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      Development and Characterization of a High Density SNP Genotyping Assay for Cattle

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          Abstract

          The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A cost-effective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of <350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds (average minor allele frequency (MAF) ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle.

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          Most cited references19

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          Linkage disequilibrium in humans: models and data.

          In this review, we describe recent empirical and theoretical work on the extent of linkage disequilibrium (LD) in the human genome, comparing the predictions of simple population-genetic models to available data. Several studies report significant LD over distances longer than those predicted by standard models, whereas some data from short, intergenic regions show less LD than would be expected. The apparent discrepancies between theory and data present a challenge-both to modelers and to human geneticists-to identify which important features are missing from our understanding of the biological processes that give rise to LD. Salient features may include demographic complications such as recent admixture, as well as genetic factors such as local variation in recombination rates, gene conversion, and the potential segregation of inversions. We also outline some implications that the emerging patterns of LD have for association-mapping strategies. In particular, we discuss what marker densities might be necessary for genomewide association scans.
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            SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries.

            High-density single-nucleotide polymorphism (SNP) arrays have revolutionized the ability of genome-wide association studies to detect genomic regions harboring sequence variants that affect complex traits. Extensive numbers of validated SNPs with known allele frequencies are essential to construct genotyping assays with broad utility. We describe an economical, efficient, single-step method for SNP discovery, validation and characterization that uses deep sequencing of reduced representation libraries (RRLs) from specified target populations. Using nearly 50 million sequences generated on an Illumina Genome Analyzer from DNA of 66 cattle representing three populations, we identified 62,042 putative SNPs and predicted their allele frequencies. Genotype data for these 66 individuals validated 92% of 23,357 selected genome-wide SNPs, with a genotypic and sequence allele frequency correlation of r = 0.67. This approach for simultaneous de novo discovery of high-quality SNPs and population characterization of allele frequencies may be applied to any species with at least a partially sequenced genome.
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              Is Open Access

              Whole genome linkage disequilibrium maps in cattle

              Background Bovine whole genome linkage disequilibrium maps were constructed for eight breeds of cattle. These data provide fundamental information concerning bovine genome organization which will allow the design of studies to associate genetic variation with economically important traits and also provides background information concerning the extent of long range linkage disequilibrium in cattle. Results Linkage disequilibrium was assessed using r2 among all pairs of syntenic markers within eight breeds of cattle from the Bos taurus and Bos indicus subspecies. Bos taurus breeds included Angus, Charolais, Dutch Black and White Dairy, Holstein, Japanese Black and Limousin while Bos indicus breeds included Brahman and Nelore. Approximately 2670 markers spanning the entire bovine autosomal genome were used to estimate pairwise r2 values. We found that the extent of linkage disequilibrium is no more than 0.5 Mb in these eight breeds of cattle. Conclusion Linkage disequilibrium in cattle has previously been reported to extend several tens of centimorgans. Our results, based on a much larger sample of marker loci and across eight breeds of cattle indicate that in cattle linkage disequilibrium persists over much more limited distances. Our findings suggest that 30,000–50,000 loci will be needed to conduct whole genome association studies in cattle.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2009
                24 April 2009
                : 4
                : 4
                : e5350
                Affiliations
                [1 ]Department of Bioinformatics and Computational Biology, George Mason University, Manassas, Virginia, United States of America
                [2 ]Bovine Functional Genomics Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, Maryland, United States of America
                [3 ]Illumina, Inc., Hayward, California, United States of America
                [4 ]Division of Animal Sciences, University of Missouri, Columbia, Missouri, United States of America
                [5 ]U.S. Meat Animal Research Center, USDA, ARS, Clay Center, Nebraska, United States of America
                [6 ]Department of Medicine, University of Maryland, Baltimore, Maryland, United States of America
                [7 ]Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
                Ohio State University Medical Center, United States of America
                Author notes

                Conceived and designed the experiments: LKM RDS JT TS TS CPVT. Performed the experiments: LKM CTL RDS JT MFA JO TS TS SM CPVT. Analyzed the data: LKM CTL RDS JT MH JO TS CPVT. Contributed reagents/materials/analysis tools: LKM CTL RDS JT MFA MH TS TS SM CPVT. Wrote the paper: LKM CTL RDS JT MFA MH JO TS TS CPVT.

                Article
                09-PONE-RA-08142R1
                10.1371/journal.pone.0005350
                2669730
                19390634
                52dec00a-0dbc-49bf-8f62-4c617be51769
                Matukumalli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 13 January 2009
                : 23 March 2009
                Page count
                Pages: 13
                Categories
                Research Article
                Computational Biology
                Genetics and Genomics
                Computational Biology/Genomics
                Computational Biology/Molecular Genetics
                Computational Biology/Population Genetics
                Genetics and Genomics/Animal Genetics
                Genetics and Genomics/Bioinformatics
                Genetics and Genomics/Complex Traits
                Genetics and Genomics/Genome Projects
                Genetics and Genomics/Genomics
                Genetics and Genomics/Population Genetics
                Molecular Biology/Bioinformatics
                Molecular Biology/Molecular Evolution

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                Uncategorized

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