Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

河北省中国荷斯坦牛产奶和体型性状遗传参数分析

旨在探究河北省中国荷斯坦牛产奶和体型性状的遗传参数,为育种提供参考。本研究收集了2012-2018年河北省133个牛场8 891头中国荷斯坦母牛第一胎次的3个产奶性状记录（产奶量、乳脂率和乳蛋白率）和26个体型性状记录,利用DMU软件,以场年季、产犊月龄、鉴定年季和鉴定员效应为固定效应,以个体的加性遗传效应为随机效应,采用AIREML结合EM算法并配合动物模型对产奶性状、体型性状进行了遗传参数估计。结果表明,产奶性状的遗传力估计值范围为0.15（产奶量）~0.32（乳脂率）,体型性状遗传力估计值范围为0.01（体型总分）~0.28（后乳房附着宽度）。产奶性状与体型性状间的遗传相关估计值范围分别是-0.43（产奶量与骨质地）~0.31（产奶量与尻部）、-0.59（乳脂率与尻部）~0.20（乳脂率与前乳头位置）以及-0.34（乳蛋白率与前乳头长度）~0.23（乳蛋白率与后乳头位置）。大部分体型性状与产奶量之间为遗传正相关,而与乳脂率和乳蛋白率为遗传负相关。加强对产奶性状及体型性状中的中高遗传力性状的选择,尤其对后乳房性状以及体型总分的选择,有利于奶牛生产性能的提高。     

Histology of corpuscles of Stannius in Lake Van fish (Alburnus tarichi Güldenstädt, 1814) (Cyprinidae)

In the present study, the location, histology and number of corpuscles of Stannius (Sc), which are endocrine glands associated with the kidneys of teleost fish, were investigated for the first time in Lake Van fish (Alburnus tarichi), an anadromous and endemic inhabiting Turkey's Lake Van Basin. The Sc, which were ovoid or spheroid and white or cream in colour, were found to vary in number between three and five among the examined fish. The glands were located in the caudal part of the kidney, and either partially or completely embedded, and found to be present on both the ventral and dorsal surface of either side of the caudal part of the kidney. The Sc were surrounded by a connective tissue capsule that penetrated and divided the gland into incomplete lobules. Two types of cells were determined in the parenchyma of the gland. Type-I cells were predominant throughout the parenchyma and larger than the second (type-II). In the type-I cells, the cytoplasm was observed as weakly or moderately eosinophilic with haematoxylin and eosin staining and weakly or moderately acidophilic with Mallory's triple staining. In the type-I cells, the cytoplasm exhibited weak to moderate periodic acid-Schiff staining and slight or uniform staining with aldehyde fuchsin. The type-II cells were round, had a darkly stained spherical nucleus and were dispersed among the type-I cells. They displayed no cytoplasmic staining with the abovementioned stains.     

抗球虫药物残留的免疫检测技术

抗球虫药物在畜禽业应用广泛,可预防治疗球虫病,提高家禽饲料转化率,提升肉品质。但是,随即会出现饲料交叉污染,对非目标动物产生毒性作用,动物性食品中药物残留超标等问题。因此,建立简便、快速、灵敏度高的检测方法检测动物源性食品中的抗球虫药物残留极为重要。基于抗原-抗体特异性结合原理的免疫分析检测技术能够完成快速检测,高通量筛选,具有特异性强、灵敏度高、操作简便、成本低的优点。本文综述了不同基质中抗球虫药物残留的免疫检测技术进展,重点介绍了酶联免疫吸附检测法、免疫层析法、荧光偏振免疫分析检测法、时间分辨荧光免疫分析检测法和生物传感器检测法。并对免疫检测技术在残留检测方面的发展趋势进行了展望,旨在为抗球虫药物的残留监控提供方法学上的参考,为新方法的建立提供思路。     

戴瑞奶绵羊产奶性状的全基因组关联分析

旨在通过对奶绵羊产奶性状的全基因组关联分析（GWAS）,寻找和定位与奶绵羊产奶性状相关的遗传标记和功能基因。本研究以135只戴瑞奶绵羊为试验材料,基于全基因组重测序技术,通过SAMTOOLS进行单核苷酸多态性（SNP）检测,使用PLINK v1.90进行质控,利用GEMMA v 0.98.1的混合线性模型对质控结果进行奶绵羊产奶性状相关的全基因组关联分析。结果表明,有1个SNP与泌乳后90天日均产奶量在全基因组范围内显著相关,8个SNPs达到潜在显著关联,相关的候选基因包括TRNAQ-CUG-2、LOC114117240、ACADL、MYL1、CHD6、SLCO3A1;有2个SNPs与150天日均产奶量达潜在显著关联,相关的候选基因包括PRMT6、RNF180;有2个SNPs与泌乳周期达潜在显著关联,相关的候选基因包括PRMT6、TRNAW-CCA-68、TRNAS-GGA-61。进一步基因功能分析推测,ACADL、SLCO3A1可能是影响奶绵羊产奶性状的候选基因。本研究为奶绵羊产奶性状的分子机制解析提供了一定的基础,为我国奶绵羊新品种培育提供了一定的理论参考。     

参考群筛选方法及规模对基因型填充准确性的影响

为探究基于A矩阵期望遗传关系最大化（maximizing the expected genetic relationship for matrix A,RELA）、基于A矩阵目标群体遗传方差最小化（minimized the target population genetic variance for matrix A,MCA）、平均亲缘关系最大化（the highest mean kinship coefficients,KIN）、随机选择（random selection,RAN）、共同祖先筛选（common ancestor,CA）等不同参考群筛选方法及参考群规模对基因型填充准确性的影响。本研究使用矮小型黄羽肉鸡作为试验群体,采用鸡600K SNP芯片（Affymetrix Axion HD genotyping array）进行基因分型,测定435羽子代公鸡45、56、70、84、91日龄体重。利用Beagle软件将低密度SNP芯片填充为高密度SNP芯片数据,比较不同参考群筛选方法、参考群规模对基因型填充准确性的影响,以及填充芯片基因组预测准确性。结果表明,使用Beagle 4.0结合系谱信息进行填充效果最佳,其次为Beagle 4.0,而Beagle 5.1填充效果最差。使用MCA方法筛选参考群进行基因型填充准确性最高,使用RAN方法筛选参考群进行基因型填充准确性最低,MCA、RELA、CA 3种方法基因型填充准确性差别较小。相比其他方法,使用MCA方法筛选个体作为参考群将低密度SNP芯片填充至高密度SNP芯片进行基因组选择的预测准确性较高,与真实高密度SNP芯片的基因组预测准确性相差甚微。随着参考群规模增大,基因型填充准确性也随之增加,但增速逐渐下降,最后趋于平缓。综上所述,可以通过参考群筛选方法构建参考群以及控制参考群规模,以保证基因型填充和基因组预测准确性并节省成本,本研究为基因型填充在畜禽遗传育种中的应用提供技术参考。     

猪瘟野毒感染抗体阻断ELISA检测方法的建立与初步评价

以猪瘟野毒E2蛋白为包被抗原、辣根过氧化物酶标记的猪瘟野毒单抗作为酶标抗体,建立检测猪瘟野毒抗体的阻断ELISA方法。猪瘟野毒E2最适包被浓度为0.03μg/mL,待检血清最适稀释度为1∶4,酶标猪瘟野毒单抗稀释度为1∶1 000。用建立的阻断ELISA方法检测369份临床阴性血清,计算阻断率,确定临界值,阻断率>40%为猪瘟野毒抗体阳性,阻断率≤40%为猪瘟野毒抗体阴性。用建立的ELISA方法检测84份血清,其中78份为免疫猪瘟疫苗的血清,6份为猪瘟病毒感染血清。结果显示,78份免疫血清均检测为猪瘟野毒抗体阴性,6份猪瘟感染血清均检测为猪瘟野毒抗体阳性。因此可初步判定该方法可用于鉴别诊断猪瘟病毒自然感染动物和C株疫苗免疫动物的血清抗体,并为临床检测猪瘟野毒抗体提供便捷、快速,精准的检测工具,对猪瘟的临床诊断、预防以及猪瘟净化工作具有非常重要的参考意义。     

噬菌体制剂对母猪生产性能、免疫指标及粪便微生物的影响

试验旨在研究噬菌体对母猪生产性能、免疫指标和粪便微生物数量的影响。试验选取96头健康长大二元母猪,随机分为4组,每组24头猪。空白对照组母猪饲喂基础日粮,试验组1、试验组2和试验组3分别在基础日粮中加入100、300、500 g/t的沙门氏菌噬菌体、大肠杆菌噬菌体和产气荚膜梭菌噬菌体组成的复合制剂,试验周期为35 d。结果显示:试验组2和试验组3相比空白对照组的窝产活仔数、断奶活仔数、平均奶均重和平均日增重均显著提高(P<0.05)。试验组1相比空白对照组的断奶活仔数、平均奶均重和平均日增重均显著提高(P<0.05)。在分娩当日,相比空白对照组,试验组3的免疫球蛋白A (IgA)含量显著提高(P<0.05)。试验组2和试验组3的免疫球蛋白M (IgM)和免疫球蛋白G (IgG)含量显著提高(P<0.05)。在断奶当日,相比空白对照组,3种剂量组的IgA、IgM和IgG含量均显著提高(P<0.05)。试验组2和试验组3的乳酸菌含量显著增加(P<0.05)。3种剂量组的大肠杆菌和沙门氏菌数量均显著降低(P<0.05)。试验表明,添加复合噬菌体制剂能提高母猪生产性能,提高机体免疫力,改善肠道微生态环境,日粮中添加300 g/t复合噬菌体制剂比较合适。     

基于机器视觉的饲料生产智能包装系统设计与应用研究

为提高饲料生产订单的包装效率、智能化程度、降低包装成本,文章基于机器视觉,从硬件选择与软件设计两个层面设计饲料生产智能包装系统。采用AutoMod软件对饲料生产智能包装系统进行仿真试验,检验整个包装系统效果。结果表明,该智能包装系统包装效率高、关键设备利用率高、系统可靠性、可行性良好。     

饲料中动物源性成分微流控检测芯片的研究

试验以"LAMP+微流控"技术为基础,开发饲料动物源性成分检测芯片。针对牛、羊、猪、鸡的16srRNA靶序列,根据LAMP引物设计软件进行4种肉类LAMP引物设计。在LAMP优化试验、芯片优化测试、特异性验证试验和芯片应用测试中,进行最佳引物筛选测试和特异性测试。结果显示,筛选出的4种肉类最佳引物特异性强,最佳反应温度为65℃。芯片特异性验证试验的归一化荧光曲线显示,试验结果准确、特异性稳定。对饲料市场购买的16份样本中检测出的动物源性成分,采用中华人民共和国农业行业标准验证,符合率100%。微流控检测芯片的开发与应用为饲料中动物源性成分检测提供新方法,为饲料产品质量安全提供保障。