GENETICS

Many questions in population genetics are concerned with reconstructing evolutionary history through time, such as inferring how population structure has changed throughout the past. Yet, many existing approaches have only an implicit temporal component, using quantities such as allele frequency or haplotype length as rough proxies for age. Recent advances in the inference of Ancestral Recombination Graphs (ARGs) have made it possible to estimate the entire sequence of local genealogies along the genome. These genealogies explicitly encode how samples are related to each other at different time points in the past, enabling the inference of how population structure has changed over time. To this end, recent work has used ARGs to define time-stratified versions of widely-used population genetics summary statistics in an attempt to capture the population structure present within a particular time window. Here, we show that naive approaches result in statistics that cannot be interpreted solely in terms of the population structure present within the time window they are targeting. To address this problem, we introduce a framework of coalescent-based time-stratified statistics, which use coalescence probabilities to partition classical summary statistics into interval-specific contributions. Using coalescent simulations, we demonstrate that these statistics accurately isolate population structure at different temporal depths and avoid spurious signals. Our results highlight the necessity of integrating coalescent theory into ARG-based temporal analyses and provide a principled and practical foundation for studying the dynamics of population structure through time.

Cell differentiation into distinct cell types generates functional specialization in eukaryotic organisms. Many fungal species undergo filamentous growth, where cells differentiate into elongated and adhesive filaments capable of expansion and invasion into new environments. The budding yeast Saccharomyces cerevisiae also undergoes filamentous growth, and the regulatory pathways that control the response have been well characterized; however, the roles essential proteins play in this response have not been systematically explored. To address this gap in understanding, we constructed a collection of 332 temperature-sensitive (ts) alleles in 320 essential genes in a strain background that undergoes filamentous growth (called {Sigma}1278b or Sigma). The ts Sigma collection showed differences in temperature sensitivity compared to a ts laboratory strain collection, revealing unexpected phenotypic diversity in essential alleles across populations of individuals. Screening the ts Sigma collection for phenotypes related to filamentous growth uncovered new phenotypes for >35% of essential alleles. New regulators of the Mitogen-Activated Protein Kinase (MAPK) pathway that regulates filamentous growth (fMAPK) were identified, including Las17, a homolog of Wiskott-Aldrich Syndrome Protein (WASP) in humans. Las17 regulated the fMAPK pathway by promoting delivery of the sensor protein, Sho1, and Rho GTPase Cdc42 to the plasma membrane. Las17 also functioned as a hub coordinating separate parallel aspects of the filamentation response. The widespread roles for essential proteins in regulating a eukaryotic differentiation response suggest broader roles essential proteins than is currently appreciated.

Epigenetic information is required for the maintenance of gene expression states through cell division. Some epigenetic information can also persist through generations, raising the possibility that epigenetic differences may arise in populations and contribute to evolutionary processes such as drift and natural selection. Evaluating this requires characterising the properties of such epimutations, so that their rate, stability and genome-wide distribution can be assessed. Recent experiments in many organisms have demonstrated that epimutations occur, but have limited stability relative to DNA sequence mutations. However, the extent to which epimutations contribute to evolution is still unclear, in part because it is not clear whether epimutations respond dynamically to environmental conditions. Here, we use the fission yeast S. pombe, in which evolutionarily relevant epimutations have been observed, but not yet systematically characterised, to test whether a physiologically relevant stress, ethanol exposure, leads to changes in the properties of epimutations. Using laboratory evolution experiments with repeated population bottlenecks to minimise selection, we show that epimutations associated with changes in small interfering (si)RNAs occur at a greater rate in ethanol. Ethanol-induced epimutations are enriched at transposable element loci and correlate with changes in transposon expression. Ethanol exposure also increases gene expression noise genome-wide and variability in growth rate; importantly, both these increases are suppressed in mutants lacking siRNAs. Together, these results demonstrate that stress can alter the generation of heritable epigenetic information, and suggest that siRNAs may have a role in promoting phenotypic variability in response to environmental stress.

Restricting the localization of centromere-specific histone H3 variant Cse4 (CENP-A in humans) to centromeric chromatin is essential for chromosome segregation. Mislocalization of overexpressed Cse4/CENP-A to non-centromeric regions contributes to chromosomal instability (CIN) in model organisms and human cells. CIN is an important hallmark of many cancers and hence defining mechanisms that prevent mislocalization of Cse4 is clinically significant. Here we report a role for YRA2 (Yeast RNA Annealing Protein 2) in ubiquitin mediated proteolysis of Cse4 to prevent its mislocalization for chromosomal stability. YRA2 was identified in a genome-wide screen for gene deletions that exhibit synthetic dosage lethality (SDL) upon overexpression of CSE4 (GALCSE4). We determined that yra2{Delta} strains exhibit increased Cse4 stability, enriched Cse4 chromatin association, reduced Cse4 ubiquitination, Cse4 mislocalization, and CIN. Defects in interaction of E3 ubiquitin ligase Psh1 with Cse4 contributes to stability of Cse4 in yra2{Delta} strains. Consistent with these results, overexpression of PSH1 suppresses GALCSE4 SDL in yra2{Delta} strain. We determined that Yra2 mediated proteolysis of Cse4 is independent of its RNA related functions as strain deleted for the C-terminal ChTOP domain of Yra2 with an intact N-terminal RNA binding domain exhibits GALCSE4 SDL and defects in Cse4 proteolysis. Furthermore, poly(A)+ RNA export mutants in YRA1 (yra1-2) and MEX67 (mex67-5), that interact with Yra2, do not exhibit GALCSE4 SDL and defects in RNA export are not observed in yra2{Delta} cells. In summary, we have defined a key role for Yra2 in preventing mislocalization of Cse4 by facilitating its proteolysis to preserve chromosomal stability. Article summaryAccurate segregation of chromosomes during cell division is essential because segregation errors are linked to cancer and developmental disorders. We investigated how cells prevent mislocalization of centromere-specific histone H3 variant Cse4, which is essential for faithful chromosome segregation. We found that the yeast RNA annealing protein Yra2 prevents Cse4 mislocalization by promoting Psh1 mediated ubiquitination and degradation of Cse4. Cells lacking Yra2 showed increased stability of Cse4, enhanced chromatin enrichment with mislocalization to non-centromeric regions and CIN. These defects were suppressed by induction of Psh1. Our findings reveal a novel role for Yra2 in regulating Cse4 levels for chromosomal stability.

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

Sequentially Markovian Coalescent (SMC) models are a central element of contemporary population genetics, underlying many inferential methods. While the SMC has been shown to closely approximate the canonical Coalescent with Recombination (CwR) in terms of low-dimensional, two-locus summaries, its effects on the deeper structural properties of Ancestral Recombination Graphs (ARGs) are less well understood. Here, we define a general SMC approximation, SMC(k), in which a single parameter k controls the physical scale over which common-ancestor events between non-overlapping ancestral segments are permitted. The model encompasses the standard SMC and SMC' as special cases and converges to the CwR as k increases, providing a tunable trade-off between computational efficiency and fidelity to the full recombination process. Using recently developed summaries of ARG structure, we show that SMC approximations systematically truncate the persistence of ancestral haplotypes across the genome, despite preserving marginal coalescent properties, and that increasing k progressively recovers this long-range ancestral structure. We implement the SMC(k) in msprime and show that, for small samples, it makes whole-chromosome simulation in species with large population-scaled recombination rates several orders of magnitude faster than the CwR. Finally, we use SMC simulations for chromosome-scale parametric bootstrapping of demographic inference and find that the SMC' captures uncertainty in SFS-based estimates remarkably well, with only modest changes as k increases despite substantial differences in long-range ARG structure. Thus, the importance of SMC approximation error depends strongly on which properties of ancestry are relevant to the downstream analysis.

In studies of assortative mating, similarity between variables measured in parents is often quantified using correlation. The order of the parents within any given pair can be arbitrary in these applications, but common correlation estimators are not robust to reordering within pairs. These unordered variable pairs are exchangeable, since the joint distributions of both orders are equal, and a given order is biased if the one variable has a lower expectation than the other. In this work, we characterize the effect of order bias on Pearson correlation estimates assuming exchangeable variables, and develop a new unbiased estimator, CorSym, that does not depend on order within each pair. Exchangeable variables have equal marginal distributions for both variables, a property accounted for by CorSym. In contrast, standard correlation estimators assume the two variables have different distributions, so biased orders skew the underlying mean, variance and covariance estimates. We show, through theory and simulations, how order bias often results in upwardly biased Pearson correlation estimates. Simulations confirm CorSym is unbiased, and validate its estimated confidence intervals. Using real admixed trios (parents and a child) from 1000 Genomes, we first demonstrate that the global ancestry of fathers and mothers are consistent with exchangeability, using both Kolmogorov-Smirnov tests and a Binomial test for order bias. However, ANCESTOR, which estimates parental global ancestry from a child's local ancestry, produces significant order biases in its output that result in substantial Pearson biases, which CorSym overcomes. Compared to ancestry proportions calculated directly on the parents, ANCESTOR also overestimates parent ancestry divergence and experiences another estimation artifact. Overall, CorSym solves an important estimation bias likely to be encountered in the study of assortative mating, providing unbiased and deterministic estimates that do not depend on the arbitrary order of the data.

Recent findings in humans and other species have revealed the presence of "mutator" alleles that increase germline mutation rate across the genome. Such mutators are expected to be selected against because of the additional deleterious alleles that they generate, to a degree that will depend on how much they increase the mutation rate in heterozygotes and homozygotes. To describe their dynamics, we develop a population genetic model of mutation rate modifiers with arbitrary dominance coefficients, in which fitness effects stem from additional germline mutations. We then use it to interpret findings for the seven human mutators identified to date. For six of the seven known mutators, the observed frequencies are well fit by the model and thus consistent with purifying selection arising solely due to their effects on germline mutation rates, although also consistent with a wide range of parameters; in two, the observed frequencies are readily explained by purely recessive fitness effects. The exception is a variant in MUTYH, which is more common than predicted under plausible parameters, for reasons that remain unclear. We also use the model to explore what types of mutators are most likely to be discovered in parents, based on identifying offspring with unexpectedly high numbers of de novo mutations. Although the two first mutators identified by this approach seem to be recessive, our modeling suggests that, for the same effect size, semi-dominant mutators are much more likely to be detected. These findings therefore imply that there are many more modifier sites with recessive effects than semi-dominant ones. More generally, our model provides a framework for interpreting properties of mutators in humans and other species and for learning about the genetic architecture of germline mutation rate variation.

The genetic basis of phenotypic differences between species is fundamental to our view of evolution. In contrast to variation within species, phenotypic differences between species can result from a long-term sequential process whereby prior steps and multiple changes at a single locus can play important roles. However, investigating the genetic basis of phenotypes that have evolved over long time periods is limited by reproductive barriers between species. In this study, we use hybrid genetic analysis to examine reproductively isolated Saccharomyces species that have diverged in their thermal growth limits over the last 15 million years. By selectively eliminating S. cerevisiae chromosomes in hybrids with S. uvarum or S. kudriavzevii, we show that most chromosomes cause a substantial loss of thermotolerance. Reciprocal loss of chromosome 5 (Chr 5) confirms that the hybrid's thermotolerance depends on the S. cerevisiae chromosome and shows that it is partially inhibited by the S. uvarum chromosome. Using CRISPR-Cas9 to induce targeted loss of heterozygosity, we find that loss of either the left or right arm of Chr 5 results in loss of thermotolerance. Fine-mapping, complementation and reciprocal hemizygosity analysis identify MMS21, which encodes an essential subunit of the Smc5-Smc6 complex, as responsible for loss of thermotolerance associated with the left arm of Chr 5. Our findings suggest that divergence in thermotolerance over long time-periods is caused by multiple loci across the genome, with each playing a critical role in enabling growth at high temperatures.

Proper transmission of cell identity between generations requires maternal epigenetic reprogramming mechanisms that prevent inappropriate inheritance of lineage-specific transcriptional programs. In Caenorhabditis elegans, loss of the H3K4me1/2 demethylase SPR-5 and the H3K9 methyltransferase MET-2 results in ectopic expression of germline genes in somatic tissues and severe developmental delay. Previous studies demonstrated that the chromatin regulator MES-4 contributes to these defects, but whether germline-specific transcription factors also participate in the ectopic transcriptional program remained unclear. Here, we investigated the role of the germline transcription factor LSL-1 in animals lacking SPR-5 and MET-2. We found that genes normally regulated by LSL-1 in the germline are significantly overrepresented among genes ectopically expressed in the soma of spr-5; met-2 progeny. Consistent with this observation, an endogenously tagged LSL-1 protein became ectopically expressed throughout somatic tissues when maternal SPR-5 and MET-2 activity was disrupted. Furthermore, depletion of LSL-1 partially suppressed the developmental delay observed in spr-5; met-2 mutants. Transcriptomic analyses revealed extensive overlap between MES-4- and LSL-1-dependent transcriptional programs, with most LSL-1-dependent genes also requiring MES-4. Notably, ectopic expression of lsl-1 itself depended on MES-4, suggesting that LSL-1 functions downstream of MES-4. Genes dependent on LSL-1 were strongly enriched for germline-associated expression programs and included previously identified direct LSL-1 targets. Together, our findings support a model in which MES-4 promotes ectopic expression of LSL-1, which in turn contributes to a shared germline-associated transcriptional program and developmental delay following failed maternal epigenetic reprogramming. These results demonstrate how lineage-restricted transcription factors cooperate with inherited chromatin states to reinforce aberrant transcriptional programs and disrupt cell fate boundaries.

We consider polygenic divergent selection in a mainland-island model, where our aim is to understand how patterns of genetic variation along the genome reflect the genetic architecture of postzygotic reproductive isolation. We derive a new expression for the effective migration rate () at both neutral and divergently selected loci (i.e. barrier loci), and develop a numerical approach to co-predict and allele frequencies at barrier loci. Using this , we predict neutral coalescence times along the genome, and show how our results for the mainland-island model can be used to obtain coalescence time predictions for nonequilibrium demographic models. We validate our approach extensively using forward-in-time individual-based simulations and show that our -based approximation remains accurate across a broad parameter range. We study both the case of a single barrier locus and a pair of barrier loci in detail to identify the conditions under which an -based approach performs well. We use our new predictions to examine how the genetic architecture of divergent selection shapes barriers to gene flow, quantifying hitchhiking effects along the genome and evaluating the effects of polygenicity on reproductive isolation. We discuss the implications for mapping barriers to gene flow using population genomic data, both for model-based inference and genome scan approaches based on summary statistics.

Genetic interactions contribute to phenotypic variation, yet most studies characterize them in a small number of reference backgrounds rather than across natural populations. Studying genetic interactions in Caenorhabditis elegans is facilitated by RNAi by feeding, which enables gene-specific knockdowns across many genetic backgrounds without the need to construct individual mutant lines. However, individually screening hundreds of isolates across many RNAi conditions is laborious. Here, we developed a pooled platform that uses whole-genome sequencing to quantify the fitness of hundreds of genetically distinct C. elegans strains competing in a single flask. We leveraged this platform to evaluate RNAi responses in a pooled population of RNAi-sensitive C. elegans strains and identified two classes of QTL that influence RNAi responses. The first class influences responses to knockdowns of specific genes and is exemplified by a QTL that is specific to knockdown of mig-6. The second class influences general RNAi-response across multiple knockdowns. One QTL in this class localized to the right arm of chromosome III and was detected by both GWA and xQTL mapping, which suggests that a common large-effect variant underlies this QTL. We identified a T96K missense change in the dsRNA receptor SID-2 as the underlying causal variant. The 96K allele resides in the extracellular domain of SID-2 and confers increased RNAi sensitivity relative to the 96T allele. We hypothesize that the 96K allele enables greater uptake of ingested lumenal dsRNA through higher-affinity binding to the dsRNA phosphate backbone. The platform we developed enables systematic mapping of genetic interactions across natural populations at a previously impractical scale.

Evolution in fluctuating environments depends critically on epistatic interactions between mutations and the pleiotropic effects these mutations have on fitness across multiple conditions. However, while these effects of epistasis and pleiotropy have been extensively studied individually, the interactions between them are less well understood. In this study, we analyze how genetic background shapes the effects of mutations across multiple environmental conditions in generic models of complex traits. We show that when epistasis is widespread and random, systematic patterns of cross-environment pleiotropy can emerge naturally. Specifically we outline two regimes, widespread random epistasis (WRE) and structured pleiotropy (SP), in which patterns of global epistasis in one environment can be predicted from those in other conditions. We outline these predictions in several classes of generic null models, and test these predictions in laboratory budding yeast by measuring the fitness effects of a large set of mutations across many genetic backgrounds in several growth environments. We find that only a subset of mutations fall within the WRE and SP regimes, but that in these cases two of our models provide accurate fits to the data. Finally, we use this framework to show how modular, pleiotropic architectures shape long-term evolutionary steady states in fluctuating environments, revealing how tradeoffs between environments can develop from the geometry of shared latent interaction structure.

Insulin/IGF signaling (IIS) inhibits the nuclear localization of the DAF-16/FOXO transcription factor to regulate longevity and stress resistance in C. elegans. In the intestine, IIS promotes DAF-16 localization to endosomes and loss of TBC-2, a RAB-5 GAP, results in increased endomembrane localization of DAF-16 at the expense of nuclear localization, decreased DAF-16 target gene expression, longevity and stress resistance. Here we found that TBC-2 differentially regulates the localization of the IIS-regulated transcription factors PQM-1 and HLH-30/TFEB. Our results suggest a broader role for TBC-2 in negatively regulating IIS and that TBC-2 likely functions at an upstream point in the IIS pathway.

Biofilm formation by the fungal pathogen Candida albicans is a major source of infection. The biofilm regulator Ume6 forms complexes with partner transcription factors Efg1, Ndt80, and Upc2 to drive expression of biofilm-related genes. Here we present chromatin immunoprecipitation with sequencing (ChIP-seq) data for Ume6-chromatin association in both wild-type and efg1{Delta}/{Delta} ndt80{Delta}/{Delta} upc2{Delta}/{Delta} backgrounds. Ume6 associates with two promoter region classes. For one class, Ume6 association is significantly decreased in the efg1{Delta}/{Delta} ndt80{Delta}/{Delta} upc2{Delta}/{Delta} background. This class includes 577 genes, many with well-established roles in biofilm formation or the related process of filamentation. For the second class, Ume6 association is unperturbed in the triple mutant background. This class includes 534 genes, some with roles in iron homeostasis (e.g., SFU1) and ergosterol synthesis (e.g., ERG11), which may contribute to the impact of Ume6 on ndt80{Delta}/{Delta} mutant azole drug sensitivity. It also includes FLO9, a putative adhesin gene that is required for the emergent biofilm state produced by efg1{Delta}/{Delta} ndt80{Delta}/{Delta} double mutants. The data show that Ume6 binding to ~half of its targets requires known partners, and Ume6 binding to ~half of its targets does not. Ume6 may bind to the latter set of promoter regions independently of any partner, or perhaps with additional partners that have yet to be discovered.

Genotype-by-environment interactions (GxE), or differences in how genotypes perform across varying environments, are a pervasive source of phenotypic variation and underlie differences in local adaptation. Though GxE is well characterized across kingdoms of life, less is known about what causes GxE interactions, particularly at the molecular level. In this study, we use allele-specific gene expression estimates in a maize (Zea mays L.) B73 x Mo17 hybrid to isolate cis-regulatory effects on gene expression for each of the two parental alleles. The hybrid was grown in two environments, and expression differences between the parental alleles were used to characterize allele-by-environment (AxE) interactions and study the influence of gene-proximal sequence variation on transcript abundance AxE. We tested the hypothesis that gene-proximal sequence variation can cause GxE in gene expression by modifying transcription factor binding. Our results show that sequence variation in gene promoter regions has a small but consistent enrichment in genes that show transcriptional AxE. Further, we demonstrate that differential transcription factor binding potential caused by sequence variation is also enriched in AxE genes. Predictive models trained on sequence and transcription factor binding variation show that while these features contain some information about whether a gene will show transcriptional AxE, they alone are not sufficient to reliably distinguish AxE genes. These findings support the hypothesis that gene expression GxE can be caused by sequence variation that modifies transcription factor binding, while also reinforcing the complex and context-specific nature of GxE interactions.

Evolutionary rescue occurs when genetic adaptation prevents extinction of a declining population. Mutation and recombination generate adaptive variation, but when their rates at adaptive loci are too low, rescue may require evolution toward higher rates of genetic change. Here, we derive and compare analytical approximations for the probability of rescue through two alternative routes: mutation-rate and recombination-rate evolution. Our model comprises two resident genotypes from which the same adaptive haplotype can arise along either route, with rescue requiring the appearance of an unlinked modifier allele that increases mutation at, or recombination between, two fitness-determining loci. We show that resident genotype composition determines the relative success of the two routes. Mutation-rate evolution yields a higher rescue probability when one resident genotype is sufficiently more common because mutation can act on either genotype whereas recombination requires both. Otherwise, the two routes yield similar rescue probabilities. Even when either route makes rescue appreciably likely, however, the modifier alleles facilitating rescue are often lost before rescue is complete. This loss is driven by multiple factors: segregation can separate the modifier from the adaptive haplotype; the fitness cost of modifier expression raises extinction risk for modifier-bearing lineages; and continued modifier-induced mutation disrupts the adaptive haplotype after it forms. The latter effect can make recombination-rate modifiers more likely to persist even when mutation-rate evolution yields a higher rescue probability. Overall, these results show that both mutation- and recombination-rate evolution can induce rescue, but successful rescue need not entail persistence of the modifier allele that facilitated it.

Population genetic dynamics are shaped by multiple evolutionary processes, including mutation, recombination, selection, and genetic drift. With the rapid growth of genomic data, modeling multilocus evolutionary dynamics and the resulting patterns of genetic variation has become increasingly important. Existing approaches often face challenges in jointly describing multiple evolutionary processes and modeling multilocus systems, motivating the development of more flexible and extensible frameworks. Here, we develop a variational framework for population genetic dynamics based on generalized gradient-flow theory, drawing on nonequilibrium thermodynamics. In our model, mutation, recombination, and selection are represented as modular variational components, with different life-cycle stages connected through a gamete-individual two-state system. Mutation and recombination act on the gamete state, selection acts on the individual state, and finite-population genetic drift is represented by a stochastic extension. Our model represents multilocus genetic variation directly in the full haplotype-frequency space, recovers classical mutation, recombination, and selection dynamics in the corresponding limits and provides a natural stochastic extension for finite populations. Numerical experiments and SLiM forward simulations show that our model captures the dynamics of allele frequencies, haplotype frequencies, and linkage disequilibrium in multilocus systems. The variational formulation opens avenues for future extensions to additional evolutionary processes and more complex multilocus systems, as well as for developing differentiable computational methods for gradient-based parameter inference and scalable genomic modeling.

Amongst measures of linkage disequilibrium, the correlation measure r2, due originally to Sewall Wright, has several advantages. Although basically a haploid statistic, it can readily be estimated using covariance and correlation from diploid data. Its population expectation can also be found from probability methods. We show that the probability of linked identity by descent, LIBD, directly estimates the expectation for r2 for Wright-Fisher models. The simplest such model for two loci is the haploid model, where each haplotype is chosen independently from haplotypes of the previous generation. This gives an equilibrium between recombination (recombination frequency c) and drift (population size N) where the expected value of r2 is approximately 1/(1+4Nc). For diploid models, the expectation is similar for low values of c, but contains a term in c2 and rises to double the haploid value for the limiting value of c = 0.5. An increase in r2 is also expected for the model of monogamous mating, as opposed to random mating for each offspring. The expectation for r2 is unaffected by the use of diploid rather than haploid data, an exception being the case of monogamous mating. Computer simulation shows close agreement of r2 with probability expectations, with minor discrepancies observed for unlinked loci. Using the probabilistic definition, r2 can be intuitively understood as a parameter shaping the effective sample size in genome-wide association studies.

The protein complex cohesin plays critical roles in genomic stability by tethering together sister chromatids at their pericentric regions and along their arms from S phase until anaphase. Cohesin-mediated pericentric cohesion prevents aneuploidy by ensuring bipolar attachment of sister kinetochores. Arm cohesion prevents loss of heterozygosity by biasing DNA repair via recombination between sister chromatids rather than between homologs. Here, we investigate in yeast whether cohesin also enhances genomic stability by suppressing unequal sister chromatid exchange (USCE) between repetitive sequences. In wild-type cells, the USCE rate between repeats 4kb apart (proximal) was 15X higher than repeats 68kb apart (distal). The USCE between distal repeats but not proximal repeats increased 4 to 7-fold in mutants with altered cohesin subunits or auxiliary factors. The level of increased distal USCE corresponded with reduced arm cohesion, reduced density of cohesion arm sites, and higher sister loci mobility. Our results suggest that high density of arm cohesion sites confines repair of DNA damage to local sequences. When the density of cohesion sites decreases, sister chromatid sequences are less confined, thereby enhancing distal repeat interactions and USCE. Another set of mutations disrupted both DNA replication and cohesin loading at the replication fork during S phase. Remarkably, distal USCE in these mutants increased approximately 100-fold and was 6-fold more likely than proximal USCE. This preferential hyperdistal USCE can be explained by an aberrant sister-chromatid structure that is normally prevented by proper coupling of cohesin function and replication.