DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power
Hormozdiari, F.; Mccaw, Z. R.; Colthurst, T.; Yun, T.; Furlotte, N.; Carroll, A.; Alipanahi, B.; McLean, C.
Show 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).
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SparsePro: an efficient fine-mapping method integrating summary statistics and functional annotations 98%
- Leveraging expression from multiple tissues using sparse canonical correlation analysis (sCCA) and aggregate tests improves the power of transcriptome-wide association studies (TWAS) 98%
- Joint Modeling of Effect Sizes for Two Correlated Traits: Characterizing Trait Properties to Enhance Polygenic Risk Prediction 97%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits 97%
- Optimizing and benchmarking polygenic risk scores with GWAS summary statistics 96%
- Single locus theory of admixture is insufficient for the study of complex traits in admixed populations 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.