Statistical methods to detect pleiotropy in human complex traits

被引:90
|
作者
Hackinger, Sophie [1 ]
Zeggini, Eleftheria [1 ]
机构
[1] Wellcome Trust Sanger Inst, Hinxton CB10 1SA, England
来源
OPEN BIOLOGY | 2017年 / 7卷 / 11期
基金
英国惠康基金;
关键词
pleiotropy; statistical methods; genome-wide association study; GENOME-WIDE ASSOCIATION; GENERALIZED ESTIMATING EQUATIONS; MULTIPLE GENETIC-VARIANTS; LD SCORE REGRESSION; BODY-MASS INDEX; MENDELIAN RANDOMIZATION; SUSCEPTIBILITY LOCI; RISK LOCI; PRINCIPAL-COMPONENTS; JOINT ANALYSIS;
D O I
10.1098/rsob.170125
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.
引用
收藏
页数:13
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