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A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements

Lookup NU author(s): Dr Holly Fisher, Dr So-Youn Shin, Professor Heather Cordell

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2017 Wiley Periodicals, Inc. Genome wide association studies (GWAS) have been very successful over the last decade at identifying genetic variants associated with disease phenotypes. However, interpretation of the results obtained can be challenging. Incorporation of further relevant biological measurements (e.g. 'omics' data) measured in the same individuals for whom we have genotype and phenotype data may help us to learn more about the mechanism and pathways through which causal genetic variants affect disease. We review various methods for causal inference that can be used for assessing the relationships between genetic variables, other biological measures, and phenotypic outcome, and present a simulation study assessing the performance of the methods under different conditions. In general, the methods we considered did well at inferring the causal structure for data simulated under simple scenarios. However, the presence of an unknown and unmeasured common environmental effect could lead to spurious inferences, with the methods we considered displaying varying degrees of robustness to this confounder. The use of causal inference techniques to integrate omics and GWAS data has the potential to improve biological understanding of the pathways leading to disease. Our study demonstrates the suitability of various methods for performing causal inference under several biologically plausible scenarios


Publication metadata

Author(s): Ainsworth HF, Shin S-Y, Cordell HJ

Publication type: Article

Publication status: Published

Journal: Genetic Epidemiology

Year: 2017

Volume: 41

Issue: 7

Pages: 577-586

Print publication date: 01/11/2017

Online publication date: 10/07/2017

Acceptance date: 19/05/2017

Date deposited: 07/06/2017

ISSN (print): 0741-0395

ISSN (electronic): 1098-2272

Publisher: Wiley

URL: https://doi.org/10.1002/gepi.22061

DOI: 10.1002/gepi.22061


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