ArticleNature genetics2023
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.
Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Genetic susceptibility to respiratory health effects from outdoor air pollution: a structured narrative review.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 2026Article
- Brain-heart-eye axis revealed by multi-organ imaging genetics and proteomics.Nature biomedical engineering · 2026Article
- Bidirectional causal association between cathepsins and neuropsychiatric disorders: univariate and multivariate Mendelian randomization study.European archives of psychiatry and clinical neuroscience · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- Empowering genetic discoveries and cardiovascular risk assessment by predicting electrocardiograms from genotype.NPJ digital medicine · 2026Article
- Genetic liability to addiction underlies comorbid bipolar and substance use disorders.medRxiv : the preprint server for health sciences · 2026Article
- The predicament of heritable confounders.Nature genetics · 2026Review
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- Structured missingness in biomedical research.Communications AI & computing · 2026Article
- The broad structure of psychopathology in the All of Us Research Program.Psychological medicine · 2025Article
- Liability threshold model-based disease risk prediction based on electronic health record phenotypes.Nature genetics · 2025Article
- Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits.bioRxiv : the preprint server for biology · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- Refining the generation, interpretation and application of multi-organ, multi-omics biological aging clocks.Nature aging · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or 'fill-in' missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.
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