PT  - JOURNAL ARTICLE
AU  - Zeynep Baskurt
AU  - Scott Mastromatteo
AU  - Jiafen Gong
AU  - Richard F. Wintle
AU  - Stephen W. Scherer
AU  - Lisa J. Strug
TI  - VikNGS: A C++ Variant Integration Kit for Next Generation Sequencing association analysis
AID  - 10.1101/504381
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 504381
4099  - http://biorxiv.org/content/early/2018/12/21/504381.short
4100  - http://biorxiv.org/content/early/2018/12/21/504381.full
AB  - Motivation Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. Unfortunately, if differential genotype uncertainty across studies is not accounted for, combining data sets can also produce spurious association results. The robust variance score statistic (RVS) for genetic association of rare and common variants has been shown to effectively adjust for bias caused by the differences in read depth in case-control genetic association studies when the two groups were sequenced using different experimental designs. To enable consortium research, the aggregation of several data sets for genetic association analysis of quantitative and binary traits with covariate adjustment is required, and we developed the Variant Integration Kit for NGS (VikNGS) that expands the functionality of RVS (vRVS) for this purpose.Results VikNGS is a fast and computationally efficient cross-platform software package that provides an implementation for vRVS, as well as conventional rare and common variant genotype-based association analysis approaches. The package includes a graphical user interface that contains power simulation functionality and data visualization tools.Availability and Implementation The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.htmlDocumentation can be found at https://VikNGSdocs.readthedocs.io/en/latest/Contact lisa.strug{at}sickkids.caSupplementary information Supplementary data are available at Bioinformatics online.