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- Publisher Website: 10.1038/s41588-018-0193-x
- Scopus: eid_2-s2.0-85052492980
- PMID: 30104760
- WOS: WOS:000443151300024
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Article: Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits
Title | Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits |
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Authors | |
Issue Date | 2018 |
Citation | Nature Genetics, 2018, v. 50, n. 9, p. 1318-1326 How to Cite? |
Abstract | © 2018, The Author(s). We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases. |
Persistent Identifier | http://hdl.handle.net/10722/276601 |
ISSN | 2023 Impact Factor: 31.7 2023 SCImago Journal Rankings: 17.300 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Yan | - |
dc.contributor.author | Qi, Guanghao | - |
dc.contributor.author | Park, Ju Hyun | - |
dc.contributor.author | Chatterjee, Nilanjan | - |
dc.date.accessioned | 2019-09-18T08:34:06Z | - |
dc.date.available | 2019-09-18T08:34:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Nature Genetics, 2018, v. 50, n. 9, p. 1318-1326 | - |
dc.identifier.issn | 1061-4036 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276601 | - |
dc.description.abstract | © 2018, The Author(s). We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases. | - |
dc.language | eng | - |
dc.relation.ispartof | Nature Genetics | - |
dc.title | Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1038/s41588-018-0193-x | - |
dc.identifier.pmid | 30104760 | - |
dc.identifier.scopus | eid_2-s2.0-85052492980 | - |
dc.identifier.volume | 50 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 1318 | - |
dc.identifier.epage | 1326 | - |
dc.identifier.eissn | 1546-1718 | - |
dc.identifier.isi | WOS:000443151300024 | - |
dc.identifier.f1000 | 733803377 | - |
dc.identifier.issnl | 1061-4036 | - |