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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ruth Johnson, Kathryn S Burch, Kangcheng Hou, Mario Paciuc, Bogdan Pasaniuc, Sriram Sankararaman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/397105d5a3a848d596928729a27ee1ab
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:397105d5a3a848d596928729a27ee1ab
record_format	dspace
spelling	oai:doaj.org-article:397105d5a3a848d596928729a27ee1ab2021-12-02T19:57:28ZEstimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits.1553-734X1553-735810.1371/journal.pcbi.1009483https://doaj.org/article/397105d5a3a848d596928729a27ee1ab2021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009483https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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.Ruth JohnsonKathryn S BurchKangcheng HouMario PaciucBogdan PasaniucSriram SankararamanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009483 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ruth Johnson Kathryn S Burch Kangcheng Hou Mario Paciuc Bogdan Pasaniuc Sriram Sankararaman Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits.
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.
format	article
author	Ruth Johnson Kathryn S Burch Kangcheng Hou Mario Paciuc Bogdan Pasaniuc Sriram Sankararaman
author_facet	Ruth Johnson Kathryn S Burch Kangcheng Hou Mario Paciuc Bogdan Pasaniuc Sriram Sankararaman
author_sort	Ruth Johnson
title	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_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_sort	estimation of regional polygenicity from gwas provides insights into the genetic architecture of complex traits.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/397105d5a3a848d596928729a27ee1ab
work_keys_str_mv	AT ruthjohnson estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits AT kathrynsburch estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits AT kangchenghou estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits AT mariopaciuc estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits AT bogdanpasaniuc estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits AT sriramsankararaman estimationofregionalpolygenicityfromgwasprovidesinsightsintothegeneticarchitectureofcomplextraits
_version_	1718375832848695296

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

Ejemplares similares