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A population genetic signal of polygenic adaptation

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Berg, Jeremy J., Coop, Graham·2014·PLoS Genetics
DOI 10.1371/journal.pgen.1004412PMC PMC4125079

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Abstract

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of [Q(ST)/F(ST)] comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

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  • One key component of the inference of polygenic adaptation is the use of an appropriate null expectation for polygenic score distributions and phenotypic differences.

    “One key component of the inference of polygenic adaptation is the use of an appropriate null expectation for polygenic score distributions and phenotypic differences [12, 42].”

    From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [12]

  • Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small, with the expected difference among populations in the trait having comparable magnitude to the classical estimate of the 'apportionment of human genetic diversity'.

    “Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small.”

    From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [12]

  • Analogous measures of population differences in quantitative phenotypic traits and genetic loci-termed QST and FST, respectively-are approximately equal in neutral evolutionary models that include genetic drift but not natural selection.

    “In other words, analogous measures of population differences in quantitative phenotypic traits and genetic loci—termed QST and FST, respectively—are approximately equal in neutral evolutionary models that include genetic drift but not natural selection.”

    From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [12]

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Authors
Berg, J. J., & Coop, G.
Affiliations
University of California, Davis (US)
Corresponding author
  • Berg, Jeremy J.
  • Coop, Graham
ORCID
Venue
PLoS Genetics
Publisher
Public Library of Science (PLoS)
Published
2014-08-07
Volume
10
Issue
8
Pages
e1004412
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1004412
PMC
PMC4125079
OpenAlex
W2116360795
Cited by
487
References
20
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FWCI
33.06
Type
journal article
Language
en
Open access
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Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
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  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation (1148897, 1262645)
  • National Institutes of Health (RO1GM107374)
  • National Institute of General Medical Sciences (R01GM083098, RO1GM83098, R01GM108779, RO1GM107374, NIH RO1GM83098, R01GM107374)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
polygenic adaptation, local adaptation, additive genetic values, population structure, genome-wide association study, skin pigmentation, height, type 2 diabetes, body mass index, inflammatory bowel disease
MeSH terms
Adaptation, Physiological, Body Height, Diabetes Mellitus, Type 2, Gene Frequency, Genetics, Population, Humans, Models, Genetic, Phenotype, Selection, Genetic, Skin Pigmentation, Inflammatory Bowel Diseases, Body Mass Index, Human Genome Project, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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