Dataset Information

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1.

ABSTRACT: BACKGROUND:The major histocompatibility complex (MHC) is present within the genomes of all jawed vertebrates. MHC genes are especially important in regulating immune responses, but even after over 80 years of research on the MHC, much remains to be learned about how it influences adaptive and innate immune responses. In most species, the MHC is highly polymorphic and polygenic. Strong and highly reproducible associations are established for chicken MHC-B haplotypes in a number of infectious diseases. Here, we report (1) the development of a high-density SNP (single nucleotide polymorphism) panel for MHC-B typing that encompasses a 209,296 bp region in which 45 MHC-B genes are located, (2) how this panel was used to define chicken MHC-B haplotypes within a large number of lines/breeds and (3) the detection of recombinants which contributes to the observed diversity. METHODS:A SNP panel was developed for the MHC-B region between the BG2 and CD1A1 genes. To construct this panel, each SNP was tested in end-point read assays on more than 7500 DNA samples obtained from inbred and commercially used egg-layer lines that carry known and novel MHC-B haplotypes. One hundred and one SNPs were selected for the panel. Additional breeds and experimentally-derived lines, including lines that carry MHC-B recombinant haplotypes, were then genotyped. RESULTS:MHC-B haplotypes based on SNP genotyping were consistent with the MHC-B haplotypes that were assigned previously in experimental lines that carry B2, B5, B12, B13, B15, B19, B21, and B24 haplotypes. SNP genotyping resulted in the identification of 122 MHC-B haplotypes including a number of recombinant haplotypes, which indicate that crossing-over events at multiple locations within the region lead to the production of new MHC-B haplotypes. Furthermore, evidence of gene duplication and deletion was found. CONCLUSIONS:The chicken MHC-B region is highly polymorphic across the surveyed 209-kb region that contains 45 genes. Our results expand the number of identified haplotypes and provide insights into the contribution of recombination events to MHC-B diversity including the identification of recombination hotspots and an estimation of recombination frequency.

SUBMITTER: Fulton JE

PROVIDER: S-EPMC4705597 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1.

Fulton Janet E JE McCarron Amy M AM Lund Ashlee R AR Pinegar Kara N KN Wolc Anna A Chazara Olympe O Bed'Hom Bertrand B Berres Mark M Miller Marcia M MM

Genetics, selection, evolution : GSE 20160107

<h4>Background</h4>The major histocompatibility complex (MHC) is present within the genomes of all jawed vertebrates. MHC genes are especially important in regulating immune responses, but even after over 80 years of research on the MHC, much remains to be learned about how it influences adaptive and innate immune responses. In most species, the MHC is highly polymorphic and polygenic. Strong and highly reproducible associations are established for chicken MHC-B haplotypes in a number of infecti ...[more]

PMID: 26743767

Similar Datasets

Project description:BackgroundHigh density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.ResultsWe report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10-15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.ConclusionsThis Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants.

Project description:Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1,243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost-only modest gains in accuracy are made as one increases panel density beyond this point.

Project description:For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees.

Project description:BackgroundA fundamental goal of single nucleotide polymorphism (SNP) genotyping is to determine the sharing of alleles between individuals across genomic loci. Such analyses have diverse applications in defining the relatedness of individuals (including unexpected relationships in nominally unrelated individuals, or consanguinity within pedigrees), analyzing meiotic crossovers, and identifying a broad range of chromosomal anomalies such as hemizygous deletions and uniparental disomy, and analyzing population structure.Principal findingsWe present SNPduo, a command-line and web accessible tool for analyzing and visualizing the relatedness of any two individuals using identity by state. Using identity by state does not require prior knowledge of allele frequencies or pedigree information, and is more computationally tractable and is less affected by population stratification than calculating identity by descent probabilities. The web implementation visualizes shared genomic regions, and generates UCSC viewable tracks. The command-line version requires pedigree information for compatibility with existing software and determining specified relationships even though pedigrees are not required for IBS calculation, generates no visual output, is written in portable C++, and is well-suited to analyzing large datasets. We demonstrate how the SNPduo web tool identifies meiotic crossover positions in siblings, and confirm our findings by visualizing meiotic recombination in synthetic three-generation pedigrees. We applied SNPduo to 210 nominally unrelated Phase I / II HapMap samples and, consistent with previous findings, identified six undeclared pairs of related individuals. We further analyzed identity by state in 2,883 individuals from multiplex families with autism and identified a series of anomalies including related parents, an individual with mosaic loss of chromosome 18, an individual with maternal heterodisomy of chromosome 16, and unexplained replicate samples.ConclusionsSNPduo provides the ability to explore and visualize SNP data to characterize the relatedness between individuals. It is compatible with, but distinct from, other established analysis software such as PLINK, and performs favorably in benchmarking studies for the analyses of genetic relatedness.

Project description:The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P-value of at least 1 × 10-10 ; MAF greater than 0.01 and call rate greater than 0.95. The FImpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave-one-out cross-validation technique that avoids iterative cross-validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high-density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.

Dataset Information

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1.

Publications

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets