Article

The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases

Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA.
Human Heredity (Impact Factor: 1.47). 02/2003; 56(1-3):73-82. DOI: 10.1159/000073735
Source: PubMed

ABSTRACT

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.

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Available from: Jason H Moore, Jun 10, 2015
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    • "Because wound repair and GIN expulsion are related to the acquired Th2 response[53,64], genes associated with these mechanisms (e.g., IL8, PF4, PPBP and AREG) could be of interest when searching for candidates to explain an adult-specific QTL, such as the QTL detected on OAR6 between 80.8 and 91.4 Mb. The large number of QTL identified in this study supports the idea that disease susceptibility is not determined by individual genes acting alone but rather by complex multi-gene interactions[65,66]. Our results are the first steps towards the identification of allelic variants that directly control the phenotypic variation observed for parasite resistance in adult Churra sheep. "
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    ABSTRACT: Background Persistence of gastrointestinal nematode (GIN) infection and the related control methods have major impacts on the sheep industry worldwide. Based on the information generated with the Illumina OvineSNP50 BeadChip (50 K chip), this study aims at confirming quantitative trait loci (QTL) that were previously identified by microsatellite-based genome scans and identifying new QTL and allelic variants that are associated with indicator traits of parasite resistance in adult sheep. We used a commercial half-sib population of 518 Spanish Churra ewes with available data for fecal egg counts (FEC) and serum levels of immunoglobulin A (IgA) to perform different genome scan QTL mapping analyses based on classical linkage analysis (LA), a combined linkage disequilibrium and linkage analysis (LDLA) and a genome-wide association study (GWAS). Results For the FEC and IgA traits, we detected a total of three 5 % chromosome-wise significant QTL by LA and 63 significant regions by LDLA, of which 13 reached the 5 % genome-wise significance level. The GWAS also revealed 10 significant SNPs associated with IgA t , although no significant associations were found for LFEC. Some of the significant QTL for LFEC that were detected by LA and LDLA on OAR6 overlapped with a highly significant QTL that was previously detected in a different half-sib population of Churra sheep. In addition, several new QTL and SNP associations were identified, some of which show correspondence with effects that were reported for different populations of young sheep. Other significant associations that did not coincide with previously reported associations could be related to the specific immune response of adult animals. Discussion Our results replicate a FEC-related QTL located on OAR6 that was previously reported in Churra sheep and provide support for future research on the identification of the allelic variant that underlies this QTL. The small proportion of genetic variance explained by the detected QTL and the large number of functional candidate genes identified here are consistent with the hypothesis that GIN resistance/susceptibility is a complex trait that is not determined by individual genes acting alone but rather by complex multi-gene interactions. Future studies that combine genomic variation analysis and functional genomic information may help elucidate the biology of GIN disease resistance in sheep.
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    • "For example, due to operational or biological constraints it is often expensive to generate large full-sib families or clonally propagated samples, which are required to properly estimate dominance or epistatic effects (Baltunis et al. 2009;Muñoz et al. 2014). On the other hand, there is much empirical evidence pointing at allelic interactions at the level of genes as an important mechanism which may contribute to developing biological processes in various species (Whitlock et al. 1995;Moore 2003;Hemani et al. 2013). This empirical evidence makes nonadditive effects indispensable to be taken into account when genetic architecture of complex traits is studied. "
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    ABSTRACT: The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects.
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    • "Therefore, although further studies are needed, these results are in line with recent trends in the field of molecular genetics, which consider the importance of testing gene networks rather than isolated gene effects for better understanding the gene–phenotype relationship in complex disorders (Gilman et al. 2012). Nonetheless, the fact that the SNP9 is included in the protective haplotype while it is detected to exert a risk effect when interacts with Val/Val genotype could suggest that the effect of this SNP may differ depending on the genetic background in which the alleles are present (Moore 2003 ). Moreover, beyond gene–gene interactions , the effect of environmental factors should also be studied. "
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    No preview · Article · Dec 2015 · The World Journal of Biological Psychiatry
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