基于元共祖的基因组联合育种模拟研究

doi:10.11843/j.issn.0366-6964.2022.07.014

摘要/Abstract

摘要： 旨在将整合元共祖的一步法(single-step genomic best linear unbiased prediction with metafounders,MF-SSGBLUP)应用到基因组联合育种中，并与其他经典基因组选择方法进行比较分析。本研究使用QMSim软件模拟3个系谱相互独立的奶牛群体；分别使用广义最小二乘法(generalized least squares，GLS)和原始方法(naïve，NAI)估计不同群体间的祖先关系矩阵Γ；将MF-SSGBLUP、SSGBLUP和BLUP用于3个模拟群体的联合育种，评估各方法在遗传参数和育种值估计方面的差异。在不同遗传力下，GLS所得的Γ矩阵在对角线元素上略低于NAI法，在非对角线元素上没有明显差异，且基因组关系矩阵与基于元共祖构建的亲缘关系矩阵对角线元素相关系数(0.750~0.775)高于基因组关系矩阵与传统的亲缘关系矩阵相关系数(0.508~0.572)。MF-SSGBLUP遗传力估计值(0.138、0.140、0.297和0.298)与当代群体遗传力(0.107和0.296)的偏差小于其余两种方法(0.145、0.173、0.273和0.340)，且MF-SSGBLUP估计育种值准确性(0.888~0.908)高于SSGBLUP法(0.863~0.876)和BLUP法(0.854~0.871)。表明，MF-SSGBLUP的遗传参数估计值无偏性更好，估计育种值准确性更高。根据上述模拟数据结果表明，在联合育种中，整合元共祖的基因组选择方法优于其他经典基因组选择方法。

关键词: 基因组选择, 元共祖, 基因组联合育种, 一步法, 模拟研究

Abstract: This study aimed to apply the single-step genomic best linear unbiased prediction with metafounders (MF-SSGBLUP) to joint genomic breeding and compare it with other classical genomic selection methods. QMSim software was used to simulate 3 dairy cattle populations with independent pedigrees; The generalized least squares (GLS) and naïve (NAI) methods were used to estimate the ancestral relationship matrix Γ between different populations. MF-SSGBLUP, SSGBLUP and BLUP were used respectively to joint breeding for the simulated populations, and the performance of each method in estimating genetic parameters and breeding values was evaluated. For different heritabilities, the Γ matrix obtained by GLS was slightly lower than that obtained by NAI method in diagonal elements, but there was no significant difference in non-diagonal elements. The correlation coefficient in diagonal elements between the genomic and genetic relationship matrices based on metafounders (0.750-0.775) was higher than that between genomic and traditional relationship matrices (0.508-0.572). The deviations of heritability estimates by MF-SSGBLUP (0.138, 0.140, 0.297 and 0.298) from the current population heritability (0.107 and 0.296) were smaller than those of the other two methods (0.145, 0.173, 0.273 and 0.340). Correspondingly, the accuracies of estimated breeding values by MF-SSGBLUP (0.888-0.908) were higher than that by SSGBLUP (0.863-0.876) and BLUP (0.854-0.871). The results showed that MF-SSGBLUP had less biased estimates of genetic parameters and breeding values with higher accuracies. Based on the simulation results, the MF-SSGBLUP performed better than other classical genomic selection methods in joint breeding.

Key words: genomic selection, metafounder, joint genomic breeding, SSGBLUP, simulation research

中图分类号:

S823.91

庞志旭, 张洪志, 乔利英, 王万年, 潘洋洋, 刘文忠. 基于元共祖的基因组联合育种模拟研究[J]. 畜牧兽医学报, 2022, 53(7): 2172-2181.

PANG Zhixu, ZHANG Hongzhi, QIAO Liying, WANG Wannian, PAN Yangyang, LIU Wenzhong. Simulation Study on Joint Genomic Breeding Using Metafounders[J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(7): 2172-2181.

参考文献 34

[1]	MEUWISSEN T H E,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense markermaps[J].Genetics,2001,157(4):1819-1829.
[2]	MEUWISSEN T,HAYES B,GODDARD M.Genomic selection:a paradigm shift in animal breeding[J].Anim Front,2016, 6(1):6-14.
[3]	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.
[4]	HAYES B J,VISSCHER P M,GODDARD M E.Increased accuracy of artificial selection by using the realized relationship matrix[J].Genet Res,2009,91(1):47-60.
[5]	HABIER D,TETENS J,SEEFRIED F R,et al.The impact of genetic relationship information on genomic breeding values in German Holstein cattle[J].Genet Sel Evol,2010,42(1):5.
[6]	LEGARRA A,AGUILAR I,MISZTAL I.A relationship matrix including full pedigree and genomic information[J].J Dairy Sci,2009,92(9):4656-4663.
[7]	CHRISTENSEN O F,LUND M S.Genomic prediction when some animals are not genotyped[J].Genet Sel Evol,2010,42(1):2.
[8]	NILFOROOSHAN M A,ZUMBACH B,JAKOBSEN J,et al.Validation of national genomicevaluations[J].Int Bull,2010(42):56-61.
[9]	EGGEN A.The development and application of genomic selection as a new breeding paradigm[J].Anim Front,2012,2(1):10-15.
[10]	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.
[11]	VANRADEN P M,VAN TASSELL C P,WIGGANS G R,et al.Invited review:reliability of genomic predictions for North American Holstein bulls[J].J Dairy Sci,2009,92(1):16-24.
[12]	全国生猪遗传改良计划(2009-2020)实施方案[J].中国牧业通讯,2010(23):25-28.The national pig improvement program (2009-2020)[J].China Animal Husbandry Bulletin,2010(23):25-28.(in Chinese)
[13]	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)
[14]	VANRADEN P M.Efficient methods to computegenomic predictions[J].J Dairy Sci,2008,91(11):4414-4423.
[15]	LEGARRA A,CHRISTENSEN O F,AGUILAR I,et al.Single Step,a general approach for genomic selection[J].Livest Sci,2014, 166:54-65.
[16]	HIDALGO A M,BASTIAANSEN J W M,LOPES M S,et al.Accuracy ofpredicted genomic breeding values in purebred and crossbred pigs[J].G3 Genes\|Genom\|Genet,2015,5(8):1575-1583.
[17]	余健,杨文静,王晔,等.多个场联合遗传评估提高基因组选择准确性[J].中国畜牧杂志,2021,57(S1):25-28.YU J,YANG W J,WANG Y,et al.Combined genetic evaluation of multiple populations improves genomic selection accuracy[J]. Chinese Journal of Animal Science,2021,57(S1):25-28.(in Chinese)
[18]	LEGARRA A,CHRISTENSEN O F,VITEZICA Z G,et al.Ancestral relationships using metafounders:finite ancestral populations and across population relationships[J].Genetics,2015,200(2):455-468.
[19]	CHRISTENSEN O F.Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation[J]. Genet Sel Evol,2012,44(1):37.
[20]	FU C K,OSTERSEN T,CHRISTENSEN O F,et al.Single-step genomic evaluation with metafounders for feed conversion ratio and average daily gain in Danish Landrace and Yorkshire pigs[J].Genet Sel Evol,2021,53(1):79.
[21]	付川珂,赵书红,李新云,等.基于元共祖的基因组选择一步法理论及研究进展[J].中国畜牧杂志,2021,57(5):1-6.FU C K,ZHAO S H,LI X Y,et al.Advances in the theories and applications of single-step genomic evaluation with metafounders[J].Chinese Journal of Animal Science,2021,57(5):1-6.(in Chinese)
[22]	SARGOLZAEI M,SCHENKEL F S.QMSim:a large-scale genome simulator for livestock[J].Bioinformatics,2009, 25(5):680-681.
[23]	CHRISTENSEN O F,MADSEN P,NIELSEN B,et al.Single-step methods for genomic evaluation in pigs[J].Animal, 2012, 6(10):1565-1571.
[24]	LOURENCO D,LEGARRA A,TSURUTA S,et al.Single-step genomic evaluations from theory to practice:using SNP chips and sequence data in BLUPF90[J].Genes (Basel),2020,11(7):790.
[25]	GARCIA-BACCINO C A,LEGARRA A,CHRISTENSEN O F,et al.Metafounders are related to Fst fixation indices and reduce bias in single-step genomic evaluations[J].Genet Sel Evol,2017,49(1):34.
[26]	PATTERSON H D,THOMPSONR.Recovery of inter-block information when block sizes are unequal[J]. Biometrika, 1971,58(3):545-554.
[27]	DEMPSTER A P,LAIRD N M,et al.Maximum likelihood from incomplete data via the EM algorithm[J].J R Stat Soc Series B Stat Methodol,1977,39(1):1-22.
[28]	JENSEN J,MÄNTYSAARI E A,MADSEN P,et al.Residual maximum likelihood estimation of (Co) variance components in multivariate mixedlinear models using average information[J].J Indian Soc Agric Stat,1997,49:215-236
[29]	KANG H,ZHOU L,MRODE R,et al.Incorporating the single-step strategy into a random regression model to enhance genomic prediction of longitudinal traits[J].Heredity (Edinb),2017,119(6):459-467.
[30]	BRADFORD H L,MASUDA Y,VANRADEN P M,et al.Modeling missing pedigree in single-step genomic BLUP[J].J Dairy Sci,2019,102(3):2336-2346.
[31]	VAN GREVENHOF E M,VANDENPLAS J,CALUS M P L.Genomic prediction for crossbred performance usingmetafounders[J]. J Anim Sci,2019,97(2):548-558.
[32]	MACEDO F L,CHRISTENSEN O F,ASTRUC J M,et al.Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups[J].Genet Sel Evol,2020,52(1):47.
[33]	KUDINOV A A,MÄNTYSAARI E A,AAMAND G P,et al.Metafounder approach for single-step genomic evaluations of Red Dairy cattle[J].J Dairy Sci,2020,103(7):6299-6310.
[34]	XIANG T,CHRISTENSEN O F,LEGARRA A.Technical note:genomic evaluation for crossbred performance in a single-step approach with metafounders[J].J Anim Sci,2017,95(4):1472-1480.