Search

The extent to which mitochondrial DNA (mtDNA) variation is involved in adaptive evolutionary change is currently being reevaluated. In particular, emerging evidence suggests that mtDNA genes coevolve with the nuclear genes with which they interact to form the energy producing enzyme complexes in the mitochondria. This suggests that intergenomic epistasis between mitochondrial and nuclear genes may affect whole-organism metabolic phenotypes. Here, we use crossed combinations of mitochondrial and nuclear lineages of the seed beetle Callosobruchus maculatus and assay metabolic rate under two different temperature regimes. Metabolic rate was affected by an interaction between the mitochondrial and nuclear lineages and the temperature regime. Sequence data suggests that mitochondrial genetic variation has a role in determining the outcome of this interaction. Our genetic dissection of metabolic rate reveals a high level of complexity, encompassing genetic interactions over two genomes, and genotype × genotype × environment interactions. The evolutionary implications of these results are twofold. First, because metabolic rate is at the root of life histories, our results provide insights into the complexity of life-history evolution in general, and thermal adaptation in particular. Second, our results suggest a mechanism that could contribute to the maintenance of nonneutral mtDNA polymorphism. [ABSTRACT FROM AUTHOR]

Sex-biased genes-genes that are differentially expressed within males and females-are nonrandomly distributed across animal genomes, with sex chromosomes and autosomes often carrying markedly different concentrations of male- and female-biased genes. These linkage patterns are often gene- and lineage-dependent, differing between functional genetic categories and between species. Although sex-specific selection is often hypothesized to shape the evolution of sex-linked and autosomal gene content, population genetics theory has yet to account for many of the gene- and lineage-specific idiosyncrasies emerging from the empirical literature. With the goal of improving the connection between evolutionary theory and a rapidly growing body of genome-wide empirical studies, we extend previous population genetics theory of sex-specific selection by developing and analyzing a biologically informed model that incorporates sex linkage, pleiotropy, recombination, and epistasis, factors that are likely to vary between genes and between species. Our results demonstrate that sex-specific selection and sex-specific recombination rates can generate, and are compatible with, the gene- and species-specific linkage patterns reported in the genomics literature. The theory suggests that sexual selection may strongly influence the architectures of animal genomes, as well as the chromosomal distribution of fixed substitutions underlying sexually dimorphic traits. [ABSTRACT FROM AUTHOR]

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic. [ABSTRACT FROM AUTHOR]

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a ''synthetic association study'' in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes. [ABSTRACT FROM AUTHOR]

Modern genomewide association studies are characterized by the problem of ''missing heritability.'' Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic ''subtypes,'' may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (e). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems. [ABSTRACT FROM AUTHOR]

Abstract: Genomewide association studies (GWAS) are being conducted to unravel the genetic etiology of complex diseases, in which complex epistasis may play an important role. One-stage method in which interactions are tested using all samples at one time may be computationally problematic, may have low power as the number of markers tested increases and may not be cost-efficient. A common two-stage method may be a reasonable and powerful approach for detecting interacting genes using all samples in both two stages. In this study, we introduce an alternative two-stage method, in which some promising markers are selected using a proportion of samples in the first stage and interactions are then tested using the remaining samples in the second stage. This two-stage method is called mixed two-stage method. We then investigate the power of both one-stage method and mixed two-stage method to detect interacting disease loci for a range of two-locus epistatic models in a case–control study design. Our results suggest that mixed two-stage method may be more powerful than one-stage method if we choose about 30% of samples for single-locus tests in the first stage, and identify less than and equal to 1% of markers for follow-up interaction tests. In addition, we compare both two-stage methods and find that our two-stage method will lose power because we only use part of samples in both two stages. [Copyright &y& Elsevier]

Motivation: Under the current era of genome-wide association study (GWAS), finding epistatic interactions in the large volume of SNP data is a challenging and unsolved issue. Few of previous studies could handle genome-wide data due to the difficulties in searching the combinatorially explosive search space and statistically evaluating high-order epistatic interactions given the limited number of samples. In this work, we propose a novel learning approach (SNPRuler) based on the predictive rule inference to find disease-associated epistatic interactions. [ABSTRACT FROM PUBLISHER]

Evolutionary theory predicts that a population in a new environment will accumulate adaptive substitutions, but precisely how they accumulate is poorly understood. The dynamics of adaptation depend on the underlying fitness landscape. Virtually nothing is known about fitness landscapes in nature, and few methods allow us to infer the landscape from empirical data. With a view toward this inference problem, we have developed a theory that, in the weak-mutation limit, predicts how a population's mean fitness and the number of accumulated substitutions are expected to increase over time, depending on the underlying fitness landscape. We find that fitness and substitution trajectories depend not on the full distribution of fitness effects of available mutations but rather on the expected fixation probability and the expected fitness increment of mutations. We introduce a scheme that classifies landscapes in terms of the qualitative evolutionary dynamics they produce. We show that linear substitution trajectories, long considered the hallmark of neutral evolution, can arise even when mutations are strongly selected. Our results provide a basis for understanding the dynamics of adaptation and for inferring properties of an organism's fitness landscape from temporal data. Applying these methods to data from a long-term experiment, we infer the sign and strength of epistasis among beneficial mutations in the Escherichia coli genome. [ABSTRACT FROM AUTHOR]

Support for the theory of ecological speciation requires evidence for ecological divergence between species which directly or indirectly causes reproductive isolation. This study investigates effects of ecological vs. genetic disparity of parental species on the presence of endogenous selection (deformation and mortality rates) and potential sources of exogenous selection (growth rates and hatch timing) on hybrids. Hybrid embryonic development is analysed in a common-garden full-sib cross of three species belonging to two different ecotypes within the Coregonus lavaretus species flock in the central Alpine region of Europe. Although hatch timing was similar across the three species, embryonic growth rates and egg sizes differed between ecotypes. This led to a mismatch between embryonic growth rate and egg size in hybrid crosses that reveals epistasis between the maternal and embryonic genomes and transgressive hatch times that were asynchronous with control crosses. A strong constraint of egg size to embryo size at late development was also evident. We argue that this demonstrates potential for coadaptation of a maternal trait (egg size) with offspring growth rate to be an important source of selection against hybridization between ecotypes with different egg sizes. Implications for the measurement and quantification of early life-history traits affected by this additive relationship, such as hatch day and larval size, are also discussed. [ABSTRACT FROM AUTHOR]

The evolutionary effects of epistasis have been primarily explored analytically and most empirical studies have utilized yeast, viral and bacterial populations. Empirical analyses in multi-cellular organisms are rare because of experimental constraints. Here, we report the results of a genome-wide scan for two-way epistasis in 16 traits related to body size and composition in F2 mice from the LG/J by SM/J intercross. We analyze two-locus genotypic values at quantitative trait loci (QTL), which provides an especially detailed view of epistatic architectures, to evaluate their predicted evolutionary consequences via Monte Carlo simulations. Epistatic profiles vary, but all traits show complicated genetic architectures which are largely hidden in single locus QTL scans. On average, detected epistatic effects are comparable in size to marginal effects. Simulations demonstrate an expected preservation, and often inflation, of heritable variance across several generations of small effective population size for many identified epistatic pairs over a range of starting allele frequencies. [ABSTRACT FROM AUTHOR]

Understanding the genetics of behavioral variation remains a fascinating but difficult problem with considerable theoretical and practical implications. We used the genome-tagged mice (GTM) and an extensive test battery of well-validated behavioral assays to scan the genome for behavioral quantitative trait loci (QTLs). The GTM are a panel of ‘speed congenic’ mice consisting of over 60 strains spanning the entire autosomal genome. Each strain harbors a small (∼23 cM) DBA/2J donor segment on a uniform C57BL/6J background. The panel allows for mapping to regions as small as 5 cM and provides a powerful new tool for increasing mapping power and replicability in the analysis of QTLs. A total of 97 loci were mapped for a variety of complex behavioral traits including hyperactivity, anxiety, prepulse inhibition, avoidance and conditional fear. A larger number of loci were recovered than generally attained from standard mapping crosses. In addition, a surprisingly high proportion of loci, 63%, showed phenotypes unlike either of the parental strains. These data suggest that epistasis decreases sensitivity of locus detection in traditional crosses and demonstrate the utility of the GTM for mapping complex behavioral traits with high sensitivity and precision.Molecular Psychiatry (2009) 14, 631–645; doi:10.1038/mp.2008.4; published online 1 April 2008 [ABSTRACT FROM AUTHOR]

A comprehensive genetic linkage map of the domestic cat X chromosome was generated with the goal o localizing the genomic position of the classic X-linked orange (O) locus. Microsatellite markers with an average spacing of 3 Mb were selected from sequence traces of the cat 1.9X whole genome sequence (WGS), including the pseudoautosomal region 1 (PAR1). Extreme variation in recombination rates (centimorgans per megabase) was observed along the X chromosome, ranging from a virtual absence of recombination events in a region estimated to he >30 Mb to recombination frequencies of 15.7 cM/Mb in a segment estimated to be <0.3 Mb. This detailed linkage map was applied to position the X-linked orange gene, placing this locus on the q arm of the X chromosome, as opposed to a previously reported location on the p arm. Fine mapping placed the locus between markers at positions 106 and 116.8 Mb in the current 1.9X-coverage sequence assembly of the cat genome. Haplotype analysis revealed potential recombination events that could reduce the size of the candidate region to 3.5 Mb and suggested multiple origins for the orange phenotype in the domestic cat. Furthermore, epistasis of orange over nonagouti was demonstrated at the genetic level. [ABSTRACT FROM AUTHOR]

The variation in several of the risk factors for osteoporotic fracture, including bone mineral density (BMD), has been shown to be strongly influenced by genetic differences. However, the genetic architecture of BMD is complex in both humans and in model organisms. We previously reported quantitative trait locus (QTL) results for BMD from a genome screen of 828 F2 progeny of Copenhagen and dark agouti rats. These progeny also provide an excellent opportunity to search for epistatic effects, or interaction between genetic loci, that contribute to fracture risk. Microsatellite marker data from a 20-cM genome screen was analyzed along with weight-adjusted bone density (DXA and pQCT) phenotypic data using the R/qtl software package. Genotype and phenotype data were permuted to determine genome-wide significance thresholds for the full model and epistasis (interaction) LOD scores corresponding to an alpha level of 0.01. A novel locus on chromosome 15 and a previously reported chromosome 14 QTL demonstrated a strong epistatic effect on BMD at the femur by DXA (LOD = 5.4). Two novel QTLs on chromosomes 2 and 12 were found to interact to affect total BMD at the femur midshaft by pQCT (LOD = 5.0). These results provide new information regarding the mode of action of previously identified QTL in the rat, as well as identifying novel loci that act in combination with known QTL or with other novel loci to contribute to BMD variation. [ABSTRACT FROM AUTHOR]

Background: The key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates. Results: We studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease. Conclusion: Besides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases. [ABSTRACT FROM AUTHOR]

Background: We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy. Results: A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes precorrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiplemarker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance. Conclusion: The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis. [ABSTRACT FROM AUTHOR]

As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genomewide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis. [ABSTRACT FROM AUTHOR]

Background: Prolificacy is the most important trait influencing the reproductive efficiency of pig production systems. The low heritability and sex-limited expression of prolificacy have hindered to some extent the improvement of this trait through artificial selection. Moreover, the relative contributions of additive, dominant and epistatic QTL to the genetic variance of pig prolificacy remain to be defined. In this work, we have undertaken this issue by performing one-dimensional and bi-dimensional genome scans for number of piglets born alive (NBA) and total number of piglets born (TNB) in a three generation Iberian by Meishan F2 intercross. Results: The one-dimensional genome scan for NBA and TNB revealed the existence of two genome-wide highly significant QTL located on SSC13 (P < 0.001) and SSC17 (P < 0.01) with effects on both traits. This relative paucity of significant results contrasted very strongly with the wide array of highly significant epistatic QTL that emerged in the bi-dimensional genome-wide scan analysis. As much as 18 epistatic QTL were found for NBA (four at P < 0.01 and five at P < 0.05) and TNB (three at P < 0.01 and six at P < 0.05), respectively. These epistatic QTL were distributed in multiple genomic regions, which covered 13 of the 18 pig autosomes, and they had small individual effects that ranged between 3 to 4% of the phenotypic variance. Different patterns of interactions (a × a, a × d, d × a and d × d) were found amongst the epistatic QTL pairs identified in the current work. Conclusions: The complex inheritance of prolificacy traits in pigs has been evidenced by identifying multiple additive (SSC13 and SSC17), dominant and epistatic QTL in an Iberian × Meishan F2 intercross. Our results demonstrate that a significant fraction of the phenotypic variance of swine prolificacy traits can be attributed to first-order gene-by-gene interactions emphasizing that the phenotypic effects of alleles might be strongly modulated by the genetic background where they segregate. [ABSTRACT FROM AUTHOR]

We recently identified several (4-8) quantitative trait loci (QTL) for 3 physical activity traits (daily distance, duration, and speed voluntarily run) in an F2 population of mice derived from an original intercross of 2 strains that exhibited large differences in activity. These QTL cumulatively explained from 11% to 34% of the variation in these traits, but this was considerably less than their total genetic variability estimated from differences among inbred strains. We therefore decided to test whether epistatic interactions might account for additional genetic variation in these traits in this same population of mice. We conducted a full genome epistasis scan for all possible interactions of QTL between each pair of 20 chromosomes. The results of this scan revealed an abundance of epistasis, with QTL throughout the genome being involved in significant interactions. Overall, epistatic effects contributed an average of 26% of the total variation among the 3 activity traits. These results suggest that epistatic interactions of genes may play as important a role in the genetic architecture of physical activity traits as single-locus effects and need to be considered in future candidate gene identification studies. [ABSTRACT FROM AUTHOR]

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene–gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available. [ABSTRACT FROM AUTHOR]

The study of genetic interactions (epistasis) is central to the understanding of genome organization and evolution. A general correlation between epistasis and genomic complexity has been recently shown, such that in simpler genomes epistasis is antagonistic on average (mutational effects tend to cancel each other out), whereas a transition towards synergistic epistasis occurs in more complex genomes (mutational effects strengthen each other). Here, we use a simple network model to identify basic features explaining this correlation. We show that, in small networks with multifunctional nodes, lack of redundancy, and absence of alternative pathways, epistasis is antagonistic on average. In contrast, lack of multi-functionality, high connectivity, and redundancy favor synergistic epistasis. Moreover, we confirm the previous finding that epistasis is a covariate of mutational robustness: in less robust networks it tends to be antagonistic whereas in more robust networks it tends to be synergistic. We argue that network features associated with antagonistic epistasis are typically found in simple genomes, such as those of viruses and bacteria, whereas the features associated with synergistic epistasis are more extensively exploited by higher eukaryotes. [ABSTRACT FROM AUTHOR]

The abundance of sex and recombination is still one of the most puzzling questions in the theory of evolution: Most models find that recombination can evolve, but only under a limited range of parameters. Here we review the major models and supporting evidence, concentrating on recent approaches where more realistic assumptions help explain the evolution of sex and recombination under a wider parameter range: finite populations, selection over long genomes, variation in recombination across the genome, and plasticity of sex and recombination. We discuss the similarities and differences between the evolution of sex and that of recombination. [ABSTRACT FROM AUTHOR]

Background: Biological robustness results from redundant pathways that achieve an essential objective, e.g. the production of biomass. As a consequence, the biological roles of many genes can only be revealed through multiple knockouts that identify a set of genes as essential for a given function. The identification of such "epistatic" essential relationships between network components is critical for the understanding and eventual manipulation of robust systems-level phenotypes. Results: We introduce and apply a network-based approach for genome-scale metabolic knockout design. We apply this method to uncover over 11,000 minimal knockouts for biomass production in an in silico genome-scale model of E. coli. A large majority of these "essential sets" contain 5 or more reactions, and thus represent complex epistatic relationships between components of the E. coli metabolic network. Conclusion: The complex minimal biomass knockouts discovered with our approach illuminate robust essential systems-level roles for reactions in the E. coli metabolic network. Unlike previous approaches, our method yields results regarding high-order epistatic relationships and is applicable at the genome-scale. [ABSTRACT FROM AUTHOR]

Background: The difficulty in elucidating the genetic basis of complex diseases roots in the many factors that can affect the development of a disease. Some of these genetic effects may interact in complex ways, proving undetectable by current single-locus methodology. Results: We have developed an analysis tool called Hypothesis Free Clinical Cloning (HFCC) to search for genome-wide epistasis in a case-control design. HFCC combines a relatively fast computing algorithm for genome-wide epistasis detection, with the flexibility to test a variety of different epistatic models in multi-locus combinations. HFCC has good power to detect multi-locus interactions simulated under a variety of genetic models and noise conditions. Most importantly, HFCC can accomplish exhaustive genome-wide epistasis search with large datasets as demonstrated with a 400,000 SNP set typed on a cohort of Parkinson's disease patients and controls. Conclusion: With the current availability of genetic studies with large numbers of individuals and genetic markers, HFCC can have a great impact in the identification of epistatic effects that escape the standard single-locus association analyses. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS. Results: The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements. Conclusion: The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware. [ABSTRACT FROM AUTHOR]

Chromosomal inversion polymorphisms are thought to play a role in adaptive divergence, but the genes conferring adaptive benefits remain elusive. Here we study 2La, a common polymorphic inversion in the African malaria vector Anopheles gambiae. The frequency of 2La varies clinally and seasonally in a pattern suggesting response to selection for aridity tolerance. By hybridizing genomic DNA from individual mosquitoes to oligonucleotide microarrays, we obtained a complete map of differentiation across the A. gambiae genome. Comparing mosquitoes homozygous for the 2La gene arrangement or its alternative (2L+a), divergence was highest at loci within the rearranged region. In the 22 Mb included within alternative arrangements, two ~1.5 Mb regions near but not adjacent to the breakpoints were identified as being significantly diverged, a conclusion validated by targeted sequencing. The persistent association of both regions with the 2La arrangement is highly unlikely given known recombination rates across the inversion in 2La heterozygotes, thus implicating selection on genes underlying these regions as factors responsible for the maintenance of 2La. Polymorphism and divergence data are consistent with a model in which the inversion is maintained by migration-selection balance between multiple alleles inside these regions, but further experiments will be needed to fully distinguish between the epistasis (coadaptation) and local adaptation models for the maintenance of 2La. [ABSTRACT FROM AUTHOR]

Mutation-selection balance in a multi-locus system is investigated theoretically, using a modification of Buhner's infinitesimal model of selection on a normally-distributed quantitative character, taking the number of mutations per individual (n) to represent the character value. The logarithm of the fitness of an individual with n mutations is assumed to be a quadratic, decreasing function of n. The equilibrium properties of infinitely large asexual populations, random-mating populations lacking genetic recombination, and random-mating populations with arbitrary recombination frequencies are investigated. With 'synergistic' epistasis on the scale of log fitness, such that log fitness declines more steeply as n increases, it is shown that equilibrium mean fitness is least for asexual populations. In sexual populations, mean fitness increases with the number of chromosomes and with the map length per chromosome. With 'diminishing returns' epistasis, such that log fitness declines less steeply as n increases, mean fitness behaves in the opposite way. Selection on asexual variants and genes affecting the rate of genetic recombination in random-mating populations was also studied. With synergistic epistasis, zero recombination always appears to be disfavoured, but free recombination is disfavoured when the mutation rate per genome is sufficiently small, leading to evolutionary stability of maps of intermediate length. With synergistic epistasis, an asexual mutant is unlikely to invade a sexual population if the mutation rate per diploid genome greatly exceeds unity. Recombination is selectively disadvantageous when there is diminishing returns epistasis. These results are compared with the results of previous theoretical studies of this problem, and with experimental data. [ABSTRACT FROM AUTHOR]

Epistasis seems to play a significant role in the manifestation of heterosis. However, the power of detecting epistatic interactions among quantitative trait loci (QTL) in segregating populations is low. We studied heterosis in Arabidopsis thalianahybrid C24 X Col-0 by testing near-isogenic lines (NILs) and their triple testcross (TTC) progenies. Our objectives were to (i) provide the theoretical basis for estimating different types of genetic effects with this experimental design, (ii) determine the extent of heterosis for seven growth-related traits, (iii) map the underlying QTL, and (iv) determine their gene action. Two substitution libraries, each consisting of 28 NILs and covering ∼61 and 39% of the Arahidopsis genome, were assayed by 110 single-nucleotide polymorphism (SNP) markers. With our novel generation means approach 38 QTL were detected, many of which confirmed heterotic QTL detected previously in the same cross with TTC progenies of recombinant inbred lines. Furthermore, many of the QTL were common for different traits and in common with the 58 QTL detected by a method that compares triplets consisting of a NIL, its recurrent parent, and their F1 cross. While the latter approach revealed mostly (75%) overdominant QTL, the former approach allowed separation of dominance and epistasis by analyzing all materials simultaneously and yielded substantial positive additive X additive effects besides directional dominance. Positive epistatic effects reduced heterosis for growth-related traits in our materials. [ABSTRACT FROM AUTHOR]

The accumulation of deleterious mutations plays a major role in evolution, and key to this are the interactions between their fitness effects, known as epistasis. Whether mutations tend to interact synergistically (with multiple mutations being more deleterious than would be expected from their individual fitness effects) or antagonistically is important for a variety of evolutionary questions, particularly the evolution of sex. Unfortunately, the experimental evidence on the prevalence and strength of epistasis is mixed and inconclusive. Here we study theoretically whether synergistic or antagonistic epistasis is likely to be favored by evolution and by how much. We find that in the presence of recombination, evolution favors less synergistic or more antagonistic epistasis whenever mutations that change the epistasis in this direction are possible. This is because evolution favors increased buffering against the effects of deleterious mutations. This suggests that we should not expect synergistic epistasis to be widespread in nature and hence that the mutational deterministic hypothesis for the advantage of sex may not apply widely. [ABSTRACT FROM AUTHOR]

We have developed a powerful experimental framework that combines competitive selection and microarray-based genetic footprinting to comprehensively reveal the genetic basis of bacterial behaviors. Application of this method to Escherichia coli motility identifies 95% of the known flagellar and chemotaxis genes, and reveals three dozen novel loci that, to varying degrees and through diverse mechanisms, affect motility. To probe the network context in which these genes function, we developed a method that uncovers genome-wide epistatic interactions through comprehensive analyses of double-mutant phenotypes. This allows us to place the novel genes within the context of signaling and regulatory networks, including the Rcs phosphorelay pathway and the cyclic di-GMP second-messenger system. This unifying framework enables sensitive and comprehensive genetic characterization of complex behaviors across the microbial biosphere. [ABSTRACT FROM AUTHOR]

Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible. [ABSTRACT FROM AUTHOR]

The role of epistasis in evolution has long been contentious. Resolving the issue requires empirical measurements that are statistically adequate and evolutionarily relevant. We review experimental methods for measuring epistasis, some that are commonly used but weak and others that are less frequently used hut stronger. We review statistical genetic methods based on analyses of variances and means as well as molecular genetic methods for detecting gene interactions. We also highlight relevant empirical studies that illustrate the implementation of particular methods. In spite of the inherent weaknesses of most methods, epistasis is surprisingly common. We conclude with a discussion of how technologies for investigating genome-wide epistasis are bridging the gap between physiological and statistical epistasis for model organisms. [ABSTRACT FROM AUTHOR]

Whether systematic genetic interactions (epistasis) occur at the genomic scale remains a challenging topic in evolutionary biology. Epistasis should make a significant contribution to variation in complex traits and influence the evolution of genetic systems as sex, diploidy, dominance, or the contamination of genomes with deleterious mutations. We have collected data from widely different organisms and quantified epistasis in a common, per-generation scale. Simpler genomes, such as those of RNA viruses, display antagonistic epistasis (mutations have smaller effects together than expected); bacterial microorganisms do not apparently deviate from independent effects, whereas in multicellular eukaryotes, a transition toward synergistic epistasis occurs (mutations have larger effects together than expected). We propose that antagonistic epistasis might be a property of compact genomes with few nonpleiotropic biological functions, whereas in complex genomes, synergism might emerge from mutational robustness. [ABSTRACT FROM AUTHOR]

Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance. [ABSTRACT FROM AUTHOR]

The mutational deterministic hypothesis for the origin and maintenance of sexual reproduction posits that sex enhances the ability of natural selection to purge deleterious mutations after recombination brings them together into single genomes. This explanation requires negative epistasis, a type of genetic interaction where mutations are more harmful in combination than expected from their separate effects. The conceptual appeal of the mutational deterministic hypothesis has been offset by our inability to identify the mechanistic and evolutionary bases of negative epistasis. Here we show that negative epistasis can evolve as a consequence of sexual reproduction itself. Using an artificial gene network model, we find that recombination between gene networks imposes selection for genetic robustness, and that negative epistasis evolves as a by-product of this selection. Our results suggest that sexual reproduction selects for conditions that favour its own maintenance, a case of evolution forging its own path. [ABSTRACT FROM AUTHOR]

Signaling by tumor necrosis factors (TNFs) plays a prominent role in mammalian development and disease. To fully understand this complex signaling pathway it is important to identify all regulators and transduction components. A single TNF family member, Eiger, is encoded in the Drosophila genome, offering the possibility of applying genetic approaches for pursuing this goal. Here we present a screen for the isolation of novel genes involved in the TNF/Eiger pathway. On the basis of Eiger's ability to potently activate Jun-N-terminal kinase (JNK) and trigger apoptosis, we used the Drosophila eye to establish an assay for dominant suppressors of this activity. In a large-scale screen the Drosophila homolog of TAB2/3 (dTAB2) was identified as an essential component of the Eiger-JNK pathway. Genetic epistasis and biochemical protein-protein interaction assays assign an adaptor role to dTAB2, linking dTRAF1 to the JNKKK dTAK1, demonstrating a conserved mechanism of TNF signal transduction in mammals and Drosophila. Thus, in contrast to morphogenetic processes, such as dorsal closure of the embryo, in which the JNK pathway is activated by the JNKKK Slipper, Eiger uses the dTAB2-dTAK1 module to induce JNK signaling activity. [ABSTRACT FROM AUTHOR]

In order to detect possible synergistic epistasis for viability in Drosophila melanogaster we assayed the relative viability of chromosomes II in: (i) panmixia, (ii) forced total homozygosity, and (iii) homozygosity for, on the average, half of their loci. As these genotypes were constructed using exactly the same set of chromosomes in the three cases, the design allows us to estimate the inbreeding depression rate at two different inbreeding levels in the absence of purging natural selection. Overall, no consistent synergistic epistasis was found. However, there was a small fraction of chromosomes whose severely deleterious effect when homozygous was almost significantly larger than expected from their viability when homozygous for half of their loci. This suggests occasional but important synergistic epistasis, which might confer evolutionary advantage to recombination in tightly linked genomes. Nevertheless, such epistasis is unlikely to be an evolutionary advantage driving the evolution of sexual anisogamous reproduction, as its contribution to overall viability is small when compared with the two-fold cost of anisogamy. [ABSTRACT FROM AUTHOR]

Fitness interactions between loci in the genome, or epistasis, can result in mutations that are individually deleterious but jointly beneficial. Such epistasis gives rise to multiple peaks on the genotypic fitness landscape. The problem of evolutionary escape from such local peaks has been a central problem of evolutionary genetics for at least 75 years. Much attention has focused on models of small populations, in which the sequential fixation of valley genotypes carrying individually deleterious mutations operates most quickly owing to genetic drift. However, valley genotypes can also be subject to mutation while transiently segregating, giving rise to copies of the high fitness escape genotype carrying the jointly beneficial mutations. In the absence of genetic recombination, these mutations may then fix simultaneously. The time for this process declines sharply with increasing population size, and it eventually comes to dominate evolutionary behavior. Here we develop an analytic expression for Ncrit, the critical population size that defines the boundary between these regimes, which shows that both are likely to operate in nature. Frequent recombination may disrupt high-fitness escape genotypes produced in populations larger than Ncrit before they reach fixation, defining a third regime whose rate again slows with increasing population size. We develop a novel expression for this critical recombination rate, which shows that in large populations the simultaneous fixation of mutations that are beneficial only jointly is unlikely to be disrupted by genetic recombination if their map distance is on the order of the size of single genes. Thus, counterintuitively, mass selection alone offers a biologically realistic resolution to the problem of evolutionary escape from local fitness peaks in natural populations. [ABSTRACT FROM AUTHOR]

Classical epistasis analysis can determine the order of function of genes in pathways using morphological, biochemical and other phenotypes. It requires knowledge of the pathway's phenotypic output and a variety of experimental expertise and so is unsuitable for genome-scale analysis. Here we used microarray profiles of mutants as phenotypes for epistasis analysis. Considering genes that regulate activity of protein kinase A in Dictyostelium, we identified known and unknown epistatic relationships and reconstructed a genetic network with microarray phenotypes alone. This work shows that microarray data can provide a uniform, quantitative tool for large-scale genetic network analysis. [ABSTRACT FROM AUTHOR]

The human UV-damaged DNA-binding protein Ddb1 associates with cullin 4 ubiquitin ligases implicated in nucleotide excision repair (NER). These complexes also contain the signalosome (CSN), but NER-relevant ubiquitination targets have not yet been identified. We report that fission yeast Ddb1, Cullin 4 (Pcu4), and CSN subunits Csn1 and Csn2 are required for degradation of the ribonucleotide reductase (RNR) inhibitor protein Spd1. Ddb1-deficient cells have >20-fold increased spontaneous mutation rate. This is partly dependent on the error-prone translesion DNA polymerases. Spd1 deletion substantially reduced the mutation rate, suggesting that insufficient RNR activity accounts for ∼50% of observed mutations. Epistasis analysis indicated that Ddb1 contributed to mutation avoidance and tolerance to DNA damage in a pathway distinct from NER. Finally, we show that Ddb1/Csn1/Cullin 4-mediated Spd1 degradation becomes essential when cells differentiate into meiosis. These results suggest that Ddb1, along with Cullin 4 and the signalosome, constitute a major pathway controlling genome stability, repair, and differentiation via RNR regulation. [ABSTRACT FROM AUTHOR]

Reproductive strategies such as sexual reproduction and recombination that involve the shuffling of parental genomes for the production of offspring are ubiquitous in nature. However, their evolutionary benefit remains unclear. Many theories have identified potential benefits, but progress is hampered by the scarcity of relevant data. One class of theories is based on the assumption that mutations affecting fitness exhibit negative epistasis. Retroviruses recombine frequently and thus provide a unique opportunity to test these theories. Using amino acid sequence data and fitness values from 9466 human immunodeficiency virus 1 (HIV-1) isolates, we find in contrast to these theories strong statistical evidence for a predominance of positive epistasis in HIV-1. [ABSTRACT FROM AUTHOR]

Genetic studies of behavior have traditionally come in two flavors: quantitative genetic studies of natural variants and single-gene studies of induced mutants. Each employed different techniques and methods of analysis toward the common, ultimate goal of understanding how genes influence behavior. With the advent of new genomic technologies, and also the realization that mechanisms underlying behavior involve a considerable degree of complex gene interaction, the traditionally separate strands of behavior genetics are merging into a single, synthetic strategy. [ABSTRACT FROM AUTHOR]

Modeling and detecting nonallelic (epistatic) effects at multiple quantitative trait loci (QTL) often assume that the study population is in zygotic equilibrium (i.e., genotypic frequencies at different loci are products of corresponding single-locus genotypic frequencies). However, zygotic associations can arise from physical linkages between different loci or from many evolutionary and demographic processes even for unlinked loci. We describe a new model that partitions the two-locus genotypic values in a zygotic disequilibrium population into equilibrium and residual portions. The residual portion is of course due to the presence of zygotic associations. The equilibrium portion has eight components including epistatic effects that can be defined under three commonly used equilibrium models, Cockerham's model, F2-metric, and F∞-metric models. We evaluate our model along with these equilibrium models theoretically and empirically. While all the equilibrium models require zygotic equilibrium, Cockerham's model is the most general, allowing for Hardy-Weinberg disequilibrium and arbitrary gene frequencies at individual loci whereas F2-metric and F∞-metric models require gene frequencies of one-half in a Hardy-Weinberg equilibrium population. In an F2 population with two unlinked loci, Cockerham's model is reduced to the F2metric model and thus both have a desirable property of orthogonality among the genic effects; the genic effects under the F∞-metric model are not orthogonal but they can be easily translated into those under the F2-metric model through a simple relation. Our model is reduced to these equilibrium models in the absence of zygotic associations. The results from our empirical analysis suggest that the residual genetic variance arising from zygotic associations can be substantial and may be an important source of bias in QTL mapping studies. [ABSTRACT FROM AUTHOR]

Recent advances in methodologies for testing epistatic interactions, combined with several successes in demonstrating genetic interaction effects in animal and human genetics, have rekindled interest in the role of epistatic influences on complex traits. It has even been suggested that the unacknowledged presence of epistasis vitiates the genetic dissection of human and animal behavior. Here we report a genome-wide interaction analysis of 1636 F2 mice to show that epistasis is of minimal importance in an animal model of anxiety. By using a sufficiently large sample of F2 animals, we provide evidence that interaction effects between any two loci contribute less than 5% to the total phenotypic variance in multiple tests of anxiety. We conclude that interactions between loci do not necessarily vitiate the genetic analysis of behavior in at least one animal model of anxiety. [ABSTRACT FROM AUTHOR]

Background: The rate at which fitness declines as an organism's genome accumulates random mutations is an important variable in several evolutionary theories. At an intuitive level, it might seem natural that random mutations should tend to interact synergistically, such that the rate of mean fitness decline accelerates as the number of random mutations is increased. However, in a number of recent studies, a prevalence of antagonistic epistasis (the tendency of multiple mutations to have a mitigating rather than reinforcing effect) has been observed. Results: We studied in silico the net amount and form of epistatic interactions in RNA secondary structure folding by measuring the fraction of neutral mutants as a function of mutational distance d. We found a clear prevalence of antagonistic epistasis in RNA secondary structure folding. By relating the fraction of neutral mutants at distance d to the average neutrality at distance d, we showed that this prevalence derives from the existence of many compensatory mutations at larger mutational distances. Conclusions: Our findings imply that the average direction of epistasis in simple fitness landscapes is directly related to the density with which fitness peaks are distributed in these landscapes. [ABSTRACT FROM AUTHOR]

Epistasis is a common and important phenomenon, as indicated by results from a number of recent experiments. Unfortunately, the discovery of epistatic quantitative trait loci (QTL) is difficult since one must search for multiple QTL simultaneously in two or more dimensions. Such a multidimensional search necessitates many statistical tests, and a high statistical threshold must be adopted to avoid false positives. Furthermore, the large number of (interaction) parameters in comparison with the number of observations results in a serious danger of overfitting and overinterpretation of the data. In this article we present a new statistical framework for mapping epistasis in inbred line crosses. It is based on reducing the high dimensionality of the problem in two ways. First, epistatic QTL are mapped in a one-dimensional genome scan for high interactions between QTL and the genetic background. Second, the dimension of the search is bounded by penalized likelihood methods. We use simulated backcross data to illustrate the new approach. [ABSTRACT FROM AUTHOR]

Presents a study which conducted a whole-genome scan to detect any epistasis in fluctuating asymmetry of centroid size in the mandibles of mice. Analysis of epistasis; Results of the genome scan for centroid size; Genomic distribution of epistatic interactions.

The pathogenesis of all forms of psoriasis remains obscure. Segregation analysis and twin studies together with ethnic differences in disease frequency all point to an underlying genetic susceptibility to psoriasis, which is both complex and likely to reflect the action of a number of genes. We performed a genome wide analysis using a total of 271 polymorphic autosomal markers on 284 sib relative pairs identified within 158 independent families. We detected evidence for linkage at 6p21 (PSORS1) with a non-parametric linkage score (NPL)=4.7, p=2 x 10-6 and at chromosome 1p (NPL=3.6, p=1.9 x 10-4) in all families studied. Significant excess (p=0.004) paternal allele sharing was detected for markers spanning the PSORS1 locus. A further three regions reached NPL scores of 2 or greater, including a region at chromosome 7 (NPL 2.1), for which linkage for a number of autoimmune disorders has been reported. Partitioning of the data set according to allele sharing at 6p21 (PSORS1) favoured linkage to chromosomes 2p (NPL 2.09) and 14q (NPL 2.0), both regions implicated in previous independent genome scans, and suggests evidence for epistasis between PSORS1 and genes at other genomic locations. This study has provided linkage evidence in favour of a novel susceptibility locus for psoriasis and provides evidence of the complex mechanisms underlying the genetic predisposition to this common skin disease. [ABSTRACT FROM AUTHOR]

The effects of assortative mating and epistasis between interspecific nuclear and cytoplasmic genomes (cytonuclear incompatibility) on levels of homozygote cytonuclear disequilibria are analysed in an effort to gain a better understanding of the processes affecting the genetic structure of hybrid populations and the nature of isolating barriers between species. Cytonuclear incompatibility has a direct effect on the level of disequilibria between maternally inherited cytoplasmic and biparentally inherited nuclear markers at the adult stage that is largely a function of (i) the strength of selection; (ii) the level of dominance of the nuclear alleles; and (iii) the level of disequilibria occurring at the seed stage. Assortative mating based on nuclear genotypes conserves cytonuclear genetic associations between the adult and the subsequent seed stage. The preservation of cytonuclear associations between the previous adult and the current progeny stages enhances the level of disequilibria at the next adult stage because the initial level of cytonuclear disequilibria in the progeny is greater before the action of selection. Under random mating, cytonuclear genetic associations are broken up in each generation, so the levels of disequilibria attained by selection are not as high as those observed with assortative mating. The changes in disequilibria between life stages in a hybrid Iris population were examined to infer the strength of selection and levels of assortative mating. Based on these estimates, it appears that the combined action of epistasis and assortative mating would be sufficient to maintain the high levels of cytonuclear disequilibria observed. [ABSTRACT FROM AUTHOR]

The article discusses how most early geneticists assumed that alleles have constant relative selective values and that the phenotype were a mosaic of unit characters. According to the author, this viewpoint was later changed when it was understood that the selective value of any gene depends on the rest of the genome and on various gene interactions (epistasis).

Background: Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested. Results: We have developed a set of three efficient programs, FastANOVA, COE and TEAM, that support epistasis test in a variety of problem settings in GWAS. These programs utilize permutation test to properly control error rate such as family-wise error rate (FWER) and false discovery rate (FDR). They guarantee to find the optimal solutions, and significantly speed up the process of epistasis detection in GWAS. Conclusions: A web server with user interface and source codes are available at the website http://www.csbio.unc. edu/epistasis/. The source codes are also available at SourceForge http://sourceforge.net/projects/epistasis/. [ABSTRACT FROM AUTHOR]