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A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank
View ORCID ProfileDiptavo Dutta, Peter VandeHaar, Laura J. Scott, Michael Boehnke, View ORCID ProfileSeunggeun Lee
doi: https://doi.org/10.1101/799791
Diptavo Dutta
1Dept. of Biostatistics, Johns Hopkins University, MD, USA
2Center for Statistical Genetics, University of Michigan, MI, USA
Peter VandeHaar
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
Laura J. Scott
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
Michael Boehnke
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
Seunggeun Lee
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
Posted October 10, 2019.
A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank
Diptavo Dutta, Peter VandeHaar, Laura J. Scott, Michael Boehnke, Seunggeun Lee
bioRxiv 799791; doi: https://doi.org/10.1101/799791
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