Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts

被引:10
|
作者
Hakenberg, Joerg [1 ]
Cheng, Wei-Yi [1 ]
Thomas, Philippe [2 ]
Wang, Ying-Chih [1 ]
Uzilov, Andrew V. [1 ]
Chen, Rong [1 ]
机构
[1] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA
[2] Humboldt Univ, Dept Comp Sci, D-10099 Berlin, Germany
来源
BMC BIOINFORMATICS | 2016年 / 17卷
关键词
Genetics; Variant annotation; Database; FUNCTIONAL ANNOTATION; SEQUENCE VARIANTS; GENETIC-VARIANTS; GENOME; MUTATIONS;
D O I
10.1186/s12859-015-0865-9
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Data from a plethora of high-throughput sequencing studies is readily available to researchers, providing genetic variants detected in a variety of healthy and disease populations. While each individual cohort helps gain insights into polymorphic and disease-associated variants, a joint perspective can bemore powerful in identifying polymorphisms, rare variants, disease-associations, genetic burden, somatic variants, and disease mechanisms. Description: We have set up a Reference Variant Store (RVS) containing variants observed in a number of large-scale sequencing efforts, such as 1000 Genomes, ExAC, Scripps Wellderly, UK10K; various genotyping studies; and disease association databases. RVS holds extensive annotations pertaining to affected genes, functional impacts, disease associations, and population frequencies. RVS currently stores 400 million distinct variants observed in more than 80,000 human samples. Conclusions: RVS facilitates cross-study analysis to discover novel genetic risk factors, gene-disease associations, potential disease mechanisms, and actionable variants. Due to its large reference populations, RVS can also be employed for variant filtration and gene prioritization.
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页数:13
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  • [1] Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts
    Jörg Hakenberg
    Wei-Yi Cheng
    Philippe Thomas
    Ying-Chih Wang
    Andrew V. Uzilov
    Rong Chen
    BMC Bioinformatics, 17