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Cited by 75Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

Estimating cross‐population genetic correlations of causal effect sizes

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Galinsky, Kevin J., Reshef, Yakir A., Finucane, Hilary K., Loh, Po‐Ru, Zaitlen, Noah, Patterson, Nick J., Brown, Brielin C., Price, Alkes L.·2019·Genetic Epidemiology
DOI 10.1002/gepi.22173PMC PMC6375794
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Abstract

Recent studies have examined the genetic correlations of single‐nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated , the cross‐population correlation of joint‐fit effect sizes at genotyped SNPs. However, the value of depends both on the cross‐population correlation of true causal effect sizes () and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio as a function of LD in each population. By applying existing methods to obtain estimates of , we can use this ratio to estimate . Our estimates of were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

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  • Differences in environmental exposure, gene-gene interactions, gene-environment interactions, historical population size dynamics, statistical noise, some potential causal effect differences, and/or other factors will further limit generalizability for genetic risk scores in an unpredictable, trait-specific fashion.

    “Differences in environmental exposure, gene-gene interactions, gene-environment interactions, historical population size dynamics, statistical noise, some potential causal effect differences, and/or other factors will further limit generalizability for genetic risk scores in an unpredictable, trait-specific fashion46–49.”

    From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [49]

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Cited by

Sorted by citations

Authors
Galinsky, K. J., Reshef, Y. A., Finucane, H. K., Loh, P., Zaitlen, N., Patterson, N. J., Brown, B. C., & Price, A. L.
Affiliations
  • Harvard University (US)
  • Broad Institute (US)
  • University of California, San Francisco (US)
  • Verily Life Sciences LLC (United States) (US)
Corresponding author
  • Brown, Brielin C.
  • Price, Alkes L.
ORCID
Venue
Genetic Epidemiology
Publisher
Wiley
Published
2018-11-25
Volume
43
Issue
2
Pages
180-188
ISSN
  • 0741-0395 (ISSN-L)
  • 1098-2272
DOI
10.1002/gepi.22173
PMC
PMC6375794
OpenAlex
W2903167678
Cited by
75
References
4
Citations by year
2026: 6 · 2025: 4 · 2024: 9 · 2023: 22 · 2022: 17 · 2021: 8 · 2020: 3 · 2019: 5 · 2014: 1
FWCI
2.45
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Genetic Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (R01 HG006399)
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 › Genetic Mapping and Diversity in Plants and Animals
Keywords
linkage disequilibrium, single nucleotide polymorphisms, genetic architecture, complex traits, type 2 diabetes, rheumatoid arthritis, European populations, East Asian populations, South Asian populations, summary statistics
MeSH terms
Adult, Aging, Arthritis, Rheumatoid, Diabetes Mellitus, Type 2, Genetics, Population, Genotype, United Kingdom, Humans, Phenotype, Biological Specimen Banks, Quantitative Trait, Heritable, Databases, Genetic

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