Crossover Interference Research Articles

Crossover interference in the mouse.

We present an analysis of crossover interference in the mouse genome, on the basis of high-density genotype data from two reciprocal interspecific backcrosses, comprising 188 meioses. Overwhelming evidence was found for strong positive crossover interference with average strength greater than that implied by the Carter-Falconer map function. There was some evidence for interchromosomal variation in the level of interference, with smaller chromosomes exhibiting stronger interference. We further compared the observed numbers of crossovers to previous cytological observations on the numbers of chiasmata and evaluated evidence for the obligate chiasma hypothesis.

Investigation of crossover interference in barley ( Hordeum vulgare L.) using the coefficient of coincidence.

Interference, the interaction between recombination events, was analysed in seven mapping populations of barley ( Hordeum vulgare L.). The coefficient of coincidence was applied to investigate the type and position of interference within the genome. Interference was analysed with respect to dependence on the recombination fraction, and simulations were used to obtain test statistics which consider the small sample size of 71-150 double haploid lines. In addition to positive interference in intermediate intervals, strong negative interference, i.e. encouraged double recombination, was found in short intervals. The relationship between recombination fraction and interference could not be described with a uniform function, neither for the entire genome nor for individual chromosomes. The analysis of the position of interference within the genome revealed that interference does not act in the same way in the whole genome. Intervals spanning the centromere exhibited significantly higher means for the coefficient of coincidence than intervals within the chromosome arms, especially with regard to small intervals. In general, positive interference was found in the chromosome arms and no or negative interference in the genetically small but physically large centromeric region.

C(3)G encodes a Drosophila synaptonemal complex protein.

The meiotic mutant c(3)G (crossover suppressor on 3 of Gowen) abolishes both synaptonemal complex (SC) formation and meiotic recombination, whereas mutations in the mei-W68 and mei-P22 genes prevent recombination but allow normal SC to form. These data, as well as a century of cytogenetic studies, support the argument that meiotic recombination between homologous chromosomes in Drosophila females requires synapsis and SC formation. We have cloned the c(3)G gene and shown that it encodes a protein that is structurally similar to SC proteins from yeast and mammals. Immunolocalization of the C(3)G protein, as well as the analysis of a C(3)G-eGFP expression construct, reveals that C(3)G is present in a thread-like pattern along the lengths of chromosomes in meiotic prophase, consistent with a role as an SC protein present on meiotic bivalents. The availability of a marker for SC in Drosophila allowed the investigation of the extent of synapsis in exchange-defective mutants. These studies indicate that SC formation is impaired in certain meiotic mutants and that the synaptic defect correlates with the exchange defects. Moreover, the observation of interference among the residual exchanges in these mutant oocytes implies that complete SC formation is not required for crossover interference in Drosophila.

Negative crossover interference in maize translocation heterozygotes.

Negative interference describes a situation where two genetic regions have more double crossovers than would be expected considering the crossover rate of each region. We detected negative crossover interference while attempting to genetically map translocation breakpoints in maize. In an attempt to find precedent examples we determined there was negative interference among previously published translocation breakpoint mapping data in maize. It appears that negative interference was greater when the combined map length of the adjacent regions was smaller. Even positive interference appears to have been reduced when the combined lengths of adjacent regions were below 40 cM. Both phenomena can be explained by a reduction in crossovers near the breakpoints or, more specifically, by a failure of regions near breakpoints to become competent for crossovers. A mathematical explanation is provided.

Statistical analysis of uniparental disomy data using hidden Markov models.

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.

Multipoint Genetic Mapping with Trisomy Data

Trisomy is the most common genetic abnormality in humans and is the leading cause of mental retardation. Although molecular studies that use a large number of highly polymorphic markers have been undertaken to understand the recombination patterns for chromosome abnormalities, there is a lack of multilocus approaches to incorporating crossover interference in the analysis of human trisomy data. In the present article, we develop two statistical methods that simultaneously use all genetic information in trisomy data. The first approach relies on a general relationship between multilocus trisomy probabilities and multilocus ordered-tetrad probabilities. Under the assumption that no more than one chiasma exists in each marker interval, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses that lead to trisomy. One limitation of the first approach is that the amount of computation increases exponentially with the number of markers. The second approach models the crossover process as a chi(2) model. We describe how to use hidden Markov models to evaluate multilocus trisomy probabilities. Our methods are applicable when both parents are available or when only the nondisjoining parent is available. For both methods, genetic distances among a set of markers can be estimated and the pattern of overall chiasma distribution can be inspected for differences in recombination between meioses exhibiting trisomy and normal meioses. We illustrate the proposed approaches through their application to a set of trisomy 21 data.

The budding yeast Msh4 protein functions in chromosome synapsis and the regulation of crossover distribution.

The budding yeast MSH4 gene encodes a MutS homolog produced specifically in meiotic cells. Msh4 is not required for meiotic mismatch repair or gene conversion, but it is required for wild-type levels of crossing over. Here, we show that a msh4 null mutation substantially decreases crossover interference. With respect to the defect in interference and the level of crossing over, msh4 is similar to the zip1 mutant, which lacks a structural component of the synaptonemal complex (SC). Furthermore, epistasis tests indicate that msh4 and zip1 affect the same subset of meiotic crossovers. In the msh4 mutant, SC formation is delayed compared to wild type, and full synapsis is achieved in only about half of all nuclei. The simultaneous defects in synapsis and interference observed in msh4 (and also zip1 and ndj1/tam1) suggest a role for the SC in mediating interference. The Msh4 protein localizes to discrete foci on meiotic chromosomes and colocalizes with Zip2, a protein involved in the initiation of chromosome synapsis. Both Zip2 and Zip1 are required for the normal localization of Msh4 to chromosomes, raising the possibility that the zip1 and zip2 defects in crossing over are indirect, resulting from the failure to localize Msh4 properly.

Genetic crossover interference in the human genome.

Positive crossover interference refers to the phenomenon that the occurrence of a crossover reduces the probability of another crossover in its vicinity. There have been studies reporting the presence of positive interference in humans. Some studies have also found evidence suggesting within and between chromosomal interference heterogeneity on some of the chromosomes. However, there has been no systematic study of interference and interference heterogeneity in the whole human genome, using pedigree data without first inferring crossovers. In this paper, we studied the Chi-square interference model and other models extensively to compare the relative performance of each of these models for accounting for interference and measuring strength of interference. Our results showed that the Chi-square model consistently fitted the data well and provided easily interpretable estimates of interference strength. The Chi-square model was then used to study interference and interference heterogeneity within and between chromosomes. We found strong evidence of positive interference in the whole human genome. Our results also indicated that the level of interference was fairly constant in most parts of the genome, but there was some evidence suggesting that the levels of interference for two of the chromosomes were different from the rest. We also found evidence of within chromosomal interference heterogeneity for several of the chromosomes.

Sample Size Requirement for Detecting Interference under the Chi-Square Model

The chi-square model (CHS) of recombination has been studied extensively, in recent years, for its ability of capturing the process and estimating the level of crossover interference. Exploration thus far shows that this model yields much better fits to human genetic data than Haldane’s no-interference model, and the explicit level of interference can be easily estimated as well. This paper provides calculations of sample sizes required to detect interference under CHS for a variety of settings. Two data types, fully informative meioses and phase-unknown backcross families, are studied. Under each setting, we calculate the number of meioses/families needed to ensure that the expected log-likelihood difference between the χ² interference model and Haldane’s no-interference model exceeds a prespecified threshold. It is found that joint consideration of multiple (more than three) markers dramatically reduces the number of meioses/families needed when compared to an analysis based on three-point data, a traditional setting for detecting interference using three-point tests. The results indicate that the numbers of meioses needed to detect interference under CHS are well within the reach of most genetic mapping studies.

Multipoint Genetic Mapping with Uniparental Disomy Data

Uniparental disomy (UPD) refers to the presence of two copies of a chromosome from one parent and none from the other parent. In genetic studies of UPDs, many genetic markers are usually used to identify the stage of nondisjunction that leads to UPD and to uncover the associated unusual patterns of recombinations. However, genetic information in such data has not been fully utilized because of the limitations of the existing statistical methods for UPD data. In the present article, we develop a multilocus statistical approach that has the advantages of being able to simultaneously consider all genetic markers for all individuals in the same analysis and to allow general models for the crossover process to incorporate crossover interference. In particular, for a general crossover-process model that assumes only that there exists in each interval at most one crossover, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses leading to UPD. We can also use this flexible approach to create genetic maps based on UPD data and to inspect recombination differences between meioses exhibiting UPD and normal meioses. The proposed method has been implemented in a computer program, and we illustrate the proposed approach through its application to a set of UPD15 data.

Characterization of Human Crossover Interference

We present an analysis of crossover interference over the entire human genome, on the basis of genotype data from more than 8,000 polymorphisms in eight CEPH families. Overwhelming evidence was found for strong positive crossover interference, with average strength lying between the levels of interference implied by the Kosambi and Carter-Falconer map functions. Five mathematical models of interference were evaluated: the gamma model and four versions of the count-location model. The gamma model fit the data far better than did any of the other four models. Analysis of intercrossover distances was greatly superior to the analysis of crossover counts, in both demonstrating interference and distinguishing between the five models. In contrast to earlier suggestions, interference was found to continue uninterrupted across the centromeres. No convincing differences in the levels of interference were found between the sexes or among chromosomes; however, we did detect possible individual variation in interference among the eight mothers. Finally, we present an equation that provides the probability of the occurrence of a double crossover between two nonrecombinant, informative polymorphisms.

Gene-centromere mapping of 312 loci in pink salmon by half-tetrad analysis

We estimated recombination rates between 312 loci and their centromeres in gynogenetic diploid pink salmon (Oncorhynchus gorbuscha) that we produced by initiating development with irradiated sperm and blocking the maternal second meiotic division. Amplified fragment length polymorphisms (AFLPs) were significantly more centromeric than loci identified by three other techniques (allozymes, microsatellites, and PCR using primer sequences from interspersed nuclear elements). The near absence of AFLPs in distal regions could limit their utility in constructing linkage maps. A large proportion of loci had frequency of second division segregation (y) values approaching 1.0, indicating near complete crossover interference on many chromosome arms. As predicted from models of chromosomal evolution in salmonids based upon results with allozyme loci, all duplicated microsatellite loci that shared alleles (isoloci) had y values of nearly 1.0.

EXO1 and MSH4 differentially affect crossing-over and segregation.

The 5'-3' exonuclease Exo1p from Saccharomyces cerevisiae is required for wild-type levels of meiotic crossing-over and normal meiotic chromosome segregation as is the meiosis-specific MutS homologue, Msh4p. Mutations in both genes reduce crossing-over by approximately two-fold, but deltamsh4 strains have significantly lower viability and a higher frequency of meiosis I non-disjunction. Epistasis analysis indicates a complex interaction between the two genes. Although crossing-over was not detectably lower in the double mutant, viability was significantly worse than either single mutant. Such a result suggests that the two genes are affecting meiotic viability by distinct mechanisms. We propose that deltaexo1 affects chromosome segregation by reducing crossing-over, while deltamsh4 affects both the frequency and distribution of crossovers. Mutation in EXO1 reduces gene conversion frequencies significantly at some but not all loci, suggesting that other enzymes are also involved in DNA resection. We propose that Exo1p plays an early role in establishing some recombination intermediates by generating single-stranded tails. The role of Msh4p is suggested to be in determining whether some recombination intermediates are resolved as crossover events and in generating crossover interference. The synergistic effect of deltaexo1deltamsh4 on spore viability suggests that the two genes have partially compensatory roles in a process affecting meiotic success.

Recombination of chromosomes 3A(m) and 5A(m) of Triticum monococcum with homeologous chromosomes 3A and 5A of wheat: the distribution of recombination across chromosomes.

Recombination of chromosomes 3A(m) and 5A(m) of Triticum monococcum with closely homeologous chromosomes 3A and 5A of T. aestivum was compared with recombination across corresponding homologous chromosome pairs. Differentiation between the homeologues impacted recombination in the proximal regions of the long arms the most and in the distal regions of the long arms the least. It is concluded that this variation principally reflects allocation of multiple crossovers across an arm and positive crossover interference across chromosome arms. Recombination rates between homeologous chromosomes 5A(m) and 5A differed in the opposite sexes.

Chromosome size-dependent control of meiotic reciprocal recombination in Saccharomyces cerevisiae: the role of crossover interference.

In the yeast Saccharomyces cerevisiae, small chromosomes undergo meiotic reciprocal recombination (crossing over) at rates (centimorgans per kilobases) greater than those of large chromosomes, and recombination rates respond directly to changes in the total size of a chromosomal DNA molecule. This phenomenon, termed chromosome size-dependent control of meiotic reciprocal recombination, has been suggested to be important for ensuring that homologous chromosomes cross over during meiosis. The mechanism of this regulation was investigated by analyzing recombination in identical genetic intervals present on different size chromosomes. The results indicate that chromosome size-dependent control is due to different amounts of crossover interference. Large chromosomes have high levels of interference while small chromosomes have much lower levels of interference. A model for how crossover interference directly responds to chromosome size is presented. In addition, chromosome size-dependent control was shown to lower the frequency of homologous chromosomes that failed to undergo crossovers, suggesting that this control is an integral part of the mechanism for ensuring meiotic crossing over between homologous chromosomes.

Microsatellite tagging of the stripe-rust resistance gene YrH52 derived from wild emmer wheat, Triticum dicoccoides, and suggestive negative crossover interference on chromosome 1B

Stripe rust caused by Puccinia striifomis West. is one of the most devastating diseases relating to wheat production. Wild emmer wheat, Triticum dicoccoides, the tetraploid progenitor of cultivated wheat, has proven to be a valuable source of novel stripe-rust resistance genes for wheat breeding. For example, T. dicoccoides accessions from Mt. Hermon, Israel, are uniformly and highly resistant to stripe-rust. The main objective of the present study is to map a stripe-rust resistance gene, derived from the unique Mt. Hermon population of wild emmer, using microsatellite markers. An F2 mapping population was established by crossing stripe-rust resistant T. dicoccoides accession H52 from Mt. Hermon with the Triticum durum cultivar Langdon. The stripe-rust resistance derived from accession H52 was found to be controlled by a single dominant gene which was temporarily designated as YrH52. Out of 120 microsatellite markers tested, 109 (91%) showed polymorphism between the parental lines. Among 79 segregating microsatellite loci generated from 56 microsatellite primer pairs, nine were linked to YrH52 with recombination frequencies of 0.02–0.35, and LOD scores of 3.56–54.22. A genetic map of chromosome 1B, consisting of ten microsatellite loci and the stripe-rust resistance gene YrH52, was constructed with a total map length of 101.5 cM. YrH52 is also closely linked to RFLP marker Nor1 with a map distance of 1.4 cM and a LOD value of 29.62. Apparent negative crossover interference was observed in chromosome 1B, especially in the region spanning the centromere. Negative crossover interference may be a common characteristic of gene-rich regions or gene clusters in specific chromosomes.

Distribution of crossing over on mouse synaptonemal complexes using immunofluorescent localization of MLH1 protein.

We have used immunofluorescent localization to examine the distribution of MLH1 (MutL homolog) foci on synaptonemal complexes (SCs) from juvenile male mice. MLH1 is a mismatch repair protein necessary for meiotic recombination in mice, and MLH1 foci have been proposed to mark crossover sites. We present evidence that the number and distribution of MLH1 foci on SCs closely correspond to the number and distribution of chiasmata on diplotene-metaphase I chromosomes. MLH1 foci were typically excluded from SC in centromeric heterochromatin. For SCs with one MLH1 focus, most foci were located near the middle of long SCs, but near the distal end of short SCs. For SCs with two MLH1 foci, the distribution of foci was bimodal regardless of SC length, with most foci located near the proximal and distal ends. The distribution of MLH1 foci indicated interference between foci. We observed a consistent relative distance (percent of SC length in euchromatin) between two foci on SCs of different lengths, suggesting that positive interference between MLH1 foci is a function of relative SC length. The extended length of pachytene SCs, as compared to more condensed diplotene-metaphase I bivalents, makes mapping crossover events and interference distances using MLH1 foci more accurate than using chiasmata.

Matrix Representation of the Haseman–Elston Method

The Hasemann–Elston method of linkage detection is based on the probabilities of a sib pair having 0, 1, or 2 alleles identical by descent (IBD) at a marker and a trait locus. These probabilities form a 3×3 matrix. Here, the characteristic values and characteristic vectors of this matrix were used to clarify the structure of the equations and to simplify calculations. As examples, the regression coefficients were derived for three genetic systems: a trait and a marker, two epistatic traits and two markers, and one trait locus and two markers. The last model was studied under the assumption of no crossover interference, the expression for allele IBD sharing at a trait locus was derived as a function of allele IBD sharing at two marker loci, and the regression is shown to be non-linear.

Meiotic chromosome morphology and behavior in zip1 mutants of Saccharomyces cerevisiae.

The yeast Zip1 protein (Zip1p) is a component of the central region of the synaptonemal complex (SC). Zip1p is predicted to form a dimer consisting of a coiled-coil domain flanked by globular domains. To analyze the organization of Zip1p within the SC, in-frame deletions of ZIP1 were constructed and analyzed. The results demonstrate that the C terminus but not the N terminus of Zip1p is required for its localization to chromosomes. Deletions in the carboxy half of the predicted coiled-coil region cause decreases in the width of the SC. Based on these results, a model for the organization of Zip1p within the SC is proposed. zip1 deletion mutations were also examined for their effects on sporulation, spore viability, crossing over, and crossover interference. The results demonstrate that the extent of synapsis is positively correlated with the levels of spore viability, crossing over, and crossover interference. In contrast, the role of Zip1p in synapsis is separable from its role in meiotic cell cycle progression. zip1 mutants display interval-specific effects on crossing over.

Stochastic modeling of the crossover process during meiosis

During meiosis, homologous chromosomes pair and each of the paired chromosomes duplicates, resulting in a bundle of four homologous chromatids. Exchanges take place after the formation of this four-strand structure, with each crossover involving two nonsister chromatids, i.e. chromatids originating from homologous chromosomes. Crossovers are precise breakage-and-reunion events which are essential for proper segregation, and can promote genetic variation. A good model for the crossover process is important to the understanding of crossing over mechanisms, to the construction of genetic maps, and to the strategy of finding disease genes. Usually randominvolvement of different pairs of chromatids (no chromatid interference) and random occurrence of crossovers on the four strand bundle (no chiasma interference) are assumed in genetic mappings, although crossover interference has been observed in many organisms. In this paper, we discuss Markov models for both chromatid interference and chiasma interference. Under these Markov models, there exists closed form expression for the probability of any data pattern that can result from the genetic studies considered in this paper. Map functions under different chromatid and chiasma interference models are compared.