GENETICS

Estimators of genetic diversity and neutrality tests derived from the site frequency spectrum (SFS), such as Wattersons{theta} W, nucleotide diversity{pi} , Tajimas D, and Fay and Wus H, are designed to be interpreted relative to a baseline defined by the standard neutral SFS. In genomic regions strongly linked to a polymorphic structural variant (SV), deviations from these baselines occur even under strict neutrality: conditioning on an SV at known frequency partitions samples into SV and non-SV haplotypes and distorts the SFS for linked neutral mutations. These deviations are well understood for genomic inversions under long-term balancing selection. However, not all SVs are under strong selection, and the evolution of some SVs may be better approximated as neutral. Here we derive analytical expectations for the unfolded (and, when necessary, folded) SFS of single nucleotide polymorphisms conditional on neutral linked polymorphic SVs, including inversions, deletions, insertions, and introgressions. We use these expectations to quantify the resulting bias in standard diversity estimators and neutrality tests as a function of SV frequency and type. Finally, we discuss approaches to build corrected estimators of diversity and neutrality tests that are unbiased/centered after accounting for the presence and frequency of the SV.

Stabilizing selection on a polygenic trait reduces the traits genetic variance by (i) generating correlations (linkage disequilibria) between opposite-effect alleles throughout the genome, and (ii) selecting against rare alleles at loci that affect the trait, eroding heterozygosity at these loci. Here, we show that the linkage disequilibria, which stabilizing selection generates on a rapid timescale, slow down the subsequent allele-frequency dynamics at individual loci, which proceed on a much longer timescale. Exploiting this separation of timescales, we obtain expressions for the expected per-generation change in minor-allele frequency at individual loci, as functions of the effect sizes at these loci, the strength of selection on the trait, its variance and heritability, and the linkage relations among loci. Using whole-genome simulations, we show that our expressions predict allele-frequency dynamics under stabilizing selection more accurately than the formulae that have previously been used for this purpose. Our results have implications for understanding the genetic architecture of complex traits.

Mislocalization of the centromeric histone H3 variant (Cse4 in budding yeast, CID in flies, CENP-A in humans) to non-centromeric regions contributes to chromosomal instability (CIN) in yeast, fly, and human cells. Overexpression and mislocalization of CENP-A has been observed in cancers, however, the mechanisms that facilitate the mislocalization of overexpressed CENP-A have not been fully explored. Defects in ubiquitin-mediated proteolysis of overexpressed Cse4 (GALCSE4) leads to its mislocalization and synthetic dosage lethality (SDL) in mutants for E3 ubiquitin ligases (Psh1, Slx5, SCFMet30, SCFCdc4), Doa1, Hir2, and Cdc7. In contrast, defects in sumoylation of GALcse4K215/216/A/R prevent its mislocalization and do not cause SDL in a psh1{Delta} strain. Here, we used a genome-wide screen to identify factors that facilitate the mislocalization of overexpressed Cse4 by characterizing suppressors of the psh1{Delta} GALCSE4 SDL. Deletions of histone H4 alleles (HHF1 or HHF2), which were among the most prominent suppressors, also suppress slx5{Delta}, cdc4-1, doa1{Delta}, hir2{Delta}, and cdc7-4 GALCSE4 SDL. Reduced dosage of H4 contributes to defects in sumoylation and reduced mislocalization of overexpressed Cse4. We determined that the hhf1-20, cse4-102, and cse4-111 mutants, which are defective in the Cse4-H4 interaction, also exhibit reduced sumoylation of Cse4 and do not display psh1{Delta} GALCSE4 SDL. In summary, we have identified genes that contribute to the mislocalization of overexpressed Cse4 and defined a role for the gene dosage of H4 in facilitating Cse4 sumoylation and mislocalization to non-centromeric regions, contributing to SDL when Cse4 is overexpressed in mutant strains.

Mismatch repair (MMR) is a highly conserved DNA repair pathway that promotes genome stability by directing the repair of errors in DNA replication. In Saccharomyces cerevisiae, MMR is initiated by either Msh2-Msh3 or Msh2-Msh6, via recognition of insertion deletion loops (IDLs; up to [~] 17 nucleotides) and misincorporation events, respectively. Both complexes recognize and bind small (1-2 nucleotide) IDLs. Once bound, MSH complexes recruit one or more downstream MLH complexes to continue repair: Mlh1-Pms1, Mlh1-Mlh2 and/or Mlh1-Mlh3. Msh2-Msh3 also promotes CAG trinucleotide repeat (TNR) expansions through specific DNA-binding to TNR DNA structures, followed by recruitment of MLH complexes. These expansions lead to genome instability that causes neurodegenerative diseases such as Huntingtons Disease in humans. Here, we defined a hierarchy of MLH function in these Msh2-Msh3-mediated pathways in vivo in S. cerevisiae. We determined that Mlh1-Pms1 is the primary MLH complex required in Msh2-Msh3-mediated MMR. In contrast, all three MLH complexes were required to promote CAG expansions, with loss of Mlh1-Pms1 or Mlh1-Mlh2 exhibiting the strongest effects. Mutations in PMS1 and MLH3 were synergistic. We propose a model in which Mlh1-Pms1 is primarily responsible for "appropriate" Msh2-Msh3-mediated MMR, while all three MLH complexes collaborate specifically in the presence of CAG structure, to promote a "pathogenic" Msh2-Msh3-mediated pathway that leads to expansions. Our model highlights the importance of DNA structure-dependent conformations in modulating MLH function.

In earlier studies, we optimized an assay system for the genome-wide detection of copy number variation (CNV) in diploid Saccharomyces cerevisiae cells, based on selection for formaldehyde plus copper (FA+Cu) resistance conferred by the amplification of a dosage-dependent reporter cassette, SFA1-CUP1. Our analyses identified a robust bias for terminal deletions of the right arm of Chr7 (Chr7R) associated with unbalanced translocations. This bias was observed at approximately constant strength across all three sites where the amplification reporter cassette was inserted, in CNV-carrying yeast clones derived both spontaneously and from mutagen-induced recombinogenic conditions. We conducted allelic mitotic recombination experiments to investigate the possibility of the presence of a fragile site on Chr7R, but the results disfavored this model, and instead indicated that the Chr7R bias applies only to non-allelic rearrangements. We validated the existence of a CNV formation bias at Chr7R through an orthologous NAHR competition approach that was independent of selection for FA+Cu resistance. Finally, we showed the in contrast to its high participation in NAHR as a recipient sequence, Chr7R becomes amplified as a translocation donor less frequently than other comparable regions of the genome. To begin unraveling the cause of this unusual behavior, we evaluated the effect of a set of candidate genes involved in chromatin mobility and sister chromatid cohesion on the rearrangement spectra involving Chr7R. We found that deletion mutations in some of these genes, particularly SAP30, attenuated the biased NAHR behavior. Taken together, our results suggested that although Chr7R is not inherently more prone to DNA breakage than other regions, once a DNA lesion is formed there, it has a higher propensity to undergo inappropriate repair leading to a chromosomal rearrangement.

Heterochromatin is a repressive, specialized chromatin structure that is central to eukaryotic transcriptional regulation and genome stability. In the budding yeast, Saccharomyces cerevisiae, heterochromatin formation requires Sir2p, Sir3p, and Sir4p, and these Sir proteins create specialized chromatin structures at telomeres and silent mating type loci. Previously, we reported that the SWI/SNF chromatin remodeling enzyme can evict Sir3 from chromatin fibers in vitro, though whether this activity contributes to the role of SWI/SNF as a transcriptional activator at euchromatic loci is unknown. Here, we characterize genetic interactions between the SIR genes (SIR2, SIR3, and SIR4) and genes encoding subunits of the chromatin remodelers SWI/SNF and INO80C, as well genes encoding the histone deacetylases Hst3 and Hst4. We find that loss of SIR genes partially rescues the growth defects of swi2, ino80, and hst3/hst4 mutants during replication stress conditions. Interestingly, partial suppression of swi2, ino80, and hst3 hst4 mutant phenotypes is due to the pseudo-diploid state of sir mutants, but a significant portion is due to more direct functional interactions. Consistent with this view, transcriptional profiling of strains lacking Swi2 or Sir3 identifies a set of genes whose expression in the M/G1 phase of the cell cycle requires SWI/SNF to antagonize the repressive impact of Sir3.

1The fate of mutations and the genetic load of populations depend on the relative importance of genetic drift and natural selection. In addition, the accuracy of numerical models of evolution depends on the strength of both selection and drift: strong selection breaks the assumptions of the nearly neutral model, and drift coupled with large sample sizes breaks Kingmans coalescent model. Thus, the regime with strong selection and large sample sizes, relevant to the study of pathogenic variation, appears particularly daunting. Surprisingly, we find that the interplay of drift and selection in that regime can be used to define asymptotically closed recursions for the distribution of allele frequencies that are accurate well beyond the strong selection limit. Selection becomes more analytically tractable when the sample size n is larger than twice the population-scaled selection coefficient: n [≥] 2Ns (4Ns in diploids). That is, when the expected number of coalescent events in the sample is larger than the number of selective events. We construct the relevant transition matrices, show how they can be used to accurately compute distributions of allele frequencies, and show that the distribution of deleterious allele frequencies is sensitive to details of the evolutionary model.

Mismatch repair (MMR) is a highly conserved DNA repair pathway that recognizes mispairs that occur spontaneously during DNA replication and coordinates their repair. In Saccharomyces cerevisiae, Msh2-Msh3 and Msh2-Msh6 initiate MMR by recognizing and binding insertion deletion loops (in/dels) up to [~] 17 nucleotides (nt.) and base-base mispairs, respectively; the two complexes have overlapping specificity for small (1-2 nt.) in/dels. The DNA-binding specificity for the two complexes resides in their respective mispair binding domains (MBDs) and have distinct DNA-binding modes. Msh2-Msh3 also plays a role in promoting CAG/CTG trinucleotide repeat (TNR) expansions, which underlie many neurodegenerative diseases such as Huntingtons Disease and Myotonic Dystrophy Type 1. Models for Msh2-Msh3s role in promoting TNR tracts expansion have invoked its specific DNA-binding activity and predict that the TNR structure alters its DNA binding and downstream activities to block repair. Using a chimeric Msh complex that replaces the MBD of Msh6 with the Msh3 MBD, we demonstrate that Msh2-Msh3 DNA-binding activity is not sufficient to promote TNR expansions. We propose a model for Msh2-Msh3-mediated TNR expansions that requires a fully functional Msh2-Msh3 including DNA binding, coordinated ATP binding and hydrolysis activities and interactions with Mlh complexes that are analogous to those required for MMR. Article SummaryThe mismatch repair (MMR) protein complex Msh2-Msh3 promotes trinucleotide repeat (TNR) expansions that can lead to neurodegenerative diseases, while the Msh2-Msh6 complex does not. We tested the hypothesis that Msh2-Msh3s specific DNA binding activity is sufficient to promote TNR expansions, using a chimeric MSH complex in vivo and in vitro. We found that the Msh2-Msh3-like DNA-binding was not sufficient to promote TNR expansions. Our findings indicate that Msh2-Msh3 plays an active, pathogenic role in promoting TNR expansions beyond simply binding to TNR structures.

The conserved C. elegans protein kinases NEKL-2 and NEKL-3 regulate multiple steps of membrane trafficking and are required for larval molting. Through a forward genetic screen we identified a loss-of-function mutation in catp-1 as a suppressor of molting defects in synthetically lethal nekl-2; nekl-3 double mutants. catp-1 is predicted to encode a membrane- associated P4-type ATPase involved in Na+-K+ exchange. Moreover, a mutation predicted to abolish CATP-1 ion-pump activity also suppressed nekl-2; nekl-3 mutants. Endogenously tagged CATP-1 was primarily expressed in epidermal (hypodermal) cells within punctate structures located at or near the apical plasma membrane. Through whole genome sequencing, we identified two additional nekl-2; nekl-3 suppressor strains containing coding-altering mutations in catp-1 but found that neither mutation, when introduced into nekl-2; nekl-3 mutants using CRISPR methods, was sufficient to elicit robust suppression of molting defects. Our data also suggested that the two catp-1 isoforms, catp-1a and catp-1b, may in some contexts be functionally redundant. On the basis of previously published studies, we tested the hypothesis that loss of catp-1 may suppress nekl-associated defects by inducing partial entry into the dauer pathway. Contrary to expectations, however, we failed to obtain evidence that loss of catp-1 suppresses nekl-2; nekl-3 defects through a dauer-associated mechanism or that loss of catp-1 leads to entry into the pre-dauer L2d stage. As such, loss of catp-1 may suppress nekl- associated molting and membrane trafficking defects by altering electrochemical gradients within membrane-bound compartments.

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high dimensional Bayesian Sparse Factor Genetic modelling to 3,385 gene expression traits from Drosophila melanogaster and from D. serrata to explore how genetic variance is distributed across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme (>3 IQR from the median) values. This observation, in the two independently sampled species, suggests that the House of Cards (HoC) model might apply not only to individual expression traits, but also to emergent co-expression phenotypes. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were outliers for multivariate factors but not for any individual traits. We observed other consistent differences between heritable multivariate factors with outlier lines versus those factors that conformed to a Gaussian distribution of genetic effects, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

The idea that sex-differences in selection drive the evolution of suppressed recombination between sex chromosomes is well-developed in population genetics. Yet, despite a now classic body of theory, empirical evidence that sexual antagonism drives the evolution of recombination suppression remains meagre and alternative hypotheses underdeveloped. We investigate whether the length of evolutionary strata formed by chromosomal inversions that expand the non-recombining sex determining region (SDR) on recombining sex chromosomes can offer an informative signature of whether, and how, selection influenced their fixation. We develop population genetic models that determine how the length of a chromosomal inversion that expands the SDR affects its fixation probability for three categories of inversions: (i) neutral, (ii) directly beneficial (i.e., due to breakpoint or position effects), and (iii) indirectly beneficial (especially those capturing sexually antagonistic loci). Our models predict that neutral inversions should leave behind a unique signature of large evolutionary strata, and that it will often be difficult or impossible to distinguish between smaller strata created by directly or indirectly beneficial inversions. An interesting and unexpected prediction of our models is that the physical location of the ancestral SDR on the sex chromosomes is the most important factor influencing the relation between inversion size and the probability of expanding the SDR. Our findings raise a suite of new questions about how physical as well as selective processes influence the evolution of recombination suppression between sex chromosomes.

Across many complex traits, genetic variants with larger effect sizes tend to occur at lower frequencies, which is often interpreted as a signature of stabilizing selection. In statistical genetics, the so-called -model captures this relationship by assuming that effect size variance is inversely proportional to heterozygosity raised to a power 0 [<=] [<=] 1. Although empirically useful, the -model is phenomenological rather than mechanistic and lacks a direct population-genetic interpretation. In this paper, we derive an alternative to the -model based on evolutionary theory. Our approach yields a linear mixed model in which the frequency dependence of effect size emerges naturally as a function of interpretable evolutionary quantities describing mutational variance, selection intensity, and coupling between the focal and selected traits. These quantities enter through two identifiable variance components that can be estimated by restricted maximum likelihood (REML). The resulting framework links a fitness-landscape model to standard mixed-model methodology, enabling both inference on evolutionary parameters and downstream prediction by best linear unbiased prediction (BLUP). In forward simulations, the model accurately recovers the focal-trait variance and generally improves genetic prediction relative to conventional -model baselines.

Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the speed of evolution and the statistics of phylogeny. However, predicting the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult both to predict theoretically and detect experimentally in sequence data. A large number of false interactions arise from stochastic linkage effects and indirect interactions, which mask true interactions. Here we develop a method to filter out false-positive interactions. We start by demonstrating that the averaging of the two-way haplotype frequencies over a multiple independent populations is necessary but not sufficient, because it still leaves high numbers of false interactions. To compensate for this residual stochastic noise, we develop a triple-way haplotype method isolating true interactions. The fidelity of the method is confirmed using simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large database sequences of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure site-site interactions from sequence data. Authors summaryInteraction of genomic sites creating "fitness landscape" is very important for predicting the escape of viruses from drugs and immune response and for passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low, due partly to a very small fraction of interaction pairs, and partly to stochastic noise intrinsic for evolution masking true interactions. Here we propose a method based on analysis of genetic sequences at three genomic sites to clean stochastic linkage and apply it to influenza virus sequence data.

Selective sweeps, resulting from the spread of beneficial, neutral, or deleterious mutations through a population, shape patterns of genetic variation at linked neutral sites. While many theoretical, computational, and statistical advances have been made in understanding the genomic signatures of selective sweeps in recombining populations, relatively less is understood in populations with little/no recombination, and arbitrary dominance and inbreeding. Using diffusion theory, we obtain the full expression for the expected site frequency spectrum (SFS) at linked neutral sites immediately post and during the fixation of moderately or strongly beneficial mutations. When a single hard sweep occurs, the SFS decays as 1/x for low derived allele frequencies (x), similar to the neutral SFS at equilibrium, whereas at higher derived allele frequencies, it follows a 1/x2 power law as also seen in a rapidly expanding neutral population. We show that these power laws are universal in the sense that they are independent of the dominance and inbreeding coefficients, and also characterize the SFS during the sweep. Additionally, we find that the derived allele frequency where the SFS shifts from the 1/x to 1/x2 power law is inversely proportional to the selection strength; thus under strong selection, the SFS follows the 1/x2 dependence for most allele frequencies. When clonal interference is pervasive, the SFS immediately post-fixation becomes U-shaped and can be approximated by the equilibrium SFS of selected sites. Our results will be important in developing statistical methods to infer the timing and strength of recent selective sweeps in asexual populations, genomic regions that lack recombination, and clonally propagating tumor populations.

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give--directly or indirectly--some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic PST used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

Numerous empirical studies have revealed epistatic interactions among deleterious variants. In this paper, we assume such interactions are widespread and analyze the resulting shift in mutational burden and average population fitness following the introduction of a strong and universal stress (hereafter "handicap"). We demonstrate that organisms with a low burden of slightly-deleterious variants (SDVs) are more likely to survive exposure to a handicap, whether genetic or environmental, leading to a purifying effect on the population. We further discuss the potential applications of harnessing such interactions for evolutionary and population studies as well as for population management.

Ancestry tracts are contiguous haplotype blocks inherited from distinct groups of common ancestors. The genomic distribution of ancestry tracts (or local ancestry) provides rich information about evolutionary mechanisms shaping the genetic composition of hybrids. The correlation structure of ancestry tracts has been particularly useful in both empirical and theoretical studies, but there is a lack of descriptive measures operating on arbitrarily large genomic blocks to summarize this correlation structure without imposing too many assumptions about admixture. We here develop an approach inspired by quantum information theory to quantify this correlation structure. The key innovation is to represent local ancestry as quantum states, where less correlation in local ancestry leads to elevated quantum entropy. By leveraging a variety of entropy measures on local ancestry signals, we show that entropy is deeply connected to co-ancestry probabilities between and within haplotypes, so that ancestral recombination graphs become pivotal to the study of entropy dynamics in admixture. We use this approach to characterize a standard neutral admixture model with an arbitrary number of sources, and recover entropic laws governing the dynamics of ancestry tracts under recombination and genetic drift, which resembles the second law of thermodynamics. In application, entropy is well-defined on arbitrarily large genomic blocks with either phased or unphased local ancestry, and is insensitive to a small amount of noise. These properties are superior to simple statistics on ancestry tracts such as tract length and junction density. Finally, we construct an entropic index reflecting the degree of intermixing among ancestry tracts over a chromosomal block. This index confirms that the Z chromosome in a previously studied butterfly hybrid zone has the least potential of ancestry mixing, thus conforming to the "large-X/Z" effect in speciation. Together, we show that quantum entropy provides a useful framework for studying ancestry tract dynamics in both theories and real systems.

Natural selection is an imperfect force that can under some conditions fail to prevent the buildup of deleterious mutations. Small population sizes and the lack of recombination are two such scenarios that reduce the efficiency of selection. Under these conditions, the disconnect between deleterious genetic load and individual fitness due to the masking of recessive deleterious mutations in heterozygous individuals may result in the emergence of pseudo-overdominance, wherein the buildup of haplotypes with complementary sets of deleterious mutations results in apparent heterozygote advantage and an increase in linked neutral diversity. In polyploids, the presence of additional allelic copies magnifies this masking effect and may therefore increase the probability of pseudo-overdominance. Here, we simulate the evolution of small diploid and autotetraploid populations to identify the conditions that support the evolution of pseudo-overdominance. We discover that pseudo-overdominance evolves under a much wider range of parameters in autotetraploids than in diploids with identical population sizes, and that in many parts of parameter space there is an inverse relationship between fitness and recombination rate. These results imply that pseudo-overdominance may be more common than previously thought. We conclude by discussing the current evidence for pseudo-overdominance in species with polyploid histories, as well as its implications in agriculture due to the prevalence of polyploidy in crops. ARTICLE SUMMARYPseudo-overdominance is an extreme evolutionary phenomenon in which low recombination rates result in the buildup of alternative mutations on disparate haplotypes, rendering recombination events maladaptive because they disrupt the masking of recessive deleterious alleles. In polyploids, additional chromosomes accentuate this masking effect, potentially increasing the likelihood of pseudo-overdominance. We used forward-in-time simulations to test the hypothesis that polyploids are more likely to experience pseudo-overdominance-and find strong evidence in support of this hypothesis. Our results have implications for the evolution of recombination rates in all species, the evolution of polyploids generally, and the efficacy of selection in polyploid crops.

Demographic inference methods in population genetics typically assume that the ancestry of a sample can be modeled by the Kingman coalescent. A defining feature of this stochastic process is that it generates genealogies that are binary trees: no more than two ancestral lineages may coalesce at the same time. However, this assumption breaks down under several scenarios. For example, pervasive natural selection and extreme variation in offspring number can both generate genealogies with "multiple-merger" events in which more than two lineages coalesce instantaneously. Therefore, detecting multiple mergers (and other violations of the Kingman assumptions) is important both for understanding which forces have shaped the diversity of a population and for avoiding fitting misspecified models to data. Current methods to detect multiple mergers in genomic data rely primarily on the site frequency spectrum (SFS). However, the signatures of multiple mergers in the SFS are also consistent with a Kingman coalescent with a time-varying population size. Here, we present a new statistical test for determining whether the Kingman coalescent with any population size history is consistent with population data. Our approach is based on information contained in the two-site joint frequency spectrum (2-SFS) for pairs of linked sites, which has a different dependence on the topologies of genealogies than the SFS. Our statistical test is global in the sense that it can detect when the genome-wide genetic diversity is inconsistent with the Kingman model, rather than detecting outlier regions, as in selection scan methods. We validate this test using simulations, and then apply it to demonstrate that genomic diversity data from Drosophila melanogaster is inconsistent with the Kingman coalescent.

Mapping the genetic basis of complex traits is complicated by the presence of epistatic interactions between loci. While work in molecular genetics identifies numerous specific genetic interactions, statistical analyses of quantitative traits frequently conclude that additive (nonepistatic) models explain most heritable variation. However, these conclusions are typically limited by the narrow range of genetic relatedness(e.g. in F1 offspring of a biparental or circular cross). Here, we use a barcoded panel of Saccharomyces cerevisiae genotypes with a broad range of relatedness to quantify the effects of epistasis on the genetic architecture of seven complex traits. We find limited contributions of epistasis to the genetic basis of these traits. These results indicate that epistasis beyond that detected in standard yeast crosses may exist, yet it contributes little to phenotypic variance in these systems.