Evidence map›Paper›PMID 34041609›Full record

ArticleHuman genetics2021

Genotype imputation in case-only studies of gene-environment interaction: validity and power.

Milda Aleknonytė-Resch, Silke Szymczak, Sandra Freitag-Wolf, Astrid Dempfle, Michael Krawczak

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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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7citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Milda Aleknonytė-ReschInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Silke SzymczakInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Sandra Freitag-WolfInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Astrid DempfleInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Michael KrawczakInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany. krawczak@medinfo.uni-kiel.de.ORCID http://orcid.org/0000-0003-2603-1502

Funding

Deutsche Forschungsgemeinschaft GRK1743
6 · The paper itself

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.

Indexed as

Gene-Environment InteractionGenotypeModels, GeneticPolymorphism, Single NucleotideAlgorithmsAllelesComputer SimulationCrohn DiseaseGene FrequencyGenome, HumanGermanyHumansLinkage Disequilibrium

Identifiers

PMID34041609
PMCPMC8263402

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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.