Recombination hotspots: Models and tools for detection

Molecular mechanisms and regulation of recombination frequency and distribution in plants

KEY MESSAGE

Recent developments in understanding the distribution and distinctive features of recombination hotspots are reviewed and approaches are proposed to increase recombination frequency in coldspot regions. Recombination events during meiosis provide the foundation and premise for creating new varieties of crops. The frequency of recombination in different genomic regions differs across eukaryote species, with recombination generally occurring more frequently at the ends of chromosomes. In most crop species, recombination is rare in centromeric regions. If a desired gene variant is linked in repulsion with an undesired variant of a second gene in a region with a low recombination rate, obtaining a recombinant plant combining two favorable alleles will be challenging. Traditional crop breeding involves combining desirable genes from parental plants into offspring. Therefore, understanding the mechanisms of recombination and factors affecting the occurrence of meiotic recombination is important for crop breeding. Here, we review chromosome recombination types, recombination mechanisms, genes and proteins involved in the meiotic recombination process, recombination hotspots and their regulation systems and discuss how to increase recombination frequency in recombination coldspot regions.

The H2-A Class II molecule α/β-chain cis-mismatch severely affects cell surface expression, selection of conventional CD4+ T cells and protection against TB infection

Introduction

To dissect the role of the part of the H2 complex comprised of the MHC-II genes in the control of tuberculosis (TB) infection, we previously established a panel of recombinant congenic mouse strains bearing different segments of the H2 ^j haplotype on the B6 (H2 ^b) genetic background. Fine genetic mapping, gene sequencing and assessment of TB phenotypes resulted in identification of the H2-Ab gene as a major factor of TB control.

Methods

We further narrowed the MHC-II H2 ^j interval by spotting a new recombination event, sequencing newly established DNA configuration and establishing a mouse strain B6.I-103 in which j/b recombination occurred within the coding sequence of the H2-Ab gene.

Results

Unexpectedly, a novel H2-Aα ^b/Aβ^jE⁰ haplotype provided exclusively high susceptibility to TB challenge. Immunologic analysis revealed an altered CD4⁺ T-cell selection and maintenance in B6.I-103 mice, as well as seriously impaired expression of the H2-Aα^b/Aβ^j molecule on the surface of antigen presenting cells. Unlike previously reported cases of Class II malfunctioning, the defective phenotype arose not from strong structural mutations, but from regular recombination events within the MHC-II recombination hot spot region.

Discussion

Our findings provide evidence that Class II α/β-chain cis-allelic mismatches created by regular genetic recombination may severely affect immune system functioning. This issue is discussed in the context of the MHC evolution.

Recombination and mutation shape variations in the major histocompatibility complex

The major histocompatibility complex (MHC) is closely associated with numerous diseases, but its high degree of polymorphism complicates the discovery of disease-associated variants. In principle, recombination and de novo mutations are two critical factors responsible for MHC polymorphisms. However, direct evidence for this hypothesis is lacking. Here, we report the generation of fine-scale MHC recombination and de novo mutation maps of ∼5 Mb by deep sequencing (> 100×) of the MHC genome for 17 MHC recombination and 30 non-recombination Han Chinese families (a total of 190 individuals). Recombination hotspots and Han-specific breakpoints are located in close proximity at haplotype block boundaries. The average MHC de novo mutation rate is higher than the genome-wide de novo mutation rate, particularly in MHC recombinant individuals. Notably, mutation and recombination generated polymorphisms are located within and outside linkage disequilibrium regions of the MHC, respectively, and evolution of the MHC locus was mainly controlled by positive selection. These findings provide insights on the evolutionary causes of the MHC diversity and may facilitate the identification of disease-associated genetic variants.

iRspot-DCC: Recombination hot/ cold spots identification based on dinucleotide-based correlation coefficient and convolutional neural network

The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences.

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN.

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

Brown fibre cotton is an environmental-friendly resource that plays a key role in the textile industry. However, the fibre quality and yield of natural brown cotton are poor, and fundamental research on brown cotton is relatively scarce. To understand the genetic basis of brown fibre cotton, we constructed linkage and association populations to systematically examine brown fibre accessions. We fine-mapped the brown fibre region, Lc1 , and dissected it into 2 loci, qBF-A07-1 and qBF-A07-2. The qBF-A07-1 locus mediates the initiation of brown fibre production, whereas the shade of the brown fibre is affected by the interaction between qBF-A07-1 and qBF-A07-2. Gh_A07G2341 and Gh_A07G0100 were identified as candidate genes for qBF-A07-1 and qBF-A07-2, respectively. Haploid analysis of the signals significantly associated with these two loci showed that most tetraploid modern brown cotton accessions exhibit the introgression signature of Gossypium barbadense. We identified 10 quantitative trait loci (QTLs) for fibre yield and 19 QTLs for fibre quality through a genome-wide association study (GWAS) and found that qBF-A07-2 negatively affects fibre yield and quality through an epistatic interaction with qBF-A07-1. This study sheds light on the genetics of fibre colour and lint-related traits in brown fibre cotton, which will guide the elite cultivars breeding of brown fibre cotton.

Highly conserved extended haplotypes of the major histocompatibility complex and their relationship to multiple sclerosis susceptibility

Objective

To determine the relationship between highly-conserved extended-haplotypes (CEHs) in the major histocompatibility complex (MHC) and MS-susceptibility.

Background

Among the ~200 MS-susceptibility regions, which are known from genome-wide analyses of single nucleotide polymorphisms (SNPs), the MHC accounts for roughly a third of the currently explained variance and the strongest MS-associations are for certain Class II alleles (e.g., HLA-DRB1*15:01; HLA-DRB1*03:01; and HLA-DRB1*13:03), which frequently reside on CEHs within the MHC.

Design/Methods

Autosomal SNPs (441,547) from 11,376 MS cases and 18,872 controls in the WTCCC dataset were phased. The most significant MS associated SNP haplotype was composed of 11 SNPs in the MHC Class II region surrounding the HLA-DRB1 gene. We also phased alleles at the HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1 loci. This data was used to probe the relationship between CEHs and MS susceptibility.

Results

We phased a total of 59,884 extended haplotypes (HLA-A, HLA-C, HLA-B, HLA-DRB1, HLA-DQB1 and SNP haplotypes) from 29,942 individuals. Of these, 10,078 unique extended haplotypes were identified. The 10 most common CEHs accounted for 22% (13,302) of the total. By contrast, the 8,446 least common extended haplotypes also accounted for approximately 20% (12,298) of the total. This extreme frequency-disparity among extended haplotypes necessarily complicates interpretation of reported disease-associations with specific HLA alleles. In particular, the HLA motif HLA-DRB1*15:01~HLA-DQB1*06:02 is strongly associated with MS risk. Nevertheless, although this motif is almost always found on the a1 SNP haplotype, it can rarely be found on others (e.g., a27 and a36), and, in these cases, it seems to have no apparent disease-association (OR = 0.7; CI = 0.3–1.3 and OR = 0.7; CI = 0.2–2.2, respectively). Furthermore, single copy carriers of the a1 SNP-haplotype without this HLA motif still have an increased disease risk (OR = 2.2; CI = 1.2–3.8). In addition, even among the set of CEHs, which carry the Class II motif of HLA-DRB1*15:01~HLA-DQB1*06:02~a1, different CEHs have differing strengths in their MS-associations.

Conclusions

The MHC in diverse human populations consists, primarily, of a very small collection of very highly-selected CEHs. Our findings suggest that the MS-association with the HLA-DRB1*15:01~HLA-DQB1*06:02 haplotype may be due primarily to the combined attributes of the CEHs on which this particular HLA-motif often resides.