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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. [ABSTRACT FROM AUTHOR]

Gene–gene interactions (G × G) potentially play a role in the etiology of complex human diseases, including inflammatory bowel disease (IBD), and may partially explain their 'missing heritability'. Using the largest genotype dataset available for IBD (16,636 Crohn's disease (CD) and 12,888 ulcerative colitis (UC) cases) we analyzed G × G with the powerful case-only (CO) design. We studied 169 single nucleotide polymorphisms (SNPs) for CD (156 for UC), previously shown to be associated with the respective diseases. To ensure the validity of the CO design, we confined our analysis to pairs of unlinked SNPs. We used principal component analysis at the center level to adjust for possible causes of genotypic association other than G × G, such as population stratification and genotyping batch effects. Results from center-wise logistic regression analyses were combined by a random effects meta-analysis. A number of nominally significant (p <.05) G × G interactions were observed, but none of these withstood the Bonferroni multiple testing correction. However, one SNP pair, comprising rs26528 in the IL27 gene and rs9297145 in the KPNA7 gene region was characterized by an interaction odds ratio of 1.18 (95% CI: 1.10–1.27) for CD and a p-value of 7.75 × 10−6. Owing to the concurrent role of the IL27 and KPNA7 genes in NF-κB signaling, a master regulator of pro- and anti-inflammatory processes in IBD, the observed interaction also has biological plausibility. We were able to exemplify the utility of the CO design for analyzing G × G, but had to recognize that such interactions are probably scarce for IBD. [ABSTRACT FROM AUTHOR]

Gene-environment interactions (G × E) have attracted considerable research interest in the past owing to their scientific and public health implications, but powerful statistical methods are required to successfully track down G × E, particularly at a genome-wide level. Previously, a case-only (CO) design has been proposed as a means to identify G × E with greater efficiency than traditional case-control or cohort studies. However, as with genotype-phenotype association studies themselves, hidden population stratification (PS) can impact the validity of G × E studies using a CO design. Since this problem has been subject to little research to date, we used comprehensive simulation to systematically assess the type I error rate, power and effect size bias of CO studies of G × E in the presence of PS. Three types of PS were considered, namely genetic-only (PS), environment-only (PS), and joint genetic and environmental stratification (PS). Our results reveal that the type I error rate of an unadjusted Wald test, appropriate for the CO design, would be close to its nominal level (0.05 in our study) as long as PS involves only one interaction partner (i.e., either PS or PS). In contrast, if the study population is stratified with respect to both G and E (i.e., if there is PS), then the type I error rate is seriously inflated and estimates of the underlying G × E interaction are biased. Comparison of CO to a family-based case-parents design confirmed that the latter is more robust against PS, as expected. However, case-parent trios may be particularly unsuitable for G × E studies in view of the fact that they require genotype data from parents and that many diseases with an environmental component are likely to be of late onset. An alternative approach to adjusting for PS is principal component analysis (PCA), which has been widely used for this very purpose in past genome-wide association studies (GWAS). However, resolving genetic PS properly by PCA requires genetic data at the population level, the availability of which would conflict with the basic idea of the CO design. Therefore, we explored three modified Wald test statistics, inspired by the genomic control (GC) approach to GWAS, as an alternative means to allow for PS. The modified statistics were benchmarked against a stratified Wald test assuming known population affiliation, which should provide maximum power under PS. Our results confirm that GC is capable of successfully and efficiently correcting the PS-induced inflation of the type I error rate in CO studies of G × E. [ABSTRACT FROM AUTHOR]

Gene-environment ( G × E) interactions have been invoked to account, at least in part, for the gap between the known heritability of common human diseases and the phenotypic variation hitherto explained by genetic variants. Noteworthy in this context, a case-only (CO) design has been proposed in the past as a means to detect G × E interactions possibly more efficiently than by using classical case-control and cohort designs. So far, however, most CO studies have followed a candidate (or single) gene approach, and the genome-wide utility of the CO design is still more or less unknown. In particular, the way in which linkage disequilibrium (LD) impacts upon the chance to detect G × E interaction through the analysis of proxy markers has not been studied in much detail before. Therefore, we systematically assessed the power to indirectly detect a given G × E interaction through exploiting LD in a CO design. Our simulations revealed a strong relationship between LD and detection power that was subsequently validated in a real colorectal cancer data set. [ABSTRACT FROM AUTHOR]