| Literature DB >> 28980259 |
Abstract
For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.Entities:
Keywords: Cross-phenotype association; Genome-wide association studies; Meta-analysis; Multivariate phenotypes; Pleiotropy; Summary statistics
Mesh:
Year: 2017 PMID: 28980259 PMCID: PMC6417431 DOI: 10.1007/978-1-4939-7274-6_22
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745