Lower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensionality Reduction

Systems and Modeling Unit, Montefiore Institute, University of Liege, Liege, Belgium.
PLoS ONE (Impact Factor: 3.23). 01/2012; 7(1):e29594. DOI: 10.1371/journal.pone.0029594
Source: PubMed


Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.

Download full-text


Available from: Elena Gusareva
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: For many complex diseases, quantitative traits contain more information than dichotomous traits. One of the approaches used to analyse these traits in family-based association studies is the quantitative transmission disequilibrium test (QTDT). The QTDT is a regression-based approach that models simultaneously linkage and association. It splits up the association effect in a between- and a within-family genetic component to adjust and test for population stratification and includes a variance components method to model linkage. We extend this approach to detect gene-gene interactions between two unlinked QTLs by adjusting the definition of the between- and within-family component and the variance components included in the model. We simulate data to investigate the influence of the epistasis model, linkage disequilibrium patterns between the markers and the QTLs, and allele frequencies on the power and type I error rates of the approach. Results show that for some of the investigated settings, power gains are obtained in comparison with FAM-MDR. We conclude that our approach shows promising results for candidate-gene studies where too few markers are available to correct for population stratification using standard methods (for example EIGENSTRAT). The proposed method is applied to real-life data on hypertension from the FLEMENGHO study.
    Full-text · Article · Mar 2012 · European journal of human genetics: EJHG
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: Genetic variations of the 5-lipoxygenase activating protein and leukotriene A4 hydrolase genes that confer an increased risk of ischemic stroke have implicated the family of leukotrienes as potential mediators of ischemic stroke. This study aimed to explore the association of ALOX5, LTA4H and LTC4S gene polymorphisms with ischemic stroke risk in a cohort of Chinese in east China. Methods: This case-control study consisted of 690 patients with ischemic stroke and 690 controls. Polymorphisms of ALOX5 rs2029253 A/G, LTA4H rs6538697 T/C, and LTC4S rs730012 A/C were genotyped by the polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) analysis. The multivariate logistic regression model was used to exclude the effects of conventional risk factors on ischemic stroke. Results: Carriers of C allele in rs730012 were more susceptible to ischemic stroke (OR: 1.37; 95%CI: 1.08-1.73; P=0.009). The rs2029253 GG genotype showed a risk-reducing effect on ischemic stroke (OR: 0.72; 95%CI: 0.55-0.93; P=0.013) while the rs6538697 CC genotype had an increased risk of ischemic stroke (OR: 1.77; 95%CI: 1.09-2.89; P=0.022). The rs730012 variant was not associated with ischemic stroke risk after adjusting confounding factors (P>0.05). Conclusion: The present study suggested that gene polymorphisms in the leukotrienes pathway may exert influences, with independent genetic effects, on ischemic stroke susceptibility in a cohort of Chinese in east China.
    No preview · Article · Jan 2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The onset of menopause has important implications on women's fertility and health. We previously identified genetic variants in genes involved in initial follicle recruitment as potential modifiers of age at natural menopause. The objective of this study was to extend our previous study, by searching for pairwise interactions between tagging single nucleotide polymorphisms (tSNPs) in the 5 genes previously selected (AMH, AMHR2, BMP15, FOXL2, GDF9). We performed a cross-sectional study among 3445 women with a natural menopause participating in the Prospect-EPIC study, a population-based prospective cohort study, initiated between 1993 and 1997. Based on the model-based multifactor dimensionality reduction (MB-MDR) test with a permutation-based maxT correction for multiple testing, we found a statistically significant interaction between rs10407022 in AMH and rs11170547 in AMHR2 (p = 0.019) associated with age at natural menopause. Rs10407022 did not have a statistically significant main effect. However, rs10407022 is an eQTL SNP that has been shown to influence mRNA expression levels in lymphoblastoid cell lines. This study provides additional insights into the genetic background of age at natural menopause and suggests a role of the AMH signaling pathway in the onset of natural menopause. However, these results remain suggestive and replication by independent studies is necessary.
    Full-text · Article · Mar 2013 · PLoS ONE
Show more