TY - JOUR
T1 - RVTESTS
T2 - An efficient and comprehensive tool for rare variant association analysis using sequence data
AU - Zhan, Xiaowei
AU - Hu, Youna
AU - Li, Bingshan
AU - Abecasis, Goncalo R.
AU - Liu, Dajiang J.
N1 - Publisher Copyright:
© The Author 2016. Published by Oxford University Press.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - Motivation: Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.
AB - Motivation: Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.
UR - http://www.scopus.com/inward/record.url?scp=84966397605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966397605&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btw079
DO - 10.1093/bioinformatics/btw079
M3 - Article
C2 - 27153000
AN - SCOPUS:84966397605
SN - 1367-4803
VL - 32
SP - 1423
EP - 1426
JO - Bioinformatics
JF - Bioinformatics
IS - 9
ER -