Multi-Ancestry Meta-Analysis of Alzheimer’s Disease

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 genomewide
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-Asianancestry
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.

Understanding the link between genetic variation and the incidence of diseases like
Alzheimer’s remains an urgent task in the genetics community. One outstanding challenge
involves incorporating people with non-European ancestries in such studies. This is an
important effort for two reasons. First, generalizing findings from homogenous populations is
difficult and hinders a broader understanding of the relevant disease. Second, such an inability
to generalize findings will likely perpetuate existing health inequalities.
It will likely take many years before enough people with non-European ancestries have
been genotyped to match the precision currently available in European-ancestry samples.
Rather than waiting for such resources to materialize, our project develops a statistical
framework that allows existing cross-ancestry data to be meta-analyzed to improve our ability to
detect genetic associations in understudied populations. The NIAGADS dataset, combined with
our methodology, will allow for a broader understanding of Alzheimer’s to be developed.