Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
Open accessAbstract
The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.
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- Authors
- Johnson, R., Burch, K. S., Hou, K., Paciuc, M., Pasaniuc, B., & Sankararaman, S.
- Affiliations
- University of California, Los Angeles (US)
- Rice University (US)
- Corresponding author
- Johnson, Ruth
- Sankararaman, Sriram
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-10-21
- Volume
- 17
- Issue
- 10
- Pages
- e1009483
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC8562817
- OpenAlex
- W3205440668
- Cited by
- 16
- References
- 34
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 9 · 2022: 3
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Update notices
- new_version (2021-11-02)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- NSF (DGE-1829071, III-1705121)
- National Institutes of Health (R35GM125055)
- Alfred P. Sloan Foundation (FG-2017-9480)
- Okawa Foundation for Information and Telecommunications
- National Human Genome Research Institute (R01HG009120, R01HG006399)
- National Cancer Institute (U01CA194393)
- 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
- genetic architecture, complex traits, causal variants, heritability enrichment, anthropometric traits, blood pressure, genomic regions, UK Biobank
- MeSH terms
- Algorithms, Blood Pressure, Humans, Genome, Human, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study
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