Comparison of genomic prediction methods for early growth traits of Inner Mongolia cashmere goats based on multi trait models
Received date: 2024-01-31
Revised date: 2024-04-25
Online published: 2024-05-06
Supported by
National Key Research and Development Program(2022YFE0113300);National Key Research and Development Program(2022YFD1300201);National Key Research and Development Program(2022YFD1300204);Inner Mongolia Autonomous Region Science and Technology Research Project(2021GG0086);Inner Mongolia Autonomous Region Higher Education Youth Science and Technology Talent Support Program(NJYT22038);Ministry of Finance and the Ministry of Agriculture and Rural Affairs, the National Technical System for Woolen Sheep Industry(CARS-39);High Level Achievement Cultivation Project of Inner Mongolia Agricultural University(QT202201);Inner Mongolia Autonomous Region Higher Education Innovation Team Development Program(NMGIRT2322);Basic Research Expenses of Universities Directly of Inner Mongolia Autonomous Region(BR220112)
Inner Mongolia cashmere goat is an excellent livestock breed formed through long-term natural selection and artificial breeding, and is currently a world-class dual-purpose breed producing cashmere and meat. Multi trait animal model is considered to significantly improve the accuracy of genetic evaluation in livestock and poultry, enabling indirect selection between traits. In this study, the pedigree, genotype, environment, and phenotypic records of early growth traits of Inner Mongolia cashmere goats were used to build multi trait animal model., Then three methods including ABLUP, GBLUP, and ssGBLUP wereused to estimate the genetic parameters and genomic breeding values of early growth traits (birth weight, weaning weight, average daily weight gain before weaning, and yearling weight). The accuracy and reliability of genomic estimated breeding value are further evaluated using the five fold cross validation method. The results showed that the heritability of birth weight estimated by three methods was 0.13-0.15, the heritability of weaning weight was 0.13-0.20, heritability of daily weight gain before weaning was 0.11-0.14, and the heritability of yearling weight was 0.09-0.14, all of which belonged to moderate to low heritability. There is a strong positive genetic correlation between weaning weight and daily weight gain before weaning, daily weight gain before weaning and yearling weight, with correlation coefficients of 0.77-0.79 and 0.56-0.67, respectively. The same pattern was found in phenotype correlation among traits. The accuracy of the estimated breeding values by ABLUP, GBLUP, and ssGBLUP methods for birth weight is 0.5047, 0.6694, and 0.7156, respectively; the weaning weight is 0.6207, 0.6456, and 0.7254, respectively; the daily weight gain before weaning was 0.6110, 0.6855, and 0.7357 respectively; and the yearling weight was 0.6209, 0.7155, and 0.7756, respectively. In summary, the early growth traits of Inner Mongolia cashmere goats belong to moderate to low heritability, and the speed of genetic improvement is relatively slow. The genetic improvement of other growth traits can be achieved through the selection of weaning weight. The ssGBLUP method has the highest accuracy and reliability in estimating genomic breeding value of early growth traits in Inner Mongolia cashmere goats, and is significantly higher than that from ABLUP method, indicating that it is the best method for genomic breeding of early growth weight in Inner Mongolia cashmere goats.
Linyu Gao, Qi Xu, Yuxiao He, Haijiao Xi, Yifan Liu, Tao Zhang, Jinquan Li, Yanjun Zhang, Ruijun Wang, Qi Lü, Bujun Mei, Rui Su, Zhiying Wang . Comparison of genomic prediction methods for early growth traits of Inner Mongolia cashmere goats based on multi trait models[J]. Hereditas(Beijing), 2024 , 46(5) : 421 -430 . DOI: 10.16288/j.yczz.24-036
| [1] | Su R, Gong G, Zhang LT, Yan XC, Wang FH, Zhang L, Qiao X, Li XK, Li JQ. Screening the key genes of hair follicle growth cycle in Inner Mongolian cashmere goat based on RNA sequencing. Arch Anim Breed, 2020, 63(1): 155-164. |
| [2] | Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819-1829. |
| [3] | Sandhu KS, Mihalyov PD, Lewien MJ, Pumphrey MO, Carter AH. Combining genomic and phenomic information for predicting grain protein content and grain yield in spring wheat. Front Plant Sci, 2021, 12: 613300. |
| [4] | Villar-Hernández BJ, Pérez-Elizalde S, Martini JWR, Toledo F, Perez-Rodriguez P, Krzuse M, García-Calvillo ID, Covarrubias-Pazaran G, Crossa J. Corrigendum to: application of multi-trait bayesian decision theory for parental genomic selection. G3 (Bethesda), 2021, 11(3): jkab034. |
| [5] | Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975, 31(2): 423-447. |
| [6] | Nilforooshan MA, Jakobsen JH, Fikse WF, Berglund B, Jorjani H. Application of a multiple-trait, multiple-country genetic evaluation model for female fertility traits. J Dairy Sci, 2010, 93(12): 5977-5986. |
| [7] | Galluzzo F, Visentin G, van Kaam JBCHM, Finocchiaro R, Biffani S, Costa A, Marusi M, Cassandro M. Genetic evaluation of gestation length in Italian Holstein breed. J Anim Breed Genet, 2024, 141(2): 113-123. |
| [8] | Adekale D, Alkhoder H, Liu ZT, Segelke D, Tetens J. Single-step SNPBLUP evaluation in six German beef cattle breeds. J Anim Breed Genet, 2023, 140(5): 496-507. |
| [9] | Lee SH, Lee SH, Park HB, Kim JM. Estimation of genetic parameters for pork belly traits. Anim Biosci, 2023, 36(8): 1156-1166. |
| [10] | Zhang X, Tsuruta S, Andonov S, Lourenco DAL, Sapp RL, Wang C, Misztal I. Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. Poult Sci, 2018, 97(5): 1511-1518. |
| [11] | Butler DG, Cullis BR, Gilmour AR, Gogel BG, Thompson R.ASReml-R Reference Manual Version 4. VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. 2017. |
| [12] | Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal, 2012, 6(10): 1565-1571. |
| [13] | Kong J, Luan S, Tan J, Sui J, Luo K, Li XP, Dai P, Meng XH, Lu X, Chen BL, Cao JW, Cao BX. Progress of study on penaeid shrimp selective breeding. J Ocean Univ China(Nat Sci Ed), 2020, 50(9): 81-97. |
| 孔杰, 栾生, 谭建, 隋娟, 罗坤, 李旭鹏, 代平, 孟宪红, 卢霞, 陈宝龙, 曹家旺, 曹宝祥. 对虾选择育种研究进展. 中国海洋大学学报(自然科学版), 2020, 50 (9): 81-97. | |
| [14] | Aguilar I, Misztal I, Tsuruta S, Wiggans GR, Lawlor TJ. Multiple trait genomic evaluation of conception rate in Holsteins. J Dairy Sci, 2011, 94(5): 2621-2624. |
| [15] | Tsuruta S, Misztal I, Aguilar I, Lawlor TJ. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J Dairy Sci, 2011, 94(8): 4198-4204. |
| [16] | Guo G, Zhao FP, Wang YC, Zhang Y, Du LX, Su GS. Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet, 2014, 15: 30. |
| [17] | Roy R, Mandal A, Notter DR. Estimates of (co)variance components due to direct and maternal effects for body weights in Jamunapari goats. Animal, 2008, 2(3): 354-359. |
| [18] | Magotra A, Bangar YC, Chauhan A, Malik BS, Malik ZS. Influence of maternal and additive genetic effects on offspring growth traits in Beetal goat. Reprod Domest Anim, 2021, 56(7): 983-991. |
| [19] | Di J, Zhang Y, Tian KC, Lazate, Liu JF, Xu XM, Zhang YJ, Zhang TH. Estimation of (co)variance components and genetic parameters for growth and wool traits of Chinese superfine merino sheep with the use of a multi-trait animal model. Livest Sci, 2011, 138(1-3): 278-288. |
| [20] | Gowane GR, Chopra A, Prakash V, Arora AL. Estimates of (co)variance components and genetic parameters for growth traits in Sirohi goat. Trop Anim Health Prod, 2011, 43(1): 189-198. |
| [21] | Barazandeh A, Moghbeli SM, Vatankhah M, Mohammadabadi M. Estimating non-genetic and genetic parameters of pre-weaning growth traits in Raini cashmere goat. Trop Anim Health Prod, 2012, 44(4): 811-817. |
| [22] | Tesema Z, Alemayehu K, Getachew T, Kebede D, Deribe B, Taye M, Tilahun M, Lakew M, Kefale A, Belayneh N, Zegeye A, Yizengaw L. Estimation of genetic parameters for growth traits and Kleiber ratios in Boer × Central Highland goat. Trop Anim Health Prod, 2020, 52(6): 3195-3205. |
| [23] | 张沅. 家畜育种学. 北京: 中国农业出版社, 2001. |
| [24] | Bangar YC, Magotra A, Yadav AS. Estimates of covariance components and genetic parameters for growth, average daily gain and Kleiber ratio in Harnali sheep. Trop Anim Health Prod, 2020, 52(5): 2291-2296. |
| [25] | Wang RJ, Liu Y, Shi Y, Qi YP, Li YB, Wang ZY, Zhang YJ, Zhao YH, Su R, Li JQ. Study of genetic parameters for pre-weaning growth traits in inner Mongolia white Arbas cashmere goats. Front Vet Sci, 2023, 9: 1026528. |
| [26] | 王大广, 赵志辉, 赵玉民, 鲍志鸿. 萨福克羊表型测定值遗传参数的估计. 中国草食动物, 2003, (S1): 67-68. |
| [27] | Baloche G, Legarra A, Sallé G, Larroque H, Astruc J-M, Robert-Granié C, Barillet F. Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. J Dairy Sci, 2014, 97(2): 1107-1116. |
| [28] | Negro A, Cesarani A, Cortellari M, Bionda A, Fresi P, Macciotta NPP, Grande S, Biffani S, Crepaldi P. A comparison of genetic and genomic breeding values in Saanen and Alpine goats. Animal, 2024, 18(4): 101118. |
| [29] | Wang FH.Design of goat SNP chip and genome-wide association analysis and genome selection of important economic traits in Inner Mongolia cashmere goats[Dissertation]. Inner Mongolia Agricultural University, 2021. |
| 王凤红.山羊SNP芯片设计与内蒙古绒山羊重要经济性状全基因组关联分析及基因组选择研究[学位论文]. 内蒙古农业大学, 2021. |
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