山西肉型猪新品系（SD—Ⅰ系）6月龄体重遗传趋势分析

用混合模型ＢＬＵＰ法对山西瘦肉型猪新品系（ＳＤ－Ⅰ系）选育过程中０－４世代４０头公猪的４４７头后裔６月龄体重进行了遗传趋势评估。结果表明，群体平均育种值随世代的增加而增加，说明ＳＤ－Ⅰ系猪选育过程中所采取的育种措施是有效的。各世代间基本一致，且除０世代外，对表型值都有正的效应。继续进行选育，６月体重有提高的可能。     

新嘉系猪选育效果的灰色预测

选用新嘉系猪０—５世代的育种材料，利用ＧＭ（１，ｌ）模型对６月龄体重、活体膘厚、臀腿围、体长指数和瘦肉率等性状的选育效果进行动态模拟和预测。结果表明，至６世代时，各主要经济性状值都将达到或超过原定的育种目标。     

亲嘉系猪选育效果的灰色预测

选用新嘉系猪０－５世代的育种材料，利用ＧＭ（１，１）模型对６月龄体重，活体膘厚、臀腿围、体长指数和瘦肉率等性状的选育效果进行动态模拟和预测，结果表明，至６世代时，各主要经济性状值都将达到或超过原定的育种目标。     

瘦肉猪专门化父杜洛克猪选育研究

始地１９９１年的瘦肉猪专门化父系各克猪选育，采用群休继代选育方法，经过Ｇ０－Ｇ３共四个世代的选育，各主选性状均已达到育种目标。经产母猪产九达１０．６头，同胞肥育猪７８．２日龄体重达９０千克，试期日增重６９２克，耗料指数３．０２，胴体瘦肉率６４．３％，品系核心６０头，与四川白猪系配套生产的杂优猪（ＤＩ），１５６．４日龄体重达９０千克，试期日增重７６７克，耗料指数３．１７，胴体瘦肉率５８．７％，肉质优     

封闭育种群的近交对猪生长发育的影响

本文研究了闭锁育种群的近交对猪生长发育的影响，结果表明：选育初期，近交的各阶段体重有一定负＊效应，随世代增加负效应减弱。断奶前绝对生长在选育＊初期受近交不良影响较大；断奶后绝对生长，各世代均＊未因近交加深而减弱，随世代进展呈现良好效应。相对＊生长，各世代不同近交组间均无明显差异，选育后期随＊近交加深呈增大趋势。近交对６月龄体重、体长、胸围＊的影响很小，各世代不同近交组间均无明显差异，随世＊代增加，对腱围则产生良好影响。适度近交对闭锁育种＊群猪的生长发育是有利的。     

山西瘦肉型猪SD-Ⅱ系6月龄体重遗传趋势分析

用混合模型BLUP法对山西瘦肉型猪SD-系选育过程中O～4世代53头公猪的603头后裔6月龄体重进行了遗传趋势评估分析。结果表明，遗传传递力与后代平均表型值的相关及其名次的秩相关均迭极显著水平；遗传趋势、表型趋势和年遗传改进量较高，．且遗传趋势与表型趋势较一致，说明SD-Ⅱ系选育过程中所要取的育种措施是有效的，继续进行选育还有提高的可能。     

山西瘦肉型猪新品系肉质特性的研究

对活重达９０ｋｇ时的３６头猪，其中２４头ＳＤ－１系继代选育４世代猪和１２头双杂交猪，ＬＬＷ×ＤＳＤ，［父本为长白猪（Ｌ）×大白猪（ＬＷ）的杂种１代（ＬＬＷ），母本为杜洛克（Ｄ）×ＳＤ－１系猪（ＳＤ）的杂种１代（ＤＳＤ）］肉质特性的研究表明ＳＤ－１系猪及其杂种猪肉质良好。杂交及肥育后期限制日粮能量水平在提高胴体瘦肉率的同时可导致肉质一定程度的下降。相关分析表明，各肉质性状间的相关均较低，但肉质与胴体瘦肉率有较密切的联系，表现出随胴体瘦肉率提高而有肉质下降的趋势。     

影响湖北白猪Ⅵ系六月龄体重有关性状的多元统计分析

通过对湖北白Ⅵ系３世代２２６头后备猪６月龄８个表型性状的通径分析、决定系数分析以及逐步多元回归分析，结果表明：影响湖北白猪Ⅵ系６月体重的主要怀状依次为胸围、体长、腹围、臀宽、体高、而臀长、半臀围地体重影响不大，６月龄体重与其它各性状都为中等以上的正相关，但各性状对６月龄体重的直接影响都不大，表现为综合性状影响，因此，对湖北白猪生长速率的选育在对本性一状直接选择的同时，表现为综合性状影响，因此，对湖     

瘦肉猪专门化父系杜洛克猪选育研究

始于1991年的瘦肉猪专门化父系杜洛克猪选育,采用群体继代选育方法,经过G_0～G_3共四个世代的选育,各主选性状均已达到育种目标。经产母猪产仔数达10.6头,同胞肥育猪178.2日龄体重达90千克,试期日增重692克,耗料指数3.02,胴体瘦肉率64.3％,品系核心群60头。与四川白猪Ⅰ系配套生产的杂优猪(DI),156.4日龄体重达90千克,试期日增重767克,耗料指数3.17,胴体瘦肉率58.7％,肉质优良。     

南昌白猪选育（第二报）

南昌白猪经４个世代选育，四世代母猪头胎产仔１０．３７头，６０日龄窝重１５１．且千克。四世代后备公猪６月龄体重９２．２千克，日增重５９１克，料重比３．０７。三点平均背膘厚２．２８厘米。后备母猪６月龄体重８４．３千克，日增重５３９克，同胞（半同胞）屠宰测定瘦肉率５７．９９％，日增重６４０克，料重比３．０９。毛色耳型基本一致，具备瘦肉型猪体型，测定的各项指标变异系数还代缩小。遗传性基本稳定。     

利用BLUP法估计荷斯坦种公牛的抗热应激育种值

以公牛女儿数大于10头为限制因子，建立线性模型Y=xb＋za＋e，对带有抗热应激$441标记的荷斯坦种公牛育种值进行了估计。结果表明：带有抗热应激S441标记的荷斯坦种公牛的平均育种值大于没有标记的群体均值。$441可以作为一种荷斯坦种公牛抗热应激分子标记应用于品种选育和生产中。应用CNDHI软件评估了2007～2011年公牛的最佳线性无偏差预测值，并依据年度均值作出遗传趋势，为选种和遗传评估提供依据。     

中国肉用西门塔尔牛场间遗传联系分析

旨在加快中国肉用西门塔尔牛的遗传进展，实现全国范围内的联合育种。本研究利用全国38家育种场和公牛站在2000—2019年出生的3 991头肉用西门塔尔牛初生重性状，使用DMU软件对场间关联率进行计算。对各场站划分关联组，并比较单场和关联组内的遗传力和估计育种值（estimated breeding value，EBV）的预测准确性。结果表明，中国肉用西门塔尔牛全国平均关联率为1.91%，大部分场间关联率处于较低水平。依据关联率可划分出两个关联组，分别包括6个和8个场，组内平均关联率分别为11.23%和12.54%。对两个关联组分别进行单场和联合估计，单场估计初生重的遗传力范围为0.32~0.44，关联组1的初生重遗传力为0.47，关联组2的初生重遗传力为0.43。两个关联组单场估计EBV的平均准确性分别为0.47和0.45，联合估计EBV的平均准确性分别为0.61和0.56。联合估计较单场估计EBV的准确性有明显提高。依据关联率划分关联组进行联合育种有利于加快中国肉用西门塔尔牛的育种进程。为推进中国肉用西门塔尔牛的育种进程，应先形成区域性联合育种，再逐步加强遗传联系，形成全国范围内的遗传关联体系。     

基于微卫星标记鉴定湖羊家系

试验旨在基于微卫星（simple sequence repeats，SSR）多态性探索一种鉴定与划分湖羊家系的方法，通过查阅文献筛选出9个分布于多个常染色体、具有高多态性信息、高杂合度的湖羊微卫星位点BM3051、HH64、TGLA137、MAF33、FCB48、VH72、INRA023、ETH152和MCM527。采集30只湖羊种公羊血液，使用试剂盒提取血液基因组DNA，合成筛选出的9个微卫星引物并使用荧光基团标记，利用降落式PCR进行扩增，扩增产物在基因分型后使用CERVUS3.0.7进行基因型分析，使用POPGENE1.32基于Nei氏遗传距离构建树状聚类图，根据图示的亲缘关系远近划分湖羊家系。结果显示，当双亲基因型未知时，9个微卫星位点的单亲累积排除概率为99.2094%；当单亲基因型已知时，9个微卫星位点的累积排除概率为99.9688%；当双亲基因型未知时9个微卫星位点的双亲累积排除概率为99.9999%，排除概率能够满足遗传育种中亲子鉴定和系谱分析的要求。根据Nei氏遗传距离构建UPGMA聚类图，将30只种公羊划分为6个家系，为育种工作提供了一定参考。本方法能够进行湖羊家系的鉴定与划分，对湖羊育种工作具有一定的应用价值。     

Identification of Hu Sheep Family Based on Microsatellite Markers

The purpose of this study was to explore identification and division methods of Hu sheep family based on simple sequence repeats(SSR) polymorphism.Nine SSR loci of BM3051,HH64,TGLA137,MAF33,FCB48,VH72,INRA023,ETH152 and MCM527 were selected,which were distributed in several autosomes,with high polymorphism and high heterozygosity.DNA was extracted from the blood samples of 30 Hu breeding rams by blood DNA extraction kit.SSR primers were synthesized and labeled with the fluorescent group.The products were amplified by touch-down PCR.After genotyping,CERVUS3.0.7 was used for genotyping analysis.POPGENE1.32 was used to construct a dendrogram based on Nei's genetic distance to divide Hu sheep families.The results showed that the cumulative exclusion probability of single parent was 99.2094% when the genotypes of both parents were unknown,99.9688% when a single parent genotypes were known,99.9999% when both parents' genotypes were unknown.Exclusion probability could meet the requirements of paternity identification and pedigree analysis in genetic breeding.UPGMA clustering map was constructed according to Nei's genetic distance,and 30 breeding rams were divided into six families,which provided a reference for sheep breeding.The method could run the family identification and division of Hu sheep,which had application value for the breeding of Hu sheep.     

基因芯片技术在晋南牛种公牛选育中的应用

为了更好的保护开发利用晋南牛,确保晋南牛的遗传多样性,本研究应用基因芯片技术,对晋南牛进行群体遗传特性的检测及后备种公牛的遗传评估,为晋南牛的分子辅助选育与保种提供理论与技术支持。采集18月龄健康、体重相近（（350±20）kg）的荷斯坦牛、和顺肉牛、西门塔尔牛、延边牛及利木赞牛血样各10份,及晋南牛后备公牛血样25份,根据不同牛品种分为6组,其中前5组每组10个重复,晋南牛后备公牛25个重复。应用Illumina SNP 50K高密度牛SNP芯片进行基因型检测,分析比较晋南牛的群体遗传特征,运用亲缘矩阵计算晋南牛后备公牛的亲缘系数,同时用BLUP进行遗传评估。结果表明,晋南牛在遗传结构上与荷斯坦牛、和顺肉牛、西门塔尔牛及利木赞牛关系较远,与延边牛较近,为中国地方品种群体;对晋南牛后备公牛进行遗传评估,得出了牛的基因组胴体重方差育种值排名,JN23的胴体重倍数性状标准差最大,从基因组水平可选作肉用种公牛;应用亲缘分析对晋南牛后备公牛家系进行分类,避免群体间的近交。本研究对晋南牛后备公牛进行了遗传评估、近交家系分析、传统表型选择及遗传疾病检测,最终选留的种公牛为JN07、JN23、JN05、JN08、JN02、JN13、JN19、JN14,通过多种选育方法结合提高了公牛的选择准确性,为晋南牛的群体选育提高奠定了基础。     

Optimizing female allocation to reproductive technologies considering merit,inbreeding and cost in nucleus breeding programmes with genomic selection

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilization and embryo transfer (JIVET) have been shown to accelerate genetic gain by increasing selection intensity and decreasing generation interval. Genomic selection (GS) increases the accuracy of selection of young candidates which can further accelerate genetic gain. Optimal contribution selection (OCS) is an effective method of keeping the rate of inbreeding at a sustainable level while increasing genetic merit. OCS could also be used to selectively and optimally allocate reproductive technologies in mate selection while accounting for their cost. This study uses stochastic simulation to simulate breeding programmes that use a combination of artificial insemination (AI) or natural mating (N), MOET and JIVET with GS. OCS was used to restrict inbreeding to 1.0% increase per generation and also to optimize use of reproductive technologies, considering their effect on genetic gain as well as their cost. Two Australian sheep breeding objectives were used as an example to illustrate the methodology—a terminal sire breeding objective (A) and a dual‐purpose self‐replacing breeding objective (B). The objective function used for optimization considered genetic merit, constrained inbreeding and cost of technologies where costs were offset by a premium paid to the seedstock breeder investing in female reproductive technologies. The premium was based on the cumulative discounted expression of genetic merit in the progeny of a commercial tier in the breeding programme multiplied by the proportion of that benefit received by the breeder. With breeding objective B, the highest premium of 64% paid to the breeder resulted in the highest allocation of reproductive technologies (4%–10% for MOET and 19%–54% for JIVET) and hence the highest annual genetic gain. Conversely, breeding objective A, which had a lower dollar value of the breeding objective and a maximum of 5% mating types for JIVET and zero for MOET were optimal, even when highest premiums were paid. This study highlights that the level of investment in breeding technologies to accelerate genetic gain depends on the investment of genetic improvement returned to the breeder per index point gain achieved. It also demonstrates that breeding programmes can be optimized including allocation of reproductive technologies at the individual animal level. Accounting for revenue to the breeder and cost of the technologies can facilitate more practical decision support for beef and sheep breeders.     

Simulation of genetic control of reproduction in beef cows. III. Within-herd breeding value estimation with known breeding dates

Procedures for breeding value estimation for reproductive traits with known breeding dates were developed and tested using results of a computer simulation model of genetic control of bovine reproduction. The model generated realized reproductive outputs as a function of underlying genetic variation in two independent traits: conception rate (CR), which was indicative of the ability to conceive given estrus, and PPI, the postpartum interval from calving to first estrus. Two scenarios were considered. In the first, all cows were assumed to be cycling at the start of breeding and to be bred artificially. For this scenario, breeding values for CR could be estimated from information on observed breeding and calving dates by using a categorical trait, multi-stage selection model. Breeding value estimation for PPI, however, required actual measurement of PPI because if PPI and CR are genetically independent and if all cows are cycling at the beginning of breeding, subsequent breeding and calving dates are independent of PPI. The second scenario recognized that not all females would be cycling at the start of breeding. For this scenario, the categorical trait, multi-stage selection model could still be applied for breeding value estimation for CR, but accumulation of data across years was complicated by a need to consider the lifetime reproductive pattern of the individual rather than just the sum of each year's performances. Breeding value estimates for PPI could be obtained from observed breeding and calving dates for this scenario, but required consideration of the distributional properties of PPI.     

Simulation study on the effects of excluding offspring information for genetic evaluation versus using genomic markers for selection in dog breeding

Different modes of selection in dogs were studied with a special focus on the availability of disease information. Canine hip dysplasia (CHD) in the German shepherd dog was used as an example. The study was performed using a simulation model, comparing cases when selection was based on phenotype, true or predicted breeding value, or genomic breeding value. The parameters in the simulation model were drawn from the real population data. The data on all parents and 40% of their progeny were assumed to be available for the genetic evaluation carried out by Gibbs sampling. With respect to the use of disease records on progeny, three scenarios were considered: random exclusion of disease data (no restrictions, N), general exclusion of disease data (G) and exclusion of disease data for popular sires (P). One round of selection was considered, and the response was expressed as change of mean CHD score, proportion of dogs scored as normal, proportion of dogs scored as clearly affected and true mean breeding value in progeny of popular sires in comparison with all sires. When no restrictions on data were applied, selection on breeding value was three times more efficient than when some systematic exclusion was practised. Higher selection response than in the exclusion cases was achieved by selecting on the basis of genomic breeding value and CHD score. Genomic selection would therefore be the method of choice in the future.     

昆明犬家系兴奋性状选育模式研究

统计昆明犬家系3个世代兴奋性测试记录和系谱数据,对昆明犬兴奋性状不同选育方法进行模拟研究。利用家系系谱资料及F3和F4世代兴奋性状数据,估算个体育种值,依据表型和育种值,对F4犬群按照个体表型、家系表型、个体育种值、家系育种值排序,各选择30%个体,寻找其在F5代中的个体,计算各种选择方法所产生后代的表型和遗传进展,评估各种选择方法对昆明犬兴奋性状选育最优方法。研究表明:选择昆明犬个体育种值好于选择昆明犬家系育种值好于选择家系表型好于选择个体表型,选择昆明犬公犬效果好于选择昆明犬母犬效果。     

价格变动对目标性状经济值及育种效果的灵敏性分析

基于鲁西牛育种、生产和市场体系,通过构建与目标性状相联系的遗传变量和经济变量的利润函数,采用偏微分预算法和基因流动法分析价格与目标性状经济值,以及目标性状经济值与育种效果之间关系的稳健性和灵敏性。结果表明,目标性状的经济值和育种效果随着市场体系的变化而变化,且育种效果的变化和目标性状经济值的变化相关联。以上结果说明,当前市场价格体系下获得的目标性状经济权重随着市场价格变化应做适宜的调整。