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A Unifying Statistical Framework to Discover Disease Genes from GWAS

View ORCID ProfileJustin N.J. McManus, Robert J. Lovelett, Daniel Lowengrub, Sarah Christensen
doi: https://doi.org/10.1101/2022.04.28.489887
Justin N.J. McManus
1Kallyope, Inc., New York, NY, USA
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  • ORCID record for Justin N.J. McManus
  • For correspondence: justin@kallyope.com
Robert J. Lovelett
1Kallyope, Inc., New York, NY, USA
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Daniel Lowengrub
1Kallyope, Inc., New York, NY, USA
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Sarah Christensen
1Kallyope, Inc., New York, NY, USA
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Article Information

doi 
https://doi.org/10.1101/2022.04.28.489887
History 
  • April 29, 2022.
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-NC-ND 4.0 International license.

Author Information

  1. Justin N.J. McManus*,1,
  2. Robert J. Lovelett1,
  3. Daniel Lowengrub1 and
  4. Sarah Christensen1
  1. 1Kallyope, Inc., New York, NY, USA
  1. ↵*Correspondence: justin{at}kallyope.com
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Posted April 29, 2022.
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A Unifying Statistical Framework to Discover Disease Genes from GWAS
Justin N.J. McManus, Robert J. Lovelett, Daniel Lowengrub, Sarah Christensen
bioRxiv 2022.04.28.489887; doi: https://doi.org/10.1101/2022.04.28.489887
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A Unifying Statistical Framework to Discover Disease Genes from GWAS
Justin N.J. McManus, Robert J. Lovelett, Daniel Lowengrub, Sarah Christensen
bioRxiv 2022.04.28.489887; doi: https://doi.org/10.1101/2022.04.28.489887

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