Statistical Methods for Testing Genetic Pleiotropy

Abstract

Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that [Formula: see text] traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

Testing cross‐phenotype effects of rare variants in longitudinal studies of complex traits

“…Genetic pleiotropy induces phenotypic correlation which is more readily detectable through cross-phenotype analyses using the extra information provided by the correlation among the phenotypes. Although several tests of pleiotropy for common variants exist, they are usually less powerful for rare variants (Schaid et al, 2016). A recent method called ‘Gene Association with Multiple Traits (GAMuT)’ (Broadaway et al, 2016) was proposed to fill this gap for rare variants.…”

Conditional analysis of multiple quantitative traits based on marginal GWAS summary statistics

“…He et al (2013), Jiang et al (2014), Zhang et al (2014), Wang et al (2016), Kim et al (2016) and Schaid et al (2016) and references therein. There are two main reasons: one is to increase statistical power and the other is to detect pleotropic effects, which may shed light on underlying biology and for possible repurposing the use of existing drugs.…”

“…There is an increasing interest in association analysis of multiple traits with many new tests being recently proposed; see, for example, He, Avery, and Lin (2013), Jiang, Li, and Zhang (2014), Zhang, Xu, Shen, Pan, and Alzheimer's Disease Neuroimaging Initiative (2014), Wang, Sha, and Zhang (2016), Kim, Zhang, Pan, and Alzheimer's Disease Neuroimaging Initiative (2016), and Schaid et al (2016) and references therein. There are two main reasons: one is to increase statistical power and the other is to detect pleotropic effects, which may shed light on underlying biology and for possible repurposing the use of existing drugs.…”

“…There are two main reasons: one is to increase statistical power and the other is to detect pleotropic effects, which may shed light on underlying biology and for possible repurposing the use of existing drugs. However, as pointed out by Schaid et al (2016), almost all existing methods test with a null hypothesis of no associated trait, which may be rejected even in the absence of pleiotropy if only one of the multiple traits is indeed associated; they cannot tell whether there is indeed pleiotropy. Accordingly, several methods have appeared to explore which of the multiple traits are indeed associated (Majumdar, Haldar, & Witte, 2016;Stephens, 2013).…”

“…Accordingly, several methods have appeared to explore which of the multiple traits are indeed associated (Majumdar, Haldar, & Witte, 2016;Stephens, 2013). In particular, Schaid et al (2016) proposed a formal testing framework to sequentially test for the number of associated traits. However, even in the presence of multiple associated traits, these approaches cannot distinguish direct and indirect associations.…”