Statistical methods for Mendelian randomization in genome-wide association studies: A review

[Display omitted] Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among co...

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Vydané v:Computational and structural biotechnology journal Ročník 20; s. 2338 - 2351
Hlavní autori: Boehm, Frederick J., Zhou, Xiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier B.V 01.01.2022
Research Network of Computational and Structural Biotechnology
Elsevier
Predmet:
biotechnology
Causal inference
Confounding
Genome-wide association study
Genomics
Horizontal pleiotropy
Mendelian randomization
observational studies
Review
risk
Causal inference
Confounding
Horizontal pleiotropy
Mendelian randomization
Genome-wide association study
Genomics
ISSN:2001-0370, 2001-0370
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Shrnutí:[Display omitted] Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.
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ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2022.05.015