Masahiro Kanai

I am a Research Fellow at Dr. Ramnik Xavier Lab, Massachusetts General Hospital & Broad Institute of MIT and Harvard. Prior to joining the lab, I completed Ph.D. in Biomedical Informatics at Harvard University, co-advised by Drs. Mark Daly and Hilary Finucane, the Analytic and Translational Genetics Unit. I received fellowships from the Nakajima Foundation and the Masason Foundation.

As a statistical geneticist, my research focuses on cross-population analysis of complex diseases and traits to better understand their genetic architecture and diversity across multiple populations. In my thesis project, I created a comprehensive atlas of putative causal variants from multiple large-scale biobanks to gain insights from complex trait fine-mapping across diverse populations. Prior to joining ATGU, I completed B.S. degree in Japan, where I worked closely with Dr. Yukinori Okada to study genetics of complex traits in the Japanese population using the BioBank Japan data.

My curriculum vitae can be found here.

News

Mar 18, 2024	The Pan-UKBB flagship manuscript (Karczewski, KJ., Gupta, R., *Kanai, M., et al.) is now available at medRxiv!
Sep 6, 2023	Kanai, M., The COVID-19 Host Genetics Initiative. (2023) is now published in Nature!
Nov 5, 2022	Kanai, M. et al. (2022) is now published in Cell Genomics!
Apr 7, 2022	Weissbrod, O., Kanai, M., *Shi, H. et al. (2022) is now published in Nature Genetics!
Sep 30, 2021	Added Sakaue, S & Kanai, M. et al. (2021).

Selected Publications and Preprints [full list]

Link to Google Scholar | ORCID | PubMed

* denotes equal contribution

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.
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.
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.
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.
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.
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.
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.
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.
Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases

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

Nature Genetics 50, 390–400 (2018)

Abstract PDF PubMed Press

Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 \times 10-8), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity, represented by the genetic links among clinical measurements, complex diseases, and relevant cell types. Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements’ relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis.