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Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at . [ABSTRACT FROM AUTHOR]

Background: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles. Results: This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach. Conclusions: In this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model. [ABSTRACT FROM AUTHOR]

The article discusses how researcher Skwark along with colleagues harnessed population genomic data to approach the challenge of epistasis analysis .Topics discussed include developing a computational method termed genome direct coupling analysis (DCA) to detect epistasis using geno type and allele frequencies estimated from genome sequences of thousands of individuals of the same species.

The vast application of fluorescent semiconductor nanoparticles (NPs) or quantum dots (QDs) has prompted the development of new, cheap and safer methods that allow generating QDs with improved biocompatibility. In this context, green or biological QDs production represents a still unexplored area. This work reports the intracellular CdTe QDs biosynthesis in bacteria. Escherichia coli overexpressing the gshA gene, involved in glutathione (GSH) biosynthesis, was used to produce CdTe QDs. Cells exhibited higher reduced thiols, GSH and Cd/Te contents that allow generating fluorescent intracellular NP-like structures when exposed to CdCl2 and K2TeO3. Fluorescence microscopy revealed that QDs-producing cells accumulate defined structures of various colors, suggesting the production of differently-sized NPs. Purified fluorescent NPs exhibited structural and spectroscopic properties characteristic of CdTe QDs, as size and absorption/emission spectra. Elemental analysis confirmed that biosynthesized QDs were formed by Cd and Te with Cd/Te ratios expected for CdTe QDs. Finally, fluorescent properties of QDs-producing cells, such as color and intensity, were improved by temperature control and the use of reducing buffers. [ABSTRACT FROM AUTHOR]

Heterosis is widely used in genetic crop improvement; however, the genetic basis of heterosis is incompletely understood. The use of whole-genome segregating populations poses a problem for establishing the genetic basis of heterosis, in that interactions often mask the effects of individual loci. However, introgression line (IL) populations permit the partitioning of heterosis into defined genomic regions, eliminating a major part of the genome-wide epistasis. In our previous study, based on mid-parental heterosis (HMP) value with single-point analysis, 42 heterotic loci (HLs) associated with six yield-related traits were detected in wild and cultivated rice using a set of 265 ILs of Dongxiang common wild rice (Oryza rufipogon Griff.). In this study, the genetic effects of HLs were determined as the combined effects of both additive and dominant gene actions, estimated from the performance values of testcross F1s and the dominance effects estimated from the HMP values of testcross F1s. We characterized the gene action type at each HL. Thirty-eight of the 42 HLs were over-dominant, and in the absence of epistasis, four HLs were dominant. Therefore, we favour that over-dominance is a major genetic basis of ‘wild-cultivar’ crosses at the single functional Mendelian locus level. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Since complete redundancy between extant duplicates (paralogs) is evolutionarily unfavorable, some degree of functional congruency is eventually lost. However, in budding yeast, experimental evidence collected for duplicated metabolic enzymes and in global physical interaction surveys had suggested widespread functional overlap between paralogs. While maintained functional overlap is thought to confer robustness against genetic mutation and facilitate environmental adaptability, it has yet to be determined what properties define paralogs that can compensate for the phenotypic consequence of deleting a sister gene, how extensive this epistasis is, and how adaptable it is toward alternate environmental states. To this end, we have performed a comprehensive experimental analysis of epistasis as indicated by aggravating genetic interactions between paralogs resulting from an ancient whole-genome duplication (WGD) event occurring in the budding yeast Saccharomyces cerevisiae, and thus were able to compare properties of large numbers of epistatic and non-epistatic paralogs with identical evolutionary times since divergence. We found that more than one-third (140) of the 399 examinable WGD paralog pairs were epistatic under standard laboratory conditions and that additional cases of epistasis became obvious only under media conditions designed to induce cellular stress. Despite a significant increase in within-species sequence co-conservation, analysis of protein interactions revealed that paralogs epistatic under standard laboratory conditions were not more functionally overlapping than those non-epistatic. As experimental conditions had an impact on the functional categorization of paralogs deemed epistatic and only a fraction of potential stress conditions have been interrogated here, we hypothesize that many epistatic relationships remain unresolved. [ABSTRACT FROM AUTHOR]