ArticleEuropean journal of human genetics : EJHG2018
Are your covariates under control? How normalization can re-introduce covariate effects.
Article in European journal of human genetics : EJHG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 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
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function.Nature communications · 2025Pooled it
- Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits.Nature human behaviour · 2023Pooled it
- Risk of in-hospital life-threatening ventricular arrhythmia or death after ST-elevation myocardial infarction vs. the Takotsubo syndrome.ESC heart failure · 2021Pooled it
- An atlas of genetic effects on the monocyte methylome across European and African populations.Genome medicine · 2026Article
- Leveraging global genetics resources to enhance polygenic prediction across ancestrally diverse populations.HGG advances · 2025Article
- Strength of Genetic Associations with Thyrotropin Values Differs Between Populations with Similarity to African and European Reference Populations.Thyroid : official journal of the American Thyroid Association · 2025Article
- Gut microbial GABA imbalance emerges as a metabolic signature in mild autism spectrum disorder linked to overrepresented Escherichia.Cell reports. Medicine · 2025Article
- Article
- Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.eLife · 2024Review
- Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases.Ophthalmology science · 2023Article
- Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility.BMC medical research methodology · 2022Article
- The winding roads to adulthood: A twin study.JCPP advances · 2021Article
- Effects of adiposity on the human plasma proteome: observational and Mendelian randomisation estimates.International journal of obesity (2005) · 2021Observational
- Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference.Genome biology · 2020Article
- An unsupervised learning approach to identify novel signatures of health and disease from multimodal data.Genome medicine · 2020Article
- A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies.PloS one · 2020Article
- Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology.American journal of human genetics · 2019Article
- A fully adjusted two-stage procedure for rank-normalization in genetic association studies.Genetic epidemiology · 2019Article
- Assessing the evidence for shared genetic risks across psychiatric disorders and traits.Psychological medicine · 2018Review
- Meta-analysis of exome array data identifies six novel genetic loci for lung function.Wellcome open research · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.
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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.