基于显性模型的基因组选择中贝叶斯方法研究

doi:10.11843/j.issn.0366-6964.2017.01.007

摘要/Abstract

摘要：

本研究旨在探索显性效应对基因组育种值估计准确性的影响。基于贝叶斯A模型，根据加性效应和显性效应相关性，提出两种子模型：1）加性效应和显性效应相互独立的BayesAD1模型；2）显性系数（Dominant coefficients）与加性效应的绝对值相互独立，并且显性系数服从正态分布的BayesAD2模型。通过模拟数据比较加性效应模型BayesAD0和两种显性效应模型下基因组估计育种值（GEBV）的准确性，并且研究不同数量性状基因座（Quantitative trait locus, QTL）数、全同胞家系内个体数和加性方差与显性方差的比重对GEBV准确性的影响。结果表明，显性模型可以减缓随着世代变化GEBV的准确性降低的趋势。另外，显性方差的比重越大，对GEBV的准确性影响越大。当加性方差与显性方差之比达到0.25时，BayesAD2有较大优势，分别比BayesAD1和BayesAD0准确性高20.3%和28.4%。全同胞数越多，GEBV的准确性越高，QTL数目增多，GEBV估计的准确性随之下降。结果显示，对显性效应占较大比重即低遗传力的性状进行育种值估计时，考虑显性效应可以提高育种值估计准确性。

Abstract:

The aim of this study was to investigate the impact of dominant effects on the predictive accuracy of genomic breeding values. Based on the dependency between additive and dominant effects using BayesA model, we proposed two submodels:1) the additive effect and dominant effect were independent of each other in BayesAD1 model; 2) the dominant coefficient and absolute values of additive effect were independent of each other in BayesAD2 model, in which the dominant coefficient followed normal distribution. Using simulated datasets, we compared the predictive accuracy of genomic estimated breeding value (GEBV) among the additive model (BayesAD0) and dominant models (BayesAD1 and BayesAD2). We further investigated the effect of number of QTLs (quantitative trait loci), size of full-sibs family and ratio of additive variance to dominant variance on predictive accuracy of GEBV. The results showed that the dominant models slowed down the declining of the predictive accuracy of GEBV in subsequent generations. Moreover, the predictive accuracy of GEBV increased as the ratio of dominant variance increase. When the ratio of additive variance to dominant variance reached 0.25, BayesAD2 was 20.3% and 28.4% higher than the accuracy of the BayesAD1 and BayesAD0, respectively. In addition, the size of full-sibs family affected the predictive accuracy of GEBV positively. And an increase in the number of QTL was accompanied by a reduction on the predictive accuracy of GEBV. These results indicate that a better prediction of genetic values is intended, when the dominant variance are large just as low-heritability traits.

中图分类号:

S813.1

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 Yan-hui, ZHU Bo, LI Jun-ya. Bayesian Models including Dominant Effects for Genomic Selection[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, 2017, 48(1): 60-67.

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.0366-6964.2017.01.007

https://www.xmsyxb.com/CN/Y2017/V48/I1/60

参考文献

［1］MEUWISSEN T H E, HAYES B J, GODDARD M E. Prediction of total genetic value using genome-wide dense marker maps ［J］. Genetics，2001，157（4）：1819-1829.
［2］GIANOLA D, DE LOS CAMPOS G, HILL W G, et al. Additive genetic variability and the Bayesian Alphabet ［J］.Genetics，2009,183（1）：347-363.
［3］王重龙, 丁向东, 刘剑锋,等. 基因组育种值估计的贝叶斯方法［J］. 遗传，2014，36（2）：111-118.
WANG C L, DING X D, LIU J F, et al. Bayesian methods for genomic breeding value estimation［J］. Hereditas (Beijing)，2014， 36（2）：111-118.（in Chinese）
［4］BOLORMAA S, PRYCE J E, ZHANG Y, et al. Non-additive genetic variation in growth, carcass and fertility traits of beef cattle ［J］. Genet Sel Evol，2015，47：26.
［5］SU G, CHRISTENSEN O, OSTERSEN T, et al. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers ［J］. PLoS One，2012，7（9）：e45293.
［6］DE ALMEIDA FILHO J E, GUIMARAES J F R, E SILVA F F, et al. The contribution of dominance to phenotype prediction in a pine breeding and simulated population ［J］. Heredity，2016，117（1）：33-41.
［7］LEE S H, VAN DER WERF J, HAYES B J, et al. Predicting unobserved phenotypes for complex traits from whole-genome SNP data ［J］. PLoS Genet，2008，4（10）：e1000231.
［8］GUO X, CHRISTENSEN O F, OSTERSEN T, et al. Genomic prediction using models with dominance and imprinting effects for backfat thickness and average daily gain in Danish Duroc pigs ［J］. Genet Sel Evol，2016，48（1）：67.
［9］DOS SANTOS J P R, VASCONCELLOS R C D C, PIRES L P M, et al. Inclusion of dominance effects in the multivariate GBLUP model ［J］. PLoS One，2016，11（4）：e0152045.
［10］WELLMANN R, BENNEWITZ J. Bayesian models with dominance effects for genomic evaluation of quantitative traits ［J］. Genet Res（Camb），2012， 94（1）：21-37.
［11］ZENG Z B, WANG T, ZOU W. Modeling quantitative trait loci and interpretation of models ［J］. Genetics，2005，169（3）：1711-1725.
［12］HENDERSON C. Best linear unbiased prediction of nonadditive genetic merits［J］. J Anim Sci，1985，60：111-117.
［13］DENIS M, BOUVET J M. Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding ［J］. Tree Genet Genomes，2013，9（1）：37-51.
［14］VITEZICA Z G, VARONA L, LEGARRA A. On the additive and dominant variance and covariance of individuals within the genomic selection scope ［J］. Genetics，2013，195（4）：1223-1230.
［15］WELLMANN R, BENNEWITZ J. The contribution of dominance to the understanding of quantitative genetic variation ［J］. Genet Res（Camb），2011，93（2）：139-154.
［16］FINLEY A O, BANERJEE S, WALDMANN P, et al. Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets ［J］. Biometrics，2009，65（2）:441-451.
［17］ZHANG Z, LIU J, DING X, et al. Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix ［J］. PLoS One，2010， 5（9）:e12648.
［18］WITTENBURG D, MELZER N, REINSCH N. Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers ［J］. BMC Genet，2011，12：74.
［19］BENNEWITZ J, MEUWISSEN T H E. The distribution of QTL additive and dominance effects in porcine F2 crosses ［J］. J Anim Breed Genet，2010，127（3）：171-179.
［20］HAYES B J, GODDARD M. The distribution of the effects of genes affecting quantitative traits in livestock ［J］. Genet Sel Evol，2001，33（3）：209-229.
［21］NISHIO M, SATOH M. Including dominance effects in the genomic BLUP method for genomic evaluation ［J］.PLoS One，2014，9（1）：e85792.
［22］LEE S H, VAN DER WERF J H. Using dominance relationship coefficients based on linkage disequilibrium and linkage with a general complex pedigree to increase mapping resolution ［J］. Genetics，2006，174（2）：1009-1016.
［23］ZENG J, TOOSI A, FERNANDO R L, et al. Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action ［J］. Genet Sel Evol，2013，45：11.
［24］ERTL J, LEGARRA A, VITEZICA Z G, et al. Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle ［J］. Genet Sel Evol，2014，46：40.
［25］CABALLERO A, KEIGHTLEY P D. A pleiotropic nonadditive model of variation in quantitative traits ［J］. Genetics，1994，138（3）：883-900.
［26］MEUWISSEN T H E, GODDARD M. Accurate prediction of genetic values for complex traits by whole-genome resequencing ［J］. Genetics，2010，185（2）：623-631.
［27］DUANGJINDA M, BERTRAND J, MISZTAL I, et al. Estimation of additive and nonadditive genetic variances in Hereford, Gelbvieh, and Charolais by method R ［J］. J Anim Sci，2001， 79（12）：2997-3001.

[1]	李超, 赵雪艳, 王永军, 王彦平, 任一帆, 李菁璇, 王怀中, 王继英, 宋勤叶. 莱芜猪和杜长大猪盲肠和结肠微生物菌群结构组成和功能分析[J]. 畜牧兽医学报, 2023, 54(12): 5033-5045.
[2]	郭艳丽, 李可强, 白少川, 王涛, 王德贺, 王麒, 李兰会. ALV-E的结构、活性调控以及对宿主功能的影响[J]. 畜牧兽医学报, 2023, 54(7): 2683-2691.
[3]	王开明, 喻宗岗, 徐雪莉, 艾妮妮, 李昕瞳, 何俊, 陶灯, 张硕, 马海明, 张跃博. 干扰Mtmr3基因对C2C12细胞增殖与分化的影响[J]. 畜牧兽医学报, 2023, 54(4): 1478-1489.
[4]	杨小耿, 张慧珠, 李键, 向华, 何翃闳. DNA甲基化在哺乳动物卵母细胞和早期胚胎发育中的研究进展[J]. 畜牧兽医学报, 2023, 54(2): 443-450.
[5]	刘继强, 郝晓东, 武丽娜, 廖诗莹, 冯羿方, 弥世荣, 刘燊, 刘建, 张龙超. 全基因组SNP分型技术在畜禽遗传育种研究中的应用[J]. 畜牧兽医学报, 2022, 53(12): 4123-4137.
[6]	王勇, 王怀栋, 郭永清, 俞英, 王楚端, 王晓铄. dsRNA和Aza-CdR转染猪肾细胞的全基因组差异甲基化区域分布特征的比较[J]. 畜牧兽医学报, 2022, 53(8): 2739-2750.
[7]	刘芳君, 蓝吴涛, 马悦悦, 李鹏飞, 张磊, 朱芷葳. miRNA-148a-3p的生物信息学分析及其在小鼠不同组织中的表达水平检测[J]. 畜牧兽医学报, 2022, 53(2): 414-422.
[8]	张海亮, 常瑶, 娄文琦, 王凯, 陈紫薇, 米思远, 温万, 王雅春. 奶牛育种中关注的新性状[J]. 畜牧兽医学报, 2021, 52(10): 2687-2697.
[9]	岳永起, 华永琳, 熊燕, 林亚秋, 熊显荣, 李键. microRNA调控动物皮下脂肪组织和肌内脂肪沉积的研究进展[J]. 畜牧兽医学报, 2021, 52(10): 2698-2709.
[10]	王欢, 邹惠影, 朱化彬, 赵善江. CRISPR/Cas9基因编辑技术在家畜育种新材料创制中的研究进展[J]. 畜牧兽医学报, 2021, 52(4): 851-861.
[11]	刘学贤, 杜斌, 郭湘, 薛骥轩, 于雷涛, 范瑞文. microRNA-146a通过靶向MAPK4和Myosin Va基因调控羊驼黑色素细胞增殖、迁移[J]. 畜牧兽医学报, 2021, 52(4): 967-975.
[12]	宋兴超, 刘琳玲, 潘虹军, 赵家平, 贾赟, 杨福合, 徐超. 水貂酪氨酸酶(TYR)基因克隆、SNPs筛查及其皮肤组织mRNA差异表达分析[J]. 畜牧兽医学报, 2021, 52(1): 66-76.
[13]	疏泽, 王立贤, 王立刚. 可变剪接及其在畜禽育种中的研究与应用[J]. 畜牧兽医学报, 2020, 51(12): 2911-2920.
[14]	张易, 白皓, 毕瑜林, 路奥, 黄艳丽, 陈国宏, 常国斌. 拷贝数变异在家禽育种中的研究进展[J]. 畜牧兽医学报, 2020, 51(11): 2633-2640.
[15]	单文娟, 代慧英, 张玉琮. 基于线粒体基因的新疆三种野兔分类及遗传多样性[J]. 畜牧兽医学报, 2020, 51(10): 2403-2412.