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使用全基因组关联研究汇总统计数据进行跨表型关联分析。

Cross-Phenotype Association Analysis Using Summary Statistics from GWAS.

作者信息

Li Xiaoyin, Zhu Xiaofeng

机构信息

Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.

出版信息

Methods Mol Biol. 2017;1666:455-467. doi: 10.1007/978-1-4939-7274-6_22.

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.

摘要

十多年来,全基因组关联研究(GWAS)一直是检测复杂性状潜在遗传变异的主要工具。最近的研究表明,同一变异或基因可能与多种性状相关,这种关联被称为跨表型(CP)关联。CP关联分析可以通过寻找对多种性状有影响的变异来提高统计效力,这通常与基因多效性相关。在本章中,我们讨论了用于分析单个标记与多变量表型之间关联的现有统计方法,介绍了一种检测CP关联的通用方法CPASSOC,并解释了在实际中如何进行分析。

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