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VikNGS: A C++ Variant Integration Kit for Next Generation Sequencing association analysis

View ORCID ProfileZeynep Baskurt, Scott Mastromatteo, Jiafen Gong, Richard F. Wintle, Stephen W. Scherer, Lisa J. Strug
doi: https://doi.org/10.1101/504381
Zeynep Baskurt
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
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Scott Mastromatteo
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
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Jiafen Gong
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
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Richard F. Wintle
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
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Stephen W. Scherer
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
4McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 0A4, Canada.
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Lisa J. Strug
1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada,
3Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Ontario, M5T3M7, Canada,
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Abstract

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.html

Documentation can be found at https://VikNGSdocs.readthedocs.io/en/latest/

Contact lisa.strug{at}sickkids.ca

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 21, 2018.
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VikNGS: A C++ Variant Integration Kit for Next Generation Sequencing association analysis
Zeynep Baskurt, Scott Mastromatteo, Jiafen Gong, Richard F. Wintle, Stephen W. Scherer, Lisa J. Strug
bioRxiv 504381; doi: https://doi.org/10.1101/504381
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VikNGS: A C++ Variant Integration Kit for Next Generation Sequencing association analysis
Zeynep Baskurt, Scott Mastromatteo, Jiafen Gong, Richard F. Wintle, Stephen W. Scherer, Lisa J. Strug
bioRxiv 504381; doi: https://doi.org/10.1101/504381

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