The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Open accessPublished version of a preprint
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.
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“Admixture, or the genetic merging of two or more parental lineages, has been demonstrated as an important contributor to modern human genetic variation (Rudan 2006; Hellenthal et al. 2014; Martin et al. 2017).”
From The Impact of Modern Admixture on Archaic Human Ancestry in Human Populations (2023) · cites this paper as Martin et al. 2017
In the citing paper
“These issues are insurmountable through statistical methods alone13, but rather motivate substantial investments in more diverse populations to produce similar-sized GWAS of biomedical phenotypes in other populations.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“The scientific basis of this imbalance has been demonstrated theoretically, in simulations, and empirically across many traits and diseases11–22.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“However, the major ethical and scientific challenge surrounding clinical implementation is that they are many-fold more accurate in European ancestry individuals than others.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“This dependence means that the most significant associations tend to be more common in the populations in which they are discovered than elsewhere13,30.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and target of prediction, a function of population history13,14.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“Eurocentric GWAS biases mean that variants associated with risk are disproportionately common and discovered in European populations, accounting for a larger fraction of the phenotypic variance there13.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“In contrast, PRS are uniformly less useful in understudied populations due to differences in genomic variation and population history13,14.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“Because the discovery sample used to construct the score consisted of individuals of European ancestry, we would not expect the predictive power of our score to be as high in other ancestry groups7,26,27.”
From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [26]
In the citing paper
“A third limit to transferability of effect estimates arises from population differences in features of correlations between nearby sites—linkage disequilibrium patterns—that influence aggregations of signals across loci [32].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [32]
In the citing paper
“As just one example, incorrect inference can readily occur if the GRS training and target datasets are from different ancestries (Martin et al., 2017).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Martin et al., 2017
In the citing paper
“Because allele frequencies, linkage disequilibrium patterns, and effect sizes of common polymorphisms vary with ancestry, the specific GPS here will not have optimal predictive power for other ethnic groups.29”
From Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations (2018) · cites this paper as [29]
In the citing paper
“As expected from previous work (Martin et al., 2017), our results indicate that genetic risk prediction is strongly affected by population structure.”
From An Unexpectedly Complex Architecture for Skin Pigmentation in Africans (2017) · cites this paper as Martin et al., 2017
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Metadata
- Authors
- Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E.
- Affiliations
- Broad Institute (US)
- Stanford University (US)
- McGill University and Génome Québec Innovation Centre (CA)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Kenny, Eimear E.
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 100
- Issue
- 4
- Pages
- 635-649
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5384097
- OpenAlex
- W2598022332
- Cited by
- 1,391
- References
- 111
- Citations by year
- 2026: 77 · 2025: 131 · 2024: 162 · 2023: 149 · 2022: 195 · 2021: 208 · 2020: 206 · 2019: 156 · 2018: 84 · 2017: 21
- FWCI
- 84.74
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-10-01)
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research
- National Institutes of Health
- Canada Research Chairs (MOP-136855)
- National Institute of Mental Health (5U01MH094432-02)
- National Human Genome Research Institute (R01GM083606, T32HG000044, U01HG005208, U01HG007417, U01HG007419, U01HG009080)
- National Institute of General Medical Sciences (T32GM007790)
- Directorate for Mathematical and Physical Sciences (1201234)
- National Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Social Sciences › Anthropology › Race, Genetics, and Society
- Keywords
- polygenic risk score, underrepresented groups, demographic history, genetic drift, allele frequency divergence, coalescent simulation, polygenic traits, height, heritability, genetic risk prediction
- MeSH terms
- Americas, Genetics, Medical, Genetics, Population, Haplotypes, Humans, Human Genome Project, Genetic Predisposition to Disease, Multifactorial Inheritance, Racial Groups
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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