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      Sequencing the mosaic genome of Brahman cattle identifies historic and recent introgression including polled

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          Abstract

          Brahman cattle have a Bos indicus and Bos taurus mosaic genome, as a result of the process used to create the breed (repeat backcrossing of Bos taurus females to Bos indicus bulls). With the aim of identifying Bos taurus segments in the Brahman genome at sequence level resolution, we sequenced the genomes of 46 influential Brahman bulls. Using 36 million variants identified in the sequences, we searched for regions close to fixation for Bos indicus or Bos taurus segments that were longer than expected by chance (from simulation of the breed formation history of Brahman cattle). Regions close to fixation for Bos indicus content were enriched for protein synthesis genes, while regions of higher Bos taurus content included genes of the G-protein coupled receptor family (including genes implicated in puberty, such as THRS). The region with the most extreme Bos taurus enrichment was on chromosome 14 surrounding PLAG1. The introgressed Bos taurus allele at PLAG1 increases stature and the high frequency of the allele likely reflects strong selection for the trait. Finally, we provide evidence that the polled mutation in Brahmans, a desirable trait under very strong recent selection, is of Celtic origin and is introgressed from Bos taurus.

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          Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.

          T Druet, I. MacLeod, B. Hayes (2014)
          Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.
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            A flexible forward simulator for populations subject to selection and demography.

            D. Paredes Hernandez (2008)
            This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). The source code (written in the C programming language) is available at http://sfscode.sourceforge.net, and a web server (http://cbsuapps.tc.cornell.edu/sfscode.aspx) allows the user to perform simulations using the high-performance computing cluster hosted by the Cornell University Computational Biology Service Unit.
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              Detection of selection signatures in dairy and beef cattle using high-density genomic information

              Fuping Zhao, Sinead McParland, Francis Kearney … (2015)
              Background Artificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs. Results Information on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (FST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the FST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global FST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection. Conclusions This study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0127-3) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                L. Koufariotis:
                ORCID: http://orcid.org/0000-0002-4917-1375
                r.koufariotis@uq.edu.au
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 10 December 2018
                Publication date PMC-release: 10 December 2018
                Publication date Collection: 2018
                Volume: 8
                Electronic Location Identifier: 17761
                Affiliations
                [1 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, Centre of Animal Science, Queensland Alliance for Agriculture and Food Innovation, , The University of Queensland, ; Brisbane, Queensland 4072 Australia
                [2 ]Australian Agricultural Company (AACo), Brisbane, Queensland 4006 Australia
                [3 ]GRID grid.492998.7, Department of Agriculture and Fisheries, ; Rockhampton, Queensland 4702 Australia
                [4 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, School of Veterinary Science, , The University of Queensland, ; Gatton, Queensland 4343 Australia
                [5 ]GRID grid.17089.37, Department of Agricultural, , Food and Nutritional Science, University of Alberta, ; Edmonton, Alberta T6G 2C8 Canada
                [6 ]ISNI 0000 0004 0407 2669, GRID grid.452283.a, Agriculture Victoria, , Agribio, Centre for Agribiosciences, ; 5 Ring Road, Bundoora, Victoria 3086 Australia
                Author information
                http://orcid.org/0000-0002-4917-1375
                http://orcid.org/0000-0002-5606-3970
                Article
                Publisher ID: 35698
                DOI: 10.1038/s41598-018-35698-5
                PMC ID: 6288114
                PubMed ID: 30531891
                SO-VID: d04c253d-6662-486d-9910-1b11d7678479
                Copyright © © The Author(s) 2018
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 16 November 2017
                Date accepted : 10 November 2018
                Funding
                Funded by: The University of Queensland
                Categories
                Subject: Article
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                issue-copyright-statement © The Author(s) 2018

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