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      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

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          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12711-015-0181-x) contains supplementary material, which is available to authorized users.

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          Most cited references80

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          Development of a high density 600K SNP genotyping array for chicken

          Background High 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. Results We 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. Conclusions This 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.
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            A review of the development of chicken lines to resolve genes determining resistance to diseases.

            The resolution of genes that determine resistance to disease is described using chicken lines maintained at the Avian Disease and Oncology Laboratory (ADOL). This description includes a summary 1) of existing selected and inbred lines differing for resistance to viral-induced tumors, i.e., Marek's disease (MD) and lymphoid leukosis (LL), and of the use of inbred and line crosses to define relevant disease-resistant genes, e.g., TV, ALVE, B, R, LY4, TH1, BU1, and IGG1; 2) of the development of TVB*/ALVE congenic lines to establish the affects of endogenous virus (EV) expression on resistance to avian leukosis virus (ALV), and methods to detect ALVE expression; 3) of the development of B congenic lines to define the influence of the MHC on MD resistance and vaccinal immunity, for producing B antisera, and for evaluating DNA sequences of Class I and II genes; and 4) of the current development of 6C.7 recombinant congenic strains (RCS) to define the role of non-MHC genes influencing susceptibility to MD and LL tumors, immune competence, and epistatic effects of genes. The procedures of pedigree mating, to avoid or maintain inbreeding, and of blood-typing, to ensure genetic purity of the lines, are also described.
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              Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and well-watered environments

              Background Identification of QTL with large phenotypic effects conserved across genetic backgrounds and environments is one of the prerequisites for crop improvement using marker assisted selection (MAS). The objectives of this study were to identify meta-QTL (mQTL) for grain yield (GY) and anthesis silking interval (ASI) across 18 bi-parental maize populations evaluated in the same conditions across 2-4 managed water stressed and 3-4 well watered environments. Results The meta-analyses identified 68 mQTL (9 QTL specific to ASI, 15 specific to GY, and 44 for both GY and ASI). Mean phenotypic variance explained by each mQTL varied from 1.2 to 13.1% and the overall average was 6.5%. Few QTL were detected under both environmental treatments and/or multiple (>4 populations) genetic backgrounds. The number and 95% genetic and physical confidence intervals of the mQTL were highly reduced compared to the QTL identified in the original studies. Each physical interval of the mQTL consisted of 5 to 926 candidate genes. Conclusions Meta-analyses reduced the number of QTL by 68% and narrowed the confidence intervals up to 12-fold. At least the 4 mQTL (mQTL2.2, mQTL6.1, mQTL7.5 and mQTL9.2) associated with GY under both water-stressed and well-watered environments and detected up to 6 populations may be considered for fine mapping and validation to confirm effects in different genetic backgrounds and pyramid them into new drought resistant breeding lines. This is the first extensive report on meta-analysis of data from over 3100 individuals genotyped using the same SNP platform and evaluated in the same conditions across a wide range of managed water-stressed and well-watered environments.
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                Author and article information

                Contributors
                jfulton@hyline.com
                amccarron@hyline.com
                alund@hyline.com
                kpinegar@hyline.com
                awolc@iastate.edu
                oc248@cam.ac.uk
                bertrand.bedhom@jouy.inra.fr
                meberres@wisc.edu
                mamiller@coh.org
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                7 January 2016
                7 January 2016
                2016
                : 48
                : 1
                Affiliations
                [ ]Hy-Line International, Dallas Center, IA USA
                [ ]Iowa State University, 239C Kildee, Ames, IA 50011 USA
                [ ]Department of Pathology and Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
                [ ]Génétique Animale et Biologie Intégrative, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
                [ ]Department of Animal Sciences, University of Wisconsin, Madison, USA
                [ ]Department of Molecular and Cellular Biology, Beckman Research Institute, City of Hope, Duarte, CA USA
                Article
                181
                10.1186/s12711-015-0181-x
                4705597
                26743767
                c58666b3-bac4-4d60-92d7-a980d0e0dd91
                © Fulton et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 November 2015
                : 23 December 2015
                Categories
                Research Article
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                © The Author(s) 2016

                Genetics
                Genetics

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