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Transethnic Genetic-Correlation Estimates from Summary Statistics

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Brown, Brielin C., Ye, Chun Jimmie, Price, Alkes L., Zaitlen, Noah·2016·The American Journal of Human Genetics
DOI 10.1016/j.ajhg.2016.05.001PMC PMC5005434

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Abstract

The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.

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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 [48]

    The Discussion (page not provided) identifies gene-gene and gene-environment interactions and statistical noise as possible reasons genetic effects differ between populations; the Introduction says such differences may make disease-risk prediction from current GWAS results problematic. The paper does not establish historical population-size dynamics or environmental exposure as specific limits on risk-score generalizability, nor does it conclude that these limits will be unpredictable and trait-specific. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Brown, B. C., Ye, C. J., Price, A. L., & Zaitlen, N.
Affiliations
  • University of California, Berkeley (US)
  • University of California, San Francisco (US)
  • Harvard University (US)
Corresponding author
Brown, Brielin C.
ORCID
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2016-07
Volume
99
Issue
1
Pages
76-88
ISSN
0002-9297 (ISSN-L)
DOI
10.1016/j.ajhg.2016.05.001
PMC
PMC5005434
OpenAlex
W2438256301
Cited by
340
References
7
Citations by year
2026: 31 · 2025: 54 · 2024: 52 · 2023: 45 · 2022: 40 · 2021: 33 · 2020: 25 · 2019: 19 · 2018: 23 · 2017: 14 · 2016: 4
FWCI
16.16
Type
journal article
Language
en
Open access
Licence
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Science Foundation (R01HG006399)
  • National Institutes of Health
  • National Heart, Lung, and Blood Institute (K25HL121295)
  • National Human Genome Research Institute (R01HG006399)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
genetic architecture, common SNPs, summary-level data, genome-wide association study, gene expression, rheumatoid arthritis, type 2 diabetes
MeSH terms
Arthritis, Rheumatoid, Body Height, Diabetes Mellitus, Type 2, Ethnicity, Genotype, Humans, Models, Genetic, Phenotype, Software, Body Mass Index, Likelihood Functions, Sample Size, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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