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      Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle

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          Abstract

          Background

          Single nucleotide polymorphism (SNP) arrays for domestic cattle have catalyzed the identification of genetic markers associated with complex traits for inclusion in modern breeding and selection programs. Using actual and imputed Illumina 778K genotypes for 3887 U.S. beef cattle from 3 populations (Angus, Hereford, SimAngus), we performed genome-wide association analyses for feed efficiency and growth traits including average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), and residual feed intake (RFI), with marker-based heritability estimates produced for all traits and populations.

          Results

          Moderate and/or large-effect QTL were detected for all traits in all populations, as jointly defined by the estimated proportion of variance explained (PVE) by marker effects (PVE ≥ 1.0%) and a nominal P-value threshold ( P ≤ 5e-05). Lead SNPs with PVE ≥ 2.0% were considered putative evidence of large-effect QTL ( n = 52), whereas those with PVE ≥ 1.0% but < 2.0% were considered putative evidence for moderate-effect QTL ( n = 35). Identical or proximal lead SNPs associated with ADG, DMI, MMWT, and RFI collectively supported the potential for either pleiotropic QTL, or independent but proximal causal mutations for multiple traits within and between the analyzed populations. Marker-based heritability estimates for all investigated traits ranged from 0.18 to 0.60 using 778K genotypes, or from 0.17 to 0.57 using 50K genotypes (reduced from Illumina 778K HD to Illumina Bovine SNP50). An investigation to determine if QTL detected by 778K analysis could also be detected using 50K genotypes produced variable results, suggesting that 50K analyses were generally insufficient for QTL detection in these populations, and that relevant breeding or selection programs should be based on higher density analyses (imputed or directly ascertained).

          Conclusions

          Fourteen moderate to large-effect QTL regions which ranged from being physically proximal (lead SNPs ≤ 3Mb) to fully overlapping for RFI, DMI, ADG, and MMWT were detected within and between populations, and included evidence for pleiotropy, proximal but independent causal mutations, and multi-breed QTL. Bovine positional candidate genes for these traits were functionally conserved across vertebrate species.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-017-3754-y) contains supplementary material, which is available to authorized users.

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          Most cited references144

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          An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

          Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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            Development and Characterization of a High Density SNP Genotyping Assay for Cattle

            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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              Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds.

              The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.
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                Author and article information

                Contributors
                cseabury@cvm.tamu.edu
                doldeschulte@cvm.tamu.edu
                msaatchi@iastate.edu
                jbeever@illinois.edu
                deckerje@missouri.edu
                yhalley@cvm.tamu.edu
                ebhattarai@cvm.tamu.edu
                mmolaei@cvm.tamu.edu
                harvey.freetly@ars.usda.gov
                slhansen@iastate.edu
                yamparah@missouri.edu
                johnsoka@wsu.edu
                kerleym@missouri.edu
                kijae@missouri.edu
                dloy@iastate.edu
                emarques@agfront.com
                neibergs@wsu.edu
                schnabelr@missouri.edu
                dshike@illinois.edu
                mspangler2@unlnotes.unl.edu
                bweaber@k-state.edu
                dorian@iastate.edu
                taylorjerr@missouri.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                18 May 2017
                18 May 2017
                2017
                : 18
                : 386
                Affiliations
                [1 ]ISNI 0000 0004 4687 2082, GRID grid.264756.4, Department of Veterinary Pathobiology, , Texas A&M University, ; College Station, 77843 USA
                [2 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Department of Animal Science, , Iowa State University, ; Ames, 50011 USA
                [3 ]ISNI 0000 0004 1936 9991, GRID grid.35403.31, Department of Animal Sciences, , University of Illinois, ; Urbana, 61801 USA
                [4 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, Division of Animal Sciences, , University of Missouri, ; Columbia, 65211 USA
                [5 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, Informatics Institute, , University of Missouri, ; Columbia, 65211 USA
                [6 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, , USDA, ARS, US Meat Animal Research Center, ; Clay Center, 68933 USA
                [7 ]ISNI 0000 0001 2157 6568, GRID grid.30064.31, Department of Animal Sciences, , Washington State University, ; Pullman, 99164 USA
                [8 ]GeneSeek a Neogen Company, Lincoln, 68521 USA
                [9 ]ISNI 0000 0004 1937 0060, GRID grid.24434.35, Department of Animal Science, , University of Nebraska, ; Lincoln, 68583 USA
                [10 ]ISNI 0000 0001 0737 1259, GRID grid.36567.31, Department of Animal Sciences and Industry, , Kansas State University, ; Manhattan, 66506 USA
                [11 ]GRID grid.148374.d, Institute of Veterinary, Animal and Biomedical Sciences, , Massey University, ; Palmerston North, New Zealand
                Article
                3754
                10.1186/s12864-017-3754-y
                5437562
                28521758
                692bbee3-f043-44bf-a4b3-88d63073ae44
                © The Author(s). 2017

                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
                : 26 October 2016
                : 3 May 2017
                Funding
                Funded by: USDA National Institute of Food and Agriculture
                Award ID: 2011-68004-30214
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                gwas,qtl,feed efficiency and growth,beef cattle
                Genetics
                gwas, qtl, feed efficiency and growth, beef cattle

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