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

Zhendong Huang, Davide Ferrari and Guoqi Qian

Computational Statistics & Data Analysis, 2017, vol. 110, issue C, 37-49

Abstract: Studying the association between a phenotype and a number of genetic variants from case-control data is an important goal in many genetic studies. Association analysis is often carried out by testing the null hypothesis that two groups of multi-dimensional data are generated by the same population. Testing based on genotype data is a challenging task as the full likelihood of the data is usually intractable. This difficulty may be tackled by composite likelihood (MCL) tests which do not entail the full likelihood. But currently available MCL tests are subject to severe power loss for involving non-informative or redundant sub-likelihoods. To reduce the power loss, a forward search and test method for simultaneous powerful group difference testing and informative sub-likelihoods composition is developed. The new 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 it achieves considerable improvement over the available tests as the modeling complexity grows. The new method is illustrated through an analysis of genotype data from a case-control study on breast cancer.

Keywords: Composite likelihood; Wald test; Forward selection; SNPs association test (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316302948
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:110:y:2017:i:c:p:37-49

DOI: 10.1016/j.csda.2016.12.004

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().