Parsimonious and powerful composite likelihood testing for group difference and genotype-phenotype association

TL;DR

This paper introduces a new composite likelihood testing method that adaptively selects informative sub-likelihoods to enhance power in high-dimensional group difference and genotype-phenotype association studies, especially in GWAS.

Contribution

It develops a forward search and testing approach that constructs a sequence of Wald-type statistics by including only informative sub-likelihoods, improving power over existing methods.

Findings

Achieves significant power improvement in simulations with complex models.

Successfully applied to GWAS breast cancer data with meaningful results.

Outperforms existing tests in scenarios with non-informative or redundant data.

Abstract

Testing the association between a phenotype and many genetic variants from case-control data is essential in genome-wide association study (GWAS). This is a challenging task as many such variants are correlated or non-informative. Similarities exist in testing the population difference between two groups of high dimensional data with intractable full likelihood function. Testing may be tackled by a maximum composite likelihood (MCL) not entailing the full likelihood, but current MCL tests are subject to power loss for involving non-informative or redundant sub-likelihoods. In this paper, we develop a forward search and test method for simultaneous powerful group difference testing and informative sub-likelihoods composition. Our method constructs a sequence of Wald-type test statistics by including only informative sub-likelihoods progressively so as to improve the test power under local sparsity alternatives. Numerical studies show that it achieves considerable improvement over the available tests as the modeling complexity grows. Our method is further validated by testing the motivating GWAS data on breast cancer with interesting results obtained.