ArticleNature genetics2017
Covariate selection for association screening in multiphenotype genetic studies.
Article in Nature genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The influence of rare variants in circulating metabolic biomarkers.PLoS genetics · 2020Pooled it
- Modeling nonlinear and interaction effects of spatiotemporal and nongenetic factors improves prediction for complex traits.Nature communications · 2026Article
- Exploring a genetic basis for the metabolic perturbations in ME/CFS using UK biobank.iScience · 2026Article
- Joint regression analysis of multiple traits based on genetic relationships.Bioinformatics advances · 2024Article
- Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder.Nature genetics · 2023Article
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.PLoS genetics · 2022Article
- Accounting for age of onset and family history improves power in genome-wide association studies.American journal of human genetics · 2022Article
- Contrasting Water Withholding Responses of Young Maize Plants Reveal Link Between Lipid Peroxidation and Osmotic Regulation Corroborated by Genetic Analysis.Frontiers in plant science · 2022Article
- Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression.Brain, behavior, & immunity - health · 2021Article
- Wavelet Screening: a novel approach to analyzing GWAS data.BMC bioinformatics · 2021Article
- Mitochondrial DNA variants modulate N-formylmethionine, proteostasis and risk of late-onset human diseases.Nature medicine · 2021Article
- Genetically Predicted Circulating C-Reactive Protein Concentration and Colorectal Cancer Survival: A Mendelian Randomization Consortium Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings.Plants (Basel, Switzerland) · 2020Article
- A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context.Nature communications · 2019Article
- Genetic effects on the commensal microbiota in inflammatory bowel disease patients.PLoS genetics · 2019Article
- Adjustment for covariates using summary statistics of genome-wide association studies.Genetic epidemiology · 2018Article
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Authors and funding
9 authors.
Funding
Abstract
Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.
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