The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE

Abstract

The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.

Identifying rare, medically-relevant genetic variation in a diverse population: opportunities and pitfalls

“…We also found that ancestry associates with the likelihood that a rare variant is correctly genotyped, with African-Americans being substantially more likely to harbor FPs. Other studies have shown that there are ancestry-associated discrepancies in accurate clinical interpretation of sequencing data, a result that, at least in part, reflects reduced representation of non-European individuals in clinical and research genetic databases [26,27]. Our results show that ancestry discrepancies also affect the technical quality of array-based detection of rare variants.…”

Strategic vision for improving human health at The Forefront of Genomics

“…For example, any given genomic variant(s) may affect more than one disease or trait (that is, pleiotropy); can confer disease risk or reduce it; and can act additively, synergistically, and/or through intermediates. New methods to analyse data that account for human diversity 103 , coupled with a growing clarity about genotype-phenotype relationships, must be developed to deduce associations and interactions among genomic variants and environmental factors, improve estimates of penetrance and expressivity, and enhance the clinical utility of genomic information for predicting risk, prognosis, treatment response, and, ultimately, clinical outcomes.…”

“…Ideally, studies should be designed for different groups, adapted for local sensibilities and situations, and consistent in capturing key information beyond participants' ancestry (for example, the physical and social environments in which they live and receive healthcare 110 ). Leveraging new insights from studies of diverse populations will require the development of robust methods for identifying signatures of natural selection, performing genotype imputation, mapping disease loci, characterizing genomic variant pathogenicity, and calculating PRSs 103,109 . Success in these efforts will yield a more-complete understanding of how the human genome functions in different environments and offer benefit to those participating in genomics research.…”

Geographic patterns of human allele frequency variation: a variant-centric perspective

“…In both of these arrays, the second and third most abundant codes are 'CuuuR' and 'RuuuR' variants. The MEGA array was uniquely designed to capture rare variation in undersampled continental groups, including African ancestries (Bien et al 2016(Bien et al , 2019. Wojcik et al (2019) found that this design improved African and African American imputation accuracy, leading to greater power to map population-specific disease risk.…”