Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens

BMC Genetics

Guosheng Su²  Mogens Sandø Lund²  Jie Wang¹  Dingming Shu¹  Chenglong Luo¹  Hao Qu¹  Tianfei Liu¹ 

[1] State Key Laboratory of Livestock and Poultry Breeding, Guangzhou 510640, China;Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark

关键词: Genomic prediction;    Cross-validation;    Breeding value;    Accuracy;   

Others  :  1085435
DOI  :  10.1186/s12863-014-0110-y

 received in 2014-04-30, accepted in 2014-10-01,  发布年份 2014

【 摘 要 】

Background

Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage.

Results

Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits.

Conclusions

The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising.

【 授权许可】

   
2014 Liu et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Colombani C, Legarra A, Fritz S, Guillaume F, Croiseau P, Ducrocq V, Robert-Granié C: Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds. J Dairy Sci 2013, 96(1):575-591.
• [2]Su G, Guldbrandtsen B, Gregersen VR, Lund MS: Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci 2010, 93(3):1175-1183.
• [3]VanRaden PM, Sullivan PG: International genomic evaluation methods for dairy cattle. Genet Sel Evol 2010, 42(1):7. BioMed Central Full Text
• [4]Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME: Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 2009, 92(2):433-443.
• [5]VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS: Invited Review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 2009, 92(1):16-24.
• [6]Tribout T, Larzul C, Phocas F: Efficiency of genomic selection in a purebred pig male line. J Anim Sci 2012, 90(12):4164-4176.
• [7]Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G: Single-step methods for genomic evaluation in pigs. Animal 2012, 6(10):1565-1571.
• [8]Duchemin S, Colombani C, Legarra A, Baloche G, Larroque H, Astruc J, Barillet F, Robert-Granie C, Manfredi E: Genomic selection in the French Lacaune dairy sheep breed. J Dairy Sci 2012, 95(5):2723-2733.
• [9]Groenen M, Megens H-J, Zare Y, Warren W, Hillier L, Crooijmans R, Vereijken A, Okimoto R, Muir W, Cheng H: The development and characterization of a 60K SNP chip for chicken. BMC Genomics 2011, 12(1):274. BioMed Central Full Text
• [10]Schaeffer LR: Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 2006, 123(4):218-223.
• [11]González-Recio O, Gianola D, Long N, Weigel KA, Rosa GJM, Avendaño S: Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 2008, 178(4):2305-2313.
• [12]González-Recio O, Gianola D, Rosa GJM, Weigel KA, Kranis A: Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens. Genet Sel Evol 2009, 41(1):3. BioMed Central Full Text
• [13]Chen CY, Misztal I, Aguilar I, Tsuruta S, Meuwissen THE, Aggrey SE, Wing T, Muir WM: Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. J Anim Sci 2011, 89(1):23-28.
• [14]Wei M, van der Werf JH: Genetic correlation and heritabilities for purebred and crossbred performance in poultry egg production traits. J Anim Sci 1995, 73(8):2220-2226.
• [15]Moyer SE, Collins WM, Skoglund WC: Heritability of body weight at three ages in cross-bred broiler chickens resulting from two systems of breeding. Poult Sci 1962, 41(5):1374-1382.
• [16]Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157(4):1819-1829.
• [17]Habier D, Fernando RL, Dekkers JCM: The impact of genetic relationship information on genome-assisted breeding values. Genetics 2007, 177(4):2389-2397.
• [18]de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM: Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 2009, 182(1):375-385.
• [19]VanRaden PM: Efficient methods to compute genomic predictions. J Dairy Sci 2008, 91(11):4414-4423.
• [20]Gianola D: Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 2006, 173(3):1761-1776.
• [21]Su G, Christensen OF, Ostersen T, Henryon M, Lund MS: Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS One 2012, 7(9):e45293.
• [22]Meuwissen THE: Accuracy of breeding values of ’unrelatedz’ individuals predicted by dense SNP genotyping. Genet Sel Evol 2009, 41(1):35. BioMed Central Full Text
• [23]Gao H, Su G, Janss L, Zhang Y, Lund MS: Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population. J Dairy Sci 2013, 96(7):4678-4687.
• [24]Goddard M: Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 2009, 136(2):245-257.
• [25]Daetwyler HD, Villanueva B, Woolliams JA: Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 2008, 3(10):e3395.
• [26]HAYES BJ, VISSCHER PM, GODDARD ME: Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 2009, 91(01):47-60.
• [27]Lund M, de Roos A, de Vries A, Druet T, Ducrocq V, Fritz S, Guillaume F, Guldbrandtsen B, Liu Z, Reents R, Schrooten C, Seefried F, Su G: A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol 2011, 43(1):43. BioMed Central Full Text
• [28]Saatchi M, McClure M, McKay S, Rolf M, Kim J, Decker J, Taxis T, Chapple R, Ramey H, Northcutt S, Bauck S, Woodward B, Dekkers J, Fernando R, Schnabel R, Garrick D, Taylor J: Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol 2011, 43(1):40. BioMed Central Full Text
• [29]Saatchi M, Schnabel R, Rolf M, Taylor J, Garrick D: Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genet Sel Evol 2012, 44(1):38. BioMed Central Full Text
• [30]Snelling WM, Allan MF, Keele JW, Kuehn LA, Thallman RM, Bennett GL, Ferrell CL, Jenkins TG, Freetly HC, Nielsen MK, Rolfe KM: Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle. J Anim Sci 2011, 89(6):1731-1741.
• [31]Lillehammer M, Meuwissen THE, Sonesson AK: Genomic selection for maternal traits in pigs. J Anim Sci 2011, 89(12):3908-3916.
• [32]Ostersen T, Christensen O, Henryon M, Nielsen B, Su G, Madsen P: Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genet Sel Evol 2011, 43(1):38. BioMed Central Full Text
• [33]Daetwyler H, Swan A, van der Werf J, Hayes B: Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation. Genet Sel Evol 2012, 44(1):33. BioMed Central Full Text
• [34]Wolc A, Stricker C, Arango J, Settar P, Fulton J, O’Sullivan N, Preisinger R, Habier D, Fernando R, Garrick D, Lamont S, Dekkers J: Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genet Sel Evol 2011, 43(1):5. BioMed Central Full Text
• [35]Su G, Madsen P, Nielsen U, Mantysaari E, Aamand G, Christensen O, Lund M: Genomic prediction for Nordic Red Cattle using one-step and selection index blending. J Dairy Sci 2012, 95(2):909-917.
• [36]Su G, Brondum RF, Ma P, Guldbrandtsen B, Aamand GR, Lund MS: Comparison of genomic predictions using medium-density (similar to 54,000) and high-density (similar to 777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. J Dairy Sci 2012, 95(8):4657-4665.
• [37]Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen THE: The accuracy of Genomic Selection in Norwegian red cattle assessed by cross-validation. Genetics 2009, 183(3):1119-1126.
• [38]Kapell D, Sorensen D, Su G, Janss L, Ashworth C, Roehe R: Efficiency of genomic selection using Bayesian multimarker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice. BMC Genet 2012, 13(1):42. BioMed Central Full Text
• [39]Legarra A, Robert-Granie C, Manfredi E, Elsen JM: Performance of genomic selection in mice. Genetics 2008, 180(1):611-618.
• [40]Lund M, Sahana G, de Koning D-J, Su G, Carlborg O: Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection. BMC Proceedings 2009, 3(Suppl 1):S1. BioMed Central Full Text
• [41]Guo G, Lund MS, Zhang Y, Su G: Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. J Anim Breed Genet 2010, 127(6):423-432.
• [42]Calus MPL, Veerkamp RF: Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 2007, 124(6):362-368.
• [43]Gao H, Christensen O, Madsen P, Nielsen U, Zhang Y, Lund M, Su G: Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genet Sel Evol 2012, 44(1):8. BioMed Central Full Text
• [44]Mrode R, Moore K, Winters M, Coffey M: Evaluating the impact of including residual polygenic effects in dairy genomic evaluations using Bayesian methods. Interbull Bulletin 2012, 46:109-114.
• [45]Sheng Z, Pettersson M, Hu X, Luo C, Qu H, Shu D, Shen X, Carlborg O, Li N: Genetic dissection of growth traits in a Chinese indigenous x commercial broiler chicken cross. BMC Genomics 2013, 14(1):151. BioMed Central Full Text
• [46]Henderson CR: Best linear unbiased estimation and prediction under a selection model. Biometrics 1975, 31(2):423-447.
• [47]Madsen P, Su G, Labouriau R, Christensen OF: DMU—A package for Analyzing Multivariate Mixed Models. 9th World Congr. Genet. Appl. Livest. Prod, Leipzig, Germany; 2010.
• [48]Gilmour AR, Thompson R, Cullis BR: Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 1995, 51(4):1440-1450.
• [49]Cohen J: Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Hillsdale, NJ; 1988.
• [50]Elston RC, Johnson W: Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach. John Wiley & Sons Ltd, United Kingdom; 2008.