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Inferring genetic causal effects on survival data with associated endo-phenotypes.

ABSTRACT: Age-at-onset phenotypes are important traits in genetic association analyses. Often, intermediate phenotypes that are related to the age-at-onset phenotype are also associated with the marker loci that are associated with the age-at-onset phenotype. In order to understand the genetic etiology of the observed associations, statistical methodology is needed to distinguish between a direct genetic effect on the age-at-onset phenotype and an indirect effect induced by the genetic association with the endo-phenotype that is correlated with the age-at-onset phenotype. In this communication, we introduce a new statistical approach to detect causal genetic effects on survival data in the presence of genetic associations with secondary phenotypes that might influence survival as well and thereby induce seemingly causal relationships. Derived using causal inference methodology, the proposed method is based on standard statistical methodology and can be implemented straight-forwardly, using standard software. Using simulation studies, the theoretical properties of the approach are verified and the power is assessed under realistic scenarios. The practical relevance of the approach is illustrated by an application to survival after cardiac surgery, where genetic components of myocardial infarctions are determined to not influence post-surgery hospital duration except through the MI-pathway.

SUBMITTER: Lipman PJ

PROVIDER: S-EPMC3125580 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Inferring genetic causal effects on survival data with associated endo-phenotypes.

Lipman Peter J PJ Liu Kuang-Yu KY Muehlschlegel Jochen Daniel JD Body Simon S Lange Christoph C

Genetic epidemiology 20101231 2

Age-at-onset phenotypes are important traits in genetic association analyses. Often, intermediate phenotypes that are related to the age-at-onset phenotype are also associated with the marker loci that are associated with the age-at-onset phenotype. In order to understand the genetic etiology of the observed associations, statistical methodology is needed to distinguish between a direct genetic effect on the age-at-onset phenotype and an indirect effect induced by the genetic association with th ...[more]

PMID: 21254219

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Project description:Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger's method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger's method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger's method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger's method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.

Dataset Information

Inferring genetic causal effects on survival data with associated endo-phenotypes.

Publications

Inferring genetic causal effects on survival data with associated endo-phenotypes.

Similar Datasets

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