畜禽基因组选择方法研究进展

畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。     

全基因组SNP分型策略及基因组预测方法的研究进展

单核苷酸多态性（single nucleotide polymorphism,SNP）是遗传学研究中重要的材料。近年来,全基因组SNP标记开发方法的发展使得研究者们能够以较低成本获得丰富的基因组标记,大大推动了基因组水平的相关研究。基因组预测从已知基因型数据和表型数据的个体建立训练模型,对未知表型的个体进行基因型和表型预测,在育种领域具有重要意义。全基因组SNP的分型策略结合基因组预测方法,构成了动物基因组选择的前沿。本文从这两个方面进行综述,以期为从事分子遗传学,尤其是复杂性状研究的研究者们提供参考。     

利用后裔测定验证大白猪基因组选择实施效果研究

旨在通过测定基因组选择选留的大白公猪的后裔生产性能,探究基因组选择实际育种效果。本研究选用913头大白猪构建参考群体,利用ssGBLUP对新出生的823头大白公猪在去势前进行第一次基因组评估,待生产性能测定后进行第二次基因组评估,最终选留10头性能差异显著的公猪留种,比较其后代生长性状表型和育种值及综合选择指数差异。结果表明,两次基因组遗传评估,达100 kg体重日龄、100 kg活体背膘厚和总产仔数3个性状基因组育种值（GEBV）估计准确性分别由0.56、0.67和0.64提高至0.73、0.73和0.67,两次基因组选择基因组母系指数相关系数为0.82,表明在去势前进行公猪基因组选择具有较高的准确性,可实现种猪早期选择。根据各性状GEBV和基因组母系指数,10头公猪被划分为高、低生产性能组,后裔测定成绩表明,两组公猪后代100 kg体重日龄表型均值之差为2.58 d,育种值之差为3.08 d,100 kg活体背膘厚表型均值之差为1.15 mm,育种值之差为1.03 mm,综合母系指数均值之差为9.3,除后代100 kg体重日龄表型均值之差外,其他差异均达到极显著水平。本研究证明,在基因组评估中具有显著差异的公猪其后代在表型值和育种值等方面均存在显著差异,通过基因组选择能够挑选出优秀种公猪,可将其遗传优势传递给后代。     

凉山48—50支半细毛羊早期及预期综合选择指数的研究

本文收集了凉山４８－５０支半细毛羊１９９０～１９９３年度的育种记录，用最小二乘法（Ｈａｒｖｅｙ程序，１９８７）校正了出生月份和出生年度的固定效应，并估测了公羊和母羊３个体重性状及５个羊毛性状的遗传参数，利用相关性综合选择指数制定原理，分别制定了公羊和母羊的早期综合选择指数（ＥＩ），并利用表型通径分析，表型遗传通径分析及遗传通径分析的方法，建立了母羊利用早期性状（初生和断奶性状）表型值预测晚期性状（     

基于SNP芯片的猪基因组选择效果分析

为探究单步基因组最佳线性无偏预测(SSGBLUP)法应用于生猪育种的选择效果，选取杜洛克、长白、大白种猪共1 996头，利用SNP芯片获得个体基因型数据，结合表型数据和系谱数据，利用HIBLUP软件的SSGBLUP模型和基于系谱的最佳线性无偏预测(PBLUP)模型分别计算估计育种值，参考全国种猪遗传评估中心的标准计算综合选择指数，利用理论准确性和后裔测定成绩评估选择效果。结果表明：通过SSGBLUP法计算达100 kg日龄、达100 kg背膘厚及总产仔数的基因组估计育种值(GEBV)与PBLUP法计算的估计育种值(EBV)的相关系数均大于0.8;达100 kg日龄、达100 kg背膘厚及总产仔数GEBV准确性相对于EBV准确性均有所提高；SSGBLUP法与PBLUP法选留的长白和大白种猪，其后代的生长速度显著优于场内选择法选留种猪的后代。本试验中，SSGBLUP法与PBLUP法均能有效提高选留种猪后代的生长速度且二者分别计算的GEBV和EBV相关性高，但SSGBLUP法选种的准确性更高，后期可利用全基因组选择对场内现有种猪进行选种指导。     

全基因组SNP分型策略及基因组预测方法的研究进展

单核苷酸多态性(single nucleotide polymorphism,SNP)是遗传学研究中重要的材料。近年来,全基因组SNP标记开发方法的发展使得研究者们能够以较低成本获得丰富的基因组标记,大大推动了基因组水平的相关研究。基因组预测从已知基因型数据和表型数据的个体建立训练模型,对未知表型的个体进行基因型和表型预测,在育种领域具有重要意义。全基因组SNP的分型策略结合基因组预测方法,构成了动物基因组选择的前沿。本文从这两个方面进行综述,以期为从事分子遗传学,尤其是复杂性状研究的研究者们提供参考。     

生物技术与美国的肉牛育种（续）

3 生理和遗传标记3.1 生理标记动物育种方法主要依赖于表型度量推知动物基因型(育种值)。表型信息包括有益性状的个体性能以及遗传相关性状的信息,其中也包括利用祖先、同胞以及后裔的性能。有些学者利用不同亲属和相关性状制定选择指数作为遗传指标,但信息仍是表型的。有关影响性状的位点和等位基因数目,以及DNA为哪种等位基因编码,在特定位点上启     

野猪、松辽黑母猪及其杂交一代多性状功能基因分子遗传标记研究

本研究旨在应用"中芯一号"家猪分子育种基因芯片了解试验猪群重要经济性状功能基因遗传变异,为选留优秀育种个体提供有效信息。选择影响猪脂肪沉积、肉质、生长、抗病和被毛表型等性状功能基因有效突变位点作为分子遗传选育标记,以野猪、松辽黑母猪及其杂交一代共计106头个体作为实验动物模型,通过"中芯一号"猪分子育种芯片检测,对试验猪不同性状功能基因有效突变位点进行统计分析。结果表明,猪脂肪沉积性状功能基因（SCD和MYH4）有效突变位点在试验猪群中全部是肌内脂肪沉积有利基因型（CC、TT）;肉质性状功能基因（PHKG1、PRKAG3和RYR1）有效突变位点,除了21头猪携带有RYR1功能基因有效突变位点对肉质性状不利的等位基因T外（杂合基因型CT）,其他个体全部为肉质性状有利基因型（CC、GG、CC）;抗病性状功能基因MUC13有效突变位点的抗病有利等位基因纯合基因型（GG）所占比例高于杂合（GA）和不利等位基因纯合基因型（AA）;生长性状功能基因（HMGA1、VRTN和CCKAR）有效突变位点检测发现,猪群中没有增加体长趋势的HMGA1突变位点的TT基因型个体,仅有1个杂合体;肋骨数功能基因VRTN有效突变位点T等位基因频率高,对个体的胸椎数有增加趋势;功能基因CCKAR有效突变位点全部是有利于增加采食及日增重的C等位基因纯合基因型;被毛表型性状KIT功能基因有效突变位点在所有检测猪个体呈现出GG基因型,说明研究猪群中不存在影响白色被毛表型的基因突变位点,与试验猪群被毛表型结果一致。以上结果为猪群进一步育种规划提供了有益信息。     

河南省种猪联合育种大约克种猪遗传进展研究报告

2005-2009年河南省种猪遗传评估中心对河南省种猪联合育种协作组织的十七家集约化种猪场的大约克种猪进行了巡回现场测定，测定数据利用多性状动物模型BLUP（最佳线性无偏预测）法，经GBS种猪遗传评估软件计算评估出每头所测个体的评估成绩，同时结合现场体型外貌鉴定，以此来确认种猪的选留，从而提高全省种猪育种技术水平和猪种质量。     

杜洛克仔猪初生重对生长和繁殖性能的影响

本研究旨在对仔猪初生重进行表型与遗传分析，探究其对生长和繁殖性能的影响，为发挥其在选种过程中的价值奠定基础。实验选取某核心场近8年杜洛克仔猪生长和繁殖性状记录，利用DMU软件和动物模型估计了初生重的加性方差、窝效应方差、表型效应方差和残差方差，计算初生重的遗传力；并估计了杜洛克仔猪初生重与主要繁殖和生长性状之间的表型和遗传相关；此外，分析了随着初生重不断增加，其对应父系指数及各性状育种值的变化情况。结果表明：性别和年份季节效应对仔猪初生重均有极显著影响；仔猪初生重遗传力达到0.24；相关性分析表明：表型与遗传相关分析结果较为相近，初生重与达115 kg体重日龄呈中等负相关，与体长和达115 kg体重日增重呈中等正相关，与达115 kg体重背膘厚、眼肌面积、总仔数、活仔数和健仔数相关均接近于0。综上，依据仔猪初生重进行早期选择，能够对种猪生长速度起到较好的选择效果，但对其他性状影响较小，建议可通过淘汰较低初生重个体（1 kg以下）来达到初选目的。     

Comparative Analysis of Different Implementation Strategies on Genomic Selection in Large White Pigs

At present, genomic selection (GS) has been applied in pigs breeding, but some implementation strategies, such as the determination of genotyping ratios or early selection rates for piglets, are required to obtain a higher benefit using this technology. The Large White pigs born from 2011 to 2016 at WENS Foodstuff Group Co.,Ltd were chose as the research objects, including more than 45 000 growth measurement records, more than 70 000 reproduction records and 2 090 individuals with genotyping-by-sequencing (GBS) data. The 440 individuals born from July to December in 2016 were used as the candidate individuals. The traits included two growth traits, age at 100 kg and backfat thickness at 100 kg, and one reproduction trait, number of total born. To compare the prediction effects, four prediction scenarios were designed according to including or ignoring the phenotypic or genotypic information of candidate individuals when predicting their breeding values. The predictive reliability of different scenarios and rankings of selection indices of individuals would be compared. The results showed that the results using the phenotypic and genotypic information was more reliable than ignoring them to predict the breeding values of candidate individuals. When genomic selection indices were calculated before and after performances testing for the growth traits, the individuals ranking in the top 30% of indices after testing were all found in the individuals ranking in the top 60% of indices before testing. If the piglets with the top 60% of traditional BLUP indices were only selected, around 15% of individuals with good genetic potentials would be omitted. This study suggests that all healthy piglets after birth are genotyped and their genomic selection indices are calculated, and then the individuals ranking in the top 60% of indices are chose to perform growth measurement.     

母系猪繁殖性状的基因组选择策略

与生长性状相比,猪的繁殖性状具有遗传力低和限性表现的特点,通过传统育种方法很难获得较高的育种值估计准确性,且无法缩短世代间隔。因此,猪的繁殖性状选育策略应与生长性状不同。基因组选择是一种基于全基因组信息的标记辅助选择。与生长性状相比,基因组选择对提高繁殖性状(如产仔数)的预测准确性更具有优势。然而,基因组选择的育种成本较高阻碍了该技术的广泛应用。本文旨在探讨母系猪繁殖性状基因组选择的参考群体构建策略,以节省基因组育种成本和加快遗传进展。     

Genomic selection strategies in dairy cattle: Strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.     

杜洛克猪专门化品系选育研究

杜洛克猪专门化品系是以丹系杜洛克为育种素材,采用不完全闭锁的群体继代选育法,运用最佳线性无偏估计(BLUP)法、综合选择指数和分子标记辅助选择(MAS)等育种新方法,主选日增重和活体背膘厚等性状。经5个世代选育,产仔数达10.48头,产活仔数9.85头;肥育期日增重831g,料重比2.55:1,活体背膘厚11.83mm;瘦肉率69.5%,肌内脂肪为2.95%,主选性状全面达到或超过育种目标,成功培育了一个高性能的专门化父本新品系(ZFD系)。     

Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs,beef cattle,and broilers

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.     

Using Progeny Testing to Evaluate the Efficiency of Genomic Selection in Large White Pigs

This study aimed to evaluate the actual genetic improvement effect of genomic selection in Large White boars through progeny testing in production performance. Nine hundred and thirteen Large White pigs were used to construct a reference group, and 823 new-born Large White boars were used to implement the first genomic selection through ssGBLUP before castration. The second genomic selection were carried out after performance testing, then 10 boars with significant difference in production performance were selected and their offsprings were compared in phenotypic values, estimated breeding values of growth traits and selection index. The results showed that the accuracies of genomic prediction on age at 100 kg body weight, 100 kg backfat thickness and total number born increased from 0.56, 0.67 and 0.64 in the first genomic selection to 0.73, 0.73 and 0.67 in the second genomic selection, respectively. The correlation coefficient of maternal selection index between the two genomic selection before castration and after performance testing was 0.82, which indicated that the first genomic selection before castration was accurate enough to make early selection on boars. According to the genomic breeding values and maternal selection index of 10 selected boars, two groups with high and low production performance were set up. The progeny testing showed that the difference of average phenotypic value between groups was 2.58 days, and the difference of average evaluated breeding value(EBV) between groups was 3.08 days in age at 100 kg body weight, those were 1.15 mm and 1.03 mm in 100 kg backfat thickness, respectively, and the difference in the mean of the comprehensive maternal index was 9.3, all the differences(except age at 100 kg body weight) were extremely significant. This study prove that the offspring of boars with significant differences in genomic evaluation have significant differences in phenotypic values and breeding values, which indicate that, through genomic selection, excellent breeding boars can be selected and their genetic superiority can be passed to their offsprings.     

国内外肉牛遗传评估体系概况

种公牛的选育是肉牛育种工作的核心。传统选育肉用种公牛需要经过后裔测定进行选择,其优点是准确性高,但存在周期长、屠宰和肉质性状难以收集、成本高等问题,致其选择效率低。自2001年全基因组选择概念提出后,该技术迅速成为动植物育种领域研究的热点。利用全基因组选择进行肉用种公牛的选育,进行早期选择从而大幅度缩短世代间隔,可以提高繁殖性状等低遗传力性状的选择准确性,加快遗传进展,并大大降低育种成本。2014年,美国安格斯协会开始应用全基因组选择技术,其他欧美发达国家也陆续使用,肉牛育种进入基因组时代。中国自2017年开始使用全基因组选择技术选择青年肉用种公牛,并于2020年在全国范围内使用该技术进行基因组遗传评估。本文综述了国内外肉牛遗传评估现状,以期为我国肉牛育种工作提供参考和借鉴。     

Accuracy of genomic prediction of purebreds for cross bred performance in pigs

In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small‐to‐moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data.     

Research Progress on Genomic Selection Methods in Livestock and Poultry

Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding.