Genome-wide association studies (GWAS) have laid the foundation for investigations
into the biology of complex traits, drug development and clinical guidelines. However,
the majority of discovery efforts are based on data from populations of European ancestry1-3.
In light of the differential genetic architecture that is known to exist between populations,
bias in representation can exacerbate existing disease and healthcare disparities.
Critical variants may be missed if they have a low frequency or are completely absent
in European populations, especially as the field shifts its attention towards rare
variants, which are more likely to be population-specific4-10. Additionally, effect
sizes and their derived risk prediction scores derived in one population may not accurately
extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic
participants in large-scale genomic studies. The Population Architecture using Genomics
and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes
in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic
and admixed populations, we describe a framework for analysing diverse populations,
identify 27 novel loci and 38 secondary signals at known loci, as well as replicate
1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size
heterogeneity across ancestries for published GWAS associations, substantial benefits
for fine-mapping using diverse cohorts and insights into clinical implications. In
the United States-where minority populations have a disproportionately higher burden
of chronic conditions13-the lack of representation of diverse populations in genetic
research will result in inequitable access to precision medicine for those with the
highest burden of disease. We strongly advocate for continued, large genome-wide efforts
in diverse populations to maximize genetic discovery and reduce health disparities.