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Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.
Nat. Genet. 55, 2269-2276 (2023)
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Genome-wide Association; Ld Score Regression; Dna
ISSN (print) / ISBN
1061-4036
e-ISSN
1546-1718
Zeitschrift
Nature Genetics
Quellenangaben
Band: 55,
Heft: 12,
Seiten: 2269-2276
Verlag
Nature Publishing Group
Verlagsort
New York, NY
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Helmholtz Pioneer Campus (HPC)
Förderungen
Lundbeckfonden (Lundbeck Foundation)
U.S. Department of Health & Human Services | National Institutes of Health (NIH)
NSF | Directorate for Biological Sciences (BIO)
NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (Information & Intelligent Systems)
NSF | BIO | Division of Biological Infrastructure (DBI)
National Science Foundation (NSF)
U.S. Department of Health & Human Services | National Institutes of Health (NIH)
NSF | Directorate for Biological Sciences (BIO)
NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (Information & Intelligent Systems)
NSF | BIO | Division of Biological Infrastructure (DBI)
National Science Foundation (NSF)