整合生物学先验信息的全基因组选择方法及其在家畜育种中的应用进展

相较于传统的育种方法,全基因组选择（genomic selection,GS）通过对拟留种的个体进行早期选择和增加选择的准确性进而加快育种的遗传进展。通过改进GS方法无法再缩短育种的世代间隔,因而如何提高GS的准确性以获得额外的遗传进展一直是GS研究的核心问题。当前,各种组学技术不断成熟,从公开的资料或前期的研究积累获取生物学先验信息已比较容易。因而,如何在GS模型中整合已知的先验信息进而提高GS的准确性以获得额外的遗传进展成为当前育种研究的热点问题。本文对生物学先验信息的类型以及整合先验信息的GS方法进行综述,探讨了这些方法在家畜育种中的应用和前景,以期为家畜育种中开展整合生物学先验信息的GS研究提供借鉴与参考。     

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1923个个体(其中公鸡1199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...     

鸡血糖性状的全基因组关联分析

旨在挖掘影响鸡血糖性状的有效SNP位点及功能基因,为优质肉鸡分子育种工作提供有效的理论支撑。本试验选取407只京星黄母鸡于98日龄屠宰,酚仿法提取血液DNA,进行深度为10×的全基因组重测序;葡萄糖氧化酶法测定血清中血糖水平,基于全基因组重测序和血糖表型数据进行全基因组关联分析（GWAS）。结果,GWAS共筛选到6个血糖相关的SNPs位点（关联阈值P<1.43×10^-6）。基因注释发现,rs734134177在UBE3D基因第8内含子上,其编码蛋白为泛素蛋白连接酶。该位点携带野生型（AA）个体的血糖水平极显著高于突变型（GG）个体（P<0.01）;rs794554022位于ACAD9基因下游D 93.5 kb处。ACAD9蛋白为酰基辅酶A脱氢酶家族的成员之一,是细胞线粒体中脂肪酰基辅酶A进行β-氧化过程中的限速酶。rs794554022位点携带野生型（AA）个体的血糖水平极显著低于携带突变型（CC）个体的（P<0.01）。以上位点可能是调控血糖水平的相关候选SNPs位点,这两个位点所在基因可能参与了肉鸡血糖代谢的调控过程,这些结果将为调控肉鸡血糖代谢进而改善肉品质的育种工作提供候选的分子标记,为肉鸡血糖代谢的调控提供了新的思路。     

蒺藜苜蓿耐酸铝性状的全基因组关联分析

铝毒害是酸性土壤耕种的主要限制因素,每年造成大量作物减产。蒺藜苜蓿是紫花苜蓿的一年生近缘种,广泛分布于世界各地,是紫花苜蓿遗传改良的重要基因资源。本研究利用蒺藜苜蓿群体的耐酸铝性状差异,进行全基因组关联分析,筛选蒺藜苜蓿耐酸铝性状相关的遗传位点,共得到58个与蒺藜苜蓿耐酸铝性状相关的SNP标记。对其周围基因进行功能注释分析,发现这些SNP位点主要参与苜蓿的细胞壁、脂质代谢、环境胁迫响应过程、氧化还原反应过程以及小分子转运等过程。最后,通过基因组选择方法将发掘SNP标记应用到蒺藜苜蓿耐酸铝性状的预测,预测准确性达到0.80,这说明本研究发掘的SNP标记可以用于蒺藜苜蓿及其近缘物种紫花苜蓿耐酸铝性状的遗传改良。     

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。     

猪主要经济性状的基因组选择研究

旨在系统比较GBLUP、SSGBLUP、BayesA、BayesB、BayesC、BayesLASSO、BSLMM和BayesR等8种方法对猪重要经济性状基因组选择的准确性。本研究以本实验室收集的2 585头大白猪达100kg日龄、达100kg背膘厚和母猪乳头数3个性状为分析对象,结合猪50K基因芯片分型数据,以加性模型为基础,利用5倍交叉验证比较8种方法的基因组选择准确性。研究发现,基因组选择的准确性与不同性状估计遗传力呈正相关。交叉验证结果表明,预测准确性最高的性状为达100kg日龄,但不同方法在不同性状中表现并不完全相同,在达100kg日龄和达100kg背膘厚中SSGBLUP基因组预测准确性均为最高,而在母猪乳头数中BayesA的基因组预测准确性最高。综上表明,对小样本开展基因组预测时,中、高等遗传力性状可以选择SSGBLUP方法,低等遗传力性状可以选择BayesA方法。如何优化和选择一种广泛适用于所有性状的方法,可能是未来研究的方向。     

全基因组关联分析(GWAS)在鸡重要经济性状基因定位中的研究进展

全基因组关联分析（GWAS）是通过分析表型性状和基因遗传变异之间的关联性从而确定影响表型性状的遗传因素的一种方法,目前已广泛应用于畜禽生产中。文章主要对GWAS在鸡生长性状、繁殖性状、蛋品质性状和屠宰性状等重要经济性状基因定位中的研究进展进行综述,旨在为鸡遗传育种和种质改良以及地方特色鸡品种的保种提供思路。     

家养动物数量性状全基因组关联研究进展

全基因组关联分析是一种旨在充分利用群体水平的连锁不平衡,以高密度SNP芯片为技术基础,在全基因组范围内定位影响表型性状的遗传因素的遗传分析方法。对群体分层的主要检测方法、基于单标记线性混合模型策略的GWAS分析方法研究进展及全基因组关联分析样本量的估计予以综述,为家养动物数量性状的主效QTC定位提供参考。     

绵羊体重性状全基因组关联分析

Motivated by mining major candidate genes across Ovine genome, the present study is to perform genome-wide association studies(GWAS) to detect genes associated with body weight traits. Using Illumina OvineSNP50 BeadChip, we performed a GWA study in 329 purebred sheep phenotyped for 6 body weight traits(birth weight, weaning weight, 6-month weight, pre-weaning gain, post-weaning gain, daily weight gain). Statistics and data analysis were based on TASSEL program,mixed linear model and the latest Ovis_aries_v3.1 genome sequence (released October 2012). The results indicated that 10 SNPs consistently reached genome-wise significant level for post-weaning gain and 22 SNPs reached chromosome-wise significant level for other body weight traits. The SNPs were within (MEF2B,RFXANK,et al) or close to some ovine genes, which were thought to be the most important candidate genes associated with body weight traits. The results will contribute to identify candidate genes for ovine body weight traits, and facilitate the potential utilization of genes involved production traits in sheep in future.     

猪肉质性状全基因组关联分析的研究进展

全基因组关联分析(genome-wide association study, GWAS)是在全基因组范围内,以单核苷酸多态性标记(single nucleotide polymorphism sign, SNPs)作为分子遗传标记,筛选出与数量性状相关的SNP、数量性状基因座(quantitative trait locus, QTL)和候选基因的有效手段。猪肉品质是猪的重要经济性状,与人们的肉食营养、肉食品加工和养猪业经济效益密切相关。本文主要对GWAS在猪肉质性状的研究应用展开论述,以期为通过GWAS鉴别影响猪肉质性状的主效基因来改善猪肉品质提供理论依据。     

利用基因组信息提高畜禽生产性能研究进展

为了满足人们对畜产品需求的快速增长,必须在加快畜禽产业发展的同时把对环境的影响降到最低,提高畜禽遗传特性有望促进这一问题的解决。进入21世纪以来,以基因组选择为核心的分子育种技术迎来了发展机遇,利用该技术可实现早期准确选择,从而大幅度缩短世代间隔,加快群体遗传进展,并显著降低育种成本。虽然在某些畜种中（如奶牛）,基因组选择取得了成功,群体也获得较大遗传进展,但仍无法满足快速增长的需求。因此,亟需寻找能够进一步加快遗传进展的方法。研究表明,在SNP标记数据中加入目标性状的已知功能基因信息,可以提高基因组育种值预测的准确性,进而加快遗传进展。而挖掘更多基因组信息的同时,开发更优化的分析方法可以更有助于目标的实现。文章总结了主要畜禽物种的可用基因组数据,包括牛、绵羊、山羊、猪和鸡以及这些数据是如何有助于鉴定影响重要性状的遗传标记和基因,从而进一步提高基因组选择的准确性。     

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。     

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.     

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.     

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。     

基因组选择在家畜改良上的研究进展

基因组选择（GS）是全基因组范围内的分子标记辅助选择,目前被证明是利用DNA标记信息改善复杂性状最有效的方法。本文简要概述了基因辅助选择以及标记辅助选择;重点介绍了GS,包括GS的实施策略与育种值估计方法,GS的准确性获得以及对GS方法的比较,总结了当前家畜上利用GS加速遗传改良的应用进展;并对家畜在GS上的应用前景进行展望。     

一种适用于单胎动物多品种选择的基因组关系矩阵

旨在提出一种新型基因组关系矩阵并验证其在多品种联合群体中的模拟应用效果。本研究利用QMsim软件模拟牛的表型数据和基因型数据;利用Gmatrix软件构建常规G阵;利用R语言构建新型G阵,新型G阵在常规G阵的基础上,将多品种联合群体的非哈代-温伯格平衡位点考虑在内;利用DMU软件使用“一步”法模型计算基因组估计育种值（estimated genomic breeding value,GEBV）;比较不同情况下使用两种G阵的GEBV预测准确性。结果表明,在不同遗传力及QTL数下,不对新型G阵使用A₂₂阵加权就能达到常规G阵使用A₂₂阵加权时的GEBV预测准确性。在系谱部分缺失时,新型G阵不加权较常规G阵加权时GEBV预测准确性高。证明,在系谱有部分缺失时,新型G阵对多品种GEBV的预测有一定优势。     

Effects of Marker Selection and Mix Time on the Coefficient of Variation (Mix Uniformity) of Broiler Feed

An experiment was conducted evaluating several markers to determine mix uniformity. Treatment diet was a corn-soybean meal-based diet formulated for broiler chicks fed from 0 to 17 d posthatch. Dietary nutrients or tracers evaluated included the following: 1) dl-Met (99%), 2) l-Lys-HCl (78%), 3) CP, 4) mixing salt (chloride ion), 5) P, 6) Mn, 7) Fe particles (#40 Red, count), 8) Fe particles (#40 Red, absorbance), 9) Fe particles (RF-Blue Lake), 10) roxarsone, and 11) semduramicin. All minor and microingredients were individually hand-weighed and added to the mixer to insure accuracy and were added at the same location for all treatments. Diets were mixed using a double ribbon mixer for 3 different mix times (0.5, 2.5, and 5.0 min). Overall, from 0.5 to 5.0 min, all markers evaluated showed a numerical reduction in percentage of CV. Crude protein and P should not be considered as markers, because many different components in the batch of feed contribute some level of CP or P, and results can be confounding. dl-Methionine (99%) and l-Lys-HCl (78%) were the only markers that statistically reduced over time and had a CV < 10% (23.86 to 9.47% and 19.75 to 8.70%, respectively). These data suggest that mixer uniformity results can be influenced by the particular marker that is chosen for mixer uniformity analysis.