Genome-wide significance testing of variation from single case exomes

被引：32

作者：

Wilfert, Amy B. ^{[1
]}

Chao, Katherine R. ^{[2
]}

Kaushal, Madhurima ^{[3
]}

Jain, Sanjay ^{[3
,4
]}

Zollner, Sebastian ^{[5
]}

Adams, David R. ^{[2
]}

Conrad, Donald F. ^{[1
,4
]}

机构：

[1] Washington Univ, Sch Med, Dept Genet, St Louis, MO 63110 USA

[2] NIH, Undiagnosed Dis Program, Bldg 10, Bethesda, MD 20892 USA

[3] Washington Univ, Sch Med, Dept Internal Med, St Louis, MO 63110 USA

[4] Washington Univ, Dept Pathol & Immunol, Sch Med, St Louis, MO 63130 USA

[5] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA

来源：

NATURE GENETICS | 2016年 / 48卷 / 12期

基金：

美国国家卫生研究院;

关键词：

SEQUENCE VARIANTS; GENETIC-VARIATION; HUMAN-DISEASE; MUTATION; ASSOCIATION; GUIDELINES; FRAMEWORK;

D O I：

10.1038/ng.3697

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Standard techniques from genetic epidemiology are ill-suited to formally assess the significance of variants identified from a single case. We developed a statistical inference framework for identifying unusual functional variation from a single exome or genome, what we refer to as the 'n-of-one' problem. Using this approach we assessed our ability to identify the causal genotypes in over 5 million simulated cases of Mendelian disease, identifying 39% of disease genotypes as the most damaging unit in a typical exome background. We applied our approach to 129 n-of-one families from the Undiagnosed Diseases Program, nominating 60% of 30 disease genes determined to be diagnostic by a standard clinical workup. Our method can currently produce well-calibrated P values when applied to single genomes, can facilitate integration of multiple data types for n-of-one analyses, and, with further work, could become a widely used epidemiological method like linkage analysis or genome-wide association analysis.

引用

页码：1455 / 1461

页数：7

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