Publications

Preprints

1. Pan-UK Biobank GWAS improves discovery, analysis of genetic architecture, and resolution into ancestry-enriched effects

   *Karczewski, K. J., *Gupta, R., *Kanai, M., ..., Lu, W., Tsuo, K., Wang, Y., Walters, R. K., Turley, P., Callier, S., Baya, N., Palmer, D. S., Goldstein, J. I., Sarma, G., Solomonson, M., Cheng, N., Bryant, S., Churchhouse, C., Cusick, C. M., Poterba, T., Compitello, J., King, D., Zhou, W., Seed, C., Finucane, H. K., Daly, M. J., Neale, B. M., Atkinson, E. G., and Martin, A. R. [Show fewer authors]

   medRxiv (2024)

   Abstract PDF Code Website

   Large biobanks, such as the UK Biobank (UKB), enable massive phenome by genome-wide association studies that elucidate genetic etiology of complex traits. However, individuals from diverse genetic ancestry groups are often excluded from association analyses due to concerns about population structure introducing false positive associations. Here, we generate mixed model associations and meta-analyses across genetic ancestry groups, inclusive of a larger fraction of the UKB than previous efforts, to produce freely-available summary statistics for 7,271 traits. We build a quality control and analysis framework informed by genetic architecture. Overall, we identify 14,676 significant loci in the meta-analysis that were not found in the European genetic ancestry group alone, including novel associations for example between CAMK2D and triglycerides. We also highlight associations from ancestry-enriched variation, including a known pleiotropic missense variant in G6PD associated with several biomarker traits. We release these results publicly alongside FAQs that describe caveats for interpretation of results, enhancing available resources for interpretation of risk variants across diverse populations.
2. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

   Jermy, B., Läll, K., Wolford, B. N., ..., Wang, Y., Zguro, K., Cheng, Y., Kanai, M., ..., Kanoni, S., Yang, Z., Hartnonen, T., Monti, R., Wanner, J., Youssef, O., Estonian Biobank research team., FinnGen., Lippert, C., Heel, D. A., Okada, Y., McCartney, D. L., Hayward, C., Marioni, R., Furini, S., Renieri, A., Martin, A. R., Neale, B., Hveem, K., Mägi, R., Palotie, A., Heyne, H., Mars, N., Ganna, A., and Ripatti, S. [Show fewer authors]

   medRxiv (2023)

   Abstract PDF

   Polygenic Scores (PGSs) offer the ability to predict genetic risk for complex disease across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant for clinical and public health decision making, it is important to account for any varying effects due to common risk factors such as age and sex. Here, we develop a novel framework to estimate for cumulative incidences over the life course and produce country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases by integrating PGS associations from 7 studies in 4 countries (N=1,197,129) with disease incidences from the Global Burden of Disease. PGSs had a significant sex-specific effect for 5 diseases (asthma, hip osteoarthritis, gout, coronary heart disease, type 2 diabetes) with all but type 2 diabetes exhibiting a larger effect in men. PGS had a larger effect in younger individuals for 13 diseases, with the effects decreasing linearly with age. We showed for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. For T2D, men and women in the top 1% reached the threshold aged 24.8 (95% CI: 22.5-27.6) and 22.3 (95% CI: 20.0-25.3) respectively. Individuals in the bottom 1% of PGS did not reach the risk threshold by age 80. Our easily extendable framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting age and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common disease.
3. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults

   Pozarickij, A., Gan, W., Lin, K., ..., Clarke, R., Fairhurst-Hunter, Z., Koido, M., Kanai, M., ..., Okada, Y., Kamatani, Y., Guo, Y., Bennett, D., Du, H., Chen, Y., Yang, L., Avery, D., Yu, M., Yu, C., Valle, D. S., Lv, J., Chen, J., Peto, R., Collins, R., Li, L., Chen, Z., Millwood, I. Y., and Walters, R. G. [Show fewer authors]

   medRxiv (2023)

   Abstract PDF

   Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, harbouring 81 novel associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated strong causal associations of specific BP traits with CVD, including systolic BP with intracranial haemorrhage, and pulse pressure with carotid plaque. The findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their role in disease risk.
4. Principled distillation of multidimensional UK Biobank data reveals insights into the correlated human phenome

   Carey, C. E., Shafee, R., Elliott, A., ..., Palmer, D. S., Compitello, J., Kanai, M., ..., Abbott, L., Schultz, P., Karczewski, K. J., Bryant, S. C., Cusick, C. M., Churchhouse, C., Howrigan, D. P., King, D., Davey Smith, G., Wedow, R., Neale, B. M., Walters, R. K., and Robinson, E. B. [Show fewer authors]

   medRxiv (2022)

   Abstract PDF

   Broad yet detailed data collected in biobanks captures variation reflective of human health and behavior, but insights are hard to extract given their complexity and scale. In the largest factor analysis to date, we distill hundreds of medical record codes, physical assays, and survey items from UK Biobank into 35 understandable latent constructs. The identified factors recapitulate known disease classifications, highlight the relevance of psychiatric constructs, improve measurement of health-related behavior, and disentangle elements of socioeconomic status. We demonstrate the power of this principled data reduction approach to clarify genetic signal, enhance discovery, and identify associations between underlying phenotypic structure and health outcomes such as mortality. We emphasize the importance of considering the interwoven nature of the human phenome when evaluating large-scale patterns relevant to public health.
5. Insights from complex trait fine-mapping across diverse populations

   Kanai, M., Ulirsch, J. C., Karjalainen, J., ..., Kurki, M., Karczewski, K. J., Fauman, E. B., Wang, Q. S., Jacobs, H., Aguet, F., Ardlie, K. G., Kerimov, N., Alasoo, K., Benner, C., Ishigaki, K., Sakaue, S., Reilly, S., The BioBank Japan Project., FinnGen., Kamatani, Y., Matsuda, K., Palotie, A., Neale, B. M., Tewhey, R., Sabeti, P. C., Okada, Y., Daly, M. J., and Finucane, H. K. [Show fewer authors]

   medRxiv (2021)

   Abstract PDF Code Data

   Despite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan, FinnGen, and UK Biobank; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.
6. Genetic variant in SPDL1 reveals novel mechanism linking pulmonary fibrosis risk and cancer protection

   Koskela, J. T., Happola, P., Liu, A., ..., FinnGen., Partanen, J., Genovese, G., Artomov, M., Myllymaki, M. N. M., Kanai, M., ..., Zhou, W., Karjalainen, J., Palviainen, T., Ronkainen, J., Sebert, S., Tukiainen, T., Palta, P., Kaprio, J., Kurki, M., Ganna, A., Palotie, A., Laitinen, T., Myllarniemi, M., and Daly, M. J. [Show fewer authors]

   medRxiv (2021)

   Abstract PDF

   Idiopathic Pulmonary Fibrosis (IPF) is a rare disease with poor prognosis. By contrast, cancer is common in any elderly population and a leading killer, but is now often curable. Of note, whereas IPF is driven by cellular senescence, cancer is characterized by uncontrolled cell division. Using data available from two large biobank-based studies (Finnish FinnGen study and UK biobank), we conducted a comprehensive analysis of the shared genetic background of IPF and cancer. In a population sample of 218,792 Finns with complete longitudinal health histories, we estimated the effect of individual genetic variants to the lifetime risk of IPF and cancer. We extend the analysis from IPF-GWAS to pan-cancer meta-analysis over FinnGen and UK Biobank and finally to the identification of genetic drivers of somatic chromosomal alterations. We detected six loci (SPDL1, MAD1L1, MAP2K1, RTEL1-STMN3, TERC-ACTRT3, OBFC1) associated with both IPF and cancer, all closely related to cellular division. However, each individual signal is found with opposite effects over the two diseases, termed as antagonistic pleiotropy. Several of these loci (TERC-ACTRT3, RTEL1-STMN3, OBFC1) are among the strongest inherited factors for constitutive telomere length variation and consistently indicate that shorter telomere length would increase the risk for IPF but protect from malignancy. However, a Finnish enriched SPDL1 missense variant and a common MAD1L1 intronic variant had no effect on telomere length but were shown to protect individuals from accumulation of somatic mutations. The decreased risk of cancer in SPDL1 and MAD1L1 variant carriers might result from a lower number of chromosomal alterations accumulated over time, conversely leading to fibrosis in the lung due to cellular senescence-induced inflammation. We hypothesize that the SPDL1 missense variant functions as gain-of-function mutation, leading to cellular senescence, a barrier to cancer and a driver of fibrosis in IPF. If translated to therapy, these findings might not only be able to offer relief to individuals with IPF, but also to protect from onset of cancer.
7. Multi-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations

   Turley, P., Martin, A. R., Goldman, G., ..., Li, H., Kanai, M., ..., Walters, R. K., Jala, J. B., Lin, K., Millwood, I. Y., Carey, C. E., Palmer, D. S., Zacher, M., Atkinson, E. G., Chen, Z., Li, L., Akiyama, M., Okada, Y., Kamatani, Y., Walters, R. G., Callier, S., Laibson, D., Meyer, M. N., Cesarini, D., Daly, M., Benjamin, D. J., and Neale, B. M. [Show fewer authors]

   bioRxiv (2021)

   Abstract PDF Code

   We present a new method, Multi-Ancestry Meta-Analysis (MAMA), which combines genome-wide association study (GWAS) summary statistics from multiple populations to produce new summary statistics for each population, identifying novel loci that would not have been discovered in either set of GWAS summary statistics alone. In simulations, MAMA increases power with less bias and generally lower type-1 error rate than other multi-ancestry meta-analysis approaches. We apply MAMA to 23 phenotypes in East-Asian- and European-ancestry populations and find substantial gains in power. In an independent sample, novel genetic discoveries from MAMA replicate strongly. ### Competing Interest Statement A.R.M has consulted for 23andMe and Illumina, and she has received speaker fees from Genentech, Illumina, and Pfizer. B.M.N. is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics, Member of the scientific advisory committee at Milken and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen. D.S.P. is an employee of Genomics plc, all contributions were performed prior to him joining the company.

2024

1. Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles

   Sakaue, S., Weinand, K., Isaac, S., ..., Dey, K. K., Jagadeesh, K., Kanai, M., ..., Watts, G. F. M., Zhu, Z., Accelerating Medicines Partnership RA/SLE Program and Network., Brenner, M. B., McDavid, A., Donlin, L. T., Wei, K., Price, A. L., and Raychaudhuri, S. [Show fewer authors]

   Nature Genetics (2024)

   Abstract PDF PubMed medRxiv Code

   Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer–gene maps from disease-relevant tissues. Building enhancer–gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer–gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer–gene maps, essential for defining noncoding variant function. SCENT is a nonparametric method that models association between chromatin accessibility and gene expression in single-cell multimodal datasets, enabling construction of cell-type-specific enhancer–gene maps to aid mapping of candidate causal variants and genes for common diseases.
2. Poor accuracy and sustainability of the first-step FIB4 EASL pathway for stratifying steatotic liver disease risk in the general population

   De Vincentis, A., Tavaglione, F., Namba, S., Kanai, M., ..., Okada, Y., Kamatani, Y., Maurotti, S., Pedone, C., Antonelli Incalzi, R., Valenti, L., Romeo, S., and Vespasiani-Gentilucci, U. [Show fewer authors]

   Alimentary Pharmacology & Therapeutics (2024)

   Abstract PubMed

   BACKGROUND AND AIMS: The European Association for the Study of the Liver introduced a clinical pathway (EASL CP) for screening significant/advanced fibrosis in people at risk of steatotic liver disease (SLD). We assessed the performance of the first-step FIB4 EASL CP in the general population across different SLD risk groups (MASLD, Met-ALD and ALD) and various age classes. METHODS: We analysed a total of 3372 individuals at risk of SLD from the 2017-2018 National Health and Nutrition Examination Survey (NHANES17-18), projected to 152.3 million U.S. adults, 300,329 from the UK Biobank (UKBB) and 57,644 from the Biobank Japan (BBJ). We assessed liver stiffness measurement (LSM) \geq8 kPa and liver-related events occurring within 3 and 10 years (3/10 year-LREs) as outcomes. We defined MASLD, MetALD, and ALD according to recent international recommendations. RESULTS: FIB4 sensitivity for LSM ≥8 kPa was low (27.7%), but it ranged approximately 80%-90% for 3-year LREs. Using FIB4, 22%-57% of subjects across the three cohorts were identified as candidates for vibration-controlled transient elastography (VCTE), which was mostly avoidable (positive predictive value of FIB4 ≥1.3 for LSM ≥8 kPa ranging 9.5%-13% across different SLD categories). Sensitivity for LSM ≥8 kPa and LREs increased with increasing alcohol intake (ALD>MetALD>MASLD) and age classes. For individuals aged \geq65 years, using the recommended age-adjusted FIB4 cut-off (\geq2) substantially reduced sensitivity for LSM ≥8 kPa and LREs. CONCLUSIONS: The first-step FIB4 EASL CP is poorly accurate and feasible for individuals at risk of SLD in the general population. It is crucial to enhance the screening strategy with a first-step approach able to reduce unnecessary VCTEs and optimise their yield.
3. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation

   Lo Faro, V., Bhattacharya, A., Zhou, W., ..., Zhou, D., Wang, Y., Läll, K., Kanai, M., ..., Lopera-Maya, E., Straub, P., Pawar, P., Tao, R., Zhong, X., Namba, S., Global Biobank Meta-analysis Initiative., Sanna, S., Nolte, I. M., Okada, Y., Ingold, N., MacGregor, S., Snieder, H., Surakka, I., Shortt, J., Gignoux, C., Rafaels, N., Crooks, K., Verma, A., Verma, S. S., Guare, L., Rader, D. J., Willer, C., Martin, A. R., Brantley, M. A., Gamazon, E. R., Jansonius, N. M., Joos, K., Cox, N. J., and Hirbo, J. [Show fewer authors]

   Cell Reports Medicine 5, 101430 (2024)

   Abstract PDF PubMed medRxiv

   Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.
4. X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements

   Scholz, M., Horn, K., Pott, J., ..., Wuttke, M., Kühnapfel, A., Nasr, M. K., Kirsten, H., Li, Y., Hoppmann, A., Gorski, M., Ghasemi, S., Li, M., Tin, A., Chai, J.-F., Cocca, M., Wang, J., Nutile, T., Akiyama, M., Åsvold, B. O., Bansal, N., Biggs, M. L., Boutin, T., Brenner, H., Brumpton, B., Burkhardt, R., Cai, J., Campbell, A., Campbell, H., Chalmers, J., Chasman, D. I., Chee, M. L., Chee, M. L., Chen, X., Cheng, C.-Y., Cifkova, R., Daviglus, M., Delgado, G., Dittrich, K., Edwards, T. L., Endlich, K., Michael Gaziano, J., Giri, A., Giulianini, F., Gordon, S. D., Gudbjartsson, D. F., Hallan, S., Hamet, P., Hartman, C. A., Hayward, C., Heid, I. M., Hellwege, J. N., Holleczek, B., Holm, H., Hutri-Kähönen, N., Hveem, K., Isermann, B., Jonas, J. B., Joshi, P. K., Kamatani, Y., Kanai, M., ..., Kastarinen, M., Khor, C. C., Kiess, W., Kleber, M. E., Körner, A., Kovacs, P., Krajcoviechova, A., Kramer, H., Krämer, B. K., Kuokkanen, M., Kähönen, M., Lange, L. A., Lash, J. P., Lehtimäki, T., Li, H., Lin, B. M., Liu, J., Loeffler, M., Lyytikäinen, L.-P., Magnusson, P. K. E., Martin, N. G., Matsuda, K., Milaneschi, Y., Mishra, P. P., Mononen, N., Montgomery, G. W., Mook-Kanamori, D. O., Mychaleckyj, J. C., März, W., Nauck, M., Nikus, K., Nolte, I. M., Noordam, R., Okada, Y., Olafsson, I., Oldehinkel, A. J., Penninx, B. W. J. H., Perola, M., Pirastu, N., Polasek, O., Porteous, D. J., Poulain, T., Psaty, B. M., Rabelink, T. J., Raffield, L. M., Raitakari, O. T., Rasheed, H., Reilly, D. F., Rice, K. M., Richmond, A., Ridker, P. M., Rotter, J. I., Rudan, I., Sabanayagam, C., Salomaa, V., Schneiderman, N., Schöttker, B., Sims, M., Snieder, H., Stark, K. J., Stefansson, K., Stocker, H., Stumvoll, M., Sulem, P., Sveinbjornsson, G., Svensson, P. O., Tai, E.-S., Taylor, K. D., Tayo, B. O., Teren, A., Tham, Y.-C., Thiery, J., Thio, C. H. L., Thomas, L. F., Tremblay, J., Tönjes, A., Most, P. J., Vitart, V., Völker, U., Wang, Y. X., Wang, C., Wei, W. B., Whitfield, J. B., Wild, S. H., Wilson, J. F., Winkler, T. W., Wong, T.-Y., Woodward, M., Sim, X., Chu, A. Y., Feitosa, M. F., Thorsteinsdottir, U., Hung, A. M., Teumer, A., Franceschini, N., Parsa, A., Köttgen, A., Schlosser, P., and Pattaro, C. [Show fewer authors]

   Nature Communications 15, 586 (2024)

   Abstract PDF PubMed

   X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.
5. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference

   Meng, X., Navoly, G., Giannakopoulou, O., ..., Levey, D. F., Koller, D., Pathak, G. A., Koen, N., Lin, K., Adams, M. J., Renterı́a Miguel E., Feng, Y., Gaziano, J. M., Stein, D. J., Zar, H. J., Campbell, M. L., Heel, D. A., Trivedi, B., Finer, S., McQuillin, A., Bass, N., Chundru, V. K., Martin, H. C., Huang, Q. Q., Valkovskaya, M., Chu, C.-Y., Kanjira, S., Kuo, P.-H., Chen, H.-C., Tsai, S.-J., Liu, Y.-L., Kendler, K. S., Peterson, R. E., Cai, N., Fang, Y., Sen, S., Scott, L. J., Burmeister, M., Loos, R. J. F., Preuss, M. H., Actkins, K. V., Davis, L. K., Uddin, M., Wani, A. H., Wildman, D. E., Aiello, A. E., Ursano, R. J., Kessler, R. C., Kanai, M., ..., Okada, Y., Sakaue, S., Rabinowitz, J. A., Maher, B. S., Uhl, G., Eaton, W., Cruz-Fuentes, C. S., Martinez-Levy, G. A., Campos, A. I., Millwood, I. Y., Chen, Z., Li, L., Wassertheil-Smoller, S., Jiang, Y., Tian, C., Martin, N. G., Mitchell, B. L., Byrne, E. M., Awasthi, S., Coleman, J. R. I., Ripke, S., PGC-MDD Working Group., China Kadoorie Biobank Collaborative Group., 23andMe Research Team., Genes and Health Research Team., BioBank Japan Project., Sofer, T., Walters, R. G., McIntosh, A. M., Polimanti, R., Dunn, E. C., Stein, M. B., Gelernter, J., Lewis, C. M., and Kuchenbaecker, K. [Show fewer authors]

   Nature Genetics 56, 222–233 (2024)

   Abstract PDF PubMed bioRxiv

   Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.
6. A genomic mutational constraint map using variation in 76,156 human genomes

   Chen, S., Francioli, L. C., Goodrich, J. K., ..., Collins, R. L., Kanai, M., ..., Wang, Q., Alföldi, J., Watts, N. A., Vittal, C., Gauthier, L. D., Poterba, T., Wilson, M. W., Tarasova, Y., Phu, W., Grant, R., Yohannes, M. T., Koenig, Z., Farjoun, Y., Banks, E., Donnelly, S., Gabriel, S., Gupta, N., Ferriera, S., Tolonen, C., Novod, S., Bergelson, L., Roazen, D., Ruano-Rubio, V., Covarrubias, M., Llanwarne, C., Petrillo, N., Wade, G., Jeandet, T., Munshi, R., Tibbetts, K., Genome Aggregation Database Consortium., O’Donnell-Luria, A., Solomonson, M., Seed, C., Martin, A. R., Talkowski, M. E., Rehm, H. L., Daly, M. J., Tiao, G., Neale, B. M., MacArthur, D. G., and Karczewski, K. J. [Show fewer authors]

   Nature 625, 92–100 (2024)

   Abstract PDF PubMed bioRxiv Website

   The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.
7. Improving fine-mapping by modeling infinitesimal effects

   Cui, R., Elzur, R. A., Kanai, M., ..., Ulirsch, J. C., Weissbrod, O., Daly, M. J., Neale, B. M., Fan, Z., and Finucane, H. K. [Show fewer authors]

   Nature Genetics 56, 162–169 (2024)

   Abstract PDF PubMed bioRxiv Code

   Fine-mapping aims to identify causal genetic variants for phenotypes. Bayesian fine-mapping algorithms (for example, SuSiE, FINEMAP, ABF and COJO-ABF) are widely used, but assessing posterior probability calibration remains challenging in real data, where model misspecification probably exists, and true causal variants are unknown. We introduce replication failure rate (RFR), a metric to assess fine-mapping consistency by downsampling. SuSiE, FINEMAP and COJO-ABF show high RFR, indicating potential overconfidence in their output. Simulations reveal that nonsparse genetic architecture can lead to miscalibration, while imputation noise, nonuniform distribution of causal variants and quality control filters have minimal impact. Here we present SuSiE-inf and FINEMAP-inf, fine-mapping methods modeling infinitesimal effects alongside fewer larger causal effects. Our methods show improved calibration, RFR and functional enrichment, competitive recall and computational efficiency. Notably, using our methods’ posterior effect sizes substantially increases polygenic risk score accuracy over SuSiE and FINEMAP. Our work improves causal variant identification for complex traits, a fundamental goal of human genetics. A downsampling approach to assess causal variant fine-mapping, replication failure rate, finds that commonly used methods may be miscalibrated. Simulations suggest this is probably due to a nonsparse genetic architecture model misspecification. Incorporating infinitesimal effects in the SuSiE and FINEMAP frameworks improves performance.

2023

1. eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

   Kerimov, N., Tambets, R., Hayhurst, J. D., ..., Rahu, I., Kolberg, P., Raudvere, U., Kuzmin, I., Chowdhary, A., Vija, A., Teras, H. J., Kanai, M., ..., Ulirsch, J., Ryten, M., Hardy, J., Guelfi, S., Trabzuni, D., Kim-Hellmuth, S., Rayner, W., Finucane, H., Peterson, H., Mosaku, A., Parkinson, H., and Alasoo, K. [Show fewer authors]

   PLoS Genetics 19, e1010932 (2023)

   Abstract PDF PubMed bioRxiv Website

   The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.
2. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

   Wang, Y., Kanai, M., Tan, T., ..., Kamariza, M., Tsuo, K., Yuan, K., Zhou, W., Okada, Y., BioBank Japan Project., Huang, H., Turley, P., Atkinson, E. G., and Martin, A. R. [Show fewer authors]

   Cell Genomics 3, 100408 (2023)

   Abstract PDF PubMed bioRxiv

   Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
3. A second update on mapping the human genetic architecture of COVID-19

   The COVID-19 Host Genetics Initiative.

   Nature 621, E7–E26 (2023)

   Abstract PDF PubMed medRxiv Website

   Investigating the role of host genetic factors in COVID-19 severity and susceptibility can inform our understanding of the underlying biological mechanisms that influence adverse outcomes and drug development. Here we present a second updated genome-wide association study (GWAS) on COVID-19 severity and infection susceptibility to SARS-CoV-2 from the COVID-19 Host Genetic Initiative (data release 7). We performed a meta-analysis of up to 219,692 cases and over 3 million controls, identifying 51 distinct genome-wide significant loci—adding 28 loci from the previous data release. The increased number of candidate genes at the identified loci helped to map three major biological pathways that are involved in susceptibility and severity: viral entry, airway defence in mucus and type I interferon.
4. GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

   International League Against Epilepsy Consortium on Complex Epilepsies.

   Nature Genetics 55, 1471–1482 (2023)

   Abstract PDF PubMed medRxiv

   Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment. Genome-wide association meta-analyses identify 26 risk loci for epilepsy, including 19 loci specific to genetic generalized epilepsy. Prioritized candidate genes implicate synaptic processes and overlap with targets of antiseizure medications.
5. Nuclear genetic control of mtDNA copy number and heteroplasmy in humans

   Gupta, R., Kanai, M., Durham, T. J., ..., Tsuo, K., McCoy, J. G., Kotrys, A. V., Zhou, W., Chinnery, P. F., Karczewski, K. J., Calvo, S. E., Neale, B. M., and Mootha, V. K. [Show fewer authors]

   Nature 620, 839–848 (2023)

   Abstract PDF PubMed medRxiv Code Press

   Mitochondrial DNA (mtDNA) is a maternally inherited, high-copy-number genome required for oxidative phosphorylation1. Heteroplasmy refers to the presence of a mixture of mtDNA alleles in an individual and has been associated with disease and ageing. Mechanisms underlying common variation in human heteroplasmy, and the influence of the nuclear genome on this variation, remain insufficiently explored. Here we quantify mtDNA copy number (mtCN) and heteroplasmy using blood-derived whole-genome sequences from 274,832 individuals and perform genome-wide association studies to identify associated nuclear loci. Following blood cell composition correction, we find that mtCN declines linearly with age and is associated with variants at 92 nuclear loci. We observe that nearly everyone harbours heteroplasmic mtDNA variants obeying two principles: (1) heteroplasmic single nucleotide variants tend to arise somatically and accumulate sharply after the age of 70 years, whereas (2) heteroplasmic indels are maternally inherited as mixtures with relative levels associated with 42 nuclear loci involved in mtDNA replication, maintenance and novel pathways. These loci may act by conferring a replicative advantage to certain mtDNA alleles. As an illustrative example, we identify a length variant carried by more than 50% of humans at position chrM:302 within a G-quadruplex previously proposed to mediate mtDNA transcription/replication switching2,3. We find that this variant exerts cis-acting genetic control over mtDNA abundance and is itself associated in-trans with nuclear loci encoding machinery for this regulatory switch. Our study suggests that common variation in the nuclear genome can shape variation in mtCN and heteroplasmy dynamics across the human population. We quantify mitochondrial DNA copy number and heteroplasmy levels and study their association with nuclear genetic loci in population-scale biobanks.
6. Leveraging fine-scale population structures for precision healthcare

   Kanai, M.

   Nature Medicine 29, 1611–1612 (2023)

   Abstract PDF PubMed

   A study investigates the links between fine-scale populations and outcomes including healthcare use and disease risk in a Los Angeles biobank, unveiling crucial insights into healthcare disparities in the USA.
7. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

   Weeks, E. M., Ulirsch, J. C., Cheng, N. Y., ..., Trippe, B. L., Fine, R. S., Miao, J., Patwardhan, T. A., Kanai, M., ..., Nasser, J., Fulco, C. P., Tashman, K. C., Aguet, F., Li, T., Ordovas-Montanes, J., Smillie, C. S., Biton, M., Shalek, A. K., Ananthakrishnan, A. N., Xavier, R. J., Regev, A., Gupta, R. M., Lage, K., Ardlie, K. G., Hirschhorn, J. N., Lander, E. S., Engreitz, J. M., and Finucane, H. K. [Show fewer authors]

   Nature Genetics 55, 1267–1276 (2023)

   Abstract PDF PubMed medRxiv Code Data

   Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox. Polygenic Priority Score (PoPS) prioritizes candidate effector genes at complex trait loci by integrating genome-wide association summary statistics with other data types. Combining PoPS with methods that leverage local genetic signals further improves the performance.
8. Analysis of genetic dominance in the UK Biobank

   Palmer, D. S., Zhou, W., Abbott, L., ..., Wigdor, E. M., Baya, N., Churchhouse, C., Seed, C., Poterba, T., King, D., Kanai, M., ..., Bloemendal, A., and Neale, B. M. [Show fewer authors]

   Science 379, 1341–1348 (2023)

   Abstract PubMed medRxiv Code Data

   Classical statistical genetics theory defines dominance as any deviation from a purely additive, or dosage, effect of a genotype on a trait, which is known as the dominance deviation. Dominance is well documented in plant and animal breeding. Outside of rare monogenic traits, however, evidence in humans is limited. We systematically examined common genetic variation across 1060 traits in a large population cohort (UK Biobank, N = 361,194 samples analyzed) for evidence of dominance effects. We then developed a computationally efficient method to rapidly assess the aggregate contribution of dominance deviations to heritability. Lastly, observing that dominance associations are inherently less correlated between sites at a genomic locus than their additive counterparts, we explored whether they may be leveraged to identify causal variants more confidently.
9. A cross-trait genetic correlation study identified eight diseases and traits associated with psoriasis

   Ogawa, K., Tsoi, L. C., Tanaka, H., Kanai, M., ..., Stuart, P. E., Nair, R. P., Tanaka, Y., Mochizuki, H., Elder, J. T., and Okada, Y. [Show fewer authors]

   Journal of Investigative Dermatology 143, 1813–1816.e2 (2023)

   PubMed
10. Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure

    Rämö, J. T., Kiiskinen, T., Seist, R., ..., Krebs, K., Kanai, M., ..., Karjalainen, J., Kurki, M., Hämäläinen, E., Häppölä, P., Havulinna, A. S., Hautakangas, H., FinnGen., Mägi, R., Palta, P., Esko, T., Metspalu, A., Pirinen, M., Karczewski, K. J., Ripatti, S., Milani, L., Stankovic, K. M., Mäkitie, A., Daly, M. J., and Palotie, A. [Show fewer authors]

    Nature Communications 14, 157 (2023)

    Abstract PDF PubMed medRxiv

    Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 \times 10-8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.
11. FinnGen provides genetic insights from a well-phenotyped isolated population

    Kurki, M. I., Karjalainen, J., Palta, P., ..., Sipilä, T. P., Kristiansson, K., Donner, K. M., Reeve, M. P., Laivuori, H., Aavikko, M., Kaunisto, M. A., Loukola, A., Lahtela, E., Mattsson, H., Laiho, P., Della Briotta Parolo, P., Lehisto, A. A., Kanai, M., ..., Mars, N., Rämö, J., Kiiskinen, T., Heyne, H. O., Veerapen, K., Rüeger, S., Lemmelä, S., Zhou, W., Ruotsalainen, S., Pärn, K., Hiekkalinna, T., Koskelainen, S., Paajanen, T., Llorens, V., Gracia-Tabuenca, J., Siirtola, H., Reis, K., Elnahas, A. G., Sun, B., Foley, C. N., Aalto-Setälä, K., Alasoo, K., Arvas, M., Auro, K., Biswas, S., Bizaki-Vallaskangas, A., Carpen, O., Chen, C.-Y., Dada, O. A., Ding, Z., Ehm, M. G., Eklund, K., Färkkilä, M., Finucane, H., Ganna, A., Ghazal, A., Graham, R. R., Green, E. M., Hakanen, A., Hautalahti, M., Hedman, Å. K., Hiltunen, M., Hinttala, R., Hovatta, I., Hu, X., Huertas-Vazquez, A., Huilaja, L., Hunkapiller, J., Jacob, H., Jensen, J.-N., Joensuu, H., John, S., Julkunen, V., Jung, M., Junttila, J., Kaarniranta, K., Kähönen, M., Kajanne, R., Kallio, L., Kälviäinen, R., Kaprio, J., FinnGen., Kerimov, N., Kettunen, J., Kilpeläinen, E., Kilpi, T., Klinger, K., Kosma, V.-M., Kuopio, T., Kurra, V., Laisk, T., Laukkanen, J., Lawless, N., Liu, A., Longerich, S., Mägi, R., Mäkelä, J., Mäkitie, A., Malarstig, A., Mannermaa, A., Maranville, J., Matakidou, A., Meretoja, T., Mozaffari, S. V., Niemi, M. E. K., Niemi, M., Niiranen, T., O Donnell, C. J., Obeidat, M. E., Okafo, G., Ollila, H. M., Palomäki, A., Palotie, T., Partanen, J., Paul, D. S., Pelkonen, M., Pendergrass, R. K., Petrovski, S., Pitkäranta, A., Platt, A., Pulford, D., Punkka, E., Pussinen, P., Raghavan, N., Rahimov, F., Rajpal, D., Renaud, N. A., Riley-Gillis, B., Rodosthenous, R., Saarentaus, E., Salminen, A., Salminen, E., Salomaa, V., Schleutker, J., Serpi, R., Shen, H.-Y., Siegel, R., Silander, K., Siltanen, S., Soini, S., Soininen, H., Sul, J. H., Tachmazidou, I., Tasanen, K., Tienari, P., Toppila-Salmi, S., Tukiainen, T., Tuomi, T., Turunen, J. A., Ulirsch, J. C., Vaura, F., Virolainen, P., Waring, J., Waterworth, D., Yang, R., Nelis, M., Reigo, A., Metspalu, A., Milani, L., Esko, T., Fox, C., Havulinna, A. S., Perola, M., Ripatti, S., Jalanko, A., Laitinen, T., Mäkelä, T. P., Plenge, R., McCarthy, M., Runz, H., Daly, M. J., and Palotie, A. [Show fewer authors]

    Nature 613, 508–518 (2023)

    Abstract PDF PubMed medRxiv Data Website Press

    Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 \times 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
12. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

    Wang, Y., Namba, S., Lopera, E., ..., Kerminen, S., Tsuo, K., Läll, K., Kanai, M., ..., Zhou, W., Wu, K.-H., Favé, M.-J., Bhatta, L., Awadalla, P., Brumpton, B., Deelen, P., Hveem, K., Lo Faro, V., Mägi, R., Murakami, Y., Sanna, S., Smoller, J. W., Uzunovic, J., Wolford, B. N., Global Biobank Meta-analysis Initiative., Willer, C., Gamazon, E. R., Cox, N. J., Surakka, I., Okada, Y., Martin, A. R., and Hirbo, J. [Show fewer authors]

    Cell Genomics 3, 100241 (2023)

    Abstract PDF PubMed Data Website

    Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

2022

1. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

   Kanoni, S., Graham, S. E., Wang, Y., ..., Surakka, I., Ramdas, S., Zhu, X., Clarke, S. L., Bhatti, K. F., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Zajac, G. J. M., Wu, K.-H. H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A. T., Wang, Z., Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Hwang, M. Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., ..., Zhou, W., Brumpton, B. M., Rasheed, H., Havulinna, A. S., Veturi, Y., Pacheco, J. A., Rosenthal, E. A., Lingren, T., Feng, Q., Kullo, I. J., Narita, A., Takayama, J., Martin, H. C., Hunt, K. A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Rasheed, A., Hindy, G., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Choudhury, A., Sengupta, D., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.-H., Matsuda, F., Jang, H.-M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Yousri, N. A., Mitchell, R. E., Chai, J. F., Aadahl, M., Bjerregaard, A. A., Yao, J., Manichaikul, A., Hwu, C.-M., Hung, Y.-J., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Matteo, F., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Ruotsalainen, S. E., Daw, E., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Vazquez-Moreno, M., Feitosa, M. F., Wojczynski, M. K., Wang, Z., Preuss, M. H., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Tsao, N. L., Verma, A., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Demirkan, A., Leonard, H. L., Marten, J., Frank, M., Schmidt, B., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Nongmaithem, S. S., Bayyana, S., Stringham, H. M., Irvin, M. R., Oldmeadow, C., Kim, H.-N., Ryu, S., Timmers, P. R. H. J., Arbeeva, L., Dorajoo, R., Lange, L. A., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Banas, B., Nardone, G. G., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A.-E., Hofer, E., Lin, M., Concas, M. P., Vaccargiu, S., Most, P. J., Pitkänen, N., Cade, B. E., Laan, S. W., Chitrala, K. N., Weiss, S., Bentley, A. R., Doumatey, A. P., Adeyemo, A. A., Lee, J. Y., Petersen, E. R. B., Nielsen, A. A., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S.-Y., Wang, J.-S., Couture, C., Lyytikäinen, L.-P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hidalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., Setten, J., Li, X., Liang, J., Tang, H., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Kawaguchi, T., Thiery, J., Bis, J. C., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Kleijn, D., Borst, G. J., Kim, E. K., Adams, H. H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W. J., Shu, X.-O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Chen, Y.-D. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J.-M., Koh, W.-P., Rhee, S. Y., Woo, J.-T., Heid, I. M., Stark, K. J., Zimmermann, M. E., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L. R., Peyser, P. A., Kato, N., Schulze, M. B., Girotto, G., Böger, C. A., Jung, B., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Tusié-Luna, T., Aguilar-Salinas, C. A., Adair, L. S., Bechayda, S. A., Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J.-Y., Zheng, W., Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C.-K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H.-L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Hunt, S. C., Walker, M., Koistinen, H. A., Chandak, G. R., Mercader, J. M., Costanzo, M. C., Jang, D., Burtt, N. P., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E., Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K.-H., McCarthy, M. I., Palmer, C. N. A., Vitart, V., Hayward, C., Simonsick, E., Duijn, C. M., Jin, Z.-B., Qu, J., Hishigaki, H., Lin, X., März, W., Gudnason, V., Tardif, J.-C., Lettre, G., Hart, L. M. ’t., Elders, P. J. M., Damrauer, S. M., Kumari, M., Kivimaki, M., Harst, P., Spector, T. D., Loos, R. J. F., Province, M. A., Parra, E. J., Cruz, M., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Rotimi, C. N., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F. A., Kiemeney, L. A. L. M., Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M.-R., Kutalik, Z., Francesco, C., Mook-Kanamori, D. O., Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Chuang, L.-M., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C.-Y., Wong, T.-Y., Khor, C. C., Li, H., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K. E., Boomsma, D. I., Willemsen, A. H. M., Cupples, L., Meurs, J. B. J., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., Vries, P. S., Morrison, A. C., Hazelhurst, S., Ramsay, M., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Baras, A., Justice, A. E., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Tamiya, G., Yamamoto, M., Heel, D. A., Trembath, R. C., Wei, W.-Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Kim, B.-J., Thorsteinsdottir, U., Stefansson, K., Zhang, J., Chen, Y., Ho, Y.-L., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K.-M., Cho, K., O’Donnell, C. J., Gaziano, J. M., Wilson, P. W. F., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Mohlke, K. L., Sun, Y. V., Morris, A. P., Boehnke, M., Brown, C. D., Natarajan, P., Deloukas, P., Willer, C. J., Assimes, T. L., and Peloso, G. M. [Show fewer authors]

   Genome Biology 23, 268 (2022)

   Abstract PDF PubMed

   BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
2. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

   Tsuo, K., Zhou, W., Wang, Y., Kanai, M., ..., Namba, S., Gupta, R., Majara, L., Nkambule, L. L., Morisaki, T., Okada, Y., Neale, B. M., Global Biobank Meta-analysis Initiative., Daly, M. J., and Martin, A. R. [Show fewer authors]

   Cell Genomics 2, 100212 (2022)

   Abstract PDF PubMed medRxiv Website

   Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.
3. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

   Kanai, M., Elzur, R., Zhou, W., ..., Global Biobank Meta-analysis Initiative., Daly, M. J., and Finucane, H. K. [Show fewer authors]

   Cell Genomics 2, 100210 (2022)

   Abstract PDF PubMed medRxiv Code Website

   Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7\times; Fisher’s exact p = 7.3 \times 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.
4. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

   Zhou, W., Kanai, M., Wu, K.-H. H., ..., Rasheed, H., Tsuo, K., Hirbo, J. B., Wang, Y., Bhattacharya, A., Zhao, H., Namba, S., Surakka, I., Wolford, B. N., Lo Faro, V., Lopera-Maya, E. A., Läll, K., Favé, M.-J., Partanen, J. J., Chapman, S. B., Karjalainen, J., Kurki, M., Maasha, M., Brumpton, B. M., Chavan, S., Chen, T.-T., Daya, M., Ding, Y., Feng, Y.-C. A., Guare, L. A., Gignoux, C. R., Graham, S. E., Hornsby, W. E., Ingold, N., Ismail, S. I., Johnson, R., Laisk, T., Lin, K., Lv, J., Millwood, I. Y., Moreno-Grau, S., Nam, K., Palta, P., Pandit, A., Preuss, M. H., Saad, C., Setia-Verma, S., Thorsteinsdottir, U., Uzunovic, J., Verma, A., Zawistowski, M., Zhong, X., Afifi, N., Al-Dabhani, K. M., Al Thani, A., Bradford, Y., Campbell, A., Crooks, K., Bock, G. H., Damrauer, S. M., Douville, N. J., Finer, S., Fritsche, L. G., Fthenou, E., Gonzalez-Arroyo, G., Griffiths, C. J., Guo, Y., Hunt, K. A., Ioannidis, A., Jansonius, N. M., Konuma, T., Lee, M. T. M., Lopez-Pineda, A., Matsuda, Y., Marioni, R. E., Moatamed, B., Nava-Aguilar, M. A., Numakura, K., Patil, S., Rafaels, N., Richmond, A., Rojas-Muñoz, A., Shortt, J. A., Straub, P., Tao, R., Vanderwerff, B., Vernekar, M., Veturi, Y., Barnes, K. C., Boezen, M., Chen, Z., Chen, C.-Y., Cho, J., Smith, G. D., Finucane, H. K., Franke, L., Gamazon, E. R., Ganna, A., Gaunt, T. R., Ge, T., Huang, H., Huffman, J., Katsanis, N., Koskela, J. T., Lajonchere, C., Law, M. H., Li, L., Lindgren, C. M., Loos, R. J. F., MacGregor, S., Matsuda, K., Olsen, C. M., Porteous, D. J., Shavit, J. A., Snieder, H., Takano, T., Trembath, R. C., Vonk, J. M., Whiteman, D. C., Wicks, S. J., Wijmenga, C., Wright, J., Zheng, J., Zhou, X., Awadalla, P., Boehnke, M., Bustamante, C. D., Cox, N. J., Fatumo, S., Geschwind, D. H., Hayward, C., Hveem, K., Kenny, E. E., Lee, S., Lin, Y.-F., Mbarek, H., Mägi, R., Martin, H. C., Medland, S. E., Okada, Y., Palotie, A. V., Pasaniuc, B., Rader, D. J., Ritchie, M. D., Sanna, S., Smoller, J. W., Stefansson, K., Heel, D. A., Walters, R. G., Zöllner, S., Martin, A. R., Willer, C. J., Daly, M. J., and Neale, B. M. [Show fewer authors]

   Cell Genomics 2, 100192 (2022)

   Abstract PDF PubMed medRxiv Website Press

   Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.
5. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative

   Bhattacharya, A., Hirbo, J. B., Zhou, D., ..., Zhou, W., Zheng, J., Kanai, M., ..., The Global Biobank Meta-analysis Initiative., Pasaniuc, B., Gamazon, E. R., and Cox, N. J. [Show fewer authors]

   Cell Genomics 2, 100180 (2022)

   Abstract PDF PubMed medRxiv Website

   The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.
6. A saturated map of common genetic variants associated with human height

   Yengo Loı̈c., Vedantam, S., Marouli, E., ..., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., Miao, J., Arias, J. D., Graham, S. E., Mukamel, R. E., Spracklen, C. N., Yin, X., Chen, S.-H., Ferreira, T., Highland, H. H., Ji, Y., Karaderi, T., Lin, K., Lüll, K., Malden, D. E., Medina-Gomez, C., Machado, M., Moore, A., Rüeger, S., Sim, X., Vrieze, S., Ahluwalia, T. S., Akiyama, M., Allison, M. A., Alvarez, M., Andersen, M. K., Ani, A., Appadurai, V., Arbeeva, L., Bhaskar, S., Bielak, L. F., Bollepalli, S., Bonnycastle, L. L., Bork-Jensen, J., Bradfield, J. P., Bradford, Y., Braund, P. S., Brody, J. A., Burgdorf, K. S., Cade, B. E., Cai, H., Cai, Q., Campbell, A., Cañadas-Garre, M., Catamo, E., Chai, J.-F., Chai, X., Chang, L.-C., Chang, Y.-C., Chen, C.-H., Chesi, A., Choi, S. H., Chung, R.-H., Cocca, M., Concas, M. P., Couture, C., Cuellar-Partida, G., Danning, R., Daw, E. W., Degenhard, F., Delgado, G. E., Delitala, A., Demirkan, A., Deng, X., Devineni, P., Dietl, A., Dimitriou, M., Dimitrov, L., Dorajoo, R., Ekici, A. B., Engmann, J. E., Fairhurst-Hunter, Z., Farmaki, A.-E., Faul, J. D., Fernandez-Lopez, J.-C., Forer, L., Francescatto, M., Freitag-Wolf, S., Fuchsberger, C., Galesloot, T. E., Gao, Y., Gao, Z., Geller, F., Giannakopoulou, O., Giulianini, F., Gjesing, A. P., Goel, A., Gordon, S. D., Gorski, M., Grove, J., Guo, X., Gustafsson, S., Haessler, J., Hansen, T. F., Havulinna, A. S., Haworth, S. J., He, J., Heard-Costa, N., Hebbar, P., Hindy, G., Ho, Y.-L. A., Hofer, E., Holliday, E., Horn, K., Hornsby, W. E., Hottenga, J.-J., Huang, H., Huang, J., Huerta-Chagoya, A., Huffman, J. E., Hung, Y.-J., Huo, S., Hwang, M. Y., Iha, H., Ikeda, D. D., Isono, M., Jackson, A. U., Jäger, S., Jansen, I. E., Johansson, I., Jonas, J. B., Jonsson, A., Jørgensen, T., Kalafati, I.-P., Kanai, M., ..., Kanoni, S., Kårhus, L. L., Kasturiratne, A., Katsuya, T., Kawaguchi, T., Kember, R. L., Kentistou, K. A., Kim, H.-N., Kim, Y. J., Kleber, M. E., Knol, M. J., Kurbasic, A., Lauzon, M., Le, P., Lea, R., Lee, J.-Y., Leonard, H. L., Li, S. A., Li, X., Li, X., Liang, J., Lin, H., Lin, S.-Y., Liu, J., Liu, X., Lo, K. S., Long, J., Lores-Motta, L., Luan, J., Lyssenko, V., Lyytikäinen, L.-P., Mahajan, A., Mamakou, V., Mangino, M., Manichaikul, A., Marten, J., Mattheisen, M., Mavarani, L., McDaid, A. F., Meidtner, K., Melendez, T. L., Mercader, J. M., Milaneschi, Y., Miller, J. E., Millwood, I. Y., Mishra, P. P., Mitchell, R. E., Møllehave, L. T., Morgan, A., Mucha, S., Munz, M., Nakatochi, M., Nelson, C. P., Nethander, M., Nho, C. W., Nielsen, A. A., Nolte, I. M., Nongmaithem, S. S., Noordam, R., Ntalla, I., Nutile, T., Pandit, A., Christofidou, P., Pärna, K., Pauper, M., Petersen, E. R. B., Petersen, L. V., Pitkänen, N., Polašek, O., Poveda, A., Preuss, M. H., Pyarajan, S., Raffield, L. M., Rakugi, H., Ramirez, J., Rasheed, A., Raven, D., Rayner, N. W., Riveros, C., Rohde, R., Ruggiero, D., Ruotsalainen, S. E., Ryan, K. A., Sabater-Lleal, M., Saxena, R., Scholz, M., Sendamarai, A., Shen, B., Shi, J., Shin, J. H., Sidore, C., Sitlani, C. M., Slieker, R. C., Smit, R. A. J., Smith, A. V., Smith, J. A., Smyth, L. J., Southam, L., Steinthorsdottir, V., Sun, L., Takeuchi, F., Tallapragada, D. S. P., Taylor, K. D., Tayo, B. O., Tcheandjieu, C., Terzikhan, N., Tesolin, P., Teumer, A., Theusch, E., Thompson, D. J., Thorleifsson, G., Timmers, P. R. H. J., Trompet, S., Turman, C., Vaccargiu, S., Laan, S. W., Most, P. J., Klinken, J. B., Setten, J., Verma, S. S., Verweij, N., Veturi, Y., Wang, C. A., Wang, C., Wang, L., Wang, Z., Warren, H. R., Bin Wei, W., Wickremasinghe, A. R., Wielscher, M., Wiggins, K. L., Winsvold, B. S., Wong, A., Wu, Y., Wuttke, M., Xia, R., Xie, T., Yamamoto, K., Yang, J., Yao, J., Young, H., Yousri, N. A., Yu, L., Zeng, L., Zhang, W., Zhang, X., Zhao, J.-H., Zhao, W., Zhou, W., Zimmermann, M. E., Zoledziewska, M., Adair, L. S., Adams, H. H. H., Aguilar-Salinas, C. A., Al-Mulla, F., Arnett, D. K., Asselbergs, F. W., Åsvold, B. O., Attia, J., Banas, B., Bandinelli, S., Bennett, D. A., Bergler, T., Bharadwaj, D., Biino, G., Bisgaard, H., Boerwinkle, E., Böger, C. A., Bønnelykke, K., Boomsma, D. I., Børglum, A. D., Borja, J. B., Bouchard, C., Bowden, D. W., Brandslund, I., Brumpton, B., Buring, J. E., Caulfield, M. J., Chambers, J. C., Chandak, G. R., Chanock, S. J., Chaturvedi, N., Chen, Y.-D. I., Chen, Z., Cheng, C.-Y., Christophersen, I. E., Ciullo, M., Cole, J. W., Collins, F. S., Cooper, R. S., Cruz, M., Cucca, F., Cupples, L. A., Cutler, M. J., Damrauer, S. M., Dantoft, T. M., Borst, G. J., Groot, L. C. P. G. M., De Jager, P. L., Kleijn, D. P. V., Silva, H., Dedoussis, G. V., Hollander, A. I., Du, S., Easton, D. F., Elders, P. J. M., Eliassen, A. H., Ellinor, P. T., Elmståhl, S., Erdmann, J., Evans, M. K., Fatkin, D., Feenstra, B., Feitosa, M. F., Ferrucci, L., Ford, I., Fornage, M., Franke, A., Franks, P. W., Freedman, B. I., Gasparini, P., Gieger, C., Girotto, G., Goddard, M. E., Golightly, Y. M., Gonzalez-Villalpando, C., Gordon-Larsen, P., Grallert, H., Grant, S. F. A., Grarup, N., Griffiths, L., Gudnason, V., Haiman, C., Hakonarson, H., Hansen, T., Hartman, C. A., Hattersley, A. T., Hayward, C., Heckbert, S. R., Heng, C.-K., Hengstenberg, C., Hewitt, A. W., Hishigaki, H., Hoyng, C. B., Huang, P. L., Huang, W., Hunt, S. C., Hveem, K., Hyppönen, E., Iacono, W. G., Ichihara, S., Ikram, M. A., Isasi, C. R., Jackson, R. D., Jarvelin, M.-R., Jin, Z.-B., Jöckel, K.-H., Joshi, P. K., Jousilahti, P., Jukema, J. W., Kähönen, M., Kamatani, Y., Kang, K. D., Kaprio, J., Kardia, S. L. R., Karpe, F., Kato, N., Kee, F., Kessler, T., Khera, A. V., Khor, C. C., Kiemeney, L. A. L. M., Kim, B.-J., Kim, E. K., Kim, H.-L., Kirchhof, P., Kivimaki, M., Koh, W.-P., Koistinen, H. A., Kolovou, G. D., Kooner, J. S., Kooperberg, C., Köttgen, A., Kovacs, P., Kraaijeveld, A., Kraft, P., Krauss, R. M., Kumari, M., Kutalik, Z., Laakso, M., Lange, L. A., Langenberg, C., Launer, L. J., Le Marchand, L., Lee, H., Lee, N. R., Lehtimäki, T., Li, H., Li, L., Lieb, W., Lin, X., Lind, L., Linneberg, A., Liu, C.-T., Liu, J., Loeffler, M., London, B., Lubitz, S. A., Lye, S. J., Mackey, D. A., Mägi, R., Magnusson, P. K. E., Marcus, G. M., Vidal, P. M., Martin, N. G., März, W., Matsuda, F., McGarrah, R. W., McGue, M., McKnight, A. J., Medland, S. E., Mellström, D., Metspalu, A., Mitchell, B. D., Mitchell, P., Mook-Kanamori, D. O., Morris, A. D., Mucci, L. A., Munroe, P. B., Nalls, M. A., Nazarian, S., Nelson, A. E., Neville, M. J., Newton-Cheh, C., Nielsen, C. S., Nöthen, M. M., Ohlsson, C., Oldehinkel, A. J., Orozco, L., Pahkala, K., Pajukanta, P., Palmer, C. N. A., Parra, E. J., Pattaro, C., Pedersen, O., Pennell, C. E., Penninx, B. W. J. H., Perusse, L., Peters, A., Peyser, P. A., Porteous, D. J., Power, C., Pramstaller, P. P., Province, M. A., Qi, Q., Qu, J., Rader, D. J., Raitakari, O. T., Ralhan, S., Rallidis, L. S., Rao, D. C., Redline, S., Reilly, D. F., Reiner, A. P., Rhee, S. Y., Ridker, P. M., Rienstra, M., Ripatti, S., Ritchie, M. D., Roden, D. M., Rosendaal, F. R., Rotter, J. I., Rudan, I., Rutters, F., Sabanayagam, C., Saleheen, D., Salomaa, V., Samani, N. J., Sanghera, D. K., Sattar, N., Schmidt, B., Schmidt, H., Schmidt, R., Schulze, M. B., Schunkert, H., Scott, L. J., Scott, R. J., Sever, P., Shiroma, E. J., Shoemaker, M. B., Shu, X.-O., Simonsick, E. M., Sims, M., Singh, J. R., Singleton, A. B., Sinner, M. F., Smith, J. G., Snieder, H., Spector, T. D., Stampfer, M. J., Stark, K. J., Strachan, D. P., Hart, L. M., Tabara, Y., Tang, H., Tardif, J.-C., Thanaraj, T. A., Timpson, N. J., Tönjes, A., Tremblay, A., Tuomi, T., Tuomilehto, J., Tusié-Luna, M.-T., Uitterlinden, A. G., Dam, R. M., Harst, P., Velde, N., Duijn, C. M., Schoor, N. M., Vitart, V., Völker, U., Vollenweider, P., Völzke, H., Wacher-Rodarte, N. H., Walker, M., Wang, Y. X., Wareham, N. J., Watanabe, R. M., Watkins, H., Weir, D. R., Werge, T. M., Widen, E., Wilkens, L. R., Willemsen, G., Willett, W. C., Wilson, J. F., Wong, T.-Y., Woo, J.-T., Wright, A. F., Wu, J.-Y., Xu, H., Yajnik, C. S., Yokota, M., Yuan, J.-M., Zeggini, E., Zemel, B. S., Zheng, W., Zhu, X., Zmuda, J. M., Zonderman, A. B., Zwart, J.-A., Chasman, D. I., Cho, Y. S., Heid, I. M., McCarthy, M. I., Ng, M. C. Y., O’Donnell, C. J., Rivadeneira, F., Thorsteinsdottir, U., Sun, Y. V., Tai, E. S., Boehnke, M., Deloukas, P., Justice, A. E., Lindgren, C. M., Loos, R. J. F., Mohlke, K. L., North, K. E., Stefansson, K., Walters, R. G., Winkler, T. W., Young, K. L., Loh, P.-R., Yang, J., Esko, T., Assimes, T. L., Auton, A., Abecasis, G. R., Willer, C. J., Locke, A. E., Berndt, S. I., Lettre, G., Frayling, T. M., Okada, Y., Wood, A. R., Visscher, P. M., and Hirschhorn, J. N. [Show fewer authors]

   Nature 610, 704–712 (2022)

   Abstract PDF PubMed bioRxiv Press

   Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.
7. The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

   Wang, Q. S., Edahiro, R., Namkoong, H., ..., Hasegawa, T., Shirai, Y., Sonehara, K., Tanaka, H., Lee, H., Saiki, R., Hyugaji, T., Shimizu, E., Katayama, K., Kanai, M., ..., Naito, T., Sasa, N., Yamamoto, K., Kato, Y., Morita, T., Takahashi, K., Harada, N., Naito, T., Hiki, M., Matsushita, Y., Takagi, H., Ichikawa, M., Nakamura, A., Harada, S., Sandhu, Y., Kabata, H., Masaki, K., Kamata, H., Ikemura, S., Chubachi, S., Okamori, S., Terai, H., Morita, A., Asakura, T., Sasaki, J., Morisaki, H., Uwamino, Y., Nanki, K., Uchida, S., Uno, S., Nishimura, T., Ishiguro, T., Isono, T., Shibata, S., Matsui, Y., Hosoda, C., Takano, K., Nishida, T., Kobayashi, Y., Takaku, Y., Takayanagi, N., Ueda, S., Tada, A., Miyawaki, M., Yamamoto, M., Yoshida, E., Hayashi, R., Nagasaka, T., Arai, S., Kaneko, Y., Sasaki, K., Tagaya, E., Kawana, M., Arimura, K., Takahashi, K., Anzai, T., Ito, S., Endo, A., Uchimura, Y., Miyazaki, Y., Honda, T., Tateishi, T., Tohda, S., Ichimura, N., Sonobe, K., Sassa, C. T., Nakajima, J., Nakano, Y., Nakajima, Y., Anan, R., Arai, R., Kurihara, Y., Harada, Y., Nishio, K., Ueda, T., Azuma, M., Saito, R., Sado, T., Miyazaki, Y., Sato, R., Haruta, Y., Nagasaki, T., Yasui, Y., Hasegawa, Y., Mutoh, Y., Kimura, T., Sato, T., Takei, R., Hagimoto, S., Noguchi, Y., Yamano, Y., Sasano, H., Ota, S., Nakamori, Y., Yoshiya, K., Saito, F., Yoshihara, T., Wada, D., Iwamura, H., Kanayama, S., Maruyama, S., Yoshiyama, T., Ohta, K., Kokuto, H., Ogata, H., Tanaka, Y., Arakawa, K., Shimoda, M., Osawa, T., Tateno, H., Hase, I., Yoshida, S., Suzuki, S., Kawada, M., Horinouchi, H., Saito, F., Mitamura, K., Hagihara, M., Ochi, J., Uchida, T., Baba, R., Arai, D., Ogura, T., Takahashi, H., Hagiwara, S., Nagao, G., Konishi, S., Nakachi, I., Murakami, K., Yamada, M., Sugiura, H., Sano, H., Matsumoto, S., Kimura, N., Ono, Y., Baba, H., Suzuki, Y., Nakayama, S., Masuzawa, K., Namba, S., Shiroyama, T., Noda, Y., Niitsu, T., Adachi, Y., Enomoto, T., Amiya, S., Hara, R., Yamaguchi, Y., Murakami, T., Kuge, T., Matsumoto, K., Yamamoto, Y., Yamamoto, M., Yoneda, M., Tomono, K., Kato, K., Hirata, H., Takeda, Y., Koh, H., Manabe, T., Funatsu, Y., Ito, F., Fukui, T., Shinozuka, K., Kohashi, S., Miyazaki, M., Shoko, T., Kojima, M., Adachi, T., Ishikawa, M., Takahashi, K., Inoue, T., Hirano, T., Kobayashi, K., Takaoka, H., Watanabe, K., Miyazawa, N., Kimura, Y., Sado, R., Sugimoto, H., Kamiya, A., Kuwahara, N., Fujiwara, A., Matsunaga, T., Sato, Y., Okada, T., Hirai, Y., Kawashima, H., Narita, A., Niwa, K., Sekikawa, Y., Nishi, K., Nishitsuji, M., Tani, M., Suzuki, J., Nakatsumi, H., Ogura, T., Kitamura, H., Hagiwara, E., Murohashi, K., Okabayashi, H., Mochimaru, T., Nukaga, S., Satomi, R., Oyamada, Y., Mori, N., Baba, T., Fukui, Y., Odate, M., Mashimo, S., Makino, Y., Yagi, K., Hashiguchi, M., Kagyo, J., Shiomi, T., Fuke, S., Saito, H., Tsuchida, T., Fujitani, S., Takita, M., Morikawa, D., Yoshida, T., Izumo, T., Inomata, M., Kuse, N., Awano, N., Tone, M., Ito, A., Nakamura, Y., Hoshino, K., Maruyama, J., Ishikura, H., Takata, T., Odani, T., Amishima, M., Hattori, T., Shichinohe, Y., Kagaya, T., Kita, T., Ohta, K., Sakagami, S., Koshida, K., Hayashi, K., Shimizu, T., Kozu, Y., Hiranuma, H., Gon, Y., Izumi, N., Nagata, K., Ueda, K., Taki, R., Hanada, S., Kawamura, K., Ichikado, K., Nishiyama, K., Muranaka, H., Nakamura, K., Hashimoto, N., Wakahara, K., Koji, S., Omote, N., Ando, A., Kodama, N., Kaneyama, Y., Maeda, S., Kuraki, T., Matsumoto, T., Yokote, K., Nakada, T.-A., Abe, R., Oshima, T., Shimada, T., Harada, M., Takahashi, T., Ono, H., Sakurai, T., Shibusawa, T., Kimizuka, Y., Kawana, A., Sano, T., Watanabe, C., Suematsu, R., Sageshima, H., Yoshifuji, A., Ito, K., Takahashi, S., Ishioka, K., Nakamura, M., Masuda, M., Wakabayashi, A., Watanabe, H., Ueda, S., Nishikawa, M., Chihara, Y., Takeuchi, M., Onoi, K., Shinozuka, J., Sueyoshi, A., Nagasaki, Y., Okamoto, M., Ishihara, S., Shimo, M., Tokunaga, Y., Kusaka, Y., Ohba, T., Isogai, S., Ogawa, A., Inoue, T., Fukuyama, S., Eriguchi, Y., Yonekawa, A., Kan-o, K., Matsumoto, K., Kanaoka, K., Ihara, S., Komuta, K., Inoue, Y., Chiba, S., Yamagata, K., Hiramatsu, Y., Kai, H., Asano, K., Oguma, T., Ito, Y., Hashimoto, S., Yamasaki, M., Kasamatsu, Y., Komase, Y., Hida, N., Tsuburai, T., Oyama, B., Takada, M., Kanda, H., Kitagawa, Y., Fukuta, T., Miyake, T., Yoshida, S., Ogura, S., Abe, S., Kono, Y., Togashi, Y., Takoi, H., Kikuchi, R., Ogawa, S., Ogata, T., Ishihara, S., Kanehiro, A., Ozaki, S., Fuchimoto, Y., Wada, S., Fujimoto, N., Nishiyama, K., Terashima, M., Beppu, S., Yoshida, K., Narumoto, O., Nagai, H., Ooshima, N., Motegi, M., Umeda, A., Miyagawa, K., Shimada, H., Endo, M., Ohira, Y., Watanabe, M., Inoue, S., Igarashi, A., Sato, M., Sagara, H., Tanaka, A., Ohta, S., Kimura, T., Shibata, Y., Tanino, Y., Nikaido, T., Minemura, H., Sato, Y., Yamada, Y., Hashino, T., Shinoki, M., Iwagoe, H., Takahashi, H., Fujii, K., Kishi, H., Kanai, M., Imamura, T., Yamashita, T., Yatomi, M., Maeno, T., Hayashi, S., Takahashi, M., Kuramochi, M., Kamimaki, I., Tominaga, Y., Ishii, T., Utsugi, M., Ono, A., Tanaka, T., Kashiwada, T., Fujita, K., Saito, Y., Seike, M., Watanabe, H., Matsuse, H., Kodaka, N., Nakano, C., Oshio, T., Hirouchi, T., Makino, S., Egi, M., Omae, Y., Nannya, Y., Ueno, T., Takano, T., Katayama, K., Ai, M., Kumanogoh, A., Sato, T., Hasegawa, N., Tokunaga, K., Ishii, M., Koike, R., Kitagawa, Y., Kimura, A., Imoto, S., Miyano, S., Ogawa, S., Kanai, T., Fukunaga, K., and Okada, Y. [Show fewer authors]

   Nature Communications 13, 4830 (2022)

   Abstract PDF PubMed

   Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection. Genetic mechanisms influencing COVID-19 susceptibility are not well understood. Here, the authors analyzed whole blood RNA-seq data of 465 Japanese individuals with COVID-19, highlighting thousands of fine-mapped variants affecting expression and splicing of genes, as well as the presence of COVID-19 severity-interaction eQTLs.
8. Inframe insertion and splice site variants in MFGE8 associate with protection against coronary atherosclerosis

   Ruotsalainen, S. E., Surakka, I., Mars, N., ..., Karjalainen, J., Kurki, M., Kanai, M., ..., Krebs, K., Graham, S., Mishra, P. P., Mishra, B. H., Sinisalo, J., Palta, P., Lehtimäki, T., Raitakari, O., Milani, L., Okada, Y., Palotie, A., Widen, E., Daly, M. J., and Ripatti, S. [Show fewer authors]

   Communications Biology 5, 802 (2022)

   Abstract PDF PubMed medRxiv Press

   Cardiovascular diseases are the leading cause of premature death and disability worldwide, with both genetic and environmental determinants. While genome-wide association studies have identified multiple genetic loci associated with cardiovascular diseases, exact genes driving these associations remain mostly uncovered. Due to Finland’s population history, many deleterious and high-impact variants are enriched in the Finnish population giving a possibility to find genetic associations for protein-truncating variants that likely tie the association to a gene and that would not be detected elsewhere. In a large Finnish biobank study FinnGen, we identified an association between an inframe insertion rs534125149 in MFGE8 (encoding lactadherin) and protection against coronary atherosclerosis. This variant is highly enriched in Finland, and the protective association was replicated in meta-analysis of BioBank Japan and Estonian biobank. Additionally, we identified a protective association between splice acceptor variant rs201988637 in MFGE8 and coronary atherosclerosis, independent of the rs534125149, with no significant risk-increasing associations. This variant was also associated with lower pulse pressure, pointing towards a function of MFGE8 in arterial aging also in humans in addition to previous evidence in mice. In conclusion, our results suggest that inhibiting the production of lactadherin could lower the risk for coronary heart disease substantially. A genome-wide association study identifies MFGE8 as protective against coronary atherosclerosis in European and East Asian populations.
9. DOCK2 is involved in the host genetics and biology of severe COVID-19

   Namkoong, H., Edahiro, R., Takano, T., ..., Nishihara, H., Shirai, Y., Sonehara, K., Tanaka, H., Azekawa, S., Mikami, Y., Lee, H., Hasegawa, T., Okudela, K., Okuzaki, D., Motooka, D., Kanai, M., ..., Naito, T., Yamamoto, K., Wang, Q. S., Saiki, R., Ishihara, R., Matsubara, Y., Hamamoto, J., Hayashi, H., Yoshimura, Y., Tachikawa, N., Yanagita, E., Hyugaji, T., Shimizu, E., Katayama, K., Kato, Y., Morita, T., Takahashi, K., Harada, N., Naito, T., Hiki, M., Matsushita, Y., Takagi, H., Aoki, R., Nakamura, A., Harada, S., Sasano, H., Kabata, H., Masaki, K., Kamata, H., Ikemura, S., Chubachi, S., Okamori, S., Terai, H., Morita, A., Asakura, T., Sasaki, J., Morisaki, H., Uwamino, Y., Nanki, K., Uchida, S., Uno, S., Nishimura, T., Ishiguro, T., Isono, T., Shibata, S., Matsui, Y., Hosoda, C., Takano, K., Nishida, T., Kobayashi, Y., Takaku, Y., Takayanagi, N., Ueda, S., Tada, A., Miyawaki, M., Yamamoto, M., Yoshida, E., Hayashi, R., Nagasaka, T., Arai, S., Kaneko, Y., Sasaki, K., Tagaya, E., Kawana, M., Arimura, K., Takahashi, K., Anzai, T., Ito, S., Endo, A., Uchimura, Y., Miyazaki, Y., Honda, T., Tateishi, T., Tohda, S., Ichimura, N., Sonobe, K., Sassa, C. T., Nakajima, J., Nakano, Y., Nakajima, Y., Anan, R., Arai, R., Kurihara, Y., Harada, Y., Nishio, K., Ueda, T., Azuma, M., Saito, R., Sado, T., Miyazaki, Y., Sato, R., Haruta, Y., Nagasaki, T., Yasui, Y., Hasegawa, Y., Mutoh, Y., Kimura, T., Sato, T., Takei, R., Hagimoto, S., Noguchi, Y., Yamano, Y., Sasano, H., Ota, S., Nakamori, Y., Yoshiya, K., Saito, F., Yoshihara, T., Wada, D., Iwamura, H., Kanayama, S., Maruyama, S., Yoshiyama, T., Ohta, K., Kokuto, H., Ogata, H., Tanaka, Y., Arakawa, K., Shimoda, M., Osawa, T., Tateno, H., Hase, I., Yoshida, S., Suzuki, S., Kawada, M., Horinouchi, H., Saito, F., Mitamura, K., Hagihara, M., Ochi, J., Uchida, T., Baba, R., Arai, D., Ogura, T., Takahashi, H., Hagiwara, S., Nagao, G., Konishi, S., Nakachi, I., Murakami, K., Yamada, M., Sugiura, H., Sano, H., Matsumoto, S., Kimura, N., Ono, Y., Baba, H., Suzuki, Y., Nakayama, S., Masuzawa, K., Namba, S., Suzuki, K., Naito, Y., Liu, Y.-C., Takuwa, A., Sugihara, F., Wing, J. B., Sakakibara, S., Hizawa, N., Shiroyama, T., Miyawaki, S., Kawamura, Y., Nakayama, A., Matsuo, H., Maeda, Y., Nii, T., Noda, Y., Niitsu, T., Adachi, Y., Enomoto, T., Amiya, S., Hara, R., Yamaguchi, Y., Murakami, T., Kuge, T., Matsumoto, K., Yamamoto, Y., Yamamoto, M., Yoneda, M., Kishikawa, T., Yamada, S., Kawabata, S., Kijima, N., Takagaki, M., Sasa, N., Ueno, Y., Suzuki, M., Takemoto, N., Eguchi, H., Fukusumi, T., Imai, T., Fukushima, M., Kishima, H., Inohara, H., Tomono, K., Kato, K., Takahashi, M., Matsuda, F., Hirata, H., Takeda, Y., Koh, H., Manabe, T., Funatsu, Y., Ito, F., Fukui, T., Shinozuka, K., Kohashi, S., Miyazaki, M., Shoko, T., Kojima, M., Adachi, T., Ishikawa, M., Takahashi, K., Inoue, T., Hirano, T., Kobayashi, K., Takaoka, H., Watanabe, K., Miyazawa, N., Kimura, Y., Sado, R., Sugimoto, H., Kamiya, A., Kuwahara, N., Fujiwara, A., Matsunaga, T., Sato, Y., Okada, T., Hirai, Y., Kawashima, H., Narita, A., Niwa, K., Sekikawa, Y., Nishi, K., Nishitsuji, M., Tani, M., Suzuki, J., Nakatsumi, H., Ogura, T., Kitamura, H., Hagiwara, E., Murohashi, K., Okabayashi, H., Mochimaru, T., Nukaga, S., Satomi, R., Oyamada, Y., Mori, N., Baba, T., Fukui, Y., Odate, M., Mashimo, S., Makino, Y., Yagi, K., Hashiguchi, M., Kagyo, J., Shiomi, T., Fuke, S., Saito, H., Tsuchida, T., Fujitani, S., Takita, M., Morikawa, D., Yoshida, T., Izumo, T., Inomata, M., Kuse, N., Awano, N., Tone, M., Ito, A., Nakamura, Y., Hoshino, K., Maruyama, J., Ishikura, H., Takata, T., Odani, T., Amishima, M., Hattori, T., Shichinohe, Y., Kagaya, T., Kita, T., Ohta, K., Sakagami, S., Koshida, K., Hayashi, K., Shimizu, T., Kozu, Y., Hiranuma, H., Gon, Y., Izumi, N., Nagata, K., Ueda, K., Taki, R., Hanada, S., Kawamura, K., Ichikado, K., Nishiyama, K., Muranaka, H., Nakamura, K., Hashimoto, N., Wakahara, K., Koji, S., Omote, N., Ando, A., Kodama, N., Kaneyama, Y., Maeda, S., Kuraki, T., Matsumoto, T., Yokote, K., Nakada, T.-A., Abe, R., Oshima, T., Shimada, T., Harada, M., Takahashi, T., Ono, H., Sakurai, T., Shibusawa, T., Kimizuka, Y., Kawana, A., Sano, T., Watanabe, C., Suematsu, R., Sageshima, H., Yoshifuji, A., Ito, K., Takahashi, S., Ishioka, K., Nakamura, M., Masuda, M., Wakabayashi, A., Watanabe, H., Ueda, S., Nishikawa, M., Chihara, Y., Takeuchi, M., Onoi, K., Shinozuka, J., Sueyoshi, A., Nagasaki, Y., Okamoto, M., Ishihara, S., Shimo, M., Tokunaga, Y., Kusaka, Y., Ohba, T., Isogai, S., Ogawa, A., Inoue, T., Fukuyama, S., Eriguchi, Y., Yonekawa, A., Kan-o, K., Matsumoto, K., Kanaoka, K., Ihara, S., Komuta, K., Inoue, Y., Chiba, S., Yamagata, K., Hiramatsu, Y., Kai, H., Asano, K., Oguma, T., Ito, Y., Hashimoto, S., Yamasaki, M., Kasamatsu, Y., Komase, Y., Hida, N., Tsuburai, T., Oyama, B., Takada, M., Kanda, H., Kitagawa, Y., Fukuta, T., Miyake, T., Yoshida, S., Ogura, S., Abe, S., Kono, Y., Togashi, Y., Takoi, H., Kikuchi, R., Ogawa, S., Ogata, T., Ishihara, S., Kanehiro, A., Ozaki, S., Fuchimoto, Y., Wada, S., Fujimoto, N., Nishiyama, K., Terashima, M., Beppu, S., Yoshida, K., Narumoto, O., Nagai, H., Ooshima, N., Motegi, M., Umeda, A., Miyagawa, K., Shimada, H., Endo, M., Ohira, Y., Watanabe, M., Inoue, S., Igarashi, A., Sato, M., Sagara, H., Tanaka, A., Ohta, S., Kimura, T., Shibata, Y., Tanino, Y., Nikaido, T., Minemura, H., Sato, Y., Yamada, Y., Hashino, T., Shinoki, M., Iwagoe, H., Takahashi, H., Fujii, K., Kishi, H., Kanai, M., Imamura, T., Yamashita, T., Yatomi, M., Maeno, T., Hayashi, S., Takahashi, M., Kuramochi, M., Kamimaki, I., Tominaga, Y., Ishii, T., Utsugi, M., Ono, A., Tanaka, T., Kashiwada, T., Fujita, K., Saito, Y., Seike, M., Watanabe, H., Matsuse, H., Kodaka, N., Nakano, C., Oshio, T., Hirouchi, T., Makino, S., Egi, M., Omae, Y., Nannya, Y., Ueno, T., Katayama, K., Ai, M., Fukui, Y., Kumanogoh, A., Sato, T., Hasegawa, N., Tokunaga, K., Ishii, M., Koike, R., Kitagawa, Y., Kimura, A., Imoto, S., Miyano, S., Ogawa, S., Kanai, T., Fukunaga, K., and Okada, Y. [Show fewer authors]

   Nature 609, 754–760 (2022)

   Abstract PDF PubMed medRxiv

   Identifying the factors underlying severe COVID-19 in the host genetics is an emerging issue. We conducted a genome-wide association study (GWAS) involving 2,393 Japanese COVID-19 cases collected in initial pandemic waves with 3,289 controls, which identified a variant on 5q35 (rs60200309-A) near DOCK2 associated with severe COVID-19 in younger (<65 ages) patients (nCase=440, odds ratio=2.01, P=1.2\times10-8). This risk allele was prevalent in East Asians but rare in Europeans, showing a value of non-European GWAS. RNA-seq of 473 bulk peripheral blood identified decreasing effect of the risk allele on DOCK2 expression in younger patients. DOCK2 expression was suppressed in severe forms of COVID-19. Single cell RNA-seq analysis (n=61) identified cell type-specific downregulation of DOCK2 and COVID-19-specific decreasing effects of the risk allele on DOCK2 in non-classical monocytes. Immunohistochemistry of lung specimens from severe COVID-19 pneumonia showed suppressed DOCK2. Moreover, inhibition of DOCK2 function using CPYPP induced much more severe pneumonia in a Syrian hamster model of SARS-CoV-2 infection characterized as weight loss, lung edema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 plays an important role in the host immune response to SARS-CoV-2 infection and development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target.
10. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

    Ramdas, S., Judd, J., Graham, S. E., ..., Kanoni, S., Wang, Y., Surakka, I., Wenz, B., Clarke, S. L., Chesi, A., Wells, A., Bhatti, K. F., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Zajac, G. J. M., Wu, K.-H. H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A. T., Wang, Z., Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Hwang, M. Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., ..., Zhou, W., Brumpton, B. M., Rasheed, H., Havulinna, A. S., Veturi, Y., Pacheco, J. A., Rosenthal, E. A., Lingren, T., Feng, Q., Kullo, I. J., Narita, A., Takayama, J., Martin, H. C., Hunt, K. A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Rasheed, A., Hindy, G., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Choudhury, A., Sengupta, D., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.-H., Matsuda, F., Jang, H.-M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Bjerregaard, A. A., Yao, J., Manichaikul, A., Lee, W.-J., Hsiung, C. A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Matteo, F., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Schönherr, S., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Ruotsalainen, S. E., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Le, P., Feitosa, M. F., Wojczynski, M. K., Hemerich, D., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Noah, T. L., Verma, A., Slieker, R. C., Lo, K. S., Zilhao, N. R., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Demirkan, A., Leonard, H. L., Marten, J., Emmel, C., Schmidt, B., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Nongmaithem, S. S., Sankareswaran, A., Irvin, M. R., Oldmeadow, C., Kim, H.-N., Ryu, S., Timmers, P. R. H. J., Arbeeva, L., Dorajoo, R., Lange, L. A., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Banas, B., Morgan, A., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A.-E., Hofer, E., Lin, M., Concas, M. P., Vaccargiu, S., Most, P. J., Pitkänen, N., Cade, B. E., Laan, S. W., Chitrala, K. N., Weiss, S., Bentley, A. R., Doumatey, A. P., Adeyemo, A. A., Lee, J. Y., Petersen, E. R. B., Nielsen, A. A., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S.-Y., Wang, J.-S., Couture, C., Lyytikäinen, L.-P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hidalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., Setten, J., He, K. Y., Tang, H., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Kawaguchi, T., Thiery, J., Bis, J. C., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Kleijn, D., Borst, G. J., Kim, E. K., Adams, H. H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W. J., Shu, X.-O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Ida Chen, Y.-D., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J.-M., Koh, W.-P., Rhee, S. Y., Woo, J.-T., Heid, I. M., Stark, K. J., Zimmermann, M. E., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Peyser, P. A., Kato, N., Schulze, M. B., Girotto, G., Böger, C. A., Jung, B., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Tusié-Luna, T., Aguilar-Salinas, C. A., Adair, L. S., Bechayda, S. A., Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J.-Y., Zheng, W., Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C.-K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H.-L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Walker, M., Scott, L. J., Koistinen, H. A., Chandak, G. R., Mercader, J. M., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K.-H., McCarthy, M. I., Palmer, C. N. A., Vitart, V., Hayward, C., Simonsick, E., Duijn, C. M., Jin, Z.-B., Lu, F., Hishigaki, H., Lin, X., März, W., Gudnason, V., Tardif, J.-C., Lettre, G., T Hart, L. M., Elders, P. J. M., Rader, D. J., Damrauer, S. M., Kumari, M., Kivimaki, M., Harst, P., Spector, T. D., Loos, R. J. F., Province, M. A., Parra, E. J., Cruz, M., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Rotimi, C. N., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F. A., Kiemeney, L., Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M.-R., Kutalik, Z., Francesco, C., Mook-Kanamori, D. O., Dijk, K., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Huey-Herng Sheu, W., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C.-Y., Wong, T.-Y., Khor, C. C., Li, H., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K. E., Boomsma, D. I., Geus, E. J. C., Cupples, L. A., Meurs, J. B. J., Ikram, A., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Tuomilehto, J., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., Vries, P. S., Morrison, A. C., Hazelhurst, S., Ramsay, M., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Baras, A., Justice, A. E., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Tamiya, G., Yamamoto, M., Heel, D. A., Trembath, R. C., Wei, W.-Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Kim, B.-J., Thorsteinsdottir, U., Stefansson, K., Zhang, J., Chen, Y. E., Ho, Y.-L., Lynch, J. A., Tsao, P. S., Chang, K.-M., Cho, K., O’Donnell, C. J., Gaziano, J. M., Wilson, P., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Boehnke, M., Million Veterans Program., Global Lipids Genetics Consortium., Struan Grant., Natarajan, P., Sun, Y. V., Morris, A. P., Deloukas, P., Peloso, G., Assimes, T. L., Willer, C. J., Zhu, X., and Brown, C. D. [Show fewer authors]

    The American Journal of Human Genetics 109, 1366–1387 (2022)

    Abstract PubMed bioRxiv

    A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
11. A first update on mapping the human genetic architecture of COVID-19

    The COVID-19 Host Genetics Initiative.

    Nature 608, E1–E10 (2022)

    Abstract PDF PubMed medRxiv Website

    The COVID-19 pandemic continues to pose a major public health threat, especially in countries with low vaccination rates. To better understand the biological underpinnings of SARS-CoV-2 infection and COVID-19 severity, we formed the COVID-19 Host Genetics Initiative. Here we present a genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries, adding 11 genome-wide significant loci compared with those previously identified. Genes at new loci, including SFTPD, MUC5B and ACE2, reveal compelling insights regarding disease susceptibility and severity.
12. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

    Winkler, T. W., Rasheed, H., Teumer, A., ..., Gorski, M., Rowan, B. X., Stanzick, K. J., Thomas, L. F., Tin, A., Hoppmann, A., Chu, A. Y., Tayo, B., Thio, C. H. L., Cusi, D., Chai, J.-F., Sieber, K. B., Horn, K., Li, M., Scholz, M., Cocca, M., Wuttke, M., Most, P. J., Yang, Q., Ghasemi, S., Nutile, T., Li, Y., Pontali, G., Günther, F., Dehghan, A., Correa, A., Parsa, A., Feresin, A., Vries, A. P. J., Zonderman, A. B., Smith, A. V., Oldehinkel, A. J., De Grandi, A., Rosenkranz, A. R., Franke, A., Teren, A., Metspalu, A., Hicks, A. A., Morris, A. P., Tönjes, A., Morgan, A., Podgornaia, A. I., Peters, A., Körner, A., Mahajan, A., Campbell, A., Freedman, B. I., Spedicati, B., Ponte, B., Schöttker, B., Brumpton, B., Banas, B., Krämer, B. K., Jung, B., Åsvold, B. O., Smith, B. H., Ning, B., Penninx, B. W. J. H., Vanderwerff, B. R., Psaty, B. M., Kammerer, C. M., Langefeld, C. D., Hayward, C., Spracklen, C. N., Robinson-Cohen, C., Hartman, C. A., Lindgren, C. M., Wang, C., Sabanayagam, C., Heng, C.-K., Lanzani, C., Khor, C.-C., Cheng, C.-Y., Fuchsberger, C., Gieger, C., Shaffer, C. M., Schulz, C.-A., Willer, C. J., Chasman, D. I., Gudbjartsson, D. F., Ruggiero, D., Toniolo, D., Czamara, D., Porteous, D. J., Waterworth, D. M., Mascalzoni, D., Mook-Kanamori, D. O., Reilly, D. F., Daw, E. W., Hofer, E., Boerwinkle, E., Salvi, E., Bottinger, E. P., Tai, E.-S., Catamo, E., Rizzi, F., Guo, F., Rivadeneira, F., Guilianini, F., Sveinbjornsson, G., Ehret, G., Waeber, G., Biino, G., Girotto, G., Pistis, G., Nadkarni, G. N., Delgado, G. E., Montgomery, G. W., Snieder, H., Campbell, H., White, H. D., Gao, H., Stringham, H. M., Schmidt, H., Li, H., Brenner, H., Holm, H., Kirsten, H., Kramer, H., Rudan, I., Nolte, I. M., Tzoulaki, I., Olafsson, I., Martins, J., Cook, J. P., Wilson, J. F., Halbritter, J., Felix, J. F., Divers, J., Kooner, J. S., Lee, J. J.-M., O’Connell, J., Rotter, J. I., Liu, J., Xu, J., Thiery, J., Ärnlöv, J., Kuusisto, J., Jakobsdottir, J., Tremblay, J., Chambers, J. C., Whitfield, J. B., Gaziano, J. M., Marten, J., Coresh, J., Jonas, J. B., Mychaleckyj, J. C., Christensen, K., Eckardt, K.-U., Mohlke, K. L., Endlich, K., Dittrich, K., Ryan, K. A., Rice, K. M., Taylor, K. D., Ho, K., Nikus, K., Matsuda, K., Strauch, K., Miliku, K., Hveem, K., Lind, L., Wallentin, L., Yerges-Armstrong, L. M., Raffield, L. M., Phillips, L. S., Launer, L. J., Lyytikäinen, L.-P., Lange, L. A., Citterio, L., Klaric, L., Ikram, M. A., Ising, M., Kleber, M. E., Francescatto, M., Concas, M. P., Ciullo, M., Piratsu, M., Orho-Melander, M., Laakso, M., Loeffler, M., Perola, M., Borst, M. H., Gögele, M., Bianca, M. L., Lukas, M. A., Feitosa, M. F., Biggs, M. L., Wojczynski, M. K., Kavousi, M., Kanai, M., ..., Akiyama, M., Yasuda, M., Nauck, M., Waldenberger, M., Chee, M.-L., Chee, M.-L., Boehnke, M., Preuss, M. H., Stumvoll, M., Province, M. A., Evans, M. K., O’Donoghue, M. L., Kubo, M., Kähönen, M., Kastarinen, M., Nalls, M. A., Kuokkanen, M., Ghanbari, M., Bochud, M., Josyula, N. S., Martin, N. G., Tan, N. Y. Q., Palmer, N. D., Pirastu, N., Schupf, N., Verweij, N., Hutri-Kähönen, N., Mononen, N., Bansal, N., Devuyst, O., Melander, O., Raitakari, O. T., Polasek, O., Manunta, P., Gasparini, P., Mishra, P. P., Sulem, P., Magnusson, P. K. E., Elliott, P., Ridker, P. M., Hamet, P., Svensson, P. O., Joshi, P. K., Kovacs, P., Pramstaller, P. P., Rossing, P., Vollenweider, P., Harst, P., Dorajoo, R., Sim, R. Z. H., Burkhardt, R., Tao, R., Noordam, R., Mägi, R., Schmidt, R., Mutsert, R., Rueedi, R., Dam, R. M., Carroll, R. J., Gansevoort, R. T., Loos, R. J. F., Felicita, S. C., Sedaghat, S., Padmanabhan, S., Freitag-Wolf, S., Pendergrass, S. A., Graham, S. E., Gordon, S. D., Hwang, S.-J., Kerr, S. M., Vaccargiu, S., Patil, S. B., Hallan, S., Bakker, S. J. L., Lim, S.-C., Lucae, S., Vogelezang, S., Bergmann, S., Corre, T., Ahluwalia, T. S., Lehtimäki, T., Boutin, T. S., Meitinger, T., Wong, T.-Y., Bergler, T., Rabelink, T. J., Esko, T., Haller, T., Thorsteinsdottir, U., Völker, U., Foo, V. H. X., Salomaa, V., Vitart, V., Giedraitis, V., Gudnason, V., Jaddoe, V. W. V., Huang, W., Zhang, W., Wei, W. B., Kiess, W., März, W., Koenig, W., Lieb, W., Gao, X., Sim, X., Wang, Y. X., Friedlander, Y., Tham, Y.-C., Kamatani, Y., Okada, Y., Milaneschi, Y., Yu, Z., Stark, K. J., Stefansson, K., Böger, C. A., Hung, A. M., Kronenberg, F., Köttgen, A., Pattaro, C., and Heid, I. M. [Show fewer authors]

    Communications Biology 5, 580 (2022)

    Abstract PDF PubMed

    Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM. A large-scale GWAS provides insight on diabetes-dependent genetic effects on the glomerular filtration rate, a common metric to monitor kidney health in disease.
13. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

    Wang, Y., Tsuo, K., Kanai, M., ..., Neale, B. M., and Martin, A. R. [Show fewer authors]

    Annual Review of Biomedical Data Science 5, 293–320 (2022)

    Abstract PDF PubMed

    Polygenic risk scores (PRS) estimate an individual’s genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
14. Genome-wide risk prediction of common diseases across ancestries in one million people

    Mars, N., Kerminen, S., Feng, Y.-C. A., Kanai, M., ..., Läll, K., Thomas, L. F., Skogholt, A. H., Briotta Parolo, P., Neale, B. M., Smoller, J. W., Gabrielsen, M. E., Hveem, K., Mägi, R., Matsuda, K., Okada, Y., Pirinen, M., Palotie, A., Ganna, A., Martin, A. R., and Ripatti, S. [Show fewer authors]

    Cell Genomics 2, 100118 (2022)

    Abstract PDF PubMed Press

    Polygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.
15. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

    *Weissbrod, O., *Kanai, M., *Shi, H., ..., Gazal, S., Peyrot, W. J., Khera, A. V., Okada, Y., The Biobank Japan Project., Martin, A. R., Finucane, H. K., and Price, A. L. [Show fewer authors]

    Nature Genetics 54, 450–458 (2022)

    Abstract PDF PubMed medRxiv Code Data

    Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements. PolyPred and PolyPred+ methods that leverage fine-mapping and non-European training data significantly improve cross-population polygenic prediction accuracy when applied to diseases and complex traits in UK Biobank populations.
16. Trans-ethnic Mendelian-randomization study reveals causal relationships between cardiometabolic factors and chronic kidney disease

    Zheng, J., Zhang, Y., Rasheed, H., ..., Walker, V., Sugawara, Y., Li, J., Leng, Y., Elsworth, B., Wootton, R. E., Fang, S., Yang, Q., Burgess, S., Haycock, P. C., Borges, M. C., Cho, Y., Carnegie, R., Howell, A., Robinson, J., Thomas, L. F., Brumpton, B. M., Hveem, K., Hallan, S., Franceschini, N., Morris, A. P., Köttgen, A., Pattaro, C., Wuttke, M., Yamamoto, M., Kashihara, N., Akiyama, M., Kanai, M., ..., Matsuda, K., Kamatani, Y., Okada, Y., Walters, R., Millwood, I. Y., Chen, Z., Davey Smith, G., Barbour, S., Yu, C., Åsvold, B. O., Zhang, H., and Gaunt, T. R. [Show fewer authors]

    International Journal of Epidemiology 50, 1995–2010 (2022)

    Abstract PDF PubMed medRxiv

    BACKGROUND: This study was to systematically test whether previously reported risk factors for chronic kidney disease (CKD) are causally related to CKD in European and East Asian ancestries using Mendelian randomization. METHODS: A total of 45 risk factors with genetic data in European ancestry and 17 risk factors in East Asian participants were identified as exposures from PubMed. We defined the CKD by clinical diagnosis or by estimated glomerular filtration rate of 25 kg/m2. CONCLUSIONS: Eight cardiometabolic risk factors showed causal effects on CKD in Europeans and three of them showed causality in East Asians, providing insights into the design of future interventions to reduce the burden of CKD.

2021

1. The power of genetic diversity in genome-wide association studies of lipids

   Graham, S. E., Clarke, S. L., Wu, K.-H. H., ..., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Hwang, M. Y., Han, S., Narita, A., Choudhury, A., Bentley, A. R., Ekoru, K., Verma, A., Trivedi, B., Martin, H. C., Hunt, K. A., Hui, Q., Klarin, D., Zhu, X., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., ..., Zhou, W., Brumpton, B. M., Rasheed, H., Ruotsalainen, S. E., Havulinna, A. S., Veturi, Y., Feng, Q., Rosenthal, E. A., Lingren, T., Pacheco, J. A., Pendergrass, S. A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Hindy, G., Rasheed, A., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.-H., Matsuda, F., Jang, H.-M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Willemsen, G., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Thuesen, B. H., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Feitosa, M. F., Wojczynski, M. K., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Kember, R. L., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Leonard, H. L., Marten, J., Schmidt, B., Arendt, M., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Ahmed, M., Jackson, A. U., Irvin, M. R., Oldmeadow, C., Kim, H.-N., Ryu, S., Timmers, P. R. H. J., Arbeeva, L., Dorajoo, R., Lange, L. A., Chai, X., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Hung, Y.-J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Gorski, M., Brumat, M., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A.-E., Hofer, E., Lin, M., Xue, C., Zhang, J., Concas, M. P., Vaccargiu, S., Most, P. J., Pitkänen, N., Cade, B. E., Lee, J., Laan, S. W., Chitrala, K. N., Weiss, S., Zimmermann, M. E., Lee, J. Y., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S.-Y., Wang, J.-S., Couture, C., Lyytikäinen, L.-P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hildalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., Setten, J., Li, X., Schwander, K., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Highland, H. M., Young, K. L., Kawaguchi, T., Thiery, J., Bis, J. C., Nadkarni, G. N., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Bhatti, K. F., Kleijn, D., Borst, G. J., Kim, E. K., Adams, H. H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W. J., Shu, X.-O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Chen, Y.-D. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J.-M., Koh, W.-P., Rhee, S. Y., Woo, J.-T., Heid, I. M., Stark, K. J., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Chen, Y. E., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L. R., Kato, N., Schulze, M. B., Girotto, G., Jung, B., Böger, C. A., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Adair, L. S., Bechayda, S. A., Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J.-Y., Zheng, W., Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C.-K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H.-L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Walker, M., Koistinen, H. A., Chandak, G. R., Yajnik, C. S., Mercader, J. M., Tusié-Luna, T., Aguilar-Salinas, C. A., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K.-H., McCarthy, M. I., Palmer, C. N. A., Vitart, V., Hayward, C., Simonsick, E., Duijn, C. M., Lu, F., Qu, J., Hishigaki, H., Lin, X., März, W., Parra, E. J., Cruz, M., Gudnason, V., Tardif, J.-C., Lettre, G., Hart, L. M., Elders, P. J. M., Damrauer, S. M., Kumari, M., Kivimaki, M., Harst, P., Spector, T. D., Loos, R. J. F., Province, M. A., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F. A., Kiemeney, L. A. L. M., Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M.-R., Kutalik, Z., Cucca, F., Mook-Kanamori, D. O., Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C.-Y., Wong, T.-Y., Khor, C. C., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K. E., Boomsma, D. I., Geus, E. J. C., Cupples, L. A., Meurs, J. B. J., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., Vries, P. S., Morrison, A. C., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Justice, A. E., Baras, A., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Wei, W.-Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Thorsteinsdottir, U., Stefansson, K., Ho, Y.-L., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K.-M., Cho, K., O’Donnell, C. J., Gaziano, J. M., Wilson, P., Rotimi, C. N., Hazelhurst, S., Ramsay, M., Trembath, R. C., Heel, D. A., Tamiya, G., Yamamoto, M., Kim, B.-J., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Boehnke, M., Natarajan, P., Peloso, G. M., Brown, C. D., Morris, A. P., Assimes, T. L., Deloukas, P., Sun, Y. V., and Willer, C. J. [Show fewer authors]

   Nature 600, 675–679 (2021)

   Abstract PDF PubMed Website

   Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.
2. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

   Polygenic Risk Score Task Force of the International Common Disease Alliance.

   Nature Medicine 27, 1876–1884 (2021)

   Abstract PDF PubMed

   Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance’s PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic. As polygenic risk scores move closer to widespread clinical use, this Perspective summarizes the benefits, risks and challenges to be overcome.
3. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

   Luo, Y., Kanai, M., Choi, W., ..., Li, X., Sakaue, S., Yamamoto, K., Ogawa, K., Gutierrez-Arcelus, M., Gregersen, P. K., Stuart, P. E., Elder, J. T., Forer, L., Schönherr, S., Fuchsberger, C., Smith, A. V., Fellay, J., Carrington, M., Haas, D. W., Guo, X., Palmer, N. D., Chen, Y.-D. I., Rotter, J. I., Taylor, K. D., Rich, S. S., Correa, A., Wilson, J. G., Kathiresan, S., Cho, M. H., Metspalu, A., Esko, T., Okada, Y., Han, B., McLaren, P. J., and Raychaudhuri, S. [Show fewer authors]

   Nature Genetics 53, 1504–1516 (2021)

   Abstract PDF PubMed medRxiv Code

   Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance. A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response.
4. A cross-population atlas of genetic associations for 220 human phenotypes

   *Sakaue, S., *Kanai, M., Tanigawa, Y., ..., Karjalainen, J., Kurki, M., Koshiba, S., Narita, A., Konuma, T., Yamamoto, K., Akiyama, M., Ishigaki, K., Suzuki, A., Suzuki, K., Obara, W., Yamaji, K., Takahashi, K., Asai, S., Takahashi, Y., Suzuki, T., Shinozaki, N., Yamaguchi, H., Minami, S., Murayama, S., Yoshimori, K., Nagayama, S., Obata, D., Higashiyama, M., Masumoto, A., Koretsune, Y., Ito, K., Terao, C., Yamauchi, T., Komuro, I., Kadowaki, T., Tamiya, G., Yamamoto, M., Nakamura, Y., Kubo, M., Murakami, Y., Yamamoto, K., Kamatani, Y., Palotie, A., Rivas, M. A., Daly, M. J., Matsuda, K., and Okada, Y. [Show fewer authors]

   Nature Genetics 53, 1415–1424 (2021)

   Abstract PDF PubMed medRxiv Website Press

   Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified  5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify  5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations.
5. Genome-wide functional screen of 3’UTR variants uncovers causal variants for human disease and evolution

   Griesemer, D., Xue, J. R., Reilly, S. K., ..., Ulirsch, J. C., Kukreja, K., Davis, J. R., Kanai, M., ..., Yang, D. K., Butts, J. C., Guney, M. H., Luban, J., Montgomery, S. B., Finucane, H. K., Novina, C. D., Tewhey, R., and Sabeti, P. C. [Show fewer authors]

   Cell 184, 5247–5260.e19 (2021)

   Abstract PubMed bioRxiv

   3’ untranslated region (3’UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3’UTRs (MPRAu) to sensitively assay 12,173 3’UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3’UTR function, suggesting that simple sequences predominately explain 3’UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3’UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.
6. Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR–FlowFISH

   Reilly, S. K., Gosai, S. J., Guiterrez, A., ..., Ulirsch, J. C., Kanai, M., ..., Berenzy, D., Kales, S., Butler, G. B., Gladden-Young, A., Finucane, H. K., Sabeti, P. C., and Tewhey, R. [Show fewer authors]

   Nature Genetics 53, 1166–1176 (2021)

   Abstract PDF PubMed bioRxiv Press

   Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed hybridization chain reaction fluorescence in situ hybridization coupled with flow cytometry (HCR–FlowFISH), a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CRISPR activity screen analysis (CASA), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene and display activating and/or silencing effects. At the cholesterol-level-associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominate causal variants and, importantly, identify their target genes. HCR–FlowFISH is a new approach to characterize CRISPR-perturbed cis-regulatory elements (CREs) via accurate quantification of native transcripts, alongside CRISPR activity screen analysis (CASA), a hierarchical Bayesian model to quantify CRE activity.
7. Mapping the human genetic architecture of COVID-19

   The COVID-19 Host Genetics Initiative.

   Nature 600, 472–477 (2021)

   Abstract PDF PubMed medRxiv Website Press

   The genetic makeup of an individual contributes to susceptibility and response to viral infection. While environmental, clinical and social factors play a role in exposure to SARS-CoV-2 and COVID-19 disease severity1,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. We describe the results of three genome-wide association meta-analyses comprised of up to 49,562 COVID-19 patients from 46 studies across 19 countries. We reported 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian Randomization analyses support a causal role for smoking and body mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19, with unprecedented speed, was made possible by the community of human genetic researchers coming together to prioritize sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
8. Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

   Wang, Q. S., Kelley, D. R., Ulirsch, J., Kanai, M., ..., Sadhuka, S., Cui, R., Albors, C., Cheng, N., Okada, Y., Aguet, F., Ardlie, K. G., MacArthur, D. G., and Finucane, H. K. [Show fewer authors]

   Nature Communications 12, 1–11 (2021)

   Abstract PDF PubMed bioRxiv Code Data

   The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes. Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci.
9. An X chromosome-wide meta-analysis based on Japanese cohorts revealed that non-autosomal variations are associated with serum urate

   Nakatochi, M., Toyoda, Y., Kanai, M., ..., Nakayama, A., Kawamura, Y., Hishida, A., Mikami, H., Matsuo, K., Takezaki, T., Momozawa, Y., Biobank Japan Project., Kamatani, Y., Ichihara, S., Shinomiya, N., Yokota, M., Wakai, K., Okada, Y., Matsuo, H., and Japan Uric Acid Genomics Consortium (Japan Urate). [Show fewer authors]

   Rheumatology 60, 4430–4432 (2021)

   PubMed
10. Genetic analyses identify widespread sex-differential participation bias

    Pirastu, N., Cordioli, M., Nandakumar, P., ..., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., ..., Rajagopal, V. M., Della Briotta Parolo, P., Baya, N., Carey, C. E., Karjalainen, J., Als, T. D., Zee, M. D., Day, F. R., Ong, K. K., Morisaki, T., Geus, E., Bellocco, R., Okada, Y., Børglum, A. D., Joshi, P., Auton, A., Hinds, D., Neale, B. M., Walters, R. K., Nivard, M. G., Perry, J. R. B., and Ganna, A. [Show fewer authors]

    Nature Genetics 53, 663–671 (2021)

    Abstract PDF PubMed bioRxiv

    Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 \times 10-36). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.
11. Population-specific causal disease effect sizes in functionally important regions impacted by selection

    Shi, H., Gazal, S., Kanai, M., ..., Koch, E. M., Schoech, A. P., Siewert, K. M., Kim, S. S., Luo, Y., Amariuta, T., Huang, H., Okada, Y., Raychaudhuri, S., Sunyaev, S. R., and Price, A. L. [Show fewer authors]

    Nature Communications 12, 1098 (2021)

    Abstract PDF PubMed bioRxiv Code

    Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82\times (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
12. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power

    Atkinson, E. G., Maihofer, A. X., Kanai, M., ..., Martin, A. R., Karczewski, K. J., Santoro, M. L., Ulirsch, J. C., Kamatani, Y., Okada, Y., Finucane, H. K., Koenen, K. C., Nievergelt, C. M., Daly, M. J., and Neale, B. M. [Show fewer authors]

    Nature Genetics 53, 195–204 (2021)

    Abstract PDF PubMed bioRxiv Code Press

    Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.

2020

1. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

   Chen, M.-H., Raffield, L. M., Mousas, A., ..., Sakaue, S., Huffman, J. E., Moscati, A., Trivedi, B., Jiang, T., Akbari, P., Vuckovic, D., Bao, E. L., Zhong, X., Manansala, R., Laplante, V., Chen, M., Lo, K. S., Qian, H., Lareau, C. A., Beaudoin, M., Hunt, K. A., Akiyama, M., Bartz, T. M., Ben-Shlomo, Y., Beswick, A., Bork-Jensen, J., Bottinger, E. P., Brody, J. A., Rooij, F. J. A., Chitrala, K., Cho, K., Choquet, H., Correa, A., Danesh, J., Di Angelantonio, E., Dimou, N., Ding, J., Elliott, P., Esko, T., Evans, M. K., Floyd, J. S., Broer, L., Grarup, N., Guo, M. H., Greinacher, A., Haessler, J., Hansen, T., Howson, J. M. M., Huang, Q. Q., Huang, W., Jorgenson, E., Kacprowski, T., Kähönen, M., Kamatani, Y., Kanai, M., ..., Karthikeyan, S., Koskeridis, F., Lange, L. A., Lehtimäki, T., Lerch, M. M., Linneberg, A., Liu, Y., Lyytikäinen, L.-P., Manichaikul, A., Martin, H. C., Matsuda, K., Mohlke, K. L., Mononen, N., Murakami, Y., Nadkarni, G. N., Nauck, M., Nikus, K., Ouwehand, W. H., Pankratz, N., Pedersen, O., Preuss, M., Psaty, B. M., Raitakari, O. T., Roberts, D. J., Rich, S. S., Rodriguez, B. A. T., Rosen, J. D., Rotter, J. I., Schubert, P., Spracklen, C. N., Surendran, P., Tang, H., Tardif, J.-C., Trembath, R. C., Ghanbari, M., Völker, U., Völzke, H., Watkins, N. A., Zonderman, A. B., Wilson, P. W. F., Li, Y., Butterworth, A. S., Gauchat, J.-F., Chiang, C. W. K., Li, B., Loos, R. J. F., Astle, W. J., Evangelou, E., Heel, D. A., Sankaran, V. G., Okada, Y., Soranzo, N., Johnson, A. D., Reiner, A. P., Auer, P. L., and Lettre, G. [Show fewer authors]

   Cell 182, 1198–1213.e14 (2020)

   Abstract PubMed bioRxiv Press

   Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 \times 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.
2. The Polygenic and Monogenic Basis of Blood Traits and Diseases

   Vuckovic, D., Bao, E. L., Akbari, P., ..., Lareau, C. A., Mousas, A., Jiang, T., Chen, M.-H., Raffield, L. M., Tardaguila, M., Huffman, J. E., Ritchie, S. C., Megy, K., Ponstingl, H., Penkett, C. J., Albers, P. K., Wigdor, E. M., Sakaue, S., Moscati, A., Manansala, R., Lo, K. S., Qian, H., Akiyama, M., Bartz, T. M., Ben-Shlomo, Y., Beswick, A., Bork-Jensen, J., Bottinger, E. P., Brody, J. A., Rooij, F. J. A., Chitrala, K. N., Wilson, P. W. F., Choquet, H., Danesh, J., Di Angelantonio, E., Dimou, N., Ding, J., Elliott, P., Esko, T., Evans, M. K., Felix, S. B., Floyd, J. S., Broer, L., Grarup, N., Guo, M. H., Guo, Q., Greinacher, A., Haessler, J., Hansen, T., Howson, J. M. M., Huang, W., Jorgenson, E., Kacprowski, T., Kähönen, M., Kamatani, Y., Kanai, M., ..., Karthikeyan, S., Koskeridis, F., Lange, L. A., Lehtimäki, T., Linneberg, A., Liu, Y., Lyytikäinen, L.-P., Manichaikul, A., Matsuda, K., Mohlke, K. L., Mononen, N., Murakami, Y., Nadkarni, G. N., Nikus, K., Pankratz, N., Pedersen, O., Preuss, M., Psaty, B. M., Raitakari, O. T., Rich, S. S., Rodriguez, B. A. T., Rosen, J. D., Rotter, J. I., Schubert, P., Spracklen, C. N., Surendran, P., Tang, H., Tardif, J.-C., Ghanbari, M., Völker, U., Völzke, H., Watkins, N. A., Weiss, S., Cai, N., Kundu, K., Watt, S. B., Walter, K., Zonderman, A. B., Cho, K., Li, Y., Loos, R. J. F., Knight, J. C., Georges, M., Stegle, O., Evangelou, E., Okada, Y., Roberts, D. J., Inouye, M., Johnson, A. D., Auer, P. L., Astle, W. J., Reiner, A. P., Butterworth, A. S., Ouwehand, W. H., Lettre, G., Sankaran, V. G., and Soranzo, N. [Show fewer authors]

   Cell 182, 1214–1231.e11 (2020)

   Abstract PDF PubMed medRxiv Press

   Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.
3. Association of the RPA3-UMAD1 locus with interstitial lung diseases complicated with rheumatoid arthritis in Japanese

   Shirai, Y., Honda, S., Ikari, K., Kanai, M., ..., Takeda, Y., Kamatani, Y., Morisaki, T., Tanaka, E., Kumanogoh, A., Harigai, M., and Okada, Y. [Show fewer authors]

   Annals of the Rheumatic Diseases 79, 1305–1309 (2020)

   Abstract PDF PubMed Press

   OBJECTIVES: The genetic background of rheumatoid arthritis-interstitial lung disease (RA-ILD) has been evaluated in Europeans, but little knowledge has been obtained in non-Europeans. This study aimed to elucidate genome-wide risk of RA-ILD in non-Europeans. METHODS: We performed an initial genome-wide association study (GWAS) of RA-ILD in the Japanese population. By conducting the meta-analysis of the three GWAS datasets of the RA cohorts and biobank of Japanese, our study included 358 RA-ILD cases and 4550 RA subjects without ILD. We then conducted the stratified analysis of the effect of the GWAS risk allele in each CT image pattern. RESULTS: We identified one novel RA-ILD risk locus at 7p21 that satisfied the genome-wide significance threshold (rs12702634 at RPA3-UMAD1, OR=2.04, 95% CI 1.59 to 2.60, p=1.5\times10-8). Subsequent stratified analysis based on the CT image patterns demonstrated that the effect size of the RA-ILD risk allele (rs12702634-C) was large with the UIP pattern (OR=1.86, 95% CI 0.97 to 3.58, p=0.062) and the probable UIP pattern (OR=2.26, 95% CI 1.36 to 3.73, p=0.0015). CONCLUSION: We revealed one novel genetic association with RA-ILD in Japanese. The RA-ILD risk of the identified variant at RPA3-UMAD1 was relatively high in the CT image patterns related to fibrosis. Our study should contribute to elucidation of the complicated aetiology of RA-ILD.
4. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases

   Ishigaki, K., Akiyama, M., Kanai, M., ..., Takahashi, A., Kawakami, E., Sugishita, H., Sakaue, S., Matoba, N., Low, S.-K., Okada, Y., Terao, C., Amariuta, T., Gazal, S., Kochi, Y., Horikoshi, M., Suzuki, K., Ito, K., Koyama, S., Ozaki, K., Niida, S., Sakata, Y., Sakata, Y., Kohno, T., Shiraishi, K., Momozawa, Y., Hirata, M., Matsuda, K., Ikeda, M., Iwata, N., Ikegawa, S., Kou, I., Tanaka, T., Nakagawa, H., Suzuki, A., Hirota, T., Tamari, M., Chayama, K., Miki, D., Mori, M., Nagayama, S., Daigo, Y., Miki, Y., Katagiri, T., Ogawa, O., Obara, W., Ito, H., Yoshida, T., Imoto, I., Takahashi, T., Tanikawa, C., Suzuki, T., Sinozaki, N., Minami, S., Yamaguchi, H., Asai, S., Takahashi, Y., Yamaji, K., Takahashi, K., Fujioka, T., Takata, R., Yanai, H., Masumoto, A., Koretsune, Y., Kutsumi, H., Higashiyama, M., Murayama, S., Minegishi, N., Suzuki, K., Tanno, K., Shimizu, A., Yamaji, T., Iwasaki, M., Sawada, N., Uemura, H., Tanaka, K., Naito, M., Sasaki, M., Wakai, K., Tsugane, S., Yamamoto, M., Yamamoto, K., Murakami, Y., Nakamura, Y., Raychaudhuri, S., Inazawa, J., Yamauchi, T., Kadowaki, T., Kubo, M., and Kamatani, Y. [Show fewer authors]

   Nature Genetics 52, 669–679 (2020)

   Abstract PDF PubMed bioRxiv Press

   The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (P < 9.58 \times 10-9). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease) and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.
5. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction

   Sakaue, S., Hirata, J., Kanai, M., ..., Suzuki, K., Akiyama, M., Lai Too, C., Arayssi, T., Hammoudeh, M., Al Emadi, S., Masri, B. K., Halabi, H., Badsha, H., Uthman, I. W., Saxena, R., Padyukov, L., Hirata, M., Matsuda, K., Murakami, Y., Kamatani, Y., and Okada, Y. [Show fewer authors]

   Nature Communications 11, 1569 (2020)

   Abstract PDF PubMed Code Press

   The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.
6. Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

   *Sakaue, S., *Kanai, M., Karjalainen, J., ..., Akiyama, M., Kurki, M., Matoba, N., Takahashi, A., Hirata, M., Kubo, M., Matsuda, K., Murakami, Y., Daly, M. J., Kamatani, Y., and Okada, Y. [Show fewer authors]

   Nature Medicine 26, 542–548 (2020)

   Abstract PDF PubMed bioRxiv Press

   While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks1, a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted2. To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898; BioBank Japan (n = 179,066), UK Biobank (n = 361,194) and FinnGen (n = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02-1.04], Pmeta = 3.9 \times 10-13) and parental lifespan (hazard ratio = 1.06[1.06-1.07], P = 2.0 \times 10-86). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals (Pheterogeneity = 9.5 \times 10-8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype-phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health.
7. Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features

   Ray, J. P., Boer, C. G., Fulco, C. P., ..., Lareau, C. A., Kanai, M., ..., Ulirsch, J. C., Tewhey, R., Ludwig, L. S., Reilly, S. K., Bergman, D. T., Engreitz, J. M., Issner, R., Finucane, H. K., Lander, E. S., Regev, A., and Hacohen, N. [Show fewer authors]

   Nature Communications 11, 1237 (2020)

   Abstract PDF PubMed

   Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we use seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in five disease-relevant immune cell lines, based on a set of features related to regulatory potential. Trait/disease-associated variants are enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 have at least one variant meeting one or both of these criteria, 5 of which are further supported by genetic fine-mapping. Our work provides a comprehensive strategy to characterize genetic variation at important disease-associated loci, and aids in the effort to identify trait causal genetic variants.
8. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits

   Matoba, N., Akiyama, M., Ishigaki, K., Kanai, M., ..., Takahashi, A., Momozawa, Y., Ikegawa, S., Ikeda, M., Iwata, N., Hirata, M., Matsuda, K., Murakami, Y., Kubo, M., Kamatani, Y., and Okada, Y. [Show fewer authors]

   Nature Human Behaviour 4, 308–316 (2020)

   Abstract PDF PubMed Press

   Dietary habits are important factors in our lifestyle, and confer both susceptibility to and protection from a variety of human diseases. We performed genome-wide association studies for 13 dietary habits including consumption of alcohol (ever versus never drinkers and drinks per week), beverages (coffee, green tea and milk) and foods (yoghurt, cheese, natto, tofu, fish, small whole fish, vegetables and meat) in Japanese individuals (n = 58,610-165,084) collected by BioBank Japan, the nationwide hospital-based genome cohort. Significant associations were found in nine genetic loci (MCL1-ENSA, GCKR, AGR3-AHR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, CYP1A2-CSK and ADORA2A-AS1) for 13 dietary traits (P < 3.8 \times 10-9). Of these, ten associations between five loci and eight traits were new findings. Furthermore, a phenome-wide association study revealed that five of the dietary trait-associated loci have pleiotropic effects on multiple human complex diseases and clinical measurements. Our findings provide new insight into the genetics of habitual consumption.

2019

1. Associations of autozygosity with a broad range of human phenotypes

   Clark, D. W., Okada, Y., Moore, K. H. S., ..., Mason, D., Pirastu, N., Gandin, I., Mattsson, H., Barnes, C. L. K., Lin, K., Zhao, J. H., Deelen, P., Rohde, R., Schurmann, C., Guo, X., Giulianini, F., Zhang, W., Medina-Gomez, C., Karlsson, R., Bao, Y., Bartz, T. M., Baumbach, C., Biino, G., Bixley, M. J., Brumat, M., Chai, J.-F., Corre, T., Cousminer, D. L., Dekker, A. M., Eccles, D. A., Eijk, K. R., Fuchsberger, C., Gao, H., Germain, M., Gordon, S. D., Haan, H. G., Harris, S. E., Hofer, E., Huerta-Chagoya, A., Igartua, C., Jansen, I. E., Jia, Y., Kacprowski, T., Karlsson, T., Kleber, M. E., Li, S. A., Li-Gao, R., Mahajan, A., Matsuda, K., Meidtner, K., Meng, W., Montasser, M. E., Most, P. J., Munz, M., Nutile, T., Palviainen, T., Prasad, G., Prasad, R. B., Priyanka, T. D. S., Rizzi, F., Salvi, E., Sapkota, B. R., Shriner, D., Skotte, L., Smart, M. C., Smith, A. V., Spek, A., Spracklen, C. N., Strawbridge, R. J., Tajuddin, S. M., Trompet, S., Turman, C., Verweij, N., Viberti, C., Wang, L., Warren, H. R., Wootton, R. E., Yanek, L. R., Yao, J., Yousri, N. A., Zhao, W., Adeyemo, A. A., Afaq, S., Aguilar-Salinas, C. A., Akiyama, M., Albert, M. L., Allison, M. A., Alver, M., Aung, T., Azizi, F., Bentley, A. R., Boeing, H., Boerwinkle, E., Borja, J. B., Borst, G. J., Bottinger, E. P., Broer, L., Campbell, H., Chanock, S., Chee, M.-L., Chen, G., Chen, Y.-D. I., Chen, Z., Chiu, Y.-F., Cocca, M., Collins, F. S., Concas, M. P., Corley, J., Cugliari, G., Dam, R. M., Damulina, A., Daneshpour, M. S., Day, F. R., Delgado, G. E., Dhana, K., Doney, A. S. F., Dörr, M., Doumatey, A. P., Dzimiri, N., Ebenesersdóttir, S. S., Elliott, J., Elliott, P., Ewert, R., Felix, J. F., Fischer, K., Freedman, B. I., Girotto, G., Goel, A., Gögele, M., Goodarzi, M. O., Graff, M., Granot-Hershkovitz, E., Grodstein, F., Guarrera, S., Gudbjartsson, D. F., Guity, K., Gunnarsson, B., Guo, Y., Hagenaars, S. P., Haiman, C. A., Halevy, A., Harris, T. B., Hedayati, M., Heel, D. A., Hirata, M., Höfer, I., Hsiung, C. A., Huang, J., Hung, Y.-J., Ikram, M. A., Jagadeesan, A., Jousilahti, P., Kamatani, Y., Kanai, M., ..., Kerrison, N. D., Kessler, T., Khaw, K.-T., Khor, C. C., Kleijn, D. P. V., Koh, W.-P., Kolcic, I., Kraft, P., Krämer, B. K., Kutalik, Z., Kuusisto, J., Langenberg, C., Launer, L. J., Lawlor, D. A., Lee, I.-T., Lee, W.-J., Lerch, M. M., Li, L., Liu, J., Loh, M., London, S. J., Loomis, S., Lu, Y., Luan, J., Mägi, R., Manichaikul, A. W., Manunta, P., Másson, G., Matoba, N., Mei, X. W., Meisinger, C., Meitinger, T., Mezzavilla, M., Milani, L., Millwood, I. Y., Momozawa, Y., Moore, A., Morange, P.-E., Moreno-Macías, H., Mori, T. A., Morrison, A. C., Muka, T., Murakami, Y., Murray, A. D., Mutsert, R., Mychaleckyj, J. C., Nalls, M. A., Nauck, M., Neville, M. J., Nolte, I. M., Ong, K. K., Orozco, L., Padmanabhan, S., Pálsson, G., Pankow, J. S., Pattaro, C., Pattie, A., Polasek, O., Poulter, N., Pramstaller, P. P., Quintana-Murci, L., Räikkönen, K., Ralhan, S., Rao, D. C., Rheenen, W., Rich, S. S., Ridker, P. M., Rietveld, C. A., Robino, A., Rooij, F. J. A., Ruggiero, D., Saba, Y., Sabanayagam, C., Sabater-Lleal, M., Sala, C. F., Salomaa, V., Sandow, K., Schmidt, H., Scott, L. J., Scott, W. R., Sedaghati-Khayat, B., Sennblad, B., Setten, J., Sever, P. J., Sheu, W. H.-H., Shi, Y., Shrestha, S., Shukla, S. R., Sigurdsson, J. K., Sikka, T. T., Singh, J. R., Smith, B. H., Stančáková, A., Stanton, A., Starr, J. M., Stefansdottir, L., Straker, L., Sulem, P., Sveinbjornsson, G., Swertz, M. A., Taylor, A. M., Taylor, K. D., Terzikhan, N., Tham, Y.-C., Thorleifsson, G., Thorsteinsdottir, U., Tillander, A., Tracy, R. P., Tusié-Luna, T., Tzoulaki, I., Vaccargiu, S., Vangipurapu, J., Veldink, J. H., Vitart, V., Völker, U., Vuoksimaa, E., Wakil, S. M., Waldenberger, M., Wander, G. S., Wang, Y. X., Wareham, N. J., Wild, S., Yajnik, C. S., Yuan, J.-M., Zeng, L., Zhang, L., Zhou, J., Amin, N., Asselbergs, F. W., Bakker, S. J. L., Becker, D. M., Lehne, B., Bennett, D. A., Berg, L. H., Berndt, S. I., Bharadwaj, D., Bielak, L. F., Bochud, M., Boehnke, M., Bouchard, C., Bradfield, J. P., Brody, J. A., Campbell, A., Carmi, S., Caulfield, M. J., Cesarini, D., Chambers, J. C., Chandak, G. R., Cheng, C.-Y., Ciullo, M., Cornelis, M., Cusi, D., Smith, G. D., Deary, I. J., Dorajoo, R., Duijn, C. M., Ellinghaus, D., Erdmann, J., Eriksson, J. G., Evangelou, E., Evans, M. K., Faul, J. D., Feenstra, B., Feitosa, M., Foisy, S., Franke, A., Friedlander, Y., Gasparini, P., Gieger, C., Gonzalez, C., Goyette, P., Grant, S. F. A., Griffiths, L. R., Groop, L., Gudnason, V., Gyllensten, U., Hakonarson, H., Hamsten, A., Harst, P., Heng, C.-K., Hicks, A. A., Hochner, H., Huikuri, H., Hunt, S. C., Jaddoe, V. W. V., De Jager, P. L., Johannesson, M., Johansson, Å., Jonas, J. B., Jukema, J. W., Junttila, J., Kaprio, J., Kardia, S. L. R., Karpe, F., Kumari, M., Laakso, M., Laan, S. W., Lahti, J., Laudes, M., Lea, R. A., Lieb, W., Lumley, T., Martin, N. G., März, W., Matullo, G., McCarthy, M. I., Medland, S. E., Merriman, T. R., Metspalu, A., Meyer, B. F., Mohlke, K. L., Montgomery, G. W., Mook-Kanamori, D., Munroe, P. B., North, K. E., Nyholt, D. R., O’connell, J. R., Ober, C., Oldehinkel, A. J., Palmas, W., Palmer, C., Pasterkamp, G. G., Patin, E., Pennell, C. E., Perusse, L., Peyser, P. A., Pirastu, M., Polderman, T. J. C., Porteous, D. J., Posthuma, D., Psaty, B. M., Rioux, J. D., Rivadeneira, F., Rotimi, C., Rotter, J. I., Rudan, I., Den Ruijter, H. M., Sanghera, D. K., Sattar, N., Schmidt, R., Schulze, M. B., Schunkert, H., Scott, R. A., Shuldiner, A. R., Sim, X., Small, N., Smith, J. A., Sotoodehnia, N., Tai, E.-S., Teumer, A., Timpson, N. J., Toniolo, D., Tregouet, D.-A., Tuomi, T., Vollenweider, P., Wang, C. A., Weir, D. R., Whitfield, J. B., Wijmenga, C., Wong, T.-Y., Wright, J., Yang, J., Yu, L., Zemel, B. S., Zonderman, A. B., Perola, M., Magnusson, P. K. E., Uitterlinden, A. G., Kooner, J. S., Chasman, D. I., Loos, R. J. F., Franceschini, N., Franke, L., Haley, C. S., Hayward, C., Walters, R. G., Perry, J. R. B., Esko, T., Helgason, A., Stefansson, K., Joshi, P. K., Kubo, M., and Wilson, J. F. [Show fewer authors]

   Nature Communications 10, 4957 (2019)

   Abstract PDF PubMed

   In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.
2. Polygenic burden in focal and generalized epilepsies

   Leu, C., Stevelink, R., Smith, A. W., ..., Goleva, S. B., Kanai, M., ..., Ferguson, L., Campbell, C., Kamatani, Y., Okada, Y., Sisodiya, S. M., Cavalleri, G. L., Koeleman, B. P. C., Lerche, H., Jehi, L., Davis, L. K., Najm, I. M., Palotie, A., Daly, M. J., Busch, R. M., and Lal, D. [Show fewer authors]

   Brain 142, 3473–3481 (2019)

   Abstract PDF PubMed

   Rare genetic variants can cause epilepsy, and genetic testing has been widely adopted for severe, paediatric-onset epilepsies. The phenotypic consequences of common genetic risk burden for epilepsies and their potential future clinical applications have not yet been determined. Using polygenic risk scores (PRS) from a European-ancestry genome-wide association study in generalized and focal epilepsy, we quantified common genetic burden in patients with generalized epilepsy (GE-PRS) or focal epilepsy (FE-PRS) from two independent non-Finnish European cohorts (Epi25 Consortium, n = 5705; Cleveland Clinic Epilepsy Center, n = 620; both compared to 20 435 controls). One Finnish-ancestry population isolate (Finnish-ancestry Epi25, n = 449; compared to 1559 controls), two European-ancestry biobanks (UK Biobank, n = 383 656; Vanderbilt biorepository, n = 49 494), and one Japanese-ancestry biobank (BioBank Japan, n = 168 680) were used for additional replications. Across 8386 patients with epilepsy and 622 212 population controls, we found and replicated significantly higher GE-PRS in patients with generalized epilepsy of European-ancestry compared to patients with focal epilepsy (Epi25: P = 1.64\times10-15; Cleveland: P = 2.85\times10-4; Finnish-ancestry Epi25: P = 1.80\times10-4) or population controls (Epi25: P = 2.35\times10-70; Cleveland: P = 1.43\times10-7; Finnish-ancestry Epi25: P = 3.11\times10-4; UK Biobank and Vanderbilt biorepository meta-analysis: P = 7.99\times10-4). FE-PRS were significantly higher in patients with focal epilepsy compared to controls in the non-Finnish, non-biobank cohorts (Epi25: P = 5.74\times10-19; Cleveland: P = 1.69\times10-6). European ancestry-derived PRS did not predict generalized epilepsy or focal epilepsy in Japanese-ancestry individuals. Finally, we observed a significant 4.6-fold and a 4.5-fold enrichment of patients with generalized epilepsy compared to controls in the top 0.5% highest GE-PRS of the two non-Finnish European cohorts (Epi25: P = 2.60\times10-15; Cleveland: P = 1.39\times10-2). We conclude that common variant risk associated with epilepsy is significantly enriched in multiple cohorts of patients with epilepsy compared to controls-in particular for generalized epilepsy. As sample sizes and PRS accuracy continue to increase with further common variant discovery, PRS could complement established clinical biomarkers and augment genetic testing for patient classification, comorbidity research, and potentially targeted treatment.
3. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

   Tin, A., Marten, J., Halperin Kuhns, V. L., ..., Li, Y., Wuttke, M., Kirsten, H., Sieber, K. B., Qiu, C., Gorski, M., Yu, Z., Giri, A., Sveinbjornsson, G., Li, M., Chu, A. Y., Hoppmann, A., O’Connor, L. J., Prins, B., Nutile, T., Noce, D., Akiyama, M., Cocca, M., Ghasemi, S., Most, P. J., Horn, K., Xu, Y., Fuchsberger, C., Sedaghat, S., Afaq, S., Amin, N., Ärnlöv, J., Bakker, S. J. L., Bansal, N., Baptista, D., Bergmann, S., Biggs, M. L., Biino, G., Boerwinkle, E., Bottinger, E. P., Boutin, T. S., Brumat, M., Burkhardt, R., Campana, E., Campbell, A., Campbell, H., Carroll, R. J., Catamo, E., Chambers, J. C., Ciullo, M., Concas, M. P., Coresh, J., Corre, T., Cusi, D., Felicita, S. C., Borst, M. H., De Grandi, A., Mutsert, R., Vries, A. P. J., Delgado, G., Demirkan, A., Devuyst, O., Dittrich, K., Eckardt, K.-U., Ehret, G., Endlich, K., Evans, M. K., Gansevoort, R. T., Gasparini, P., Giedraitis, V., Gieger, C., Girotto, G., Gögele, M., Gordon, S. D., Gudbjartsson, D. F., Gudnason, V., Haller, T., Hamet, P., Harris, T. B., Hayward, C., Hicks, A. A., Hofer, E., Holm, H., Huang, W., Hutri-Kähönen, N., Hwang, S.-J., Ikram, M. A., Lewis, R. M., Ingelsson, E., Jakobsdottir, J., Jonsdottir, I., Jonsson, H., Joshi, P. K., Josyula, N. S., Jung, B., Kähönen, M., Kamatani, Y., Kanai, M., ..., Kerr, S. M., Kiess, W., Kleber, M. E., Koenig, W., Kooner, J. S., Körner, A., Kovacs, P., Krämer, B. K., Kronenberg, F., Kubo, M., Kühnel, B., La Bianca, M., Lange, L. A., Lehne, B., Lehtimäki, T., Liu, J., Loeffler, M., Loos, R. J. F., Lyytikäinen, L.-P., Magi, R., Mahajan, A., Martin, N. G., März, W., Mascalzoni, D., Matsuda, K., Meisinger, C., Meitinger, T., Metspalu, A., Milaneschi, Y., O’Donnell, C. J., Wilson, O. D., Gaziano, J. M., Mishra, P. P., Mohlke, K. L., Mononen, N., Montgomery, G. W., Mook-Kanamori, D. O., Müller-Nurasyid, M., Nadkarni, G. N., Nalls, M. A., Nauck, M., Nikus, K., Ning, B., Nolte, I. M., Noordam, R., O’Connell, J. R., Olafsson, I., Padmanabhan, S., Penninx, B. W. J. H., Perls, T., Peters, A., Pirastu, M., Pirastu, N., Pistis, G., Polasek, O., Ponte, B., Porteous, D. J., Poulain, T., Preuss, M. H., Rabelink, T. J., Raffield, L. M., Raitakari, O. T., Rettig, R., Rheinberger, M., Rice, K. M., Rizzi, F., Robino, A., Rudan, I., Krajcoviechova, A., Cifkova, R., Rueedi, R., Ruggiero, D., Ryan, K. A., Saba, Y., Salvi, E., Schmidt, H., Schmidt, R., Shaffer, C. M., Smith, A. V., Smith, B. H., Spracklen, C. N., Strauch, K., Stumvoll, M., Sulem, P., Tajuddin, S. M., Teren, A., Thiery, J., Thio, C. H. L., Thorsteinsdottir, U., Toniolo, D., Tönjes, A., Tremblay, J., Uitterlinden, A. G., Vaccargiu, S., Harst, P., Duijn, C. M., Verweij, N., Völker, U., Vollenweider, P., Waeber, G., Waldenberger, M., Whitfield, J. B., Wild, S. H., Wilson, J. F., Yang, Q., Zhang, W., Zonderman, A. B., Bochud, M., Wilson, J. G., Pendergrass, S. A., Ho, K., Parsa, A., Pramstaller, P. P., Psaty, B. M., Böger, C. A., Snieder, H., Butterworth, A. S., Okada, Y., Edwards, T. L., Stefansson, K., Susztak, K., Scholz, M., Heid, I. M., Hung, A. M., Teumer, A., Pattaro, C., Woodward, O. M., Vitart, V., and Köttgen, A. [Show fewer authors]

   Nature Genetics 51, 1459–1474 (2019)

   Abstract PDF PubMed

   Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.
4. Characterizing rare and low-frequency height-associated variants in the Japanese population

   Akiyama, M., Ishigaki, K., Sakaue, S., ..., Momozawa, Y., Horikoshi, M., Hirata, M., Matsuda, K., Ikegawa, S., Takahashi, A., Kanai, M., ..., Suzuki, S., Matsui, D., Naito, M., Yamaji, T., Iwasaki, M., Sawada, N., Tanno, K., Sasaki, M., Hozawa, A., Minegishi, N., Wakai, K., Tsugane, S., Shimizu, A., Yamamoto, M., Okada, Y., Murakami, Y., Kubo, M., and Kamatani, Y. [Show fewer authors]

   Nature Communications 10, 4393 (2019)

   Abstract PDF PubMed Press

   Human height is a representative phenotype to elucidate genetic architecture. However, the majority of large studies have been performed in European population. To investigate the rare and low-frequency variants associated with height, we construct a reference panel (N = 3,541) for genotype imputation by integrating the whole-genome sequence data from 1,037 Japanese with that of the 1000 Genomes Project, and perform a genome-wide association study in 191,787 Japanese. We report 573 height-associated variants, including 22 rare and 42 low-frequency variants. These 64 variants explain 1.7% of the phenotypic variance. Furthermore, a gene-based analysis identifies two genes with multiple height-increasing rare and low-frequency nonsynonymous variants (SLC27A3 and CYP26B1; PSKAT-O < 2.5 \times 10-6). Our analysis shows a general tendency of the effect sizes of rare variants towards increasing height, which is contrary to findings among Europeans, suggesting that height-associated rare variants are under different selection pressure in Japanese and European populations.
5. A catalog of genetic loci associated with kidney function from analyses of a million individuals

   Wuttke, M., Li, Y., Li, M., ..., Sieber, K. B., Feitosa, M. F., Gorski, M., Tin, A., Wang, L., Chu, A. Y., Hoppmann, A., Kirsten, H., Giri, A., Chai, J.-F., Sveinbjornsson, G., Tayo, B. O., Nutile, T., Fuchsberger, C., Marten, J., Cocca, M., Ghasemi, S., Xu, Y., Horn, K., Noce, D., Most, P. J., Sedaghat, S., Yu, Z., Akiyama, M., Afaq, S., Ahluwalia, T. S., Almgren, P., Amin, N., Ärnlöv, J., Bakker, S. J. L., Bansal, N., Baptista, D., Bergmann, S., Biggs, M. L., Biino, G., Boehnke, M., Boerwinkle, E., Boissel, M., Bottinger, E. P., Boutin, T. S., Brenner, H., Brumat, M., Burkhardt, R., Butterworth, A. S., Campana, E., Campbell, A., Campbell, H., Canouil, M., Carroll, R. J., Catamo, E., Chambers, J. C., Chee, M.-L., Chee, M.-L., Chen, X., Cheng, C.-Y., Cheng, Y., Christensen, K., Cifkova, R., Ciullo, M., Concas, M. P., Cook, J. P., Coresh, J., Corre, T., Sala, C. F., Cusi, D., Danesh, J., Daw, E. W., Borst, M. H., De Grandi, A., Mutsert, R., Vries, A. P. J., Degenhardt, F., Delgado, G., Demirkan, A., Di Angelantonio, E., Dittrich, K., Divers, J., Dorajoo, R., Eckardt, K.-U., Ehret, G., Elliott, P., Endlich, K., Evans, M. K., Felix, J. F., Foo, V. H. X., Franco, O. H., Franke, A., Freedman, B. I., Freitag-Wolf, S., Friedlander, Y., Froguel, P., Gansevoort, R. T., Gao, H., Gasparini, P., Gaziano, J. M., Giedraitis, V., Gieger, C., Girotto, G., Giulianini, F., Gögele, M., Gordon, S. D., Gudbjartsson, D. F., Gudnason, V., Haller, T., Hamet, P., Harris, T. B., Hartman, C. A., Hayward, C., Hellwege, J. N., Heng, C.-K., Hicks, A. A., Hofer, E., Huang, W., Hutri-Kähönen, N., Hwang, S.-J., Ikram, M. A., Indridason, O. S., Ingelsson, E., Ising, M., Jaddoe, V. W. V., Jakobsdottir, J., Jonas, J. B., Joshi, P. K., Josyula, N. S., Jung, B., Kähönen, M., Kamatani, Y., Kammerer, C. M., Kanai, M., ..., Kastarinen, M., Kerr, S. M., Khor, C.-C., Kiess, W., Kleber, M. E., Koenig, W., Kooner, J. S., Körner, A., Kovacs, P., Kraja, A. T., Krajcoviechova, A., Kramer, H., Krämer, B. K., Kronenberg, F., Kubo, M., Kühnel, B., Kuokkanen, M., Kuusisto, J., La Bianca, M., Laakso, M., Lange, L. A., Langefeld, C. D., Lee, J. J.-M., Lehne, B., Lehtimäki, T., Lieb, W., Lim, S.-C., Lind, L., Lindgren, C. M., Liu, J., Liu, J., Loeffler, M., Loos, R. J. F., Lucae, S., Lukas, M. A., Lyytikäinen, L.-P., Mägi, R., Magnusson, P. K. E., Mahajan, A., Martin, N. G., Martins, J., März, W., Mascalzoni, D., Matsuda, K., Meisinger, C., Meitinger, T., Melander, O., Metspalu, A., Mikaelsdottir, E. K., Milaneschi, Y., Miliku, K., Mishra, P. P., Mohlke, K. L., Mononen, N., Montgomery, G. W., Mook-Kanamori, D. O., Mychaleckyj, J. C., Nadkarni, G. N., Nalls, M. A., Nauck, M., Nikus, K., Ning, B., Nolte, I. M., Noordam, R., O’Connell, J., O’Donoghue, M. L., Olafsson, I., Oldehinkel, A. J., Orho-Melander, M., Ouwehand, W. H., Padmanabhan, S., Palmer, N. D., Palsson, R., Penninx, B. W. J. H., Perls, T., Perola, M., Pirastu, M., Pirastu, N., Pistis, G., Podgornaia, A. I., Polasek, O., Ponte, B., Porteous, D. J., Poulain, T., Pramstaller, P. P., Preuss, M. H., Prins, B. P., Province, M. A., Rabelink, T. J., Raffield, L. M., Raitakari, O. T., Reilly, D. F., Rettig, R., Rheinberger, M., Rice, K. M., Ridker, P. M., Rivadeneira, F., Rizzi, F., Roberts, D. J., Robino, A., Rossing, P., Rudan, I., Rueedi, R., Ruggiero, D., Ryan, K. A., Saba, Y., Sabanayagam, C., Salomaa, V., Salvi, E., Saum, K.-U., Schmidt, H., Schmidt, R., Schöttker, B., Schulz, C.-A., Schupf, N., Shaffer, C. M., Shi, Y., Smith, A. V., Smith, B. H., Soranzo, N., Spracklen, C. N., Strauch, K., Stringham, H. M., Stumvoll, M., Svensson, P. O., Szymczak, S., Tai, E.-S., Tajuddin, S. M., Tan, N. Y. Q., Taylor, K. D., Teren, A., Tham, Y.-C., Thiery, J., Thio, C. H. L., Thomsen, H., Thorleifsson, G., Toniolo, D., Tönjes, A., Tremblay, J., Tzoulaki, I., Uitterlinden, A. G., Vaccargiu, S., Dam, R. M., Harst, P., Duijn, C. M., Velez Edward, D. R., Verweij, N., Vogelezang, S., Völker, U., Vollenweider, P., Waeber, G., Waldenberger, M., Wallentin, L., Wang, Y. X., Wang, C., Waterworth, D. M., Bin Wei, W., White, H., Whitfield, J. B., Wild, S. H., Wilson, J. F., Wojczynski, M. K., Wong, C., Wong, T.-Y., Xu, L., Yang, Q., Yasuda, M., Yerges-Armstrong, L. M., Zhang, W., Zonderman, A. B., Rotter, J. I., Bochud, M., Psaty, B. M., Vitart, V., Wilson, J. G., Dehghan, A., Parsa, A., Chasman, D. I., Ho, K., Morris, A. P., Devuyst, O., Akilesh, S., Pendergrass, S. A., Sim, X., Böger, C. A., Okada, Y., Edwards, T. L., Snieder, H., Stefansson, K., Hung, A. M., Heid, I. M., Scholz, M., Teumer, A., Köttgen, A., and Pattaro, C. [Show fewer authors]

   Nature Genetics 51, 957–972 (2019)

   Abstract PDF PubMed

   Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
6. Genome-wide meta-analysis identifies multiple novel loci associated with serum uric acid levels in Japanese individuals

   *Nakatochi, M., *Kanai, M., *Nakayama, A., ..., *Hishida, A., *Kawamura, Y., Ichihara, S., Akiyama, M., Ikezaki, H., Furusyo, N., Shimizu, S., Yamamoto, K., Hirata, M., Okada, R., Kawai, S., Kawaguchi, M., Nishida, Y., Shimanoe, C., Ibusuki, R., Takezaki, T., Nakajima, M., Takao, M., Ozaki, E., Matsui, D., Nishiyama, T., Suzuki, S., Takashima, N., Kita, Y., Endoh, K., Kuriki, K., Uemura, H., Arisawa, K., Oze, I., Matsuo, K., Nakamura, Y., Mikami, H., Tamura, T., Nakashima, H., Nakamura, T., Kato, N., Matsuda, K., Murakami, Y., Matsubara, T., Naito, M., Kubo, M., Kamatani, Y., Shinomiya, N., Yokota, M., Wakai, K., Okada, Y., and Matsuo, H. [Show fewer authors]

   Communications Biology 2, 115 (2019)

   Abstract PDF PubMed Press

   Gout is a common arthritis caused by elevated serum uric acid (SUA) levels. Here we investigated loci influencing SUA in a genome-wide meta-analysis with 121,745 Japanese subjects. We identified 8948 variants at 36 genomic loci (P<5 \times 10-8) including eight novel loci. Of these, missense variants of SESN2 and PNPLA3 were predicted to be damaging to the function of these proteins; another five loci-TMEM18, TM4SF4, MXD3-LMAN2, PSORS1C1-PSORS1C2, and HNF4A-are related to cell metabolism, proliferation, or oxidative stress; and the remaining locus, LINC01578, is unknown. We also identified 132 correlated genes whose expression levels are associated with SUA-increasing alleles. These genes are enriched for the UniProt transport term, suggesting the importance of transport-related genes in SUA regulation. Furthermore, trans-ethnic meta-analysis across our own meta-analysis and the Global Urate Genetics Consortium has revealed 15 more novel loci associated with SUA. Our findings provide insight into the pathogenesis, treatment, and prevention of hyperuricemia/gout.
7. Clinical use of current polygenic risk scores may exacerbate health disparities

   Martin, A. R., Kanai, M., Kamatani, Y., ..., Okada, Y., Neale, B. M., and Daly, M. J. [Show fewer authors]

   Nature Genetics 51, 584–591 (2019)

   Abstract PDF PubMed bioRxiv Code Press

   Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
8. GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture

   Matoba, N., Akiyama, M., Ishigaki, K., Kanai, M., ..., Takahashi, A., Momozawa, Y., Ikegawa, S., Ikeda, M., Iwata, N., Hirata, M., Matsuda, K., Kubo, M., Okada, Y., and Kamatani, Y. [Show fewer authors]

   Nature Human Behaviour 3, 471–477 (2019)

   Abstract PDF PubMed Press

   Cigarette smoking is a risk factor for a wide range of human diseases1. To investigate the genetic components associated with smoking behaviours in the Japanese population, we conducted a genome-wide association study of four smoking-related traits using up to 165,436 individuals. In total, we identified seven new loci, including three loci associated with the number of cigarettes per day (EPHX2-CLU, RET and CUX2-ALDH2), three loci associated with smoking initiation (DLC1, CXCL12-TMEM72-AS1 and GALR1-SALL3) and LINC01793-MIR4432HG, associated with the age of smoking initiation. Of these, three loci (LINC01793-MIR4432HG, CXCL12-TMEM72-AS1 and GALR1-SALL3) were found by conducting an additional sex-stratified genome-wide association study. This additional analysis showed heterogeneity of effects between sexes. The cross-sex linkage disequilibrium score regression2,3 analysis also indicated that the genetic component of smoking initiation was significantly different between the sexes. Cross-trait linkage disequilibrium score regression analysis and trait-relevant tissue analysis showed that the number of cigarettes per day has a specific genetic background distinct from those of the other three smoking behaviours. We also report 11 diseases that share genetic basis with smoking behaviours. Although the current study should be carefully considered owing to the lack of replication samples, our findings characterized the genetic architecture of smoking behaviours. Further studies in East Asian populations are warranted to confirm our findings.
9. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population

   Suzuki, K., Akiyama, M., Ishigaki, K., Kanai, M., ..., Hosoe, J., Shojima, N., Hozawa, A., Kadota, A., Kuriki, K., Naito, M., Tanno, K., Ishigaki, Y., Hirata, M., Matsuda, K., Iwata, N., Ikeda, M., Sawada, N., Yamaji, T., Iwasaki, M., Ikegawa, S., Maeda, S., Murakami, Y., Wakai, K., Tsugane, S., Sasaki, M., Yamamoto, M., Okada, Y., Kubo, M., Kamatani, Y., Horikoshi, M., Yamauchi, T., and Kadowaki, T. [Show fewer authors]

   Nature Genetics 51, 379–386 (2019)

   Abstract PDF PubMed Press

   To understand the genetics of type 2 diabetes in people of Japanese ancestry, we conducted A meta-analysis of four genome-wide association studies (GWAS; 36,614 cases and 155,150 controls of Japanese ancestry). We identified 88 type 2 diabetes-associated loci (P 0.6) with the lead variants. Among the 28 missense variants, three previously unreported variants had distinct minor allele frequency (MAF) spectra between people of Japanese and European ancestry (MAFJPN > 0.05 versus MAFEUR < 0.01), including missense variants in genes related to pancreatic acinar cells (GP2) and insulin secretion (GLP1R). Transethnic comparisons of the molecular pathways identified from the GWAS results highlight both ethnically shared and heterogeneous effects of a series of pathways on type 2 diabetes (for example, monogenic diabetes and beta cells).
10. Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population

    Hirata, J., Hosomichi, K., Sakaue, S., Kanai, M., ..., Nakaoka, H., Ishigaki, K., Suzuki, K., Akiyama, M., Kishikawa, T., Ogawa, K., Masuda, T., Yamamoto, K., Hirata, M., Matsuda, K., Momozawa, Y., Inoue, I., Kubo, M., Kamatani, Y., and Okada, Y. [Show fewer authors]

    Nature Genetics 51, 470–480 (2019)

    Abstract PDF PubMed Press

    To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data (n = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype-phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes.