ArticleHuman genetics2021
Genotype imputation in case-only studies of gene-environment interaction: validity and power.
Article in Human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 2026Article
- Advances in haplotype phasing and genotype imputation.Nature reviews. Genetics · 2026Review
- Missing genotype imputation in non-model species using self-organizing maps.Molecular ecology resources · 2025Article
- Gene‒Environment Interaction Between CAST Gene and Eye-Rubbing in the Chinese Keratoconus Cohort Study: A Case-Only Study.Investigative ophthalmology & visual science · 2024Article
- The effect of FTO gene rs9939609 polymorphism on the association between colorectal cancer and different types of dietary fat intake: a case-control study.Journal of physiological anthropology · 2023Article
- Genome-wide case-only analysis of gene-gene interactions with known Parkinson's disease risk variants reveals link between LRRK2 and SYT10.NPJ Parkinson's disease · 2023Article
- An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts.Frontiers in dementia · 2023Article
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5 authors.
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Abstract
Case-only (CO) studies are a powerful means to uncover gene-environment (G × E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G × E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G × E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G × E interaction.
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