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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
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  • ORCID record for Diptavo Dutta
Peter VandeHaar
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
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Laura J. Scott
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
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Michael Boehnke
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
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Seunggeun Lee
2Center for Statistical Genetics, University of Michigan, MI, USA
3Dept. of Biostatistics, University of Michigan, MI, USA
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  • ORCID record for Seunggeun Lee
  • For correspondence: leeshawn@umich.edu
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Posted October 10, 2019.
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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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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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