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Genetic variation in mitochondrial DNA (mtDNA) provides adaptive potential although the underlying genetic architecture of fitness components within mtDNAs is not known. To dissect functional variation within mtDNAs, we first identified naturally occurring mtDNAs that conferred high or low fitness in Saccharomyces cerevisiae by comparing growth in strains containing identical nuclear genotypes but different mtDNAs. During respiratory growth under temperature and oxidative stress conditions, mitotype effects were largely independent of nuclear genotypes even in the presence of mito--nuclear interactions. Recombinant mtDNAs were generated to determine fitness components within high- and low-fitness mtDNAs. Based on phenotypic distributions of isogenic strains containing recombinant mtDNAs, we found that multiple loci contributed to mitotype fitness differences. These mitochondrial loci interacted in epistatic, nonadditive ways in certain environmental conditions. Mito--mito epistasis (i.e., nonadditive interactions between mitochondrial loci) influenced fitness in progeny from four different crosses, suggesting that mito--mito epistasis is a widespread phenomenon in yeast and other systems with recombining mtDNAs. Furthermore, we found that interruption of coadapted mito--mito interactions produced recombinant mtDNAs with lower fitness. Our results demonstrate that mito--mito epistasis results in functional variation through mitochondrial recombination in fungi, providing modes for adaptive evolution and the generation of mito--mito incompatibilities. [ABSTRACT FROM AUTHOR]

The impact of epistasis on the evolution of multi-locus traits depends on recombination. While sexually reproducing eukaryotes recombine so frequently that epistasis between polymorphisms is not considered to play a large role in short-term adaptation, many bacteria also recombine, some to the degree that their populations are described as "panmictic" or "freely recombining." However, whether this recombination is sufficient to limit the ability of selection to act on epistatic contributions to fitness is unknown. We quantify homologous recombination in five bacterial pathogens and use these parameter estimates in a multilocus model of bacterial evolution with additive and epistatic effects. We find that even for highly recombining species (e.g., Streptococcus pneumoniae or Helicobacter pylori), selection on weak interactions between distant mutations is nearly as efficient as for an asexual species, likely because homologous recombination typically transfers only short segments. However, for strong epistasis, bacterial recombination accelerates selection, with the dynamics dependent on the amount of recombination and the number of loci. Epistasis may thus play an important role in both the short- and long-term adaptive evolution of bacteria, and, unlike in eukaryotes, is not limited to strong effect sizes, closely linked loci, or other conditions that limit the impact of recombination. [ABSTRACT FROM AUTHOR]

Gene interactions, or epistasis, play a large role in determining evolutionary outcomes. The ruggedness of fitness landscapes, and thus the predictability of evolution and the accessibility of high-fitness genotypes, is determined largely by the pervasiveness of epistasis and the degree of correlation between similar genotypes. We created all possible pairings of three sets of five beneficial first-step mutations fixed during adaptive walks under three different regimes: selection on growth rate alone, on growth rate and thermal stability, and on growth rate and pH stability. All 30 double-mutants displayed negative, antagonistic epistasis with regard to growth rate and fitness, but positive epistasis and additivity were common for the stability phenotypes. This suggested that biophysically simple phenotypes, such as capsid stability, may on average behave more additively than complex phenotypes like viral growth rate. Growth rate epistasis was also smaller in magnitude when the individual effects of single mutations were smaller. Significant sign epistasis, such that the effect of a mutation that is beneficial in the wild-type background is deleterious in combination with a second mutation, emerged more frequently in intragenic mutational pairings than in intergenic pairs, and was evident in nearly half of the double-mutants, indicating that the fitness landscape is moderately uncorrelated and of intermediate ruggedness. Together, our results indicated that mutations may interact additively with regard to phenotype when considered at a basic, biophysical level, but that epistasis arises as a result of pleiotropic interactions between the individual components of complex phenotypes and diminishing returns arising from intermediate phenotypic optima. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity, and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty can be problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here, we report an advanced recombinant inbred line (RIL) quantitative trait locus mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs at present, was created by hybridization of 16 wild isolates, experimental evolution for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of singlenucleotide polymorphisms known to segregate in natural populations, and complements existing C. elegans mapping resources by providing fine resolution and high nucleotide diversity across > 95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad-sense heritability in the CeMEE. While simulations show that we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits do not feature such alleles. We instead find that a significant fraction of trait variance, approaching 40% for fertility, can be explained by sign epistasis with main effects below the detection limit. In congruence, phenotype prediction from genomic similarity, while generally poor (r² <10%), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms. [ABSTRACT FROM AUTHOR]

Frequency-independent selection is generally considered as a force that acts to reduce the genetic variation in evolving populations, yet rigorous arguments for this idea are scarce. When selection fluctuates in time, it is unclear whether frequencyindependent selection may maintain genetic polymorphism without invoking additional mechanisms. We show that constant frequency-independent selection with arbitrary epistasis on a well-mixed haploid population eliminates genetic variation if we assume linkage equilibrium between alleles. To this end, we introduce the notion of frequency-independent selection at the level of alleles, which is sufficient to prove our claim and contains the notion of frequency-independent selection on haploids. When selection and recombination are weak but of the same order, there may be strong linkage disequilibrium; numerical calculations show that stable equilibria are highly unlikely. Using the example of a diallelic two-locus model, we then demonstrate that frequency-independent selection that fluctuates in time can maintain stable polymorphism if linkage disequilibrium changes its sign periodically. We put our findings in the context of results from the existing literature and point out those scenarios in which the possible role of frequencyindependent selection in maintaining genetic variation remains unclear. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges regarding the use of genetic mapping to detect epistasis. It is noted that multiple testing is a common challenge in genome-wide scans for epistasis while statistical power is a related difficulty that genome-wide scans for epistasis face. It is mentioned that as inbred lines allows unambiguous cataloging of the variants, using them as the founders of crosses is desirable.

High-order epistasis has been observed in many genotype-phenotype maps. These multi-way interactions between mutations may be useful for dissecting complex traits and could have profound implications for evolution. Alternatively, they could be a statistical artifact. High-order epistasis models assume the effects of mutations should add, when they could in fact multiply or combine in some other nonlinear way. A mismatch in the "scale" of the epistasis model and the scale of the underlying map would lead to spurious epistasis. In this article, we develop an approach to estimate the nonlinear scales of arbitrary genotype-phenotype maps. We can then linearize these maps and extract high-order epistasis. We investigated seven experimental genotype-phenotype maps for which high-order epistasis had been reported previously. We find that five of the seven maps exhibited nonlinear scales. Interestingly, even after accounting for nonlinearity, we found statistically significant high-order epistasis in all seven maps. The contributions of high-order epistasis to the total variation ranged from 2.2 to 31.0%, with an average across maps of 12.7%. Our results provide strong evidence for extensive high-order epistasis, even after nonlinear scale is taken into account. Further, we describe a simple method to estimate and account for nonlinearity in genotype-phenotype maps. [ABSTRACT FROM AUTHOR]

Short tandem repeats (STRs) are hypervariable genetic elements that occur frequently in coding regions. Their high mutation rate readily generates genetic variation, contributing to adaptive evolution and human diseases. We previously reported that natural ELF3 polyglutamine variants cause reciprocal genetic incompatibilities in two divergent Arabidopsis thaliana backgrounds. Here, we dissect the genetic architecture of this incompatibility, revealing as many as four loci putatively interacting with ELF3. We were able to specifically identify one such ELF3-interacting gene, LSH9. We further used a yeast two-hybrid strategy to identify proteins whose physical interactions with ELF3 were affected by polyglutamine tract length. We found two proteins for which this was the case, ELF4 and AtGLDP1. Using these two approaches, we identify specific genetic interactions and physical mechanisms by which the ELF3 polyglutamine tract may mediate the observed genetic incompatibilities. Our work elucidates how STR variation, which is generally underascertained in population-scale sequencing, can contribute to phenotypic variation. Furthermore, our results support our proposal that highly variable STR loci can contribute to the epistatic component of heritability. [ABSTRACT FROM AUTHOR]

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fastevolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans. [ABSTRACT FROM AUTHOR]

Various models describe asexual evolution by mutation, selection, and drift. Some focus directly on fitness, typically modeling drift but ignoring or simplifying both epistasis and the distribution of mutation effects (traveling wave models). Others follow the dynamics of quantitative traits determining fitness (Fisher's geometric model), imposing a complex but fixed form of mutation effects and epistasis, and often ignoring drift. In all cases, predictions are typically obtained in high or low mutation rate limits and for long-term stationary regimes, thus losing information on transient behaviors and the effect of initial conditions. Here, we connect fitness-based and trait-based models into a single framework, and seek explicit solutions even away from stationarity. The expected fitness distribution is followed over time via its cumulant generating function, using a deterministic approximation that neglects drift. In several cases, explicit trajectories for the full fitness distribution are obtained for arbitrary mutation rates and standing variance. For nonepistatic mutations, especially with beneficial mutations, this approximation fails over the long term but captures the early dynamics, thus complementing stationary stochastic predictions. The approximation also handles several diminishing returns epistasis models (e.g., with an optimal genotype); it can be applied at and away from equilibrium. General results arise at equilibrium, where fitness distributions display a "phase transition" with mutation rate. Beyond this phase transition, in Fisher's geometric model, the full trajectory of fitness and trait distributions takes a simple form; robust to the details of the mutant phenotype distribution. Analytical arguments are explored regarding why and when the deterministic approximation applies. [ABSTRACT FROM AUTHOR]

Recent studies of protein evolution contend that the longer an amino acid substitution is present at a site, the less likely it is to revert to the amino acid previously occupying that site. Here we study this phenomenon of decreasing reversion rates rigorously and in a much more general context. We show that, under weak mutation and for arbitrary fitness landscapes, reversion rates decrease with time for any site that is involved in at least one epistatic interaction. Specifically, we prove that, at stationarity, the hazard function of the distribution of waiting times until reversion is strictly decreasing for any such site. Thus, in the presence of epistasis, the longer a particular character has been absent from a site, the less likely the site will revert to its prior state. We also explore several examples of this general result, which share a common pattern whereby the probability of having reverted increases rapidly at short times to some substantial value before becoming almost flat after a few substitutions at other sites. This pattern indicates a characteristic tendency for reversion to occur either almost immediately after the initial substitution or only after a very long time. [ABSTRACT FROM AUTHOR]

The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes—including the mean and SD of selection and epistasis coefficients—it was often unable to explain the full structure of fitness landscapes. [ABSTRACT FROM AUTHOR]

Mitochondrial (mtDNA) and nuclear genes have to operate in a coordinated manner to maintain organismal function, and the regulation of this homeostasis presents a substantial source of potential epistatic (G x G) interactions. How these interactions shape the fitness landscape is poorly understood. Here we developed a novel mitonuclear epistasis model, using selected strains of the Drosophila Genetic Reference Panel (DGRP) and mitochondrial genomes from within Drosophila melanogaster and D. simulans to test the hypothesis that mtDNA x nDNA interactions influence fitness. In total we built 72 genotypes (12 nuclear backgrounds x 6 mtDNA haplotypes, with 3 from each species) to dissect the relationship between genotype and phenotype. Each genotype was assayed on four food environments. We found considerable variation in several phenotypes, including development time and egg-to-adult viability, and this variation was partitioned into genetic (G), environmental (E), and higher-order (G x G, G x E, and G x G x E) components. Food type had a significant impact on development time and also modified mitonuclear epistases, evidencing a broad spectrum of G x G x E across these genotypes. Nuclear background effects were substantial, followed by mtDNA effects and their G x G interaction. The species of mtDNA haplotype had negligible effects on phenotypic variation and there was no evidence that mtDNA variation has different effects on male and female fitness traits. Our results demonstrate that mitonuclear epistases are context dependent, suggesting the selective pressure acting on mitonuclear genotypes may vary with food environment in a genotype-specific manner. [ABSTRACT FROM AUTHOR]

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction.We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of caseonly tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi. [ABSTRACT FROM AUTHOR]

Much of our knowledge about the fitness effects of new mutations has been gained from mutation accumulation (MA) experiments. Yet the fitness effect of single mutations is rarely measured in MA experiments. This raises several issues, notably for inferring epistasis for fitness. The acceleration of fitness decline in MA lines has been taken as evidence for synergistic epistasis, but establishing the role of epistasis requires measuring the fitness of genotypes carrying known numbers of mutations. Otherwise, accelerating fitness loss could be explained by increased genetic mutation rates. Here we segregated mutations accumulated over 4800 generations in haploid and diploid MA lines of the yeast Saccharomyces cerevisiae. We found no correspondence between an accelerated fitness decline and synergistic epistasis among deleterious mutations in haploid lines. Pairs of mutations showed no overall epistasis. Furthermore, several lines of evidence indicate that genetic mutation rates did not increase in the MA lines. Crucially, segregant fitness analyses revealed that MA accelerated in both haploid and diploid lines, even though the fitness of diploid lines was nearly constant during the MA experiment. This suggests that the accelerated fitness decline in haploids was caused by cryptic environmental factors that increased mutation rates in all lines during the last third of the lines' transfers. In addition, we provide new estimates of deleterious mutation rates, including lethal mutations, and highlight that nearly all the mutational load we observed was due to one or two mutations having a large effect on fitness. [ABSTRACT FROM AUTHOR]

Additive genetic variance in natural populations is commonly estimated using mixed models, in which the covariance of the genetic effects is modeled by a genetic similarity matrix derived from a dense set of markers. An important but usually implicit assumption is that the presence of any nonadditive genetic effect increases only the residual variance and does not affect estimates of additive genetic variance. Here we show that this is true only for panels of unrelated individuals. In the case that there is genetic relatedness, the combination of population structure and epistatic interactions can lead to inflated estimates of additive genetic variance. [ABSTRACT FROM AUTHOR]

Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species. [ABSTRACT FROM AUTHOR]

Genetic interactions can strongly influence the fitness effects of individual mutations, yet the impact of these epistatic interactions on evolutionary dynamics remains poorly understood. Here we investigate the evolutionary role of epistasis over 50,000 generations in a well-studied laboratory evolution experiment in Escherichia coli. The extensive duration of this experiment provides a unique window into the effects of epistasis during long-term adaptation to a constant environment. Guided by analytical results in the weak-mutation limit, we develop a computational framework to assess the compatibility of a given epistatic model with the observed patterns of fitness gain and mutation accumulation through time. We find that a decelerating fitness trajectory alone provides little power to distinguish between competing models, including those that lack any direct epistatic interactions between mutations. However, when combined with the mutation trajectory, these observables place strong constraints on the set of possible models of epistasis, ruling out many existing explanations of the data. Instead, we find that the data are consistent with a "two-epoch" model of adaptation, in which an initial burst of diminishing-returns epistasis is followed by a steady accumulation of mutations under a constant distribution of fitness effects. Our results highlight the need for additional DNA sequencing of these populations, as well as for more sophisticated models of epistasis that are compatible with all of the experimental data. [ABSTRACT FROM AUTHOR]

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits. [ABSTRACT FROM AUTHOR]

The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/. [ABSTRACT FROM AUTHOR]

Empirical evidence for diminishing fitness returns of beneficial mutations supports Fisher's geometric model. We show that a similar pattern emerges through the phenomenon of regression to the mean and that few studies correct for it. Although biases are often small, regression to the mean has overemphasized diminishing returns and will hamper cross-study comparisons unless corrected for. [ABSTRACT FROM AUTHOR]

Mitochondria are essential multifunctional organelles whose metabolic functions, biogenesis, and maintenance are controlled through genetic interactions between mitochondrial and nuclear genomes. In natural populations, mitochondrial efficiencies may be impacted by epistatic interactions between naturally segregating genome variants. The extent that mitochondrial-nuclear epistasis contributes to the phenotypic variation present in nature is unknown. We have systematically replaced mitochondrial DNAs in a collection of divergent Saccharomyces cerevisiae yeast isolates and quantified the effects on growth rates in a variety of environments. We found that mitochondrial-nuclear interactions significantly affected growth rates and explained a substantial proportion of the phenotypic variances under some environmental conditions. Naturally occurring mitochondrial-nuclear genome combinations were more likely to provide growth advantages, but genetic distance could not predict the effects of epistasis. Interruption of naturally occurring mitochondrial-nuclear genome combinations increased endogenous reactive oxygen species in several strains to levels that were not always proportional to growth rate differences. Our results demonstrate that interactions between mitochondrial and nuclear genomes generate phenotypic diversity in natural populations of yeasts and that coadaptation of intergenomic interactions likely occurs quickly within the specific niches that yeast occupy. This study reveals the importance of considering allelic interactions between mitochondrial and nuclear genomes when investigating evolutionary relationships and mapping the genetic basis underlying complex traits. [ABSTRACT FROM AUTHOR]

Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, perse, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the heterozygosity, for models where frequencies are widely dispersed, such as for selectively neutral mutations, contributions of epistatic variance are always small. Epistasis may be important in understanding the genetic architecture, for example, of function or human disease, but that does not imply that loci exhibiting it will contribute much genetic variance. Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution. [ABSTRACT FROM AUTHOR]

The identification and validation of gene-gene interactions is a major challenge in human studies. Here, we explore an approach for studying epistasis in humans using a Drosophila melanogaster model of neonatal diabetes mellitus. Expression of the mutant preproinsulin (hINSC96Y) in the eye imaginal disc mimics the human disease: it activates conserved stress-response pathways and leads to cell death (reduction in eye area). Dominant-acting variants in wild-derived inbred lines from the Drosophila Genetics Reference Panel produce a continuous, highly heritable distribution of eye-degeneration phenotypes in a hINSC96Y background. A genome-wide association study (GWAS) in 154 sequenced lines identified a sharp peak on chromosome 3L, which mapped to a 400-bp linkage block within an intron of the gene sulfateless (sfl). RNAi knockdown of sfl enhanced the eye-degeneration phenotype in a mutant-hINS-dependent manner. RNAi against two additional genes in the heparan sulfate (HS) biosynthetic pathway (ttv and botv), in which sfl acts, also modified the eye phenotype in a hINSC96Y-dependent manner, strongly suggesting a novel link between HS-modified proteins and cellular responses to misfolded proteins. Finally, we evaluated allele-specific expression difference between the two major sfl-intronic haplotypes in heterozygtes. The results showed significant heterogeneity in marker-associated gene expression, thereby leaving the causal mutation(s) and its mechanism unidentified. In conclusion, the ability to create a model of human genetic disease, map a QTL by GWAS to a specific gene, and validate its contribution to disease with available genetic resources and the potential to experimentally link the variant to a molecular mechanism demonstrate the many advantages Drosophila holds in determining the genetic underpinnings of human disease. [ABSTRACT FROM AUTHOR]

Haloperidol is an efficacious antipsychotic drug that has serious, unpredictable motor side effects that limit its utility and cause noncompliance in many patients. Using a drug-placebo diallel of the eight founder strains of the Collaborative Cross and their F1 hybrids, we characterized aggregate effects of genetics, sex, parent of origin, and their combinations on haloperidol response. Treating matched pairs of both sexes with drug or placebo, we measured changes in the following: open field activity, inclined screen rigidity, orofacial movements, prepulse inhibition of the acoustic startle response, plasma and brain drug level measurements, and body weight. To understand the genetic architecture of haloperidol response we introduce new statistical methodology linking heritable variation with causal effect of drug treatment. Our new estimators, "difference of models" and "multiple-impute matched pairs", are motivated by the Neyman-Rubin potential outcomes framework and extend our existing Bayesian hierarchical model for the diallel (Lenarcic et al. 2012). Drug-induced rigidity after chronic treatment was affected by mainly additive genetics and parent-of-origin effects (accounting for 28% and 14.8% of the variance), with NZO/HILtJ and 129S1/SvlmJ contributions tending to increase this side effect. Locomotor activity after acute treatment, by contrast, was more affected by strain-specific inbreeding (12.8%). In addition to drug response phenotypes, we examined diallel effects on behavior before treatment and found not only effects of additive genetics (10.2-53.2%) but also strong effects of epistasis (10.64-25.2%). In particular: prepulse inhibition showed additivity and epistasis in about equal proportions (26.1% and 23.7%); there was evidence of nonreciprocal epistasis in pretreatment activity and rigidity; and we estimated a range of effects on body weight that replicate those found in our previous work. Our results provide the first quantitative description of the genetic architecture of haloperidol response in mice and indicate that additive, dominance-like inbreeding and parent-of-origin effects contribute strongly to treatment effect heterogeneity for this drug. [ABSTRACT FROM AUTHOR]

Despite the accumulation of substantial quantities of information about epistatic interactions among both deleterious and beneficial mutations in a wide array of experimental systems, neither consistent patterns nor causal explanations for these interactions have yet emerged. Furthermore, the effects of mutations depend on the environment in which they are characterized, implying that the environment may also influence epistatic interactions. Recent work with beneficial mutations for the single-stranded DNA bacteriophage ID11 demonstrated that interactions between pairs of mutations could be understood by means of a simple model that assumes that mutations have additive phenotypic effects and that epistasis arises through a nonlinear phenotype-fitness map with a single intermediate optimum. To determine whether such a model could also explain changes in epistatic patterns associated with changes in environment, we measured epistatic interactions for these same mutations under conditions for which we expected to find the wild-type ID11 at different distances from its phenotypic optimum by assaying fitnesses at three different temperatures: 33º, 37º, and 41º. Epistasis was present and negative under all conditions, but became more pronounced as temperature increased. We found that the additive-phenotypes model explained these patterns as changes in the parameters of the phenotype-fitness map, but that a model that additionally allows the phenotypes to vary across temperatures performed significantly better. Our results show that ostensibly complex patterns of fitness effects and epistasis across environments can be explained by assuming a simple structure for the genotype-phenotype relationship. [ABSTRACT FROM AUTHOR]

A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive x additive, dominance x dominance, additive x dominance, and dominance x additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive x additive (18%), dominance x dominance (14%), additive x dominance (48%), and dominance x additive (15%) variances. Tiller number is contributed by additive (17%), additive x additive (22%), and dominance x additive (43%) variances. Grain number is mainly contributed by additive (42%), additive x additive (19%), and additive x dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive x additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species. [ABSTRACT FROM AUTHOR]

Insulin and target of rapamycin (TOR) signaling pathways converge to maintain growth so a proportionate body form is attained. Insufficiency in either insulin or TOR results in developmental growth defects due to low ATP level. Spargel is the Drosophila homolog of PGC-1, which is an omnipotent transcriptional coactivator in mammals. Like its mammalian counterpart, Spargel/dPGC-1 is recognized for its role in energy metabolism through mitochondrial biogenesis. An earlier study demonstrated that Spargel/dPGC-1 is involved in the insulin-TOR signaling, but a comprehensive analysis is needed to understand exactly which step of this pathway Spargel/PGC-1 is essential. Using genetic epistasis analysis, we demonstrated that a Spargel gain of function can overcome the TOR and S6K mediated cell size and cell growth defects in a cell autonomous manner. Moreover, the tissue-restricted phenotypes of TOR and S6k mutants are rescued by Spargel overexpression. We have further elucidated that Spargel gain of function sets back the mitochondrial numbers in growth-limited TOR mutant cell clones, which suggests a possible mechanism for Spargel action on cells and tissue to attain normal size. Finally, excess Spargel can ameliorate the negative effect of FoxO overexpression only to a limited extent, which suggests that Spargel does not share all of the FoxO functions and consequently cannot significantly rescue the FoxO phenotypes. Together, our observation established that Spargel/dPGC-1 is indeed a terminal effector in the insulin-TOR pathway operating below TOR, S6K, Tsc, and FoxO. This led us to conclude that Spargel should be incorporated as a new member of this growth-signaling pathway. [ABSTRACT FROM AUTHOR]

It has increasingly been recognized that adapting populations of microbes contain not one, but many lineages continually arising and competing at once. This process, termed "clonal interference," alters the rate and dynamics of adaptation and biases winning mutations toward those with the largest selective effect. Here we uncovered a dramatic example of clonal interference between multiple similar mutations occurring at the same locus within replicate populations of Methylobacterium extorquens AM1. Because these mutational events involved the transposition of an insertion sequence into a narrow window of a single gene, they were both readily detectable at low frequencies and could be distinguished due to differences in insertion sites. This allowed us to detect up to 17 beneficial alleles of this type coexisting in a single population. Despite conferring a large selective benefit, the majority of these alleles rose and then fell in frequency due to other lineages emerging that were more fit. By comparing allele-frequency dynamics to the trajectories of fitness gains by these populations, we estimated the fitness values of the genotypes that contained these mutations. Collectively across all populations, these alleles arose upon backgrounds with a wide range of fitness values. Within any single population, however, multiple alleles tended to rise and fall synchronously during a single wave of multiple genotypes with nearly identical fitness values. These results suggest that alleles of large benefit arose repeatedly in failed "soft sweeps" during narrow windows of adaptation due to the combined effects of epistasis and clonal interference. [ABSTRACT FROM AUTHOR]

Behavior is a complex trait that results from interactions among multiple genes and the environment. Both additive and nonadditive effects are expected to contribute to broad-sense heritability of complex phenotypes, although the relative contribution of each of these mechanisms is unknown. Here, we mapped genetic variation in the correlated phenotypes of thermal preference and isothermal dispersion in the nematode Caenorhabditis elegans. Genetic variation underlying these traits is characterized by a set of linked quantitative trait loci (QTL) that interact in a complex epistatic network. In particular, two loci located on the X chromosome interact with one another to generate extreme thermophilic behavior and are responsible for ~50% of the total variation observed in a cross between two parental lines, even though these loci individually explain very little of the among-line variation. Our results demonstrate that simultaneously considering the influence of a quantitative trait locus (QTL) on multiple scales of behavior can inform the physiological mechanism of the QTL and show that epistasis can explain significant proportions of otherwise unattributed variance within populations. [ABSTRACT FROM AUTHOR]

We study the trajectory of an allele that affects a polygenic trait selected toward a phenotypic optimum. Furthermore, conditioning on this trajectory we analyze the effect of the selected mutation on linked neutral variation. We examine the well- characterized two-locus two-allele model but we also provide results for diallelic models with up to eight loci. First, when the optimum phenotype is that of the double heterozygote in a two-locus model, and there is no dominance or epistasis of effects on the trait, the trajectories of selected mutations rarely reach fixation; instead, a polymorphic equilibrium at both loci is approached. Whether a polymorphic equilibrium is reached (rather than fixation at both loci) depends on the intensity of selection and the relative distances to the optimum of the homozygotes at each locus. Furthermore, if both loci have similar effects on the trait, fixation of an allele at a given locus is less likely when it starts at low frequency and the other locus is polymorphic (with alleles at intermediate frequencies). Weaker selection increases the probability of fixation of the studied allele, as the polymorphic equilibrium is less stable in this case. When we do not require the double heterozygote to be at the optimum we find that the polymorphic equilibrium is more difficult to reach, and fixation becomes more likely. Second, increasing the number of loci decreases the probability of fixation, because adaptation to the optimum is possible by various combinations of alleles. Summaries of the genealogy (height, total length, and imbalance) and of sequence polymorphism (number of polymorphisms, frequency spectrum, and haplotype structure) next to a selected locus depend on the frequency that the selected mutation approaches at equilibrium. We conclude that multilocus response to selection may in some cases prevent selective sweeps from being completed, as described in previous studies, but that conditions causing this to happen strongly depend on the genetic architecture of the trait, and that fixation of selected mutations is likely in many instances. [ABSTRACT FROM AUTHOR]

The classic diallel takes a set of parents and produces offspring from all possible mating pairs. Phenotype values among the offspring can then be related back to their respective parentage. When the parents are diploid, sexed, and inbred, the diallel can characterize aggregate effects of genetic background on a phenotype, revealing effects of strain dosage, heterosis, parent of origin, epistasis, and sex-specific versions thereof. However, its analysis is traditionally intricate, unforgiving of unplanned missing information, and highly sensitive to imbalance, making the diallel unapproachable to many geneticists. Nonetheless, imbalanced and incomplete diallels arise frequently, albeit unintentionally, as by-products of larger-scale experiments that collect F1 data, for example, pilot studies or multiparent breeding efforts such as the Collaborative Cross or the Arabidopsis MAGIC lines. We present a general Bayesian model for analyzing diallel data on dioecious diploid inbred strains that cleanly decomposes the observed patterns of variation into biologically intuitive components, simultaneously models and accommodates outliers, and provides shrinkage estimates of effects that automatically incorporate uncertainty due to imbalance, missing data, and small sample size. We further present a model selection procedure for weighing evidence for or against the inclusion of those components in a predictive model. We evaluate our method through simulation and apply it to incomplete diallel data on the founders and F1's of the Collaborative Cross, robustly characterizing the genetic architecture of 48 phenotypes. [ABSTRACT FROM AUTHOR]

The human mutation rate per nucleotide site per generation (μ) can be estimated from data on mutation rates at loci causing Mendelian genetic disease, by comparing putatively neutrally evolving nucleotide sequences between humans and chimpanzees and by comparing the genome sequences of relatives. Direct estimates from genome sequencing of relatives suggest that μ is about 1.1 x 10-8, which is about twofold lower than estimates based on the human-chimp divergence. This implies that an average of ~70 new mutations arise in the human diploid genome per generation. Most of these mutations are paternal in origin, but the male:female mutation rate ratio is currently uncertain and might vary even among individuals within a population. On the basis of a method proposed by Kondrashov and Crow, the genome-wide deleterious mutation rate (U) can be estimated from the product of the number of nucleotide sites in the genome, μ, and the mean selective constraint per site. Although the presence of many weakly selected mutations in human noncoding DNA makes this approach somewhat problematic, estimates are U ≈ 2.2 for the whole diploid genome per generation and ~0.35 for mutations that change an amino acid of a protein-coding gene. A genome-wide deleterious mutation rate of 2.2 seems higher than humans could tolerate if natural selection is "hard," but could be tolerated if selection acts on relative fitness differences between individuals or if there is synergistic epistasis. I argue that in the foreseeable future, an accumulation of new deleterious mutations is unlikely to lead to a detectable decline in fitness of human populations. [ABSTRACT FROM AUTHOR]

Bayesian hierarchical shrinkage methods have been widely used for quantitative trait locus mapping. From the computational perspective, the application of the Markov chain Monte Carlo (MCMC) method is not optimal for high-dimensional problems such as the ones arising in epistatic analysis. Maximum a posteriori (MAP) estimation can be a faster alternative, but it usually produces only point estimates without providing any measures of uncertainty (i.e., interval estimates). The variational Bayes method, stemming from the mean field theory in theoretical physics, is regarded as a compromise between MAP and MCMC estimation, which can be efficiently computed and produces the uncertainty measures of the estimates. Furthermore, variational Bayes methods can be regarded as the extension of traditional expectation-maximization (EM) algorithms and can be applied to a broader class of Bayesian models. Thus, the use of variational Bayes algorithms based on three hierarchical shrinkage models including Bayesian adaptive shrinkage, Bayesian LASSO, and extended Bayesian LASSO is proposed here. These methods performed generally well and were found to be highly competitive with their MCMC counterparts in our example analyses. The use of posterior credible intervals and permutation tests are considered for decision making between quantitative trait loci (QTL) and non-QTL. The performance of the presented models is also compared with R/qtlbim and R/BhGLM packages, using a previously studied simulated public epistatic data set. [ABSTRACT FROM AUTHOR]

We investigate the generation and decay of interspecific disequilibrium (ID) between organelle and symbiont genomes as a function of the rate of horizontal transmission. We show that rare horizontal transmission greatly diminishes the covariance between organelle and symbiont genomes. This result has two important implications. First, a low level of ID does not indicate low levels of vertical transmission. Second, even with low levels of horizontal transmission, the additive effects of host and symbiont loci will determine the response to selection, while epistatic effects will not be selectable. [ABSTRACT FROM AUTHOR]

Disruption of interactions among ensembles of epistatic loci has been shown to contribute to reproductive isolation among various animal and plant species. Under the Bateson-Dobzhansky-Muller model, such interspecific incompatibility arises as a byproduct of genetic divergence in each species, and the Orr-Turelli model indicates that the number of loci involved in incompatible interactions may "snowball" over time. We address the combined effect of multiple incompatibility loci on the rate of introgression at neutral marker loci across the genome. Our analysis extends previous work by accommodating sex specificity: differences between the sexes in the expression of incompatibility, in rates of crossing over between neutral markers and incompatibility loci, and in transmission of markers or incompatibility factors. We show that the evolutionary process at neutral markers in a genome subject to incompatibility selection is well approximated by a purely neutral process with migration rates appropriately scaled to reflect the influence of selection targeted to incompatibility factors. We confirm that in the absence of sex specificity and functional epistasis among incompatibility factors, the barrier to introgression induced by multiple incompatibility factors corresponds to the product of the barriers induced by the factors individually. A new finding is that barriers to introgression due to sex-specific incompatibility depart in general from multiplicativity. Our partitioning of variation in relative reproductive rate suggests that such departures derive from associations between sex and incompatibility and between sex and neutral markers. Concordant sex-specific incompatibility (for example, greater impairment of male hybrids or longer map lengths in females) induces lower barriers (higher rates of introgression) than expected under multiplicativity, and discordant sex-specific incompatibility induces higher barriers. [ABSTRACT FROM AUTHOR]

Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods ["universal kriging" (UK) and "simple kriging" (SK)] are presented. As a novelty, we suggest use of the family of Matérn covariance functions to model the covariance structure of SNP vectors. A genomic best linear unbiased prediction (GBLUP) is applied as a reference method. The three approaches are compared in a whole-genome simulation study considering additive, additive-dominance, and epistatic gene-action models. Predictive performance is measured in terms of correlation between true and predicted GVs and average true GVs of the individuals ranked best by prediction. We show that UK outperforms GBLUP in the presence of dominance and epistatic effects. In a limiting case, it is shown that the genomic covariance structure proposed by VanRaden (2008) can be considered as a covariance function with corresponding quadratic variogram. We also prove theoretically that if a specific linear relationship exists between covariance matrices for two linear mixed models, the GVs resulting from BLUP are linked by a scaling factor. Finally, the relation of kriging to other models is discussed and further options for modeling the covariance structure, which might be more appropriate in the genomic context, are suggested. [ABSTRACT FROM AUTHOR]

In most organisms, the segregation of chromosomes during the first meiotic division is dependent upon at least one crossover (CO) between each pair of homologous chromosomes. COs can result from chromosome double-strand breaks (DSBs) that are induced and preferentially repaired using the homologous chromosome as a template. The PCH2 gene of budding yeast is required to establish proper meiotic chromosome axis structure and to regulate meiotic interhomolog DSB repair outcomes. These roles appear conserved in the mouse ortholog of PCH2, Trip13, which is also involved in meiotic chromosome axis organization and the regulation of DSB repair. Using a combination of genetic and physical assays to monitor meiotic DSB repair, we present data consistent with pch2Δ mutants showing defects in suppressing intersister DSB repair. These defects appear most pronounced in dmc1Δ mutants, which are defective for interhomolog repair, and explain the previously reported observation that pch2Δ dmc1Δ cells can complete meiosis. Results from genetic epistasis analyses involving spo13Δ, rad54Δ, and mek1/MEK1 alleles and an intersister recombination reporter assay are also consistent with Pch2 acting to limit intersister repair. We propose a model in which Pch2 is required to promote full Mek1 activity and thereby promotes interhomolog repair. [ABSTRACT FROM AUTHOR]

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F10 generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F2 loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation. [ABSTRACT FROM AUTHOR]

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene--gene and gene--environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fun- damental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics. [ABSTRACT FROM AUTHOR]

Directional epistasis describes a situation in which epistasis consistently increases or decreases the effect of allele substitutions, thereby affecting the amount of additive genetic variance available for selection in a given direction. This study applies a recent parameterization of directionality of epistasis to empirical data. Data stems from a QTL mapping study on an intercross between inbred mouse (Mus musculus) strains LG/J and SM/J, originally selected for large and small body mass, respectively. Results show a negative average directionality of epistasis for body-composition traits, predicting a reduction in additive allelic effects and in the response to selection for increased size. Focusing on average modification of additive effect of single loci, we find a more complex picture, whereby the effects of some loci are enhanced consistently across backgrounds, while effects of other loci are decreased, potentially contributing to either enhancement or reduction of allelic effects when selection acts at single loci. We demonstrate and discuss how the interpretation of the overall measurement of directionality depends on the complexity of the genotype-phenotype map. The measure of directionality changes with the power of scale in a predictable way; however, its expected effect with respect to the modification of additive genetic effects remains constant. [ABSTRACT FROM AUTHOR]

Cellular responses to environmental stimuli require conserved signal transduction pathways. In budding yeast (Saccharomyces cerevisiae), nutrient limitation induces morphological changes that depend on the protein kinase A (PKA) pathway and the Kss1 mitogen-activated protein kinase (MAPK) pathway. It was unclear to what extent and at what level there is synergy between these two distinct signaling modalities. We took a systematic genetic approach to clarify the relationship between these inputs. We performed comprehensive epistasis analysis of mutants lacking different combinations of all relevant pathway components. We found that these two pathways contribute additively to nutrient limitationinduced haploid invasive growth. Moreover, full derepression of either pathway rendered it individually sufficient for invasive growth and thus, normally, both are required only because neither is maximally active. Furthermore, in haploids, the MAPK pathway contributes more strongly than the PKA pathway to cell elongation and adhesion, whereas nutrient limitation-induced unipolar budding is independent of both pathways. In contrast, in diploids, upon nutrient limitation the MAPK pathway regulates cell elongation, the PKA pathway regulates unipolar budding, and both regulate cell adhesion. Thus, although there are similarities between haploids and diploids, cell type-specific differences clearly alter the balance of the signaling inputs required to elicit the various nutrient limitation-evoked cellular behaviors. [ABSTRACT FROM AUTHOR]