ArticleAmerican journal of human genetics2012
A permutation procedure to correct for confounders in case-control studies, including tests of rare variation.
Article in American journal of human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Kernel-smoothed permutation for extreme P-value estimation in genetic association studies.Genetics · 2026Article
- Hierarchical association of COPD to principal genetic components of biological systems.PloS one · 2023Article
- Confound-leakage: confound removal in machine learning leads to leakage.GigaScience · 2022Article
- Interacting brains revisited: A cross-brain network neuroscience perspective.Human brain mapping · 2022Article
- Taking population stratification into account by local permutations in rare-variant association studies on small samples.Genetic epidemiology · 2021Article
- Controlling for human population stratification in rare variant association studies.Scientific reports · 2021Article
- Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion.Molecular psychiatry · 2021Article
- A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease.Nature communications · 2020Article
- Rare variant association testing in the non-coding genome.Human genetics · 2020Review
- Long noncoding RNA DLEU2 predicts a poor prognosis and enhances malignant properties in laryngeal squamous cell carcinoma through the miR-30c-5p/PIK3CD/Akt axis.Cell death & disease · 2020Article
- Gene association detection via local linear regression method.Journal of human genetics · 2020Article
- A case-control collapsing analysis identifies retinal dystrophy genes associated with ophthalmic disease in patients with no pathogenic ABCA4 variants.Genetics in medicine : official journal of the American College of Medical Genetics · 2019Article
- Sub-pathway based approach to systematically track candidate sub-pathway biomarkers for heart failure.Experimental and therapeutic medicine · 2019Article
- Identification of lncRNA competitively regulated subpathways in myocardial infarction.Experimental and therapeutic medicine · 2019Article
- Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis.Experimental and therapeutic medicine · 2019Article
- Identification of Aberrantly Expressed lncRNAs Involved in Orthodontic Force Using a Subpathway Strategy.Computational and mathematical methods in medicine · 2019Article
- Multisample adjusted U-statistics that account for confounding covariates.Statistics in medicine · 2018Article
- Powerful and robust cross-phenotype association test for case-parent trios.Genetic epidemiology · 2018Article
- Integrated case-control and somatic-germline interaction analyses of melanoma susceptibility genes.Biochimica et biophysica acta. Molecular basis of disease · 2018Article
- A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.American journal of human genetics · 2018Article
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6 authors.
Funding
Abstract
Many case-control tests of rare variation are implemented in statistical frameworks that make correction for confounders like population stratification difficult. Simple permutation of disease status is unacceptable for resolving this issue because the replicate data sets do not have the same confounding as the original data set. These limitations make it difficult to apply rare-variant tests to samples in which confounding most likely exists, e.g., samples collected from admixed populations. To enable the use of such rare-variant methods in structured samples, as well as to facilitate permutation tests for any situation in which case-control tests require adjustment for confounding covariates, we propose to establish the significance of a rare-variant test via a modified permutation procedure. Our procedure uses Fisher's noncentral hypergeometric distribution to generate permuted data sets with the same structure present in the actual data set such that inference is valid in the presence of confounding factors. We use simulated sequence data based on coalescent models to show that our permutation strategy corrects for confounding due to population stratification that, if ignored, would otherwise inflate the size of a rare-variant test. We further illustrate the approach by using sequence data from the Dallas Heart Study of energy metabolism traits. Researchers can implement our permutation approach by using the R package BiasedUrn.
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