Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels
TL;DR: Investigation of how well an additive genetic value can be predicted using various sets of approximately 3000 SNPs selected out of the 54 001 SNPs in an Illumina BovineSNP50 BeadChip high density panel found a low density SNP panel allows for reasonably good prediction of future breeding values.
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Abstract: In human and animal genetics dense single nucleotide polymorphism (SNP) panels are widely used to describe genetic variation. In particular genomic selection in dairy cattle has become a routinely applied tool for prediction of additive genetic values of animals, especially of young selection candidates. The aim of the study was to investigate how well an additive genetic value can be predicted using various sets of approximately 3000 SNPs selected out of the 54 001 SNPs in an Illumina BovineSNP50 BeadChip high density panel. Effects of SNPs from the nine subsets of the 54 001 panel were estimated using a model with a random uncorrelated SNPs effect based on a training data set of 1216 Polish Holstein-Friesian bulls whose phenotypic records were approximated by deregressed estimated breeding values for milk, protein, and fat yields. Predictive ability of the low density panels was assessed using a validation data set of 622 bulls. Correlations between direct and conventional breeding values routinely estimated for the Polish population were similar across traits and clearly across sets of SNPs. For the training data set correlations varied between 0.94 and 0.98, for the validation data set between 0.25 and 0.46. The corresponding correlations estimated using the 54 001 panel were: 0.98 for the three traits (training), 0.98 (milk and fat yields, validation), and 0.97 (protein yield, validation). The optimal subset consisted of SNPs selected based on their highest effects for milk yield obtained from the evaluation of all 54 001 SNPs. A low density SNP panel allows for reasonably good prediction of future breeding values. Even though correlations between direct and conventional breeding values were moderate, for young selection candidates a low density panel is a better predictor than a commonly used average of parental breeding values.
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Citations
High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.
TL;DR: Results indicate that accurate genotype imputation to medium density is achievable with low-density genotype panels with at least 6,000 SNPs, and the most accurate SNP selection method for panels with <9,000SNPs was that based on MAF and LD pattern within genomic blocks.
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Evaluation of developed low-density genotype panels for imputation to higher density in independent dairy and beef cattle populations
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References
PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
Shaun Purcell,Shaun Purcell,Benjamin M. Neale,Benjamin M. Neale,Kathe Todd-Brown,Lori Thomas,Manuel A. R. Ferreira,David Bender,David Bender,Julian Maller,Julian Maller,Pamela Sklar,Pamela Sklar,Paul I.W. de Bakker,Paul I.W. de Bakker,Mark J. Daly,Mark J. Daly,Pak C. Sham +17 more
TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
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Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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Prediction of total genetic value using genome-wide dense marker maps.
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Invited review: Genomic selection in dairy cattle: progress and challenges.
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1.7K
•Journal Article
Invited review: Genomic selection in dairy cattle: progress and challenges (vol 92, pg 433, 2009)
TL;DR: The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams, and the increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBv only.
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