Multivariate generalized linear model for genetic pleiotropy

Daniel J. Schaid, Xingwei Tong, Anthony Batzler, Jason P. Sinnwell, Jiang Qing, Joanna M. Biernacka

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases.Yet, most 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 fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy.We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, 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 a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

Original languageEnglish (US)
Pages (from-to)111-128
Number of pages18
JournalBiostatistics
Volume20
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Constrained model
  • Estimating equations
  • Multivariate analysis
  • Sequential testing.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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