ArticleNature genetics2016
A multiple-phenotype imputation method for genetic studies.
Article in Nature genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
71 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions.European journal of human genetics : EJHG · 2021Pooled it
- Imputing Phenotypes for Genome-wide Association Studies.American journal of human genetics · 2016Pooled it
- Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026Article
- Pleiotropic shared heritability quantifies the shared genetic variance of common diseases.Nature genetics · 2026Article
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 2026Article
- The predicament of heritable confounders.Nature genetics · 2026Review
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Improving accuracy in genome-wide association studies: a two-step approach for handling below limit of detection biomarker measurements.NAR genomics and bioinformatics · 2025Article
- Liability threshold model-based disease risk prediction based on electronic health record phenotypes.Nature genetics · 2025Article
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- Rapid and accurate multi-phenotype imputation for millions of individuals.Nature communications · 2025Article
- Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings.Translational psychiatry · 2024Review
- A novel phenotype imputation method with copula model.BMC bioinformatics · 2024Article
- Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.Nature genetics · 2024Article
- A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data.Nature communications · 2024Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.