A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies

Abstract

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.

Evidence for epistasis between hemoglobin C and immune genes in human P. falciparum malaria: a family study in Burkina Faso

“…25 Table 1 presents the SNPs that were selected on the basis of their association with malaria phenotypes. [22][23][24][27][28][29][30][31][32]46,[53][54][55][56][57] The PGMDR is a score-based multifactor dimensionality reduction method that uses the same data reduction strategy as does the original multifactor dimensionality reduction method to detect nonlinear genetic interactions, 58 that is to classify multilocus genotype combinations as high risk or low risk ones.…”

“…59 The PGMDR method extends the generalized multifactor dimensionality reduction one, and offers handling different family types and sizes as well as patterns of missing data. 25 Briefly, we used a 10-fold cross-validation procedure. The informative sibs were randomly divided into nearly 10 nearly equal subsets, and the cross-validation was repeated 10 times.…”

“…25 Results Table 1 summarizes the association results of the studied single-nucleotide polymorphisms (SNPs) with mild malaria, maximum parasitemia and asymptomatic parasitemia, and also reports previously published association studies. In addition, we assessed the family-based association of NCR3 and TNF polymorphisms with asymptomatic parasitemia, of LTA and IL12B polymorphisms with mild malaria and maximum parasitemia, and of IL4-590 with asymptomatic parasitemia, maximum parasitemia or mild malaria.…”

UGMDR: a unified conceptual framework for detection of multifactor interactions underlying complex traits

“…In sexual reproduction, corresponding to a real individual, there exists a pseudo individual, called the nontransmitted sib, who is composed of two gametids complementary, respectively, to the egg and the sperm that unite to develop into the zygote. The nontransmitted sib can serve as an internal control to test the null hypothesis (that is, equi-probable transmission) when his/her genotype can be inferred from the genetic information on the real individual and other pedigree member(s) (Lou et al, 2008). When no genetic information on the pedigree member(s) is available (for example, singletons or founders in pedigrees), the nontransmitted sib is considered as being missing.…”

“…To overcome these weaknesses, MDR has been extended to survival analysis (Gui et al, 2011;Lee et al, 2012), case-control study of structured populations (Niu et al, 2011), family study (Martin et al, 2006) and inclusion of covariates (Lee et al, 2007). The existing generalized MDR (GMDR) (Lou et al, 2007(Lou et al, , 2008 is still limited in tackling diverse phenotypes and samples, specifically being unable to analyze ordinal or polytomous nominal data, survival data and multivariate phenotypes, correct for population stratification in unrelateds and unify both unrelated and family samples into a joint analysis. Recently, Chen et al (2014) developed a GMDR method for unifying analyses of both unrelateds and families for dichotomous and continuous traits based on an adjustment with the principal components on the phenotypes but not on the markers.…”