基于简化基因组测序技术和基因芯片技术比较研究黄羽肉鸡基因组选择

旨在比较简化基因组测序技术和基因芯片技术实施基因组选择的基因组估计育种值（GEBV）准确性。本研究在AH肉鸡资源群体F₂代中随机选取395个个体（其中公鸡212只,母鸡183只,来自8个半同胞家系）,同时采用10×SLAF测序技术和Illumina Chicken 60K SNP芯片进行基因标记分型。采用基因组最佳无偏估计法（GBLUP）和BayesCπ对6周体重、12周体重、日均增重、日均采食量、饲料转化率和剩余采食量等6个性状进行GEBV准确性比较研究,并采用5折交叉验证法验证。结果表明,采用同一基因标记分型平台,两种育种值估计方法所得GEBV准确性差异不显著（P>0.05）;不同的性状对基因标记分型平台的选择存在差异,对于6周体重,使用基因芯片可获得更高的GEBV准确性（P<0.05）,对于剩余采食量,则使用简化基因组测序可获得更高的GEBV准确性（P<0.05）。综合6个性状GEBV均值比较,两个基因标记分型平台之间差异不到0.01,高通量测序技术和基因芯片技术都可以用于黄羽肉鸡基因组选择。     

杜洛克公猪背膘厚度、日增重、日采食量和饲料效率相关性状的遗传参数估计

对384头扬翔华系杜洛克公猪生长育肥阶段背膘厚度(BF)、日增重(ADG)、日采食量(ADFI)、饲料转化率(FCR)和剩余采食量(RFI)5个性状用DMU程序进行遗传参数估计探究杜洛克公猪的遗传参数。结果表明, ADFI和RFI的遗传方差占表型方差的比例分别为87.73%和84.91%;BF和ADG的遗传方差占表型方差的比例分别是58.77%和58.91%;RFI与BF、ADG的表型相关系数均为零,而遗传相关系数分别为-0.22和0.15;RFI与ADFI的表型、遗传相关系数分别为0.98和0.99,RFI与FCR的表型、遗传相关系数分别为0.44和0.81。多性状模型与单性状模型的估计遗传力基本趋于一致,ADG和FCR遗传力分别是0.27和0.19,属于低遗传力性状;而BF、ADFI和RFI的估计遗传力在0.31～0.46范围,属于中等遗传力性状。     

提高猪饲料效率的测定与选择

为提高猪饲料效率的选择,本试验测定一些与猪饲料效率相关的生产性状并进行遗传评估。方法：测定60头军牧1号白猪后备公猪的采食量、体增重、背膘厚等生产性状,用猪剩余采食量（RFI）和饲料转化率（FCR）作为评价饲料效率的两个指标,并对其遗传参数进行评估。结果：测定期内军牧1号公猪群体FCR均值为2．61,RFI的标准差为77．52。RFI与FCR的遗传力分别是0．35、0．33,RFI与ADFI（日采食量）、ADG（日增重）、BF（背膘厚）的遗传相关分别是0．89、0．12、-0．05,FCR与ADFI、ADG、BF的遗传相关分别是0．55、-0．65、-0．11。结论：军牧1号白猪品种内饲料效率存在较大的遗传差异,由于RFI与ADG遗传相关很低,因此用RFI作为选择性状可有效提高猪的饲料效率。     

基于猪剩余采食量的分化选择与遗传进展

猪剩余采食量(Residual Feed Intake,RFI)是一个有效评价生猪饲料效率的新性状,用剩余采食量来提高生猪饲料效率是当前养猪界的重要研究方向。本试验测定了2个世代198头军牧1号公猪的周采食量、日增重(ADG)和背膘(BF)等性状,和母猪453头母猪日增重(ADG)和背膘(BF)等性状,然后建立了饲料效率育种值估计模型,以剩余采食量(RFI)为主选性状,ADG和BF为辅助约束性状,通过遗传评估按育种值高低分为高RFI和低RFI 2个群体。在这2个分化群体内进行同质选配2个世代后计算其遗传进展。结果显示,RFI、ADG、BF的遗传力分别为0.15、0.22、0.28。G0代选择群体高、低RFI之间,RFI、ADG、BF性状的育种值差异分别是1.48、0.40、0.56个标准差;G1代高、低RFI测定群体之间3个性状的育种值差异分别是1.52、0.56、0.18个标准差。原始群体与G1代测定群体相比,高RFI群体的RFI、ADG、BF的遗传进展分别是0.732、-0.274、0.101个标准差,低RFI群体的RFI、ADG、BF的遗传进展是-0.788、0.283、-0.079个标准差。此外,肉质检测结果显示2群体间无显著差异。结果表明,用RFI作为主选性状可有效改变猪的饲料效率,建立饲料效率分化品系,遗传进展良好。     

快大型黄羽肉鸡肉品质性状的遗传参数估计和关键基因挖掘

旨在挖掘快大型黄羽肉鸡胸肌肉品质性状的重要候选区间和基因。本研究以1 923只快大型黄羽肉鸡为素材，于56日龄屠宰并测定屠宰和胸肌肉品质性状；利用“京芯一号”55K SNP芯片进行基因分型，利用传统最佳线性无偏预测（BLUP）、基因组最佳线性无偏预测（GBLUP）和全基因组关联分析（GWAS）等方法进行遗传参数估计和QTL区间/关键基因的检测。结果显示，胸肌pH、肉色L_{24 h}^*。同时发现，位于5号染色体上的2个单倍型对胸肌pH、肉色性状均有极显著影响。以上结果为黄羽肉鸡肉品质遗传选择方案优化和分子育种技术研发奠定了重要基础。     

基于多层感知机的绵羊限性性状基因组选择模拟研究

旨在将多层感知机(multilayer perceptron, MLP)应用于绵羊限性性状基因组选择中，并在多种情况下与其他经典基因组选择方法进行比较分析。本研究利用Qmsim软件模拟2个绵羊群体Pop1和Pop2的表型数据和基因型数据。在MLP中使用人工神经网络(artificial neural network, ANN),线性模型中使用约束性最大似然法(residual maximum likelihood, REML)估计不同群体的遗传参数。利用Python语言自编MLP模型，利用DMU软件实现最佳线性无偏预测(best linear unbiased prediction, BLUP)、基因组最佳线性无偏预测(genomic BLUP)和一步法(single-step GBLUP, SSGBLUP)模型，评估不同情况下各方法遗传力(heritability,h²)和育种值估计方面的差异。各情况下，MLP和SSGBLUP均显著(P<0.05)优于GBLUP和BLUP。在3种情况下MLP的h²估值与SSGBLUP差异不显著：h     

结合GWAS先验标记信息的肉鸡RFI性状全基因组选择研究

旨在比较结合全基因组关联分析（genome-wide association study,GWAS）先验标记信息的基因组育种值（genomic estimated breeding value,GEBV）估计与基因组最佳线性无偏预测（genomic best linear unbiased prediction,GBLUP）方法对鸡剩余采食量性状育种值估计的准确性,为提高基因组选择准确性提供理论与技术支持。本研究选用广西金陵花鸡3个世代共2 510个个体作为素材,其中公鸡1 648只,母鸡862只,以42～56日龄期间的剩余采食量（residual feed intake,RFI）为目标性状,将试验群体随机分为两组,其中一组作为先验标记信息发现群体,用于GWAS分析并筛选最显著的top5%、top10%、top15%和top20%的位点作为先验标记信息;另外一组分别结合不同的先验标记信息进行遗传参数估计并比较基因组育种值的预测准确性,使用重复10次的五倍交叉验证法获取准确性,随后两组群体再进行交叉验证。研究结果表明,GBLUP计算RFI的遗传力为0.153,预测准确性为0.387～0.429,结合GWAS先验标记信息的基因组选择方法计算RFI的遗传力为0.139～0.157,预测准确性为0.401～0.448。将GWAS结果中P值最显著的top10%～top15%的SNPs作为先验信息整合至基因组选择模型中可以将RFI的预测准确性提升2.10%～5.17%。     

优质矮小型黄羽肉鸡饲料利用效率相关性状的遗传参数估计

以优质矮小型黄羽肉鸡作为研究材料,结合采食量自动测定设备记录的采食量和体重数据,对30个家系的1 158个个体的饲料利用效率相关性状进行遗传力估计,并分析其与生长性状、部分屠体性状的相关关系。结果表明,44~83日龄剩余采食量的遗传力为0.35,44~83日龄饲料转化比的遗传力为0.22,两者呈显著的表型和遗传相关(r_p=0.66,r_g=0.75)。对两者进行选择会对生长性状和部分屠体性状带来不同程度的间接选择反应。饲料转化比与日均采食量的遗传相关为0.44,剩余采食量与日均采食量的遗传相关程度更高,为0.75。如果单纯从降低采食量的角度考虑,选择低剩余采食量比选择低料重比的效果可能会更好。     

比较机器学习等算法对肉鸡产蛋性状育种值估计的准确性

我国白羽肉鸡育种中,通过遗传途径提高产蛋数和控制合适的蛋重是培育优良品系的一个重要方面。为探索适合我国白羽肉鸡育种中的基因组选择模型,本研究以2 474只白羽肉鸡品系的产蛋性状为研究对象,主要分析了机器学习算法KAML、BLUP(包括：PBLUP、GBLUP、SSGBLUP)和Bayes(包括：Bayes A、Bayes B和Bayes Cπ)方法对产蛋数和蛋重性状的预测准确性,准确性以5倍交叉验证进行评估。利用系谱以及基因组信息估计了产蛋数和蛋重性状的遗传力和遗传相关。结果表明,产蛋数性状遗传力为0.061~0.16,属于低遗传力性状;蛋重遗传力为0.28~0.39,属于中等遗传力性状;产蛋数与蛋重是中等遗传负相关(-0.518~-0.184),不同阶段产蛋数之间是强的遗传正相关(0.736~0.998)。不同模型预测43周产蛋数和52周蛋重的育种值估计准确性结果表明,KAML方法对两者的预测准确性分别为0.115和0.266,与GBLUP方法(准确性分别为0.118和0.283)和SSGBLUP方法(准确性分别为0.136和0.259)的准确性差异显著,同时显著低于Bayes方法(准确性分别为0.230~0.239、0.336~0.340)的预测准确性, PBLUP方法预测准确性最低(准确性分别为0.095和0.246)。因此,在白羽肉鸡产蛋数和蛋重性状中应用Bayes方法将获得最高的育种值估计准确性。     

内蒙古绒山羊绒毛性状基因组育种值估计准确性研究

为探究一步法基因组最佳线性无偏预测(SSGBLUP)法应用于内蒙古绒山羊育种的选择效果,本研究基于课题组前期积累的健康状况良好的内蒙古绒山羊(阿尔巴斯型)2 256只个体的70 K SNP芯片测序数据,收集整理1至8岁个体的绒毛性状(绒长、绒细和产绒量)生产性能数据和系谱记录,通过设定SSGBLUP法中H逆矩阵的不同矩阵参数(ω,τ)进行基因组育种值估计,并利用五倍交叉验证法评价基因组育种值估计的准确性。结果表明：随着ω的不断增加,SSGBLUP法用于内蒙古绒山羊绒毛性状的基因组育种值估计准确性越高。结合ABLUP和GBLUP的遗传参数估计结果可知,当τ为0.3、ω为0.9时,内蒙古绒山羊绒毛性状的基因组选择准确性较好。其中,绒长的准确性为0.702 8,绒细准确性为0.668 2,产绒量准确性为0.713 1。对SSGBLUP方法的H矩阵选择合适的尺度参数可提高内蒙古绒山羊绒毛性状基因组育种值估计的准确性,加快种群的遗传改良,缩短世代间隔。     

Comparison between Reduced-Representation Genome Sequencing and SNP Chip for Genomic Selection in Yellow-feathered Broiler

This study aimed to compare the accuracy of the genomic estimated breeding value (GEBV) using reduced-representation genome sequencing technology and SNP chip technology to implement genomic selection. A total of 395 individuals (212♂+ 183♀, from 8 half-sib families) were randomly selected from F₂ generation of AH broiler resource population, and genotyped with 10×specific-locus amplified fragment sequencing (SLAF-seq) and Illumina Chicken 60K SNP BeadChip. Genomic best linear unbiased prediction (GBLUP) and BayesCπ were used to compare the accuracy of genomic estimated breeding values (GEBV) for 6 traits: body weight at the 6^th week, body weight at the 12^th week, average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR) and residual feed intake (RFI). A 5-fold cross validation procedure was used to verify the accuracies of GEBV between prediction models and between genotyping platforms. The results showed that there was no significant difference between accuracies of GEBV predicted by GBLUP and BayesCπ using the same genotyping platform(P>0.05). The superiority of the two genotyping platforms was different for different traits. For body weight at the 6^th week, the accuracy of GEBV was higher using chip SNPs (P<0.05). On the contrary, the accuracy was higher using SLAF-seq for residual feed intake (P<0.05). Comprehensive comparison of the means of GEBV for 6 traits, the difference between the two genotyping platforms was less than 0.01, therefore, both high throughput sequencing and chip SNPs can be used for genomic selection in yellow-feathered broiler.     

Residual intake and body weight gain: a new measure of efficiency in growing cattle

Interest in improving feed efficiency in cattle is intensifying. Residual feed intake (RFI), which is the difference between expected intake and that predicted based on energy demands, is now the most commonly used measure of feed efficiency over a given time period. However, RFI, as commonly defined, is independent of growth rate, which may affect its acceptance by industry. Residual BW gain (RG) has also been proposed as a measure of feed efficiency and is represented as the residuals from a multiple regression model regressing ADG on both DMI and BW. In this study, we propose a new trait, residual intake and BW gain (RIG), which retains the favorable characteristic of both RFI and RG being independent of BW, but animals superior for RIG have, on average, both greater ADG and reduced DMI. Phenotypic and genetic analyses were undertaken on up to 2,605 purebred performance-tested bulls. Clear phenotypic differences in DMI and ADG existed between animals divergent for RIG. The heritability of RIG was 0.36 ± 0.06, which is consistent with the heritability estimates of RFI and other feed efficiency traits measured in the study. The RIG trait was both phenotypically and genetically negatively correlated with DMI and positively correlated with ADG; no correlation existed between RIG and BW. The advantages of both reduced daily DMI and greater ADG in animals superior for RIG are demonstrated compared with animals superior for either RFI or RG.     

Genome‐wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single‐step genomic best linear unbiased prediction method

The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome‐wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single‐step genomic best linear unbiased prediction (ssGBLUP)‐based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny‐tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP‐based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.     

Feed conversion ratio,residual feed intake and cholecystokinin type A receptor gene polymorphisms are associated with feed intake and average daily gain in a Chinese local chicken population

Background: The feed conversion ratio(FCR) and residual feed intake(RFI) are common indexes in measuring feed efficiency for livestock. RFI is a feed intake adjusted for requirements for maintenance and production so these two traits are related. Similarly, FCR is related to feed intake and weight gain because it is their ratio. Cholecystokinin type A receptor(CCKAR) plays an important role in animal digestive process. We examined the interplay of these three parameters in a local Chinese chicken population.Results: The feed intake(FI) and body weights(BW) of 1,841 individuals were monitored on a daily basis from 56 to 105 d of age. There was a strong correlation between RFI and average daily feed intake(ADFI) and a negative correlation between the FCR and daily gain(r_g=-0.710). Furthermore, we identified 51 single nucleotide polymorphisms(SNPs) in the CCKAR and 4 of these resulted in amino acid mutations. The C334A mutation was specifically associated with FI and the expected feed intake(EFI)(P 0.01) and significantly associated with the average daily gain(ADG)(P 0.05). G1290A was significantly associated with FI and EFI(P 0.05).Conclusion: FCR is apply to weight selecting, and RFI is more appropriate if the breeding focus is feed intake. And C334A and G1290A of the CCKAR gene can be deemed as candidate markers for feed intake and weight gain.     

Variance components due to direct genetic, maternal genetic and permanent environmental effect for growth and feed-efficiency traits in young male Japanese Black cattle

Variance components and genetic parameters were estimated using data recorded on 740 young male Japanese Black cattle during the period from 1971 to 2003. Traits studied were feed intake (FI), feed‐conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG), metabolic body weight (MWT) at the mid‐point of the test period and body weight (BWT) at the finish of the test (345 days). Data were analysed using three alternative animal models (direct, direct + maternal environmental, and direct + maternal genetic effects). Comparison of the log likelihood values has shown that the direct genetic effect was significant (p < 0.05) for all traits and that the maternal environmental effects were significant (p < 0.05) for MWT and BWT. The heritability estimates were 0.20 ± 0.12 for FI, 0.14 ± 0.10 for FCR, 0.33 ± 0.14 for RFI, 0.19 ± 0.12 for ADG, 0.30 ± 0.14 for MWT and 0.30 ± 0.13 for BWT. The maternal effects (maternal genetic and maternal environmental) were not important in feed‐efficiency traits. The genetic correlation between RFI and ADG was stronger than the corresponding correlation between FCR and ADG. These results provide evidence that RFI should be included for genetic improvement in feed efficiency in Japanese Black breeding programmes.     

猪PLA2G4A基因单核苷酸多态性及其与饲料报酬性状的关联分析

猪肉是世界第一大肉类消费品,通过提高饲料报酬来降低猪的养殖成本有巨大的经济意义。PLA2G4A基因作为早期脂肪分化的重要基因,已经在多个物种上进行研究。本实验以2016年出生于北京中育种猪有限责任公司的166头大白猪为材料,研究PLA2G4A基因与平均日采食量、饲料转化率、剩余饲料采食量和平均日增重4个饲料报酬性状的关系。结果发现:猪PLA2G4A位于外显子1、12、13、21的4个SNP位点;内含子2、2、11、12、16的5个SNP位点,共计9个SNP位点;对这9个SNP和4个饲料报酬相关性状进行关联分析发现,位于内含子16的SNP位点与猪的平均日增重有极显著相关,而其他SNP位点与这4种饲料报酬性状均无显著相关。     

Genetic relationship between different measures of feed efficiency and its component traits in Japanese Black (Wagyu) bulls

Genetic parameters of average daily gain (ADG), metabolic body weight (MWT), body weight at finish (BWF), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI) were estimated in 740 Japanese Black bulls. RFI was calculated as the difference between actual and expected feed intake predicted by the residual of multiple regression (RFI_phe) and genetic regression (RFI_gen) from the multivariate analysis for DFI, MWT, and ADG. The estimations were made for the test periods of 140 days (77 bulls) and 112 days (663 bulls). The mean for RFI_phe was close to zero and RFI_gen was negative. Most of the traits studied were moderately heritable (ranging from 0.24 to 0.49), except for ADG and FCR (0.20 and 0.15, respectively). The genetic correlations among growth traits (ADG, MWT and BWF) and between DFI and growth traits were high, while the phenotypic correlations between them were moderate to high. The genetic and phenotypic correlations between RFI_phe and RFI_gen were > 0.95 implying that they are regarded as the same trait and the genetic correlations of RFI (RFI_phe and RFI_gen) with FCR and DFI were favorably high. RFI_phe was phenotypically independent of its component traits, MWT (r_p = − 0.01) and ADG (r_p = 0.01). RFI_gen was genetically independent of MWT (r_g = − 0.07), while there was a weak genetic relationship (r_g = 0.18) between RFI_gen and ADG. These results provide evidence that RFI_gen should be included for genetic improvement of feed efficiency in Japanese Black breeding program.