Article

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: 10.99). 10/2009; 85(3):309-20. DOI: 10.1016/j.ajhg.2009.08.006
Source: PubMed

ABSTRACT 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.

Download full-text

Full-text

Available from: Scott M Williams, Jul 07, 2015
0 Followers
 · 
121 Views
  • Source
    • "The non-additive effects of gene-gene interactions, i.e., epistasis, are believed to be an important contributor to the complex relationship between genetic and phenotypic variations [6] [11] [12] [24] [28]. The focus of recent disease association research is shifting from identifying single locus susceptibility to quantifying interaction effects between multiple candidate loci throughout the human genome [11] [12] [29]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We analyzed two West African samples (Guinea-Bissau: n=289 cases and 322 controls; The Gambia: n=240 cases and 248 controls) to evaluate single-nucleotide polymorphisms (SNPs) in Epiregulin (EREG) and V-ATPase (T-cell immune regulator 1 (TCIRG1)) using single and multilocus analyses to determine whether previously described associations with pulmonary tuberculosis (PTB) in Vietnamese and Italians would replicate in African populations. We did not detect any significant single locus or haplotype associations in either sample. We also performed exploratory pairwise interaction analyses using Visualization of Statistical Epistasis Networks (ViSEN), a novel method to detect only interactions among multiple variables, to elucidate possible interaction effects between SNPs and demographic factors. Although we found no strong evidence of marginal effects, there were several significant pairwise interactions that were identified in either the Guinea-Bissau or the Gambian samples, two of which replicated across populations. Our results indicate that the effects of EREG and TCIRG1 variants on PTB susceptibility, to the extent that they exist, are dependent on gene-gene interactions in West African populations as detected with ViSEN. In addition, epistatic effects are likely to be influenced by inter- and intra-population differences in genetic or environmental context and/or the mycobacterial lineages causing disease.Genes and Immunity advance online publication, 5 June 2014; doi:10.1038/gene.2014.28.
    Genes and Immunity 06/2014; 15(6). DOI:10.1038/gene.2014.28 · 3.79 Impact Factor
  • Source
    • "These effects are called epistatic interactions (epistasis) (Moore and Williams, 2009). Many studies demonstrated existence of epistatic interactions in such diseases as breast cancer (Ritchie et al., 2001), coronary heart disease (Nelson et al., 2001) and Alzheimer's disease (Zubenko et al., 2001). "
    [Show abstract] [Hide abstract]
    ABSTRACT: There are many algorithms for detecting epistatic interactions in GWAS. However, most of these algorithms are applicable only for detecting two-locus interactions. Some algorithms are designed to detect only two-locus interactions from the beginning. Others do not have limits to the order of interactions, but in practice take very long time to detect higher order interactions in real data of GWAS. Even the better ones take days to detect higher order interactions in WTCCC data. We propose a fast algorithm for detection of high order epistatic interactions in GWAS. It runs k-means clustering algorithm on the set of all SNPs. Then candidates are selected from each cluster. These candidates are examined to find the causative SNPs of k-locus interactions. We use mutual information from information theory as the measure of association between genotypes and phenotypes. We tested the power and speed of our method on extensive sets of simulated data. The results show that our method has more or equal power, and runs much faster than previously reported methods. We also applied our algorithm on each of seven diseases in WTCCC data to analyze up to 5-locus interactions. It takes only a few hours to analyze 5-locus interactions in one dataset. From the results we make some interesting and meaningful observations on each disease in WTCCC data. In this study, a simple yet powerful two-step approach is proposed for fast detection of high order epistatic interaction. Our algorithm makes it possible to detect high order epistatic interactions in GWAS in a matter of hours on a PC.
    Computational biology and chemistry 06/2014; DOI:10.1016/j.compbiolchem.2014.01.005 · 1.60 Impact Factor
  • Source
    • "Epistasis (gene-gene interaction) detection is receiving an increasing amount of research attention in large-scale genetic association studies [Cordell, 2009]. Interactions between genes are fundamentally important to understand the structure and function of genetic pathways of complex genetic systems [Moore and Williams, 2009; Phillips, 2008] and have been suggested potentially to uncover the " missing heritability " in genetic association studies [Manolio et al., 2009]. Detecting epistatic interactions in the genome-wide scale is both statistically and computationally challenging. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/.
    Genetic Epidemiology 11/2013; 37(7). DOI:10.1002/gepi.21754 · 2.95 Impact Factor