| Title: | DeepNull models non-linear covariate effects to improve phenotypic prediction and association power |
| Journal: | Nature Communications |
| Published: | 11 Jan 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35017556/ |
| DOI: | https://doi.org/10.1038/s41467-021-27930-0 |
| URL: | https://www.nature.com/articles/s41467-021-27930-0.pdf |
Publication 7212
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Abstract
Genome-wide association studies (GWASs) 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).8 Authors
- Zachary R. McCaw
- Thomas Colthurst
- Taedong Yun
- Nicholas A. Furlotte
- Andrew Carroll
- Babak Alipanahi
- Cory Y. McLean
- Farhad Hormozdiari
Enabling scientific discoveries that improve human health