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Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits

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...

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Autores principales: Johnson, Ruth, Burch, Kathryn S., Hou, Kangcheng, Paciuc, Mario, Pasaniuc, Bogdan, Sankararaman, Sriram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562817/
https://www.ncbi.nlm.nih.gov/pubmed/34673766
http://dx.doi.org/10.1371/journal.pcbi.1009483
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author Johnson, Ruth
Burch, Kathryn S.
Hou, Kangcheng
Paciuc, Mario
Pasaniuc, Bogdan
Sankararaman, Sriram
author_facet Johnson, Ruth
Burch, Kathryn S.
Hou, Kangcheng
Paciuc, Mario
Pasaniuc, Bogdan
Sankararaman, Sriram
author_sort Johnson, Ruth
collection PubMed
description 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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spelling pubmed-85628172021-11-03 Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits Johnson, Ruth Burch, Kathryn S. Hou, Kangcheng Paciuc, Mario Pasaniuc, Bogdan Sankararaman, Sriram PLoS Comput Biol Research Article 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. Public Library of Science 2021-10-21 /pmc/articles/PMC8562817/ /pubmed/34673766 http://dx.doi.org/10.1371/journal.pcbi.1009483 Text en © 2021 Johnson et al 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 Research Article
Johnson, Ruth
Burch, Kathryn S.
Hou, Kangcheng
Paciuc, Mario
Pasaniuc, Bogdan
Sankararaman, Sriram
Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title_full Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title_fullStr Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title_full_unstemmed Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title_short Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
title_sort estimation of regional polygenicity from gwas provides insights into the genetic architecture of complex traits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562817/
https://www.ncbi.nlm.nih.gov/pubmed/34673766
http://dx.doi.org/10.1371/journal.pcbi.1009483
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