Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Abstract

Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.

Human genetic admixture through the lens of population genomics

“…In practice, genetic ancestry of individuals from admixed populations is not fully known and is inferred, often using reference panels that are collated to represent the source populations [4,[27][28][29][30]38]. In the following sections, we discuss aspects of human evolution that are commonly inferred from patterns of genetic variation in admixed populations, particularly genetic ancestry.…”

“…Estimation of ancestry proportions under this model of admixture often relies on identifying a subset of loci with particularly large allele frequency differences between the source populations, known as Ancestry Informative Markers (AIMs) [47]. With further developments in genome sequencing increasing the density of loci across genomes, recent methods often incorporate linkage information or model small allele frequency changes over many loci, producing estimates of global ancestry proportions, as well as local ancestry along an admixed individual's genome [4,[27][28][29][30]38]. Mechanistic models of admixture complement empirical studies to improve our intuition of admixture dynamics and interpretation of empirical results [15,42,[48][49][50][51].…”

“…Recent methodological improvements have increased the efficiency and performance of local ancestry calling (i.e. the assignment of genomic segments to their population of origin; some early scalable algorithmic implementations are given in [7,[27][28][29]; figure 1c). These advances have enabled the use of local ancestry patterns in admixed populations to infer demographic history, adaptation and the genetic bases of complex traits.…”

Ethnic Diversity and Warfarin Pharmacogenomics

“…Consistent with being admixed populations, MAFs for Hispanics/Latinos are usually lower than those for Whites (decrease progressively with increasing individual proportion of African ancestry), with one large cohort of >1,000 Brazilians reporting MAFs of 0.11, 0.05, and 0.33 for CYP2C9*2 , CYP2C9*3 , and VKORC1 -1639G > A , respectively, ( Rodrigues-Soares and Suarez-Kurtz, 2019 ). However, some MAFs may be higher in admixed populations for traits that have been under differential selection pressure among the ancestral populations or among deeply divergent populations ( Lin et al, 2021 ).…”

Polygenic risk, population structure and ongoing difficulties with race in human genetics

“…[ 72 ]), or an ‘all-purpose’ PRS is designed to capture and combine predictive elements from a global population sample (encompassing multiple subpopulations) into a single risk predictor that can be universally applied (e.g. [ 70 , 73 ]). In the former case, appropriate clinical application requires assigning the patient to a population/ancestry category prior to risk estimation; there are several difficulties with this.…”