Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments t...

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Autores principales:	Jingshu Wang, Qingyuan Zhao, Jack Bowden, Gibran Hemani, George Davey Smith, Dylan S Small, Nancy R Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/9916134a7aa848008f5f368315c5af5c
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spelling	oai:doaj.org-article:9916134a7aa848008f5f368315c5af5c2021-12-02T20:02:41ZCausal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.1553-73901553-740410.1371/journal.pgen.1009575https://doaj.org/article/9916134a7aa848008f5f368315c5af5c2021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009575https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.Jingshu WangQingyuan ZhaoJack BowdenGibran HemaniGeorge Davey SmithDylan S SmallNancy R ZhangPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 6, p e1009575 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Jingshu Wang Qingyuan Zhao Jack Bowden Gibran Hemani George Davey Smith Dylan S Small Nancy R Zhang Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
description	Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.
format	article
author	Jingshu Wang Qingyuan Zhao Jack Bowden Gibran Hemani George Davey Smith Dylan S Small Nancy R Zhang
author_facet	Jingshu Wang Qingyuan Zhao Jack Bowden Gibran Hemani George Davey Smith Dylan S Small Nancy R Zhang
author_sort	Jingshu Wang
title	Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
title_short	Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
title_full	Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
title_fullStr	Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
title_full_unstemmed	Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
title_sort	causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9916134a7aa848008f5f368315c5af5c
work_keys_str_mv	AT jingshuwang causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT qingyuanzhao causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT jackbowden causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT gibranhemani causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT georgedaveysmith causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT dylanssmall causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments AT nancyrzhang causalinferenceforheritablephenotypicriskfactorsusingheterogeneousgeneticinstruments
_version_	1718375674185515008

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

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