Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

Abstract

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Genetic susceptibility to infectious diseases: Current status and future perspectives from genome-wide approaches

“…These percentages clearly highlight the need to extend GWASs analysis for infectious diseases to under-represented populations. This is required to ensure that the benefits of research are equally distributed, especially in light of the recent evidence of limited portability of GWAS results across populations (Martin et al, 2017).…”

Father Absence and Accelerated Reproductive Development in Non-Hispanic White Women in the United States

“…Data include measures of family structure, reproductive development, and genome-wide molecular genetic data (Harris 2010; Harris et al 2013). Given the complications inherent in working with genetic data in diverse samples (Martin et al 2017; Wojcik et al 2017), we restrict our primary analytic sample to 2,681 unrelated non-Hispanic white women, and supplementary analyses of sisters to 411 genetically confirmed non-Hispanic white sisters. Add Health genetic data are available on roughly 800 non-Hispanic black and 700 Hispanic females for future research.…”

“…Population stratification is a potential confounder in genetic association studies (Hamer and Sirota 2000) and, by extension, polygenic score analysis (Belsky and Israel 2014; Martin et al 2017). GWAS and polygenic scoring rely on a subset of genetic markers to act as proxies for unmeasured genetic variation.…”

Integrative omics for health and disease

“…In the genomics space, with an individual’s genotype and a database of results from GWAS, one can compute a polygenic risk score to assess an individual’s risk of disease 4,77 (for recent reviews on methodology, see REFS 22,78). A major obstacle remains in building such frameworks for multiple omics profiles, which is likely to face some of the same challenges, such as the difficulty in applying results discovered in one population to individuals in another 79,80 .…”