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Vereeniging BKB Louwid Office - Tel: 016 423 5377 Rudi Nagel - Cell: 082 905 6066 BKB Louwid North Tel: 058 813 1071 10. Dundee BKB Louwid Office - Tel: 034 218 1261/5 Walter Pretorius - Cell: 082 828 8593 BKB Louwid East Tel: 058 303 4466 Home of Agriculture [email protected] Q-8238- BKB Brangus Joernaal Livestock Map Ad 2015.indd 1 2014/11/26 3:23 PM genomic selection for Feed Efficiency Jeremy F. Taylor, Robert D. Schnabel and Jared E. Decker Division of Animal Sciences | University of Missouri, Columbia MO Genomic Selection (GS) is a method for estimating the genetic merit of an animal using as little as a set of genotypes on the animal. The process requires the formation of a training population in which animals with phenotypes (e.g., feed intake, growth and residual feed intake (RFI)) are genotyped with a high density genotyping assay which may comprise from 50,000 to 800,000 DNA variants which are called single nucleotide polymorphisms (SNPs and pronounced as “snips”) and these genotypes and phenotypes are used to build a statistical model which allows the prediction of genetic merit on new animals (not in the training population) using only a set of genotypes produced on those animals. Once such a model has been developed, we need to characterize how well it performs and so we usually validate the model by genotyping a second set of animals of the same breed that also have phenotypes on the same trait(s), predicting their genetic merit using the model and then finally correlating these predictions with the animal’s phenotypes. This correlation has a theoretical maximum value of the square root of the heritability (amount of variation in the trait that is caused by genetics) and so dividing the correlation by the square root of the heritability provides an accuracy that is between 0 and 1. The technology was invented by Meuwissen et al. (2001) but was not tested in agricultural species until we were able to discover a lot of SNP variants by inexpensively sequencing many animals and then building the first high-density SNP chip, the Illumina BovineSNP50 (Matukumalli et al. 2009) which contained 54,001 SNPs and became available for genotyping cattle in December, 2006. In the U.S., the USDA National Institute of Food and Agriculture has 5. Producers often ask if they can stop recording data if they invested significant research funding into developing molecular utilize this technology. However, if data are not routinely estimates of breeding value (MBVs) for feed efficiency in cattle and collected on the traits of interest which allows us to periodically swine. This research has revealed a number of important features of update the prediction model, accuracies will begin to decrease GS that impacts the utility of the technology: as the tested animals become less and less related to the animals that were used to originally train the prediction model. 1. The technology works only within breeds. If we train a model to predict MBVs for feed efficiency in Angus cattle, the model has Genomic selection is not a silver bullet. However it offers some no predictive capability in other breeds of cattle – not even Red advantages that we cannot realize any other way. For example, we Angus which is a closely related breed. can obtain reasonably accurate estimates of genetic merit in young 2. Accuracy of MBVs depends on the amount of data available calves, essentially reducing the need to progeny test bulls before we on animals in the training population, the heritability of the trait select the best. Perhaps more interestingly, the accuracies of MBVs and how closely related these animals are to those new animals is the same in males and females and so for the first time we have for which we wish to generate MBVs. Typically, we need to the ability to accurately identify the best females in the population assemble training populations of size at least 1,000 animals and utilize these in multiple ovulation and embryo transfer programs before we can achieve worthwhile accuracies. Saatchi et al. along with the use of sexed semen to produce very high merit young (2011, 2012) have shown that we can achieve accuracies of at bulls to sire the next generation as yearling bulls. This has been fully least 0.22 to 0.76 for the traits routinely recorded by beef breed capitalized upon in the dairy industry but is only just beginning to associations using training populations of size 2,239 to 2,856 gain traction in the beef industry. animals that themselves have moderately accurate EPDs. 3. We have found the heritability of dry matter intake and residual Other than the need to gather phenotypic data on the traits that are feed intake to be in the range 0.21-0.49 in U.S. taurine beef important to the industry, the only limitation to the technology is breeds (Saatchi et al. 2014) which is certainly sufficient to allow the current high cost of genotyping. This has been perceived as an the generation of MBVs. impediment to technology adoption by many seedstock producers, 4. Because accuracy declines as the prediction model is applied particularly those who sell all the bulls the produce anyway – and in animals that are less and less related to the animals that were the technology may add a cost that they are unable to fully recapture in the training program, if we train a model to predict MBVs in since the low utilization of artificial insemination in the commercial U.S. Angus, the accuracy of the resulting MBVs will be beef industry limits the value of individual natural service bulls. considerably less in South African Angus even though South Africa may have used imported U.S. Angus genetics in their It is unlikely that the cost of genotyping will decrease significantly breeding program. in the next few years even though work is currently underway to 81 identify the genetic variants that have the largest effect References on each of the most important traits. This will enable us to Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell genotype many fewer SNPs at lower cost than the high- J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP. 2009. Development and density chips that are currently used, while still obtaining characterization of a high density SNP genotyping assay for cattle. PLoS One. 4:e5350. MBVs with worthwhile, but, lower, accuracies. This may Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using allow a much greater adoption of the genome-wide dense marker maps. Genetics. 157:1819-29. technology within the industry; however, Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, the technology really seems to need a Taxis TM, Chapple RH, Ramey HR, Northcutt SL, restructuring of the industry to realize Bauck S, Woodward B, Dekkers JC, Fernando RL, its full potential. The beef industry in the Schnabel RD, Garrick DJ, Taylor JF. 2011. Accuracies U.S. is highly fragmented and animals of genomic breeding values in American Angus beef change hands many times between cattle using K-means clustering for cross-validation. birth and final processing. This means Genet Sel Evol. 43:40. that the traits that are important change Saatchi M, Schnabel RD, Rolf MM, Taylor JF, Garrick depending on where you are within DJ. 2012. Accuracy of direct genomic breeding values the production chain. To capture the for nationally evaluated traits in US Limousin and value of genetic improvement and Simmental beef cattle. Genet Sel Evol. 44:38. present the correct market signals to Saatchi M, Beever JE, Decker JE, Faulkner DB, Freetly seedstock producers may require the HC, Hansen SL, Yampara-Iquise H, Johnson KA, formation of strategic alliances among Kachman SD, Kerley MS, Kim J, Loy DD, Marques E, groups of seedstock producers and Neibergs HL, Pollak EJ, Schnabel RD, Seabury CM, groups of commercial producers to Shike DW, Snelling WM, Spangler ML, Weaber RL, defray the cost of genotyping their Garrick DJ, Taylor JF.