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。     

利用连锁不平衡进行畜禽QTL定位的研究进展

一般家养畜禽群体规模小、选择强度高，连锁不平衡（linkage disequilibrium , LD）现象普遍存在，尤其在杂种或新培育品种群体中更为突出。随着分子遗传学的发展，我们可以直接选择影响性状的基因或包含基因的染色体区域（QTL），可分型的遗传标记和QTL之间是否存在连锁不平衡是畜禽群体中QTL定位和应用的关键。目前，畜禽群体中用基因组范围内SNPs的连锁不平衡精细定位影响畜禽重要经济性状的QTL正受到广泛关注。作者从LD定位QTL的原理、畜禽群体中LD程度、利用LD进行QTL定位的试验设计及统计分析方法、LD在畜禽QTL精细定位中的应用等几个方面进行了综述。并进一步阐述了畜禽群体中利用LD进行标记辅助选择（MAS）的策略，对LD在畜禽QTL定位及标记辅助选择方面的应用进行了新的探讨。     

湖北白猪优质系主要生长和胴体性状的遗传参数估测

应用非求导约束最大似然法（DFREML）对湖北白猪优质系生长和胴体性状的遗传参数进行估计。结果表明,湖北白猪优质系生长性状平均日增重（ADG）、90 kg体质量日龄（AGE）、达90 kg体质量背膘厚（BF）和饲料转化率（FCR）的遗传力估计值分别为0.42、0.34、0.22和0.48。各性状的窝效应变化范围为0.09~0.31。胴体性状遗传力变化范围为0.13~0.72,其中瘦肉率、胴体直长、肋膘厚、腿臀比、眼肌面积和肌内脂肪含量的遗传力估计值分别为0.65、0.72、0.48、0.67和0.32,均属于中高遗传力性状。生长性状ADG/AGE、ADG/BF、ADG/FCR、AGE/BF、AGE/FCR、BF/FCR的遗传相关分别为-0.72、0.25、-0.48、-0.17、0.55和-0.04。胴体性状肌内瘦肉率、眼肌面积和腿臀比与脂肪含量的遗传相关分别为-0.28、-0.15和-0.23,眼肌面积、腿臀比与瘦肉率的遗传相关分别为0.62、0.59。     

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。     

快速型黄羽肉鸡饲料利用效率性状的基因组选择研究

旨在探究快速型黄羽肉鸡饲料利用效率性状的遗传参数,评估不同方法所得估计育种值的准确性。本研究以自主培育的快速型黄羽肉鸡E系1 923个个体（其中公鸡1 199只,母鸡724只）为研究素材,采用"京芯一号"鸡55K SNP芯片进行基因分型。分别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）和一步法（SSGBLUP）3种方法,基于加性效应模型进行遗传参数估计,通过10倍交叉验证比较3种方法所得估计育种值的准确性。研究性状包括4个生长性状和4个饲料利用效率性状：42日龄体重（BW42D）、56日龄体重（BW56D）、日均增重（ADG）、日均采食量（ADFI）和饲料转化率（FCR）、剩余采食量（RFI）、剩余增长体重（RG）、剩余采食和增长体重（RIG）。结果显示,4个饲料利用效率性状均为低遗传力（0.08~0.20）,其他生长性状为中等偏低遗传力（0.11~0.35）;4个饲料利用效率性状间均为高度遗传相关,RFI、RIG与ADFI间为中度遗传相关,RFI与ADG间无显著相关性,RIG与ADG间为低度遗传相关。本研究在获得SSGBLUP方法的最佳基因型和系谱矩阵权重比基础上,比较8个性状的估计育种值准确性,SSGBLUP方法获得的准确性分别比传统BLUP和GBLUP方法提高3.85%~14.43%和5.21%~17.89%。综上,以RIG为选择指标能够在降低日均采食量的同时保持日均增重,比RFI更适合快速型黄羽肉鸡的选育目标;采用最佳权重比进行SSGBLUP分析,对目标性状估计育种值的预测性能最优,建议作为快速型黄羽肉鸡基因组选择方法。     

湖北白猪优质系主要生长和胴体性状的遗传参数估测

本研究应用多性状动物模型DFREML方法估计了湖北白猪优质系生长和胴体性状的遗传参数。生长性状：平均日增重（ADG）、90 kg体重日龄（DAYS）、达90 kg体重背膘厚（BF）和饲料转化率（FCR）的遗传力估计值分别为0.42、0.34、0.22和0.48。各性状均存在一定的窝效应,变化范围为0.09～0.31。胴体性状：宰前活重、胴体重、屠宰率、瘦肉率、眼肌面积、肋膘厚、腿臀比、胴体直长、肋皮厚和肌内脂肪含量的遗传力分别为0.13、0.21、0.32、0.45、0.67、0.52、0.48、0.65、0.72和0.32。生长性状ADG/DAYS、ADG/BF、ADG/FCR、DAYS/BF、DAYS/FCR、BF/FCR的遗传相关分别为-0.72、0.25、-0.48、-0.17、0.55和-0.04。胴体性状眼肌面积、腿臀比与瘦肉率的遗传相关分别为0.62、0.59,肌内脂肪含量与瘦肉率、眼肌面积和腿臀比的遗传相关分别为-0.28、-0.15和-0.23。     

BC1群体单标记分析检测QTL的效率

[目的]为QTL定位中进一步估计QTL的位置和影响效应提供必要的依据。[方法]以BC1设计为资源群体,通过计算机模拟研究了不同群体规模、标记-QTL间图距、性状遗传力和QTL效应（QTL方差占加性遗传方差的比例）对单标记分析检测QTL效率的影响。[结果]表明：资源群体规模较大,标记与QTL的间距较小（或标记与QTL连锁紧密）,目标性状的遗传力较高,且QTL效应较大时,采用单标记分析方法检测QTL的检出率较高。当所检测的标记距离QTL较近时,获得相同的QTL检出率所需的资源群体规模更小。[结论]QTL效应对QTL检出率的影响会受性状遗传力的制约,如果性状的遗传力过低,即使QTL方差占遗传方差的比例很高,也很难获得理想的QTL检出率。     

标记辅助选择(MAS)及在畜禽育种工作中的应用

近年来，标记辅助选择（MAS）的应用研究，已越来越受到畜禽育种工作者的重视。应用MAS，可以更加深入地研究数量性状遗传，使遗传学研究有了新的突破。LaMe和Thompson（1990）采用模拟研究MAS与传统指数选择相比较，结果表明：对遗传力低和家系共同效应大的性状，采用MAS的效果可比传统指数选择效果提高3～4倍。Kash等（1990）对奶牛的后备公牛采用MAS的遗传改进也表明可比传统后裔测验方法提高20％～30％。所以，MAS的应用研究前景十分广阔。     

长白和大白猪主要生长性状的遗传参数估计

旨在分析母猪的出生年份、出生季节、初生重、开测日龄等固定效应对长白、大白猪主要生长性状的影响,并对目标生长性状进行遗传参数估计（遗传力、遗传方差、表型相关和遗传相关）,为猪的遗传改良提供基本依据。本试验利用GLM模型分析试验猪群（398头长白猪和1 176头大白猪）的固定效应对猪生长性状的影响,并采用多性状动物模型对目标性状进行遗传参数估计。目标生长性状包括达100 kg体重日龄（age to 100 kg,AGE）、达100 kg背膘厚（backfat to 100 kg,BF）、100 kg平均日增重（average daily gain to 100 kg,ADG）。研究表明,在大白和长白猪中,猪的出生年、出生季、初生重以及开测日龄对生长性状均具有极显著的影响（P<0.001）;长白猪的AGE、ADG和BF的遗传力分别为0.321、0.327和0.324,大白猪对应性状的遗传力分别为0.454、0.469和0.408;长白猪的ADG和AGE之间的遗传相关、表型相关分别为-0.990、-0.995,大白猪的ADG和AGE之间的遗传相关、表型相关分别为-0.993、-0.998,均呈现较强的负相关。长白、大白猪的生长性状（AGE、ADG、BF）均属于中等遗传力性状,其出生年份、出生季节、初生重和开测日龄对猪的生长性状影响较大。在遗传参数估计分析时,提高样本数量并提升表型数据质量,可以增加遗传参数估计的可靠性。本研究中的生长性状遗传参数估计结果较为可靠,可为后续的遗传改良提供参考。     

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。     

Genomic selection

Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.     

A method to detect recombination between a genetic marker and the QTL that it marks

Some individual genetic markers show strong and apparently consistent effects on trait merit and are taken as causative mutations that can be used directly as fixed effects in marker‐assisted selection programs. If the effect of such a marker is seen to decrease over time, key reasons include epistasis, where the effect depends on genetic background, and recombination, where the marker is in fact not causative, and strong linkage disequilibrium between the marker and the causative QTL is breaking down. This paper presents a method to detect the latter scenario, including calculation of the probability of a recombinant haplotype for each gamete contributing to each individual in a pedigree. This method requires only pedigree, phenotypes and genotypic information on the single marker. Missing marker genotypes are handled by the method, but with diminishing power. For biallelic markers, strong QTL effects are needed for the method to be of clear value. Given suitable results, breeders may chose to eliminate certain individuals from the breeding program in order to continue using the single genetic marker under high linkage disequilibrium with the causative QTL. Alternatively, other linked markers might be sought that can be used individually or in haplotype tests to restore strong LD for marker‐assisted selection.     

Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Linkage disequilibrium and selection response in two-stage marker-assisted selection of dairy cattle over several generations

A stochastic simulation was carried out to investigate the advantage of marker‐assisted selection (MAS) in comparison with traditional selection over several generations. The selection goal was a sex‐limited trait or a linear combination of traits with a polygenic component, two unlinked additive QTL and a non‐genetic component. The simulated QTL were moderate or large and the allele frequencies were varied. Two stages of selection among the male offspring were carried out. In the first stage marker information was used to select among full sibs (MAS) or one full sib was chosen at random. In the second stage young bulls were selected based on a progeny test. The response in total genetic gain was faster with MAS than with traditional selection and persisted over several generations. With a QTL of moderate size and initial allele frequencies of the favourable allele of 0.05 the response with MAS was 6% higher than with traditional selection in the sires selected after progeny test. MAS in a within‐family two‐stage selection scheme improved the genetic merit of selected bulls even when linkage disequilibrium between QTL and polygenes was initially increased.     

畜禽基因组选择的研究进展

基因组选择（genomic selection,GS）是继标记辅助选择（marker-assisted selection,MAS）之后发展起来的新一代畜禽遗传评估的新方法。近年来,不少学者从各方面对GS在畜禽育种中的运用进行了研究,结果发现,与其他方法相比,GS优势明显,是当前畜禽遗传育种领域的研究热点。作者系统地阐述了GS估计染色体片段效应的方法及其准确性比较,详细介绍了影响GS准确性的因素、GS的经济效益,以及世界各国学者研究GS在实际育种中的应用情况,最后简述了中国畜禽育种开展GS策略所面临的挑战及其展望。     

标记辅助导入中不同背景选择方法的比较

模拟比较了随机选择、标记值选择及BLUP选择3种背景选择方法在标记辅助导入(利用标记辅助将供体群中的一个有利QTL等位基因导入到受体群中)的选择效果。前景选择是借助与目标基因连锁的两侧标记对目标基因进行间接选择。研究结果表明,在背景选择中,利用标记值选择能使受体基因组很快得到恢复,2个世代的回交就能恢复90%以上,4个世代的回交就能完全恢复。利用BLUP选择虽然不能使受体基因组迅速全部恢复,但能使特定的背景性状得到最大的遗传进展。     

QTL基因型值对标记辅助导入的影响

在标记辅助导入QTL的过程中,利用指数法进行背景选择。研究结果表明基因型值的大小对于导入QTL的频率和两个背景QTL的频率以及受体遗传背景的恢复影响很小,对于两个背景性状来说,基因型值较大时要比较小时获得更大的遗传进展。对于前景性状来说,在横交阶段,基因型值较大时要比较小时获得更大的遗传进展;而在回交阶段,结果正好相反。     

Marker‐assisted selection reduces expected inbreeding but can result in large effects of hitchhiking

We used computer simulations to investigate to what extent true inbreeding, i.e. identity‐by‐descent, is affected by the use of marker‐assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h² = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (α = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.     

Efficiency of quantitative trait loci-assisted selection in correlations between identified and residual genotypes

This study quantified the efficiency of quantitative traits loci (QTL)‐assisted selection in the presence of correlations (ρ_qr) between identified (q) and residual (r) genotypes. Two levels of heritability (h² = 0.1 or 0.3), two levels of correlation (ρ_qr = ?0.3 or 0.3) and five proportions of genetic variance explained by QTL detected (= 0.1, 0.2, 0.4, 0.6 or 0.8) were combined to give 20 scenarios in all. QTL‐assisted selection placed a larger index weight on the QTL genotype than on the phenotype in 17 of 20 scenarios, yielding a greater response in the QTL genotype than in residual genotype. Although QTL‐assisted selection was superior to phenotypic selection in all 20 scenarios, QTL‐assisted selection showed a greater advantage over phenotypic selection when ρ_qr was positive than when ρ_qr was negative. Doubling the proportion of detected QTL variance to genetic variance does not result in a twofold increase in the genetic response to QTL‐assisted selection, suggesting that economic returns diminish for each additional cost of detecting extra QTL. The correlation between q and r would make the interpretation (or prediction) of QTL effects difficult and QTL‐assisted selection strategy must consider the joint effect of q and r. When q and r are not independent, a failure to account for ρ_qr in QTL‐assisted selection would underestimate the genetic responses when ρ_qr is positive, but overestimate the genetic responses when ρ_qr is negative. Estimation bias is more serious at high heritability than at low heritability. Accounting for ρ_qr would improve the efficiency of QTL‐assisted selection and the accuracy of QTL detection. The generalized procedure developed in this study allows for quantifying the efficiency of QTL‐assisted selection and assessing estimation bias for ignoring the correlation between q and r for all possible combinations of h², ρ_qr, and .     

Genomic best linear unbiased prediction method reflecting the degree of linkage disequilibrium

The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP‐LD). In the three GBLUP‐LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP‐LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP‐LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP‐LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP‐LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP‐LD methods were lower than GBLUP and Bayesian lasso.