Parameters affecting genome simulation for evaluating genomic selection method

The present study investigated the parameter settings for obtaining a simulated genome at steady state of allele frequency (mutation–drift equilibrium) and linkage disequilibrium (LD), and evaluated the impact of whether or not the simulated genome reached steady state of allele frequency and LD on the accuracy of genomic estimated breeding values (GEBVs). After 500 to 50 000 historical generations, the base population and subsequent seven generations were generated as recent populations. The allele frequency distribution of the last generations of the historical population and LD in the base population were calculated when varying the values of five parameters: initial minor allele frequency, mutation rate, effective population size, number of markers and chromosome length. The accuracies of GEBVs in the last generation of the recent population were calculated by genomic best linear unbiased prediction. The number of historical generations required to reach mutation–drift equilibrium depended on the initial allele frequency and mutation rate. Regardless of the parameters, LD reached a steady state before allele frequency distribution reached mutation–drift equilibrium. The accuracies of GEBVs largely reflect the extent of linkage disequilibrium with the exception of varying chromosome length, although there were no associations between the accuracies of GEBVs and allele frequency distribution.     

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.     

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0.38...     

Genomic selection strategies to improve maternal traits in Norwegian White Sheep

This study tested and compared different implementation strategies for genomic selection for Norwegian White Sheep, aiming to increase genetic gain for maternal traits. These strategies were evaluated for their genetic gain ingrowth, carcass and maternal traits, total genetic gain, a weighted sum of the gain in each trait and rates of inbreeding through a full-scale stochastic simulation. Results showed genomic selection schemes to increase genetic gain for maternal traits but reduced genetic gain for other traits. This could also be obtained by selecting rams for artificial selection at a higher age. Implementation of genomic selection in the current breeding structure increased genetic gain for maternal traits up to 57%, outcompeted by reducing the generation interval for artificial insemination rams from current 3 to 2 years. Then, total genetic gain for maternal traits increased by 65%–77% and total genetic gain by18%–20%, but at increased rates of inbreeding.     

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。     

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。     

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G‐matrices) across these two breeds. Single‐trait genomic best linear unbiased prediction (GBLUP) model and two‐trait GBLUP model were used for single‐breed and two‐breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two‐breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two‐breed predictions. Weighting the two‐breed G‐matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two‐breed predictions.     

全基因组选择模型研究进展及展望

全基因组选择是一种利用覆盖全基因组的高密度标记进行选择育种的新方法,可通过早期选择缩短世代间隔,提高育种值估计准确性等加快遗传进展,尤其对低遗传力、难测定的复杂性状具有较好的预测效果,真正实现了基因组技术指导育种实践。随着芯片和测序技术日趋成熟,高密度标记芯片检测成本不断降低,全基因组选择模型的不断升级和优化,预测准确性不断提高,全基因组选择已成为动物遗传改良的重要手段和研究热点。目前,全基因组选择已经成为奶牛遗传评估的标准方法,并取得重要进展,在其它物种中的应用正在逐步开展。本文主要对全基因组选择的统计模型发展进行综述,总结全基因组选择在动物遗传育种中的应用现状,讨论当前存在的问题,并对全基因组选择模型的发展方向和应用前景进行展望。     

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。     

紫花苜蓿QTL与全基因组选择研究进展及其应用

四倍体紫花苜蓿是重要的豆科牧草之一,由于其复杂的遗传背景与二倍体作物相比遗传作图与重要性状数量性状位点(quantitative trait locus,QTL)定位研究相对滞后。然而,二倍体苜蓿的相关研究起步较早,已经建立了高密度遗传图谱和物理图谱,这些研究为四倍体苜蓿遗传作图与QTL定位奠定了基础。随着第三代分子标记与测序技术的快速发展,极大地促进了四倍体苜蓿的高密度遗传图谱构建与QTL定位研究,并借助分子标记辅助育种技术对提高苜蓿选育效率,加速育种进程具有重要意义。本文对苜蓿遗传图谱构建与QTL定位研究及发展趋势进行了总结,并对苜蓿关联作图与全基因组选择的研究进展及应用前景加以概述,旨在为读者就相关研究领域有较全面的了解。     

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。     

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium‐density single nucleotide polymorphism (SNP) array. Here, the impact of lower‐density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)‐flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree‐based prediction (0.50–0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL‐flanking SNP (0.65–0.72) was similar to the panel with 35K SNP (0.65–0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r² ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long‐range LD likely contributed to the accurate genomic predictions with the low‐density SNP panels. Population structure analysis supported the hypothesis that long‐range LD in this population may be caused by admixture. Results suggest that lower‐cost, low‐density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.     

Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins

Genome‐assisted prediction of genetic merit of individuals for a quantitative trait requires building statistical models that can handle data sets consisting of a massive number of markers and many fewer observations. Numerous regression models have been proposed in which marker effects are treated as random variables. Alternatively, multivariate dimension reduction techniques [such as principal component regression (PCR) and partial least‐squares regression (PLS)] model a small number of latent components which are linear combinations of original variables, thereby reducing dimensionality. Further, marker selection has drawn increasing attention in genomic selection. This study evaluated two dimension reduction methods, namely, supervised PCR and sparse PLS, for predicting genomic breeding values (BV) of dairy bulls for milk yield using single‐nucleotide polymorphisms (SNPs). These two methods perform variable selection in addition to reducing dimensionality. Supervised PCR preselects SNPs based on the strength of association of each SNP with the phenotype. Sparse PLS promotes sparsity by imposing some penalty on the coefficients of linear combinations of original SNP variables. Two types of supervised PCR (I and II) were examined. Method I was based on single‐SNP analyses, whereas method II was based on multiple‐SNP analyses. Supervised PCR II was clearly better than supervised PCR I in predictive ability when evaluated on SNP subsets of various sizes, and sparse PLS was in between. Supervised PCR II and sparse PLS attained similar predictive correlations when the size of the SNP subset was below 1000. Supervised PCR II with 300 and 500 SNPs achieved correlations of 0.54 and 0.59, respectively, corresponding to 80 and 87% of the correlation (0.68) obtained with all 32 518 SNPs in a PCR model. The predictive correlation of supervised PCR II reached a plateau of 0.68 when the number of SNPs increased to 3500. Our results demonstrate the potential of combining dimension reduction and variable selection for accurate and cost‐effective prediction of genomic BV.     

Prediction of response to marker-assisted and genomic selection using selection index theory

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS). By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection. Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding. Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.     

基于元共祖的基因组联合育种模拟研究

旨在将整合元共祖的一步法(single-step genomic best linear unbiased prediction with metafounders,MF-SSGBLUP)应用到基因组联合育种中,并与其他经典基因组选择方法进行比较分析。本研究使用QMSim软件模拟3个系谱相互独立的奶牛群体;分别使用广义最小二乘法(generalized least squares,GLS)和原始方法(naïve,NAI)估计不同群体间的祖先关系矩阵Γ;将MF-SSGBLUP、SSGBLUP和BLUP用于3个模拟群体的联合育种,评估各方法在遗传参数和育种值估计方面的差异。在不同遗传力下,GLS所得的Γ矩阵在对角线元素上略低于NAI法,在非对角线元素上没有明显差异,且基因组关系矩阵与基于元共祖构建的亲缘关系矩阵对角线元素相关系数(0.750~0.775)高于基因组关系矩阵与传统的亲缘关系矩阵相关系数(0.508~0.572)。MF-SSGBLUP遗传力估计值(0.138、0.140、0.297和0.298)与当代群体遗传力(0.107和0.296)的偏差小于其余两种方法(0.145、0.173、0.273和0.340),且MF-SSGBLUP估计育种值准确性(0.888~0.908)高于SSGBLUP法(0.863~0.876)和BLUP法(0.854~0.871)。表明,MF-SSGBLUP的遗传参数估计值无偏性更好,估计育种值准确性更高。根据上述模拟数据结果表明,在联合育种中,整合元共祖的基因组选择方法优于其他经典基因组选择方法。     

Genomic selection strategies in dairy cattle: Strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.     

Linkage disequilibrium and genomic scan to detect selective loci in cattle populations adapted to different ecological conditions in Ethiopia

Despite the wide range of observed phenotypic diversities and adaptation to different ecological conditions, little has been studied regarding the genetics of adaptation in the genome of indigenous cattle breeds of developing countries. Here, we investigated the linkage disequilibrium (LD) and identified the subset of outlier loci that are highly differentiated among cattle populations adapted to different ecological conditions in Ethiopia. Specifically, we genotyped 47 unrelated animals sampled from high‐ versus low‐altitude environments using a Bovine 50K SNP BeadChip. Linkage disequilibrium was assessed using both D′ and r² between adjacent SNPs. We calculated F_ST and heterozygosity at different significance levels as measures of genetic differentiation for each locus between high‐ and low‐altitude populations following the hierarchical island model approach. We identified 816 loci (p < 0.01) showing selection signals and are associated with genes that might have roles in local adaptation. Some of them are associated with candidate genes that are involved in metabolism (ATP2A3, CA2, MYO18B, SIK3, INPP4A, and IREB2), hypoxia response (BDNF, TFRC, and PML) and heat stress (PRKDC, CDK1, and TFDC). Average r² and D′ values were 0.14 ± 0.21 and 0.57 ± 0.34, respectively, for a minor allele frequency (MAF) ≥ 0.05 and were found to increase with increasing MAF value. The outlier loci identified in the studied Ethiopian cattle populations indicate the presence of genetic variation produced/shaped by adaptation to different environmental conditions and provide a basis for further validation and functional analysis using a reasonable sample size and high‐density markers.     

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.     

畜禽基因组选择方法研究进展

畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。     

Research Progress on Genomic Selection Methods in Livestock and Poultry

Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding.