Dissecting the genetics of complex traits using summary association statistics

被引：258

作者：

Pasaniuc, Bogdan ^{[1
,2
]}

Price, Alkes L. ^{[3
,4
,5
]}

机构：

[1] Univ Calif Los Angeles, Dept Human Genet, Los Angeles, CA 90095 USA

[2] Univ Calif Los Angeles, Dept Pathol & Lab Med, Los Angeles, CA 90095 USA

[3] Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA

[4] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[5] Broad Inst, Program Med & Populat Genet, Cambridge, MA 02142 USA

来源：

NATURE REVIEWS GENETICS | 2017年 / 18卷 / 02期

基金：

美国国家卫生研究院;

关键词：

GENOME-WIDE ASSOCIATION; SUSCEPTIBILITY LOCI; DIRECT IMPUTATION; CAUSAL VARIANTS; RARE VARIANTS; HUMAN-DISEASES; JOINT ANALYSIS; METAANALYSIS; ARCHITECTURE; RISK;

D O I：

10.1038/nrg.2016.142

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

引用

页码：117 / 127

页数：11

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