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Principal Investigator  
Principal Investigator's Name: Daniel Benjamin
Institution: National Bureau of Economic Research
Department: Economics of Aging
Country:
Proposed Analysis: Genome-Wide Association Studies (GWAS) are powerful techniques for linking genetic variation to complex phenotypes but have limited utility in small samples. Because the overwhelming majority of genotyped cohorts consist of individuals of European ancestry, conducting well-powered GWAS in diverse populations will be a challenge for years to come. This project overcomes this barrier by developing a meta-analysis framework for GWAS conducted in populations of different ancestries. In particular, multi-ancestry meta-analysis (MAMA) implements a generalized method of moments estimator based on differences in local linkage disequilibrium (LD) structure across the relevant populations. In doing so, MAMA allows for genetic signal in one population to be shared across other populations, substantially boosting statistical power in small samples and allowing for novel genetic associations to be detected. The primary goal of our proposed project is to use MAMA to jointly analyze GWAS summary statistics for Alzheimer’s disease corresponding to several different ancestries. Preliminary applications of MAMA to other phenotypes has yielded many additional genome-wide significant loci for each ancestry. MAMA summary statistics can be interpreted similar to the original GWAS summary statistics, but with greater statistical power. We hope to incorporate NIAGADS data from the study Kunkle et al. (2020) into our analysis pipeline. We will jointly analyze these data with summary statistics from Kunkle et al. (2018) and from Zhou et al. (2018), studies based on European-ancestry and East-Asian-ancestry samples, respectively. We anticipate that doing so will yield many novel genetic discoveries about the genetics of Alzheimer’s disease in each of these populations.
Additional Investigators  
Investigator's Name: Patrick Turley
Proposed Analysis: Genome-Wide Association Studies (GWAS) are powerful techniques for linking genetic variation to complex phenotypes but have limited utility in small samples. Because the overwhelming majority of genotyped cohorts consist of individuals of European ancestry, conducting well-powered GWAS in diverse populations will be a challenge for years to come. This project overcomes this barrier by developing a meta-analysis framework for GWAS conducted in populations of different ancestries. In particular, multi-ancestry meta-analysis (MAMA) implements a generalized method of moments estimator based on differences in local linkage disequilibrium (LD) structure across the relevant populations. In doing so, MAMA allows for genetic signal in one population to be shared across other populations, substantially boosting statistical power in small samples and allowing for novel genetic associations to be detected. The primary goal of our proposed project is to use MAMA to jointly analyze GWAS summary statistics for Alzheimer’s disease corresponding to several different ancestries. Preliminary applications of MAMA to other phenotypes has yielded many additional genome-wide significant loci for each ancestry. MAMA summary statistics can be interpreted similar to the original GWAS summary statistics, but with greater statistical power. We hope to incorporate NIAGADS data from the study Kunkle et al. (2020) into our analysis pipeline. We will jointly analyze these data with summary statistics from Kunkle et al. (2018) and from Zhou et al. (2018), studies based on European-ancestry and East-Asian-ancestry samples, respectively. We anticipate that doing so will yield many novel genetic discoveries about the genetics of Alzheimer’s disease in each of these populations.
Investigator's Name: David Cesarini
Proposed Analysis: Genome-Wide Association Studies (GWAS) are powerful techniques for linking genetic variation to complex phenotypes but have limited utility in small samples. Because the overwhelming majority of genotyped cohorts consist of individuals of European ancestry, conducting well-powered GWAS in diverse populations will be a challenge for years to come. This project overcomes this barrier by developing a meta-analysis framework for GWAS conducted in populations of different ancestries. In particular, multi-ancestry meta-analysis (MAMA) implements a generalized method of moments estimator based on differences in local linkage disequilibrium (LD) structure across the relevant populations. In doing so, MAMA allows for genetic signal in one population to be shared across other populations, substantially boosting statistical power in small samples and allowing for novel genetic associations to be detected. The primary goal of our proposed project is to use MAMA to jointly analyze GWAS summary statistics for Alzheimer’s disease corresponding to several different ancestries. Preliminary applications of MAMA to other phenotypes has yielded many additional genome-wide significant loci for each ancestry. MAMA summary statistics can be interpreted similar to the original GWAS summary statistics, but with greater statistical power. We hope to incorporate NIAGADS data from the study Kunkle et al. (2020) into our analysis pipeline. We will jointly analyze these data with summary statistics from Kunkle et al. (2018) and from Zhou et al. (2018), studies based on European-ancestry and East-Asian-ancestry samples, respectively. We anticipate that doing so will yield many novel genetic discoveries about the genetics of Alzheimer’s disease in each of these populations.
Investigator's Name: Mohan Ramanujan
Proposed Analysis: Genome-Wide Association Studies (GWAS) are powerful techniques for linking genetic variation to complex phenotypes but have limited utility in small samples. Because the overwhelming majority of genotyped cohorts consist of individuals of European ancestry, conducting well-powered GWAS in diverse populations will be a challenge for years to come. This project overcomes this barrier by developing a meta-analysis framework for GWAS conducted in populations of different ancestries. In particular, multi-ancestry meta-analysis (MAMA) implements a generalized method of moments estimator based on differences in local linkage disequilibrium (LD) structure across the relevant populations. In doing so, MAMA allows for genetic signal in one population to be shared across other populations, substantially boosting statistical power in small samples and allowing for novel genetic associations to be detected. The primary goal of our proposed project is to use MAMA to jointly analyze GWAS summary statistics for Alzheimer’s disease corresponding to several different ancestries. Preliminary applications of MAMA to other phenotypes has yielded many additional genome-wide significant loci for each ancestry. MAMA summary statistics can be interpreted similar to the original GWAS summary statistics, but with greater statistical power. We hope to incorporate NIAGADS data from the study Kunkle et al. (2020) into our analysis pipeline. We will jointly analyze these data with summary statistics from Kunkle et al. (2018) and from Zhou et al. (2018), studies based on European-ancestry and East-Asian-ancestry samples, respectively. We anticipate that doing so will yield many novel genetic discoveries about the genetics of Alzheimer’s disease in each of these populations.