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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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-06-01
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| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009575&type=printable |
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| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-9916134a7aa848008f5f368315c5af5c |
| institution | OA Journals |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-9916134a7aa848008f5f368315c5af5c2025-08-20T02:23:18ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042021-06-01176e100957510.1371/journal.pgen.1009575Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.Jingshu WangQingyuan ZhaoJack BowdenGibran HemaniGeorge Davey SmithDylan S SmallNancy R ZhangOver 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.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009575&type=printable |
| spellingShingle | 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. PLoS Genetics |
| title | 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_short | Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments. |
| title_sort | causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009575&type=printable |
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