Epistasis and Its Implications for Personal Genetics

Computational Genetics Laboratory, Department of Genetics and Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA.
The American Journal of Human Genetics (Impact Factor: 10.93). 10/2009; 85(3):309-20. DOI: 10.1016/j.ajhg.2009.08.006
Source: PubMed


The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

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    • "Most evolutionary processes, from adaptation to speciation, are influenced by the presence of epistasis, or interactions between genetic loci. Epistasis has been implicated in the evolution of sexual reproduction (Otto & Gerstein, 2006), and an understanding of epistasis is essential to the field of personal genomics and to the unraveling of the genetic architectures of complex diseases (Moore & Williams, 2009; Green et al., 2010). "
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    ABSTRACT: Horizontal gene transfer and recombination occur across many groups of viruses and play key roles in important viral processes such as host-range expansion and immune-system avoidance. To have any predictive power regarding the ability of viruses to readily recombine, we must determine the extent to which epistasis restricts the success of recombinants, particularly as it relates to the genetic divergence between parental strains. In any hybridization event, the evolutionary success or failure of hybrids is largely determined by the pervasiveness of epistasis in the parental genomes. Recombination has previously been shown to incur steep fitness costs in highly divergent viruses as a result of disrupted epistatic interactions. We used a pair of bacteriophages of the family Microviridae to demonstrate that epistasis may evidence itself in the form of fitness costs even in the case of the exchange of alleles at a locus with amino acid divergence as low as 1%. We explored a possible biophysical source of epistasis in the interaction of viral coat and scaffolding proteins and examined a recovery mutation that likely repairs interactions disrupted by recombination.
    Full-text · Article · Oct 2015 · PeerJ
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    • "To this end, evolution has favored complex robust systems resistant to variations (Moore and Williams, 2009). A compelling argument in favor of this hypothesis is the redundancy rate in biological networks. "
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    ABSTRACT: During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).
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    • "Diagnostic categories have been the basis for neuroimaging and genome-wide association studies (Garvey et al., 1986; Clayton, 1990; Moore and Williams, 2009; Savitz and Drevets, 2009), but disease-specific risk factors identified by extant investigations have been equivocal based on the current categorical system of mood disorder diagnoses (Dolan et al., 1993; Sheline et al., 1999; Ravnkilde et al., 2003; Lange et al., 2004; Drevets, 2007; Hamilton et al., 2008; Surguladze et al., 2008). This has led to the pursuit of intermediate phenotypes, sometimes called endophenotypes . "
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    No preview · Article · Mar 2015 · Brain
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