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DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power

View ORCID ProfileZachary R. McCaw, View ORCID ProfileThomas Colthurst, View ORCID ProfileTaedong Yun, View ORCID ProfileNicholas A. Furlotte, View ORCID ProfileAndrew Carroll, View ORCID ProfileBabak Alipanahi, View ORCID ProfileCory Y. McLean, View ORCID ProfileFarhad Hormozdiari
doi: https://doi.org/10.1101/2021.05.26.445783
Zachary R. McCaw
1Google Health, Palo Alto, CA, USA
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Thomas Colthurst
2Google Health, Cambridge, MA, USA
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Taedong Yun
2Google Health, Cambridge, MA, USA
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Nicholas A. Furlotte
1Google Health, Palo Alto, CA, USA
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Andrew Carroll
1Google Health, Palo Alto, CA, USA
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Babak Alipanahi
1Google Health, Palo Alto, CA, USA
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Cory Y. McLean
2Google Health, Cambridge, MA, USA
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  • For correspondence: cym@google.com fhormoz@google.com
Farhad Hormozdiari
2Google Health, Cambridge, MA, USA
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  • For correspondence: cym@google.com fhormoz@google.com
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Abstract

Genome-wide association studies (GWAS) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n=370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

Competing Interest Statement

All authors are employees of Google LLC. This study was funded by Google LLC.

Footnotes

  • These authors jointly supervised this work: Cory Y. McLean, Farhad Hormozdiari

  • Added the XGBoost and sex-specific spline results; Included the functional analysis of DeepNull; Additional simulations; Added genome-wide significant level comparison of DeepNull vs Baseline and Second-order (Supplementary Figs 16-25); author order updated; Supplemental files updated.

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 September 27, 2021.
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DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power
Zachary R. McCaw, Thomas Colthurst, Taedong Yun, Nicholas A. Furlotte, Andrew Carroll, Babak Alipanahi, Cory Y. McLean, Farhad Hormozdiari
bioRxiv 2021.05.26.445783; doi: https://doi.org/10.1101/2021.05.26.445783
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DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power
Zachary R. McCaw, Thomas Colthurst, Taedong Yun, Nicholas A. Furlotte, Andrew Carroll, Babak Alipanahi, Cory Y. McLean, Farhad Hormozdiari
bioRxiv 2021.05.26.445783; doi: https://doi.org/10.1101/2021.05.26.445783

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