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。     

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。     

基于SNP芯片的猪基因组选择效果分析

为探究单步基因组最佳线性无偏预测(SSGBLUP)法应用于生猪育种的选择效果，选取杜洛克、长白、大白种猪共1 996头，利用SNP芯片获得个体基因型数据，结合表型数据和系谱数据，利用HIBLUP软件的SSGBLUP模型和基于系谱的最佳线性无偏预测(PBLUP)模型分别计算估计育种值，参考全国种猪遗传评估中心的标准计算综合选择指数，利用理论准确性和后裔测定成绩评估选择效果。结果表明：通过SSGBLUP法计算达100 kg日龄、达100 kg背膘厚及总产仔数的基因组估计育种值(GEBV)与PBLUP法计算的估计育种值(EBV)的相关系数均大于0.8;达100 kg日龄、达100 kg背膘厚及总产仔数GEBV准确性相对于EBV准确性均有所提高；SSGBLUP法与PBLUP法选留的长白和大白种猪，其后代的生长速度显著优于场内选择法选留种猪的后代。本试验中，SSGBLUP法与PBLUP法均能有效提高选留种猪后代的生长速度且二者分别计算的GEBV和EBV相关性高，但SSGBLUP法选种的准确性更高，后期可利用全基因组选择对场内现有种猪进行选种指导。     

我国荷斯坦青年公牛基因组选择效果分析

本研究基于我国荷斯坦奶牛基因组遗传评估和生产性能测定（DHI）结果,旨在分析我国荷斯坦公牛基因组选择的效果。选择1 686头既有基因组遗传评估成绩又有后裔测定成绩的荷斯坦公牛,利用2019年12月基因组遗传评估结果及其女儿的产奶和体型性状数据,通过R软件与Excel计算公牛基因组评估结果与公牛女儿表型数据间的相关性,对我国荷斯坦青年公牛基因组选择效果进行分析。相关性分析结果表明,荷斯坦公牛的基因组性能指数（GCPI）与后裔测定性能指数（CPI）呈正相关（r_s>0.3）,其中产奶量和体细胞评分的基因组育种值（GEBV）与估计育种值（EBV）呈较强的正相关（0.4 < r_s < 0.8）。对公牛女儿表型数据分析结果表明,女儿产奶量、乳蛋白率、乳脂率与肢蹄评分的表型值与公牛GEBV分组趋势一致,且公牛不同产奶性状GEBV组间的女儿性状表型值大部分达到极显著差异（P<0.01）;北京及上海地区公牛产奶性状、体细胞评分和肢蹄评分的GEBV分组与女儿表型值趋势较其他省市（地区）更一致,且GEBV高组与低组之间差值均高于其他省市（地区）。基于1 686头荷斯坦公牛基因组选择及其女儿表型数据的分析结果表明,我国荷斯坦公牛的基因组遗传评估准确性较好,其中产奶量、乳蛋白率、体细胞评分和肢蹄评分的表型数据更好地反映了基因组选择的效果;北京及上海地区较其他省市（地区）更能反映我国荷斯坦公牛基因组选择的效果。     

基于基因组和系谱信息的不同选配方案效果模拟研究

基因组选配（genomic mating,GM）是利用基因组信息进行优化的选种选配,可以有效控制群体近交水平的同时实现最大化的遗传进展。但基因组选配是对群体中所有个体进行选配,这与实际的育种工作有点相悖。本研究模拟了遗传力为0.5的9 000头个体的基础群数据,每个世代根据GEBV选择30头公畜、900头母畜作为种用个体,而后使用基因组选配、同质选配、异质选配、随机交配4种不同的选配方案。其中基因组选配中分别选取遗传进展最大的解、家系间方差最大的解、近交最小的解所对应的交配方案进行选育。每种方案选育5个世代,比较其后代群体的平均GEBV、每世代的遗传进展、近交系数、遗传方差,并重复5次取平均值。结果表明,3种基因组选配方案的ΔG均显著高于随机交配和异质选配（P<0.01）,而且,选取遗传进展最大的基因组选配方案的ΔG比同质选配还高出4.3%。3种基因组选配的方案的ΔF比同质选配低22.2%~94.1%,而且选取近交最小的基因组选配方案ΔF比异质选配低11.8%。同质选配的遗传方差迅速降低,在第5世代显著低于除基因组选配中选择遗传进展最大的方案以外的所有方案（P<0.05）,3种基因组选配方案的遗传方差比同质选配高10.8%~32.2%。这表明基因组选配不仅可以获得比同质选配更高的遗传进展,同时有效的降低了近交水平,并且减缓了遗传方差降低速度,保证了一定的遗传变异。基因组选配作为一种有效的可持续育种方法,在畜禽育种中开展十分有必要。     

基于表型和基因组信息评价北京油鸡保种群保种情况

旨在利用表型和基因组信息对北京油鸡随机交配保种群体近交系数、有效群体大小进行研究,评价北京油鸡保种群体保种情况。本研究以国家级北京油鸡保种场北京油鸡2019年随机交配保种群体40只鸡为研究对象,对表型记录进行整理,同时利用基因组SNP信息,使用PLINK软件分别计算基于ROH的近交系数（F_ROH）、基于纯合基因型的近交系数（F_HOM）、基于联合配子之间相关性的近交系数（F_UNI）;使用GCTA和R软件计算基于基因组关系G矩阵的近交系数（F_GRM）;使用SNeP软件估计北京油鸡历史世代的有效群体大小;使用NeEstimator软件估计基于连锁不平衡方法的当前世代的有效群体大小;使用R软件的PerformanceAnalytics包对F_ROH、F_HOM、F_GRM和F_UNI等不同算法所得近交系数进行相关性分析,评价北京油鸡的保种情况。结果显示,1979—2019年以来,北京油鸡保种群体凤冠、胫羽、五趾等典型的外貌特征明显且百分比稳定;保种群有效群体大小从98世代前的595逐渐降至13世代前的176;2019年北京油鸡随机交配保种群体F_ROH为0.079 8,与F_GRM显著相关（P<0.01）,且相关系数为0.45;除此之外,F_HOM与F_GRM,F_HOM与F_UNI以及F_GRM与F_UNI之间也存在较高的线性相关。北京油鸡1979—2019年以来近交系数增长缓慢,国家级北京油鸡保种场随机交配保种群体的保种工作是十分有效的。基于目前情况,本研究建议每年随机选取一定数量的北京油鸡随机交配保种群体的个体,进行全基因组二代重测序检测,有利于对保种状况进行动态监控,以便随时调整保种工作方案。     

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%。     

广灵驴分子系谱的建立及群体遗传结构分析

旨在对广灵县优种驴场保种群体进行调查的基础上构建分子系谱,并对其种群的遗传结构进行分析。本研究采集保种群成年、体况良好的广灵驴（体重350~400 kg）颈静脉血10 mL（n=107）,其中公驴13份,母驴94份,抗凝处理后提取全血DNA。采用12个微卫星标记进行荧光PCR扩增后,用ABI3730测序仪进行分型。分型结果采用Cervus 2.0和Pedigraph 2.4软件构建分子系谱,同时采用STRUCTURE2.3和Fstat软件计算群体遗传参数,采用R语言的hclust函数绘制7头公驴及其后代的系统发生NJ树（邻接树）。结果,对107头种驴进行了分子系谱构建,找到了30头子代的父亲和7头子代的母亲,系谱可靠性>90%;微卫星标记的平均观测杂合度（H_Obs）和多态信息含量（PIC）分别为0.676 5和0.593 9,标记遗传多样性较高;NJ树对7个公驴家系进行了聚类;群体分化系数（F_ST）为0.184,群体平均近交系数（F_IT）为0.033,群体内近交系数（F_IS）为-0.238,且群体处于哈代-温伯格平衡状态,存在很弱的近交。本研究建立了广灵驴保种群可靠性较高的分子系谱并对其遗传结构进行了分析,证明该群体遗传多态性较高,群体近交系数较低,处于较好的保种状态,具有较大的品种资源开发潜力。     

河北省大白猪繁殖性状基因组选择参考群体构建和应用

联合育种是我国生猪遗传改良计划的重要工作，联合育种能够扩大群体规模，增加群体内遗传变异，提高育种值估计的准确性，且相较于传统育种方法对低遗传力的繁殖性状有着更明显的效果。本研究收集了河北大好河山养殖有限公司、河北裕丰京安养殖有限公司、石家庄清凉山养殖有限公司（以下分别简称大好河山、京安和清凉山）3家育种场共6 790条大白猪的繁殖性状，构建了基因组选择合并参考群体，通过基因型填充将纽勤50K(Geneseek)芯片基因型填充到液相50K，采用一步法进行基因组联合遗传评估。结果表明：清凉山与裕丰京安两场遗传背景相近，大好河山场与其他两场存在较远的联系；基于系谱信息预测大好河山个体的总产仔数育种值准确性为0.170，基因组预测准确性则为0.324；通过联合基因组遗传评估，总产仔数基因组预测的准确性进一步提升至0.347，比基于单场系谱信息提高了104%。本研究表明通过基因型填充统一各场SNP芯片类型，构建河北省大白猪繁殖性状基因组选择参考群，从而进行联合基因组选择是可行的，尤其对提高常规育种进展缓慢的繁殖性状意义重大。     

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     

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.     

Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population

The objective of this study was to assess the effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci (QTL) detection and genomic evaluation using a simulated cattle population. Twelve generations (G1–G12) were simulated from the base generation (G0). The recent population had different effective population sizes, heritability, and number of QTL. G0–G4 were used for pedigree information. A total of 300 genotyped bulls from G5–G10 were randomly selected. Their progenies were generated in G6–G11 with different numbers of progeny per bull. Scenarios were considered according to the number of progenies and whether the genotypes were possessed by the bulls or the progenies. A genome‐wide association study and genomic evaluation were performed with a single‐step genomic best linear unbiased prediction method to calculate the power of QTL detection and the genomic estimated breeding value (GEBV). We found that genotyped bulls could be available for QTL detection depending on conditions. Additionally, using a reference population, including genotyped bulls, which had more progeny phenotypes, enabled a more accurate prediction of GEBV. However, it is desirable to have more than 4,500 individuals consisting of both genotypes and phenotypes for practical genomic evaluation.     

The unified approach to the use of genomic and pedigree information in genomic evaluations revisited

Previous proposals for a unified approach for amalgamating information from animals with or without genotypes have combined the numerator relationship matrix A with the genomic relationship G estimated from the markers. These approaches have resulted in biased genomic EBV (GEBV), and methodology was developed to overcome these problems. Firstly, a relationship matrix, G(FG) , based on linkage analysis was derived using the same base population as A, which (i) utilizes the genomic information on the same scale as the pedigree information and (ii) permits the regression coefficients used to propagate the genomic data from the genotyped to ungenotyped individuals to be calculated in the light of the genomic information, rather than ignoring it. Secondly, the elements of G were regressed back towards their expected values in the A matrix to allow for their estimation errors. These developments were combined in a methodology LDLAb and tested on simulated populations where either parents were phenotyped and offspring genotyped or vice versa. The LDLAb method was demonstrated to be a unified approach that maximized accuracy of GEBV compared to previous methodologies and removed the bias in the GEBV. Although LDLAb is computationally much more demanding than MLAC, it demonstrates how to make best use the marker information and also shows the computational problems that need to be solved in the future to make best use of the marker data.     

Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.     

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.     

The importance of haplotype length and heritability using genomic selection in dairy cattle

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites ( h ²) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h ² = 0.02 to 0.93 for h ² = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h ² = 0.02 to 0.86 for h ² = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.