Cordell, H. J. Detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet. 10, 392-404

Institute of Human Genetics, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK.
Nature Reviews Genetics (Impact Factor: 39.79). 06/2009; 10(6):392-404. DOI: 10.1038/nrg2579
Source: PubMed

ABSTRACT Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

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    • "On the other hand, while IGF2BP2, HHEX and PPARG that were not independently associated, turned out to be significant in the multivariate context. IRS-1 and CAPN10 that were associated individually failed to show association in the multivariate analysis, both these situations conforming probably to the phenomenon of epistasis (Cordell, 2010; Culverhouse et al., 2002); where as in the first case, the genes with no independent effect, turn out to be significant in the presence of other genes, in case of the latter the potential independent effects of IRS-1 and CAPN10 were probably masked by other genes. "
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    ABSTRACT: Fifteen SNPs from nine different genes were genotyped on 1379 individuals, 758 T2DM patients and 621 controls, from the city of Hyderabad, India, using Sequenom Massarray platform. These data were analyzed to examine the role of gene–gene and gene–environment interactions in the manifestation of T2DM. The multivariate analysis suggests that TCF7L2, CDKAL1, IGF2BP2, HHEX and PPARG genes are significantly associated with T2DM, albeit only the first two of the above 5 were associated in the univariate analysis. Significant gene–gene and gene–environment interactions were also observed with reference to TCF7L2, CAPN10 and CDKAL1 genes, highlighting their importance in the pathophysiology of T2DM. In the analysis for cumulative effect of risk alleles, SLC30A8 steps in as significant contributor to the disease by its presence in all combinations of risk alleles. A striking difference between risk allele categories, 1–4 and 5–6, was evident in showing protective and susceptible roles, respectively, while the latter was characterized by the presence of TCF7L2 and CDKAL1 variants. Overall, these two genes TCF7L2 and CDKAL1 showed strong association with T2DM, either individually or in interaction with the other genes. However, we need further studies on gene–gene and gene–environment interactions among heterogeneous Indian populations to obtain unequivocal conclusions that are applicable for the Indian population as a whole.
    09/2015; 5. DOI:10.1016/j.mgene.2015.05.001
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    • "The Random Forest algorithm is a tree-based ensemble machine learning tool more suited to detect evidence of polygenic adaptation since it search for correlation and interactions among loci (Goldstein et al. 2011; Boulesteix et al. 2012). The efficiency of Random Forest approach in finding a group of covarying markers that differentiate complex traits has been shown in several medicine and agriculture studies (Shi et al. 2005; Cordell 2009; Tang et al. 2009; Xu et al. 2011; Poland et al. 2012; Mokry et al. 2013; Jarquín et al. 2014) but still infrequent in evolutionary molecular [but see Brieuc et al. 2015; Pavey et al. 2015)]. As recommended by Strobl et al. (2009) and Chen and Ishwaran (2012), a total of 100 forests (runs) sets with different seed numbers were computed to ensure randomness of the test. "
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    ABSTRACT: Parallel changes in body shape may evolve in response to similar environmental conditions, but whether such parallel phenotypic changes share a common genetic basis is still debated. The goal of this study was to assess if parallel phenotypic changes could be explained by genetic parallelism, multiple genetic routes or both. We first provide evidence for parallelism in fish shape using geometric morphometrics among 300 fish representing five species pairs of Lake Whitefish. Using a genetic map comprising 3438 RAD-seq SNPs, we then identified quantitative trait loci (QTL) underlying body shape traits in a backcross family reared in the laboratory. A total of 138 body shape QTL were identified in this cross, thus revealing a highly polygenic architecture of body shape in Lake Whitefish. Thirdly, we tested for evidence of genetic parallelism among independent wild populations using both a single-locus method (outlier analysis) and a polygenic approach (analysis of co-variation among markers). The single-locus approach provided limited evidence for genetic parallelism. However, the polygenic analysis revealed genetic parallelism for three of the five lakes, which differed from the two other lakes. These results provide evidence for both genetic parallelism and multiple genetic routes underlying parallel phenotypic evolution in fish shape among populations occupying similar ecological niches. Copyright © 2015 Author et al.
    G3-Genes Genomes Genetics 07/2015; 5:1481-1491. DOI:10.1534/g3.115.019067 · 2.51 Impact Factor
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    • ", 2015 ) . As recommended in the literature , a variable is considered a confounder when it is associated with both variables of interest ( association with both the outcome and studied SNPs for a P - value < 0 . 2 ) ( Cordell , 2009 ; Maldonado and Greenland , 1993 ) . "
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    ABSTRACT: The aim of this study was to analyze hypotheses-driven gene-environment and gene-gene interactions in smoked (crack) cocaine addiction by evaluating childhood neglect and polymorphisms in mineralocorticoid and glucocorticoid receptor genes (NR3C2 and NR3C1, respectively). One hundred thirty-nine crack/cocaine-addicted women who completed 3 weeks of follow-up during early abstinence composed our sample. Childhood adversities were assessed using the Childhood Trauma Questionnaire (CTQ), and withdrawal symptoms were assessed using the Cocaine Selective Severity Assessment (CSSA) scale. Conditional logistic regression with counterfactuals and generalized estimating equation modeling were used to test gene-environment and gene-gene interactions. We found an interaction between the rs5522-Val allele and childhood physical neglect, which altered the risk of crack/cocaine addiction (Odds ratio = 4.0, P = 0.001). Moreover, a NR3C2-NR3C1 interaction (P = 0.002) was found modulating the severity of crack/cocaine withdrawal symptoms. In the post hoc analysis, concomitant carriers of the NR3C2 rs5522-Val and NR3C1 rs6198-G alleles showed lower overall severity scores when compared to other genotype groups (P-values ≤ 0.035). This gene-environment interaction is consistent with epidemiological and human experimental findings demonstrating a strong relationship between early life stress and the hypothalamic-pituitary-adrenal (HPA) axis dysregulation in cocaine addiction. Additionally, this study extended in crack/cocaine addiction the findings previously reported for tobacco smoking involving an interaction between NR3C2 and NR3C1 genes. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Psychiatric Research 06/2015; 68. DOI:10.1016/j.jpsychires.2015.06.008 · 4.09 Impact Factor
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