Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (23): 5125-5131.doi: 10.3864/j.issn.0578-1752.2021.23.016

• ANIMAL SCIENCE·VETERINARY SCIENCE·RESOURCE INSECT • Previous Articles     Next Articles

Comparison of Genomic Prediction Accuracy for Meat Type Chicken Carcass Traits Based on GBLUP and BayesB Method

ZHU Mo1,2(),ZHENG MaiQing2,CUI HuanXian2,ZHAO GuiPing2(),LIU Yang1()   

  1. 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095
    2State Key Laboratory of Animal Nutrition/Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193
  • Received:2020-11-10 Accepted:2021-04-07 Online:2021-12-01 Published:2021-12-06
  • Contact: GuiPing ZHAO,Yang LIU E-mail:2018105017@njau.edu.cn;zhaoguiping@caas.cn;yangliu@njau.edu.cn

RichHTML

38

PDF

250

Abstract:

【Objective】 Predicting the breeding value is the core content of livestock breeding and the accurate predicting of breeding value is an important approach to improve the selection accuracy of breeding. Carcass traits are economic important traits for broilers. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool because of its accurate prediction of breeding values of animals without own phenotypic information. At present, there are few reports on genomic selection on carcass traits for broilers. The purpose of this study was to compare the accuracy of genomic prediction of broiler carcass traits by using GBLUP and BayesB method. 【Method】 This study investigated the efficiency of genomic prediction in a white-feathered broiler population, which was collected from 3 362 white-feathered broilers. Five carcass traits, including breast muscle rate (BrR), breast muscle weight (BrW), carcass weight (CW), thigh muscle rate (ThR) and thigh muscle weight (ThW) were taken into account. All the individuals were genotyped with “IASCHICK” chicken 55 K SNP array. PLINK software was used for the quality control of genotype data. GBLUP and BayesB method were implemented with ASReml and hibayes package in R software, respectively. Generation validation were utilized to evaluate the accuracy of genomic prediction for twenty replicates for each trait.【Result】The results showed that the accuracy of genomic selection was almost positively correlated with the heritability of each trait. The validation results indicated that the prediction accuracy of BrR was the highest with GBLUP method, and the accuracies of BrR, BrW, CW, ThR and ThW were 0.3262, 0.2871, 0.2780, 0.2153, and 0.2126, respectively. Meanwhile, the accuracy of BrR was the highest with BayesB method, and the accuracies of BrR, BrW, CW, ThR and ThW were 0.3765, 0.2257, 0.1376, 0.2525, and 0.2844, respectively. The results showed that the prediction accuracy of BayesB method was slightly higher than that of GBLUP method. It took about 1 h and 7 h for GBLUP and BayesB method, respectively, to carry out the calculating procedure for one trait. 【Conclusion】 In conclusion, the prediction accuracy of BrR, ThR and ThW with BayesB method was higher than that with GBLUP method, while the prediction accuracy of BrW and CW with GBLUP method was higher than that with BayesB method. However, the calculating time for BayesB method was longer than that for GBLUP method. In breeding practice, the balance of prediction accuracy and computational efficiency should be comprehensively considered to predict the genomic estimated breeding value.

Key words: chicken, genomic selection, generation validation, accuracy

Table 1

Descriptive statistics for each carcass trait"

性状
Trait		 个体数
Count		 最小值
Minimum		 最大值
Maximum		 平均值
Mean		 标准差
Standard deviation		 变异系数
Coefficient of variation (%)
胸肌率BrR (%) 2409 15.34 26.79 20.97 1.90 9.05
胸肌重BrW (g) 2409 220.20 686.40 434.84 86.34 19.86
屠体重CW (g) 2421 1165.20 2650.00 1879.51 268.18 14.27
腿肌率ThR (%) 2405 18.50 26.72 22.51 1.29 5.71
腿肌重ThW (g) 2405 242.80 691.60 464.53 74.73 16.09

Table 2

Results of heritability for each carcass trait"

性状
Trait		 遗传力(标准误) Heritability (SE)
系谱亲缘关系
Pedigree relationship		 全基因组亲缘关系
Genomic relationship
BrR 0.40 (0.05) 0.37 (0.03)
BrW 0.34 (0.05) 0.26 (0.03)
CW 0.35 (0.05) 0.20 (0.03)
ThR 0.25 (0.05) 0.18 (0.03)
ThW 0.31 (0.05) 0.18 (0.03)

Table 3

Results of generation validation based on GBLUP and BayesB method for each carcass trait"

性状
Trait		 准确性Accuracy
GBLUP BayesB
BrR 0.3262 0.3765
BrW 0.2871 0.2257
CW 0.2780 0.1376
ThR 0.2153 0.2525
ThW 0.2126 0.2844

Fig. 1

The comparison of computing performance of GBLUP and BayesB method for each carcass trait"

[1] HENDERSON C R. Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975, 31(2):423-447.
doi: 10.2307/2529430
[2] LANDE R, THOMPSON R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics, 1990, 124(3):743.
doi: 10.1093/genetics/124.3.743
[3] GODDARD M E, HAYES B J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 2009, 10(6):381-391.
doi: 10.1038/nrg2575
[4] MEUWISSEN T H, HAYES B J, GODDARD M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4):1819-1829.
doi: 10.1093/genetics/157.4.1819
[5] GODDARD M E, HAYES B J, MEUWISSEN T H E. Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie, 2011, 128(6):409-421.
[6] YANG J, BENYAMIN B, MCEVOY B P, GORDON S, HENDERS A K, NYHOLT D R, MADDEN P A, HEATH A C, MARTIN N G, MONTGOMERY G W, GODDARD M E, VISSCHER P M. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 2010, 42(7):565-569.
doi: 10.1038/ng.608
[7] VANRADEN P M. Efficient methods to compute genomic predictions. Journal of Dairy Science, 2008, 91(11):4414-4423.
doi: 10.3168/jds.2007-0980
[8] AGUILAR I, MISZTAL I, JOHNSON D, LEGARRA A, TSURUTA S, LAWLOR T. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 2010, 93(2):743-752.
doi: 10.3168/jds.2009-2730
[9] HABIER D, FERNANDO R L, KIZILKAYA K, GARRICK D J. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 2011, 12:186.
doi: 10.1186/1471-2105-12-186
[10] YI N, XU S. Bayesian LASSO for quantitative trait loci mapping. Genetics, 2008, 179(2):1045-1055.
doi: 10.1534/genetics.107.085589
[11] International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 2004, 432(7018):695-716.
doi: 10.1038/nature03154
[12] KRANIS A, GHEYAS A A, BOSCHIERO C, TURNER F, YU L, SMITH S, TALBOT R, PIRANI A, BREW F, KAISER P, HOCKING P M, FIFE M, SALMON N, FULTON J, STROM T M, HABERER G, WEIGEND S, PREISINGER R, GHOLAMI M, QANBARI S, SIMIANER H, WATSON K A, WOOLLIAMS J A, BURT D W. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics, 2013, 14:59.
doi: 10.1186/1471-2164-14-59
[13] LIU R, XING S, WANG J, ZHENG M, CUI H, CROOIJMANS R P M A, LI Q, ZHAO G, WEN J. A new chicken 55K SNP genotyping array. BMC Genomics, 2019, 20(1):410.
doi: 10.1186/s12864-019-5736-8
[14] ZHANG Z, XU Z Q, LUO Y Y, ZHANG H B, GAO N, HE J L, JI C L, ZHANG D X, LI J Q, ZHANG X Q. Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population. Journal of Animal Science, 2017, 95(1):72-80.
[15] LIU T, QU H, LUO C, SHU D, WANG J, LUND M S, SU G. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genetics, 2014, 15:110.
doi: 10.1186/s12863-014-0110-y
[16] WOLC A, DROBIK-CZWARNO W, JANKOWSKI T, ARANGO J, SETTAR P, FULTON J E, FERNANDO R L, GARRICK D J, DEKKERS J C M. Accuracy of genomic prediction of shell quality in a White Leghorn line. Poultry Science, 2020, 99(6):2833-2840.
doi: 10.1016/j.psj.2020.01.019
[17] CHEN C Y, MISZTAL I, AGUILAR I, TSURUTA S, MEUWISSEN T H E, AGGREY S E, WING T, MUIR W M. Genome-wide marker- assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. Journal of Animal Science, 2011, 89(1):23-28.
doi: 10.2527/jas.2010-3071
[18] GONZáLEZ-RECIO O, GIANOLA D, ROSA G J, WEIGEL K A, KRANIS A. Genome-assisted prediction of a quantitative trait measured in parents and progeny: Application to food conversion rate in chickens. Genetics, Selection, Evolution: GSE, 2009, 41:3.
doi: 10.1186/1297-9686-41-3
[19] LIU T, LUO C, WANG J, MA J, SHU D, LUND M S, SU G, QU H. Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens. PLoS ONE, 2017, 12(3):e0173620.
doi: 10.1371/journal.pone.0173620
[20] PURCELL S, NEALE B, TODD-BROWN K, THOMAS L, FERREIRA M A R, BENDER D, MALLER J, SKLAR P, DE BAKKER P I W, DALY M J, SHAM P C. PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 2007, 81(3):559-575.
doi: 10.1086/519795
[21] GILMOUR A R, GOGEL B J, CULLIS B R, WELHAM S, THOMPSON R. ASReml user guide release 4.1 structural specification. Hemel Hempstead, UK: VSN International Ltd, 2015.
[22] 李晶, 王杰, 康慧敏, 刘冉冉, 李华, 赵桂苹. 基于BLUP和GBLUP方法估计北京油鸡胴体和肉质性状遗传参数的差异. 畜牧兽医学报, 2020, 51(1):35-42.
LI J, WANG J, KANG H M, LIU R R, LI H, ZHAO G P. The difference of genetic parameters for carcass and meat quality traits by BLUP and GBLUP methods in Beijing You chicken. Acta Veterinaria et Zootechnica Sinica, 2020, 51(1):35-42. (in Chinese)
[23] ØDEGåRD J, MEUWISSEN T H E. Estimation of heritability from limited family data using genome-wide identity-by-descent sharing. Genetics, Selection, Evolution : GSE, 2012, 44:16.
doi: 10.1186/1297-9686-44-16
[24] LEE S H, GODDARD M E, VISSCHER P M, VAN DER WERF J H. Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits. Genetics, Selection, Evolution : GSE, 2010, 42:22.
doi: 10.1186/1297-9686-42-22
[25] VEERKAMP R F, MULDER H A, THOMPSON R, CALUS M P L. Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. Journal of Dairy Science, 2011, 94(8):4189-4197.
doi: 10.3168/jds.2011-4223
[26] ZENG J, PSZCZOLA M, WOLC A, STRABEL T, FERNANDO R L, GARRICK D J, DEKKERS J C M. Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods. BMC Proceedings, 2012, 6(2):S7.
[27] TENG J, GAO N, ZHANG H, LI X, LI J, ZHANG H, ZHANG X, ZHANG Z. Performance of whole genome prediction for growth traits in a crossbred chicken population. Poultry Science, 2019, 98(5):1968-1975.
doi: 10.3382/ps/pey604
[28] LOPES M S, BOVENHUIS H, VAN SON M, NORDBø Ø, GRINDFLEK E H, KNOL E F, BASTIAANSEN J W M. Using markers with large effect in genetic and genomic predictions. Journal of Animal Science, 2017, 95(1):59-71.
[29] MOORE J K, MANMATHAN H K, ANDERSON V A, POLAND J A, MORRIS C F, HALEY S D. Improving genomic prediction for pre-harvest sprouting tolerance in wheat by weighting large-effect quantitative trait loci. Crop Science, 2017, 57(3):1315-1324.
doi: 10.2135/cropsci2016.06.0453
[30] TIEZZI F, MALTECCA C. Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix. Genetics, Selection, Evolution: GSE, 2015, 47:24.
doi: 10.1186/s12711-015-0100-1
[31] ZHANG Z, ERBE M, HE J, OBER U, GAO N, ZHANG H, SIMIANER H, LI J. Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix. G3 (Bethesda, Md.), 2015, 5(4):615-627.
[32] ZHANG Z, OBER U, ERBE M, ZHANG H, GAO N, HE J, LI J, SIMIANER H. Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies. PLoS ONE, 2014, 9(3):e93017.
doi: 10.1371/journal.pone.0093017
[1] SHU JingTing,SHAN YanJu,JI GaiGe,ZHANG Ming,TU YunJie,LIU YiFan,JU XiaoJun,SHENG ZhongWei,TANG YanFei,LI Hua,ZOU JianMin. Relationship Between Expression Levels of Guangxi Partridge Chicken m6A Methyltransferase Genes, Myofiber Types and Myogenic Differentiation [J]. Scientia Agricultura Sinica, 2022, 55(3): 589-601.
[2] ZHANG YaNan,JIN YongYan,ZHUANG ZhiWei,WANG Shuang,XIA WeiGuang,RUAN Dong,CHEN Wei,ZHENG ChunTian. Comparison of Shell Mechanical Property, Ultrastructure and Component Between Chicken and Duck Eggs [J]. Scientia Agricultura Sinica, 2022, 55(24): 4957-4968.
[3] TU YunJie,JI GaiGe,ZHANG Ming,LIU YiFan,JU XiaoJun,SHAN YanJu,ZOU JianMin,LI Hua,CHEN ZhiWu,SHU JingTing. Screening of Wnt3a SNPs and Its Association Analysis with Skin Feather Follicle Density Traits in Chicken [J]. Scientia Agricultura Sinica, 2022, 55(23): 4769-4780.
[4] HUANG XunHe,WENG ZhuoXian,LI WeiNa,WANG Qing,HE DanLin,LUO Wei,ZHANG XiQuan,DU BingWang. Genetic Diversity of Indigenous Yellow-Feathered Chickens in Southern China Inferred from Mitochondrial DNA D-Loop Region [J]. Scientia Agricultura Sinica, 2022, 55(22): 4526-4538.
[5] WANG ZhePeng,ZHOU WenXin,HE JunXi,HU QiaoYan,ZHAO JiaYue. Association of Levels of Cholecystokinin A Receptor Expression and Sequence Variants with Feed Conversion Efficiency of Lueyang Black-Boned Chicken [J]. Scientia Agricultura Sinica, 2022, 55(22): 4539-4549.
[6] GUO Jun,WANG KeHua,HAN Wei,DOU TaoCun,WANG XingGuo,HU YuPing,MA Meng,QU Liang. Analysis of Indirect Genetic Effects on Body Weight of 42 Day-Old Rugao Yellow Chickens [J]. Scientia Agricultura Sinica, 2022, 55(19): 3854-3861.
[7] YaTing JIA,HuiHui HU,YaJun ZHAI,Bing ZHAO,Kun HE,YuShan PAN,GongZheng HU,Li YUAN. Molecular Mechanism of Regulation by H-NS on IncFⅡ Plasmid Transmission of Multi-drug Resistant Chicken Escherichia coli [J]. Scientia Agricultura Sinica, 2022, 55(18): 3675-3684.
[8] ZHOU Jun,LIN Qing,SHAO BaoQuan,REN DuanYang,LI JiaQi,ZHANG Zhe,ZHANG Hao. Evaluating the Application Effect of Single-Step Genomic Selection in Pig Populations [J]. Scientia Agricultura Sinica, 2022, 55(15): 3042-3049.
[9] LÜ ZhiWei,DU Kang,ZHOU ZhiGuo,ZHAO WenQing,HU Wei,ZHAO JianMing,ZHU SuQin,WANG YouHua. Research on Senescence Process and Suitable Indicators of Maize Ear Leaves [J]. Scientia Agricultura Sinica, 2022, 55(12): 2311-2323.
[10] ZHANG NingBo,HAN ZhaoQing,JIN TaiHua,ZHUANG GuiYu,LI JiongKui,ZHENG QuanSheng,LI YongZhu. Comparison Analysis on Eggshell Quality, Biochemical Index of Calcium Metabolism and Calcium Binding Protein CaBP-D28k mRNA Expression Between Langya Chicken and Its Synthetic Lines [J]. Scientia Agricultura Sinica, 2021, 54(9): 2017-2026.
[11] WANG GuangYu,LI Qing,TANG WenQian,WANG HuHu,XU XingLian,QIU WeiFen. Effects of nuoB on Physiological Properties of Pseudomonas fragi and Its Spoilage Potential in Chilled Chicken [J]. Scientia Agricultura Sinica, 2021, 54(8): 1761-1771.
[12] YuYan YANG,YaoWen LI,Shuang XING,MinHong ZHANG,JingHai FENG. The Temperature-Humidity Index Estimated by the Changes of Surface Temperature of Broilers at Different Ages [J]. Scientia Agricultura Sinica, 2021, 54(6): 1270-1279.
[13] TANG XiuJun,FAN YanFeng,JIA XiaoXu,GE QingLian,LU JunXian,TANG MengJun,HAN Wei,GAO YuShi. Genetic Diversity and Origin Characteristics of Chicken Species Based on Mitochondrial DNA D-loop Region [J]. Scientia Agricultura Sinica, 2021, 54(24): 5302-5315.
[14] TANG ZhenShuang,YIN Dong,YIN LiLin,MA YunLong,XIANG Tao,ZHU MengJin,YU Mei,LIU XiaoLei,LI XinYun,QIU XiaoTian,ZHAO ShuHong. To Evaluate the “Two-Step” Genomic Selection Strategy in Pig by Simulation [J]. Scientia Agricultura Sinica, 2021, 54(21): 4677-4684.
[15] YU BaoJun,DENG ZhanZhao,XIN GuoSheng,CAI ZhengYun,GU YaLing,ZHANG Juan. Correlation Analysis of Inosine Monophosphate Specific Deposition Related LNC_003828-gga-miR-107-3P-MINPP1 in Jingyuan Chicken Muscle Tissue [J]. Scientia Agricultura Sinica, 2021, 54(19): 4229-4242.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd