ArticleNature communications2022
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development.Frontiers in genetics · 2022Pooled it
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- The predicament of heritable confounders.Nature genetics · 2026Review
- Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.Biostatistics (Oxford, England) · 2025Article
- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- Greater value add from electronic health records than polygenic risk scores for predicting myocardial infarction in machine learning.Communications medicine · 2025Article
- Forward and reverse genomic screens enhance the understanding of phenotypic variation in a large Chinese rhesus macaque cohort.Nature communications · 2025Article
- Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.Communications medicine · 2025Article
- The Significant Effects of Threshold Selection for Advancing Nitrogen Use Efficiency in Whole Genome of Bread Wheat.Plant direct · 2025Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- Learning genotype-phenotype associations from gaps in multi-species sequence alignments.Briefings in bioinformatics · 2024Article
- Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.Scientific reports · 2024Article
- Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.Respiratory research · 2024Article
- Statistical inference with large-scale trait imputation.Statistics in medicine · 2024Article
- Deep learning captures the effect of epistasis in multifactorial diseases.Frontiers in medicine · 2024Article
- Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.medRxiv : the preprint server for health sciences · 2023Article
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Multi-PGS enhances polygenic prediction by combining 937 polygenic scores.Nature communications · 2023Article
- Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models.Nature genetics · 2023Article
Corrections and comments
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8 authors.
Funding
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).
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