猪主要经济性状的基因组选择研究

doi:10.11843/j.issn.0366-6964.2019.02.023

畜牧兽医学报 ›› 2019, Vol. 50 ›› Issue (2): 439-445.doi: 10.11843/j.issn.0366-6964.2019.02.023

猪主要经济性状的基因组选择研究

彭潇^1,2, 尹立林^1,2, 梅全顺^1,2, 王海燕^1,2, 刘小磊^1,2, 朱猛进^1,2, 李新云^1,2, 付亮亮^1,2*, 赵书红^1,2*

1. 华中农业大学, 农业动物遗传育种与繁殖教育部重点实验室, 武汉 430070;
2. 生猪健康养殖协同创新中心, 武汉 430070

收稿日期:2018-07-19 出版日期:2019-02-23 发布日期:2019-02-23
通讯作者: 付亮亮,主要从事小鼠和猪功能基因组研究,E-mail:fuliangliang2011@163.com;赵书红,主要从事动物基因组与育种研究,E-mail:shzhao@mail.hzau.edu.cn
作者简介:彭潇(1993-),男,硕士,山东临沂人,主要从事动物遗传育种研究,E-mail:pengxiao0201@163.com
基金资助:
国家生猪产业技术体系项目（CARS-35）；国家自然科学基金地区间国际重大合作项目（CGIAR31361140365）；国家自然科学基金（31672391）；华中农业大学大北农青年学者提升专项资助项目（2017DBN019）

A Study of Genome Selection Based on the Porcine Major Economic Traits

PENG Xiao^1,2, YIN Lilin^1,2, MEI Quanshun^1,2, WANG Haiyan^1,2, LIU Xiaolei^1,2, ZHU Mengjin^1,2, LI Xinyun^1,2, FU Liangliang^1,2*, ZHAO Shuhong^1,2*

1. Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
2. The Cooperative Innovation Center for Sustainable Pig Production, Wuhan 430070, China

Received:2018-07-19 Online:2019-02-23 Published:2019-02-23

摘要/Abstract

摘要：

旨在系统比较GBLUP、SSGBLUP、BayesA、BayesB、BayesC、BayesLASSO、BSLMM和BayesR等8种方法对猪重要经济性状基因组选择的准确性。本研究以本实验室收集的2 585头大白猪达100 kg日龄、达100 kg背膘厚和母猪乳头数3个性状为分析对象，结合猪50K基因芯片分型数据，以加性模型为基础，利用5倍交叉验证比较8种方法的基因组选择准确性。研究发现，基因组选择的准确性与不同性状估计遗传力呈正相关。交叉验证结果表明，预测准确性最高的性状为达100 kg日龄，但不同方法在不同性状中表现并不完全相同，在达100 kg日龄和达100 kg背膘厚中SSGBLUP基因组预测准确性均为最高，而在母猪乳头数中BayesA的基因组预测准确性最高。综上表明，对小样本开展基因组预测时，中、高等遗传力性状可以选择SSGBLUP方法，低等遗传力性状可以选择BayesA方法。如何优化和选择一种广泛适用于所有性状的方法，可能是未来研究的方向。

Abstract:

This study aimed to systematically compare the accuracy of genomic selection for important economic traits in pigs by using 8 models including GBLUP, SSGBLUP, BayesA, BayesB, BayesC, BayesLASSO, BSLMM and BayesR. The data of age at 100 kg, backfat thickness at 100 kg and teat numbers were collected from 2 585 Yorkshire sows and all the individuals were genotyped by using PorcineSNP50K Beadchip. The accuracy of genomic selection for the 8 models were compared by a 5-fold cross-validation procedure based on additive model. The results demonstrated that the accuracy of genomic selection were positively correlated with the calculated heritabilities of different traits. The cross-validation analysis indicated that the prediction accuracy of age at 100 kg was the highest among the 3 different traits, but different models performed dissimilarly in different traits. The prediction accuracy of SSGBLUP was the highest for both age at 100 kg and backfat thickness at 100 kg, and the prediction accuracy of BayesA was the highest for teat numbers. In conclusion, SSGBLUP model can be used for the traits with moderate and high heritabilities when conducting genomic prediction for small sample size and BayesA is suitable to the traits with low heritability. How to optimize and select a model that is applicable to all traits may be a research direction in the future.

中图分类号:

S114
S828.2

彭潇, 尹立林, 梅全顺, 王海燕, 刘小磊, 朱猛进, 李新云, 付亮亮, 赵书红. 猪主要经济性状的基因组选择研究[J]. 畜牧兽医学报, 2019, 50(2): 439-445.

PENG Xiao, YIN Lilin, MEI Quanshun, WANG Haiyan, LIU Xiaolei, ZHU Mengjin, LI Xinyun, FU Liangliang, ZHAO Shuhong. A Study of Genome Selection Based on the Porcine Major Economic Traits[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, 2019, 50(2): 439-445.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.xmsyxb.com/CN/10.11843/j.issn.0366-6964.2019.02.023

https://www.xmsyxb.com/CN/Y2019/V50/I2/439

参考文献

[1] HILL W G.Applications of population genetics to animal breeding,from wright,fisher and lush to genomic prediction[J]. Genetics, 2014,196(1):1-16.
[2] WALSH B.Quantitative genetics,version 3.0:where have we gone since 1987 and where are we headed?[J]. Genetica, 2009,136(2):213-223.
[3] SOBCZY-SKA M,BLICHARSKI T.Phenotypic and genetic variation in longevity of Polish Landrace sows[J].J Anim Breed Gene,2015,132(4):318-327.
[4] HARTLEY H O,RAO J N K.Maximum-likelihood estimation for the mixed analysis of variance model[J]. Biometrika, 1967,54(1-2):93-108.
[5] MEYER K.Restricted Maximum Likelihood to estimate variance components for mixed models with two random factors[J]. Genet Sel Evol,1987,19(1):49-68.
[6] BASELGA M,CAMACHO J.MIVQUE estimation of genetic parameters for traits of dam and young traits in a prolific species[J]. J Anim Breed Genet,1993,110(1-6):253-263.
[7] 张勤,丁向东,陈瑶生.种猪遗传评估技术研发与评估系统应用[J].中国畜牧杂志,2015,51(8):61-65,84.
ZHANG Q,DING X D,CHEN Y S.Development and application of swine genetic evaluation system in China[J].Chinese Journal of Animal Science,2015,51(8):61-65,84.(in Chinese)
[8] HENDERSON C R.Best linear unbiased estimation and prediction under a selection model[J].Biometrics, 1975,31(2):423-447.
[9] MEUWISEN T H,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics,2001,157(4):1819-1829.
[10] VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci,2008,91(11):4414-4423.
[11] AGUILAR I,MISZTAL I,JOHNSON D L,et al.Hot topic:a unified approach to utilize phenotypic,full pedigree,and genomic information for genetic evaluation of Holstein final score[J].J Dairy Sci,2010,93(2):743-752.
[12] VERBYLA K L,HAYES B J,BOWMAN P J,et al.Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle[J].Genet Res (Camb),2009,91(5):307-311.
[13] VERBYLA K L,BOWMAN P J,HAYES B J,et al.Sensitivity of genomic selection to using different prior distributions[J]. BMC Proc,2010,4(Suppl 1):S5.
[14] HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12(1):186.
[15] MOSER G,LEE S H,HAYES B J,et al.Simultaneous discovery,estimation and prediction analysis of complex traits using a bayesian mixture model[J].PLoS Genet,2015,11(4):e1004969.
[16] DE LOS CAMPOS G,NAYA H,GIANOLA D,et al.Predicting quantitative traits with regression models for dense molecular markers and pedigree[J].Genetics,2009,182(1):375-385.
[17] ZHOU X,CARBONETTA P,STEPHENS M.Polygenic modeling with bayesian sparse linear mixed models[J].PLoS Genet,2013,9(2):e1003264.
[18] CLARK S A,HICKEY J M,VAN DER WERF J H J.Different models of genetic variation and their effect on genomic evaluation[J].Genet Sel Evol,2011,43(1):18.
[19] CHRISTENSEN O F,MADSEN P,NIELSEN B,et al.Single-step methods for genomic evaluation in pigs[J].Animal, 2012, 6(10):1565-1571.
[20] DO D N,JANSS L L G,JENSEN J,et al.SNP annotation-based whole genomic prediction and selection:an application to feed efficiency and its component traits in pigs[J].J Anim Sci,2015,93(5):2056-2063.
[21] 王延晖,朱波,李俊雅.基于显性模型的基因组选择中贝叶斯方法研究[J].畜牧兽医学报,2017,48(1):60-67.
WANG Y H,ZHU B,LI J Y.Bayesian models including dominant effects for genomic selection[J].Acta Veterinaria et Zootechnica Sinica,2017,48(1):60-67.(in Chinese)
[22] ZHONG S Q,DEKKERS J C M,FERNANDO R L,et al.Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines:a Barley case study[J].Genetics,2009,182(1):355-364.
[23] HAYES B J,BOWMAN P J,CHAMBERLAIN A J,et al.Invited review:genomic selection in dairy cattle:progress and challenges[J]. J Dairy Sci,2009,92(2):433-443.
[24] 王晨,秦珂,薛明,等.全基因组选择在猪育种中的应用[J].畜牧兽医学报,2016,47(1):1-9.
WANG C,QIN K,XUE M,et al.Application of genomic selection in swine breeding[J].Acta Veterinaria et Zootechnica Sinica, 2016,47(1):1-9.(in Chinese)
[25] TOMIYAMA M,KANETANI T,TATSUKAWA Y,et al.Genetic parameters for preweaning and early growth traits in Berkshire pigs when creep feeding is used[J].J Anim Sci,2010,88(3):879-884.
[26] LI X W,KENNEDY B W.Genetic parameters for growth rate and backfat in Canadian Yorkshire,Landrace,Duroc,and Hampshire pigs[J].J Anim Sci,1994,72(6):1450-1454.
[27] 张锁宇,邱小田,丁向东,等.利用关联组估计中国大白、长白及杜洛克猪繁殖性状的遗传参数[J].畜牧兽医学报,2016,47(3):429-438.
ZHANG S Y,QIU X T,DING X D,et al.Estimation of genetic parameters by connecting groups for reproductive traits of Yorkshire,Landrace and Duroc pigs in China[J].Acta Veterinaria et Zootechnica Sinica,2016,47(3):429-438.(in Chinese)
[28] BRYNER S M,MABRY J W,BERTRAND J K,et al.Estimation of direct and maternal heritability and genetic correlation for backfat and growth rate in swine using data from centrally tested Yorkshire boars[J].J Anim Sci,1992, 70(6):1755-1759.
[29] ØDEGÅRD J,MEUWISSEN T H E.Estimation of heritability from limited family data using genome-wide identity-by-descent sharing[J].Genet Sel Evol,2012,44(1):16.
[30] LEGARRA A,AGUILAR I,MISZTAL I.A relationship matrix including full pedigree and genomic information[J].J Dairy Sci,2009,92(9):4656-4663.
[31] NADAF J,RIGGIO V,YU T P,et al.Effect of the prior distribution of SNP effects on the estimation of total breeding value[J].BMC Proc,2012,6(Suppl 2):S6.
[32] DONG L S,XIAO S J,WANG Q R,et al.Comparative analysis of the GBLUP,emBayesB,and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea)[J].BMC Genomics,2016,17(1):460.
[33] MOSER G,KHATKAR M S,HAYES B J,et al.Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers[J].Genet Sel Evol,2010,42(1):37.
[34] MEUWISSEN T H E,ODEGARD J,ANDERSEN-RANBERG I,et al.On the distance of genetic relationships and the accuracy of genomic prediction in pig breeding[J].Genet Sel Evol,2014,46(1):49.

猪主要经济性状的基因组选择研究

A Study of Genome Selection Based on the Porcine Major Economic Traits

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	尹立林, 马云龙, 项韬, 朱猛进, 余梅, 李新云, 刘小磊, 赵书红. 全基因组选择模型研究进展及展望[J]. 畜牧兽医学报, 2019, 50(2): 233-242.
[2]	董祎鑫, 李想, 亓建刚, 罗汉鹏, 窦金焕, 刘林, 李锡智, 王雅春. 中国荷斯坦牛产后0~35 d繁殖疾病遗传参数估计[J]. 畜牧兽医学报, 2019, 50(2): 280-286.