Invited Review: Genomic selection in dairy cattle: Progress and challenges

Department of Primary Industries Victoria,Biosciences Research Division, Bundoora, Australia.
Journal of Dairy Science (Impact Factor: 2.57). 03/2009; 92(2):433-43. DOI: 10.3168/jds.2008-1646
Source: PubMed


A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. 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. 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, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.

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    • "In a landmark article, Meuwissen et al. (2001) proposed a new method termed genomic selection (GS), which uses information from genome-wide markers to predict phenotypes. GS has been widely used by animal breeders (Hayes et al. 2009b). More recently, GS studies have been conducted in crop breeding as well as in forest tree populations (Crossa et al. 2010, 2013 Heffner et al. 2011; Burgueno et al. 2012; Ornella et al. 2012; Resende et al. 2012a,b,c). "
    Dataset: 1991.full

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    • "The results also agree with those from Meuwissen et al. (2001) and Hayes et al. (2009), who point out that the size of the TP also influences the accuracy of the prediction of the TGV. Saatchi et al. (2010) "
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    ABSTRACT: Abstract The genomic value is the best prediction of the genotypic value of an animal, whose accuracy varies in function of several factors. The objectives of this study were to compare, through simulation, the accuracy of the genomic values of animals predicted through analysis with two alternative models, and to obtain the genetic correlation between these and the true genetic values simulated. A population with an effective size of 800 individuals was simulated and 100 generations were used to generate linkage disequilibrium. Then, another population was simulated with 14 generations, a panel of 53 010 single nucleotide polymorphisms (SNPs), placed randomly in 30 chromosomes and 540 quantitative traits loci. The genotypes and phenotypes of 6400 animals were also simulated using a heritability=0.40, and considering only the additive effects. Four sets of molecular values were trained using the SNP markers of generations 7 to 10, and the corresponding adjusted genetic values. When the training population (TP) was larger in size, the accuracy estimators (R 2) were higher. Similarly, when the evaluation population (EP) was closer to the TP, the R 2 estimators were higher and they became smaller when the distance between TP and EP increased. The estimate of the prediction error variance (PEV) was lower (0.15±0.01) when TP and EP were closer, regardless of the size of the TP, generation 10 for TP and generation 11 for EP. On the contrary, the highest PEV (0.29±0.02) was obtained when the TP included animals from generation seven and generation 14 was evaluated; that is, when the distance between TP and EP was greater.
    Agrociencia 09/2015; 49(6):613-622. · 0.26 Impact Factor
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    • "lection and genomic selection (GS) (e.g., Hayes et al. 2009). The discovery and exploitation of genotype2phenotype associations in an increasing number of agricultural species has the potential to dramatically accelerate food improvement (McClure et al. 2014). "
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    ABSTRACT: Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid imputation accuracy. However, in non-model organisms, genetic and physical maps are often either of poor quality or are completely absent and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for non-model organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation are often not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes and maize, we compare LD-kNNi to several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best existing methods and exhibits the least bias in allele frequency estimates.
    G3-Genes Genomes Genetics 09/2015; DOI:10.1534/g3.115.021667 · 3.20 Impact Factor
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