Genotype and SNP calling from next-generation sequencing data

被引：952

作者：

Nielsen, Rasmus ^{[1
,2
,3
]}

Paul, Joshua S. ^{[4
]}

Albrechtsen, Anders ^{[2
]}

Song, Yun S. ^{[3
,4
]}

机构：

[1] Univ Calif Berkeley, Dept Integrat Biol, Berkeley, CA 94720 USA

[2] Univ Copenhagen, Ctr Bioinformat, DK-2100 Copenhagen O, Denmark

[3] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

[4] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

来源：

NATURE REVIEWS GENETICS | 2011年 / 12卷 / 06期

基金：

美国国家科学基金会;

关键词：

GENOME-WIDE ASSOCIATION; SHORT READ ALIGNMENT; HIGH-THROUGHPUT; REVEALS; IMPUTATION; INFERENCE; ULTRAFAST; GRAPHS; ERROR; PHASE;

D O I：

10.1038/nrg2986

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low-to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.

引用

页码：443 / 451

页数：9

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