Invited review: Genomic selection in dairy cattle: Progress and challenges
ABSTRACT 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.
- SourceAvailable from: Robert R. Fitak
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- "Large-scale analyses of genetic variation, or polymorphisms, within a species or population can uncover additional candidates for selection through dense genome scans of population divergence or hitchhiking (Ellegren 2008). For example, genome-wide analysis of single nucleotide polymorphisms (SNPs) in cattle has identified loci linked to milk production traits (Pryce et al. 2010) and this knowledge has been implemented in breeding programs designed to improve production traits through the process of genomic selection (reviewed by Hayes et al. 2009; Schefers & Weigel 2012). Furthermore, these genomic scans of polymorphism can inform assessments of demographic history; where population bottlenecks and small population sizes, often associated with mammalian megafauna, can obscure the ability to detect patterns of selection in genomes (Akey et al. 2004; Pool et al. 2010). "
ABSTRACT: The single-humped dromedary (Camelus dromedarius), is the most numerous and widespread of domestic camel species and is a significant source of meat, milk, wool, transportation, and sport for millions of people. Dromedaries are particularly well adapted to hot, desert conditions and harbor a variety of biological and physiological characteristics with evolutionary, economic, and medical importance. To understand the genetic basis of these traits, an extensive resource of genomic variation is required. In this study, we assembled at 65x coverage, a 2.06 Gb draft genome of a female dromedary whose ancestry can be traced to an isolated population from the Canary Islands. We annotated 21,167 protein-coding genes and estimated ~33.7% of the genome to be repetitive. A comparison with the recently published draft genome of an Arabian dromedary resulted in 1.91 Gb of aligned sequence with a divergence of 0.095%. An evaluation of our genome with the reference revealed that our assembly contains more error-free bases (91.2%) and fewer scaffolding errors. We identified ~1.4 million single nucleotide polymorphisms with a mean density of 0.71 x 10(-3) per base. An analysis of demographic history indicated that changes in effective population size corresponded with recent glacial epochs. Our de novo assembly provides a useful resource of genomic variation for future studies of the camel's adaptations to arid environments and economically important traits. Furthermore, these results suggest that draft genome assemblies constructed with only two differently sized sequencing libraries can be comparable to those sequenced using additional library sizes; highlighting that additional resources might be better placed in technologies alternative to short-read sequencing to physically anchor scaffolds to genome maps. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.Molecular Ecology Resources 07/2015; DOI:10.1111/1755-0998.12443 · 5.63 Impact Factor
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- "Genomic selection (GS) is a relatively new breeding methodology (Hayes et al., 2009; Lorenz et al., 2011) which is increasingly attractive for the genetic improvement of various species because of its potential to increase the rate of genetic gain (Rutkoski et al., 2013). Genomic selection refers to the use of large numbers of single nucleotide polymorphisms (SNPs) spread across the genome for breeding value estimation and subsequent selection of individuals based on gnomically enhanced breeding values. "
ABSTRACT: Genomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. Genotypic imputation is defined as the prediction of genotypes for both unrelated individuals and parent-offspring trios at the single nucleotide polymorphism (SNP) locations in a sample of individuals for which assays are not directly available. Several imputation methods are available for imputation designed for livestock population. Machine learning methods have been used in genetic studies to build models capable of predicting missing values of a marker. In this study, strategies and factors affecting the imputation accuracy of parent-offspring trios were compared using two Machine Learning methods namely K-Nearest neighbour (KNN) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Two datasets of D1 (100 trios with 5k SNPs) and D2 (500 trios with 5k SNPs) were simulated. The methods were compared in terms of imputation accuracy and computation time and factors affecting imputation accuracy (sample size). Comparison of two methods for imputation showed that the KNN outperformed AB for imputation accuracy. The time of computation was different between methods. The KNN was the fastest algorithm. Accuracy of imputation increased with increasing number of trios. Simulation datasets showed that our methods performed very well for imputation of un-typed SNPs and can be used as an alternative for imputation of parent-offspring trios than other methods.Research Opinions in Animal and Veterinary Sciences 06/2015; 5(7):295-299.
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- "A balance between genetic improvement and conservation programs should be carefully considered. In addition, due to the low number of animals, genomic selection does not seem possible in very small populations (Hayes et al., 2009); therefore there is the need to identify other strategies to use marker information for selection purposes. Candidate gene markers, already shown to affect milk production traits in cosmopolitan breeds, could provide useful information for the characterization of minor breeds and for the evaluation of marker assisted selection programs considering also conservation strategies. "
ABSTRACT: Reggiana is a local dairy cattle breed that currently counts about 2,000 cows reared mainly in the province of Reggio Emilia (North of Italy). Reggiana cows are less productive than Holstein cows opening questions about the profitability of this breed whose milk is used to produce a niche brand of Parmigiano-Reggiano cheese. For these reasons, protein content and yield of the Reggiana milk are very important traits. With the aim to identify markers that could be useful to describe genetic variability in this breed and eventually to use them in conservation and marker assisted selection programs, we genotyped 22 DNA polymorphisms of 17 candidate genes (ABCG2, CRH, CSN3, CYP11B1, DGAT1, FGF2, GH1, GHR, KIT, LEP, LGB, ORL1, POU1F1, PRLR, SPP1, STAT1 and TG) in 128 Reggiana sires for which semen was available and we analysed association between 20 polymorphic markers and milk yield, protein yield (PY), fat yield (FY), protein percentage (PP) and fat percentage (FP) estimated breeding values. Five markers, four of which on Bos taurus chromosome (BTA) 14 (DGAT1 p.K232A, CYP11B1 p.A30V, TG g.9509279 C>T, CRH p.S45D) and one on BTA19 (GH1 p.L153V), were highly associated (P<0.01) with several production traits (PP, FY and PY; FP; PY; FY; PP and FP; respectively). This study is the largest investigation of molecular markers carried out in this breed so far and represents one of the few attempts to identify DNA markers affecting production traits in a cattle breed constituted by a very small population. Obtained results could open new possibilities for conservation and breeding programs in Reggiana cattle.Livestock Science 04/2015; 176. DOI:10.1016/j.livsci.2015.03.022 · 1.10 Impact Factor