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Variable prediction accuracy of polygenic scores within an ancestry group

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Mostafavi, Hakhamanesh, Harpak, Arbel, Agarwal, Ipsita, Conley, Dalton, Pritchard, Jonathan K, Przeworski, Molly·2020·eLife
DOI 10.7554/elife.48376PMC PMC7067566

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Abstract

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

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  • GWAS results and downstream analyses of behavioral phenotypes and some anthropometric phenotypes are affected by demographic and indirect genetic effects.

    “Consistent with previous studies13,14,19,40, we found that GWAS results and downstream analyses of behavioral phenotypes (for example, educational attainment, smoking behavior) as well as some anthropometric phenotypes (for example, height, BMI) are affected by demographic and indirect genetic effects.”

    From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [40]

    In Results (no page number available), the paper reports that BMI and educational-attainment polygenic-score accuracy varies with age and socioeconomic status, and discusses indirect genetic effects for educational attainment. It suggests assortative mating may affect height prediction, but does not establish the claimed effects for smoking behavior; its account of the mechanisms is also more cautious than the citing sentence. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Mostafavi, H., Harpak, A., Agarwal, I., Conley, D., Pritchard, J. K., & Przeworski, M.
Affiliations
  • Columbia University (US)
  • Princeton University (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
  • Mostafavi, Hakhamanesh
  • Harpak, Arbel
  • Przeworski, Molly
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2020-01-30
Volume
9
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.48376
PMC
PMC7067566
OpenAlex
W3004083863
Cited by
457
References
17
Citations by year
2026: 62 · 2025: 71 · 2024: 65 · 2023: 68 · 2022: 83 · 2021: 55 · 2020: 42 · 2019: 11
FWCI
36.36
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of General Medical Sciences (GM121372)
  • National Human Genome Research Institute (HG008140)
  • Robert Wood Johnson Foundation (84337817)
  • Simons Foundation (633313)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
Keywords
polygenic risk score, prediction accuracy, genetic ancestry, portability, socioeconomic status, age, sex differences, UK Biobank, clinical application, health disparities
MeSH terms
Adult, Age Factors, Aged, Female, Gene Frequency, Genetics, Population, United Kingdom, Humans, Male, Middle Aged, Sex Factors, Socioeconomic Factors, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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