ArticleHuman genomics2009
Recommendations for using standardised phenotypes in genetic association studies.
Article in Human genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Differences in the skeletal muscle transcriptome profile associated with extreme values of fatty acids content.BMC genomics · 2016Article
- Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population.BMC genomics · 2014Article
- Genetic variation in antioxidant enzymes and lung function.Free radical biology & medicine · 2012Article
- A novel lung disease phenotype adjusted for mortality attrition for cystic fibrosis genetic modifier studies.Pediatric pulmonology · 2011Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Genetic association studies of complex traits often rely on standardised quantitative phenotypes, such as percentage of predicted forced expiratory volume and body mass index to measure an underlying trait of interest (eg lung function, obesity). These phenotypes are appealing because they provide an easy mechanism for comparing subjects, although such standardisations may not be the best way to control for confounders and other covariates. We recommend adjusting raw or standardised phenotypes within the study population via regression. We illustrate through simulation that optimal power in both population- and family-based association tests is attained by using the residuals from within-study adjustment as the complex trait phenotype. An application of family-based association analysis of forced expiratory volume in one second, and obesity in the Childhood Asthma Management Program data, illustrates that power is maintained or increased when adjusted phenotype residuals are used instead of typical standardised quantitative phenotypes.
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