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Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context
Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461826/ https://www.ncbi.nlm.nih.gov/pubmed/37585442 http://dx.doi.org/10.1371/journal.pgen.1010852 |
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author | Willis, Thomas W. Wallace, Chris |
author_facet | Willis, Thomas W. Wallace, Chris |
author_sort | Willis, Thomas W. |
collection | PubMed |
description | Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. |
format | Online Article Text |
id | pubmed-10461826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104618262023-08-29 Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context Willis, Thomas W. Wallace, Chris PLoS Genet Methods Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Public Library of Science 2023-08-16 /pmc/articles/PMC10461826/ /pubmed/37585442 http://dx.doi.org/10.1371/journal.pgen.1010852 Text en © 2023 Willis, Wallace https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Methods Willis, Thomas W. Wallace, Chris Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title | Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title_full | Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title_fullStr | Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title_full_unstemmed | Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title_short | Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context |
title_sort | accurate detection of shared genetic architecture from gwas summary statistics in the small-sample context |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461826/ https://www.ncbi.nlm.nih.gov/pubmed/37585442 http://dx.doi.org/10.1371/journal.pgen.1010852 |
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