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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。 相似文献
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蒺藜苜蓿是继拟南芥和水稻之后又一个进行全基因组测序的植物,利用蒺藜苜蓿的基因组序列,开发出可以在其他豆科植物上应用的分子标记,即穿梭标记,已成为缺乏基因组信息或基因组复杂的豆科植物基因组学及分子遗传学研究的有效手段。天蓝苜蓿和金花菜是我国最重要的两种一年生苜蓿,由于缺乏有效的分子标记,这两种苜蓿在基因组水平上的研究很少。SLAF-seq是近年来开发出的一种简化基因组测序技术,具有高通量、准确性、成本低、周期短的优点,已在众多物种的全基因组SNP标记开发上得到应用。本研究通过SLAF-seq技术对12份蒺藜苜蓿、天蓝苜蓿和金花菜材料进行简化基因组测序,共得到28.04×106个读长的测序数据,276432个高质量的SLAF标签,其中58748个SLAF标签为多态性标签,平均测序深度为17.44。在58748个多态性SLAF标签中,共检测出次要基因型频率(MAF)大于0.05的SNP标记189133个。本研究开发出的SNP标记可用于一年生苜蓿的遗传多样性、遗传图谱构建和重要农艺性状的QTL定位等的研究,其种间穿梭的特性可为苜蓿属种间基因组排列顺序、系统进化关系、比较图谱构建等方面的研究提供帮助。 相似文献
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《国外畜牧学(猪与禽)》2021,41(4)
全基因组序列数据的使用在家畜育种计划中具有巨大的潜力,可以提高发现变异基因的能力,同时能更准确和更持久地预测育种值而不是标记阵列。要了解家畜基因组序列数据的全部潜力,需要从大量的个体,甚至要从数百万个个体上获得基因组序列和表型数据,从而准确地估测构成数量性状基础的大量致病变异的影响。 相似文献
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为挖掘影响地方鸡肉色性状的有效SNP位点及功能基因，给儋州鸡育种工作提供有效的数据基础和理论支撑，利用10×深度全基因组重测序技术对200只儋州鸡个体的肉色性状数据进行全基因组关联分析。结果显示，与肉色性状相关的全基因组和潜在显著水平的SNP位点分别为6个和13个，分别位于儋州鸡1、2、4、5号和Z染色体。通过KEGG通路分析和GO注释，预测ACAA2、ACSS3基因可作为儋州鸡肉色性状的重要候选基因。这些结果将为儋州鸡的育种提供候选分子标记，为地方鸡标记辅助选择提供新的思路。 相似文献
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全基因组重测序(whole genome resequencing, WGRS)是对已知参考基因组序列的物种进行不同个体间的全基因组水平的测序,具有检测变异类型丰富、高性价比、应用广泛等优点。随着测序成本的降低和畜禽基因组测序工作的完成,全基因组重测序技术已成为畜禽遗传变异研究的重要工具。全基因组重测序技术可获得大量基因变异信息,包括单核苷酸多态性(single nucleotide polymorphism, SNP)、插入缺失(insertion/deletion, InDel)、结构变异(structural variation, SV)和拷贝数变异(copy number variation, CNV)等,丰富了现有的基因组序列,形成的大量数据集为探索畜禽表型性状和遗传改良提供了一个基因组信息库,以促进对畜禽遗传资源的深入研究与利用。作者概述了全基因组重测序技术及其关键影响因素(测序深度、序列比对和变异检测),重点综述了该技术在重要畜禽(牛、羊、猪、鸡)研究领域的应用进展,并对将来侧重于整合分析重测序数据、精准表型记录和多组学信息的研究趋势进行了展望。 相似文献
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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。 相似文献
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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. 相似文献
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Gopal R. Gowane Sang Hong Lee Sam Clark Nasir Moghaddar Hawlader A. Al‐Mamun Julius H. J. van der Werf 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2019,136(5):390-407
Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree‐based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single‐Step approach (SSGBLUP) using both. For a scenario with no‐selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single‐Step approach to obtain accurate and unbiased prediction of GEBV. 相似文献
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J.M. Hickey B.P. Kinghorn B. Tier S.A. Clark J.H.J. van der Werf G. Gorjanc 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2013,130(4):259-269
Long‐range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off‐diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of individuals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype‐based prediction methods was not higher than those based on unphased genotype information. 相似文献
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Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low‐density SNP genotypes 
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下载免费PDF全文 F.B. Lopes X.‐L. Wu H. Li J. Xu T. Perkins J. Genho R. Ferretti R.G. Tait Jr S. Bauck G.J.M. Rosa 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2018,135(1):14-27
Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP‐LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP‐LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de‐regressed EBV was slightly small (i.e. 0.87%–18.75%). The present study also compared the performance of five genomic prediction models and two cross‐validation methods. The five genomic models predicted EBV and de‐regressed EBV of the ten traits similarly well. Of the two cross‐validation methods, leave‐one‐out cross‐validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. 相似文献
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Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle 下载免费PDF全文
下载免费PDF全文 Shinichiro Ogawa Hirokazu Matsuda Yukio Taniguchi Toshio Watanabe Akiko Takasuga Yoshikazu Sugimoto Hiroaki Iwaisaki 《Animal Science Journal》2016,87(1):3-12
Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction. 相似文献
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Green RD 《Journal of animal science》2009,87(2):793-800
The past century has seen animal breeding and genetics evolve and expand from definition and validation of basic population genetics theory to development of selection index theory to today's relatively sophisticated genetic prediction systems enabling industry genetic improvement. The end of the first century of the American Society of Animal Science coincides with the rapid movement of the field into the era of genome-enabled genetic improvement and precision management systems. Led by recent research infrastructure investments by the United States and international partners to develop chicken, bovine, swine, ovine, and equine "genomic toolboxes," the animal breeding community is poised to play a crucial role in the century to come. These genomic toolboxes provide the needed platforms for developing whole-genome selection programs based on linkage disequilibrium for a wide spectrum of traits; allow the opportunity to redefine genetic prediction based on allele sharing as opposed to traditional pedigree relationships; and provide for the first time simultaneous information upon which to practice genetic selection and plan precision management of specific genotypes, all early in the life of the animal. An area of major focus will be mining of the genomes through systems biology approaches to better understand gene and metabolic networks--what has previously been lumped into poorly understood genotype by environment and genotype by genotype interactions. Perhaps the greatest obstacle to the successful merger of genomic and quantitative approaches will be the lack of necessary animal resource populations to appropriately define and measure phenotypes (i.e., the so-called phenomic gap) for difficult-to-measure traits such as resistance to disease and stress, adaptability, longevity, and efficiency of nutrient utilization. Additionally, because of de-emphasis of quantitative genetics and animal breeding programs in academia over the past quarter century, a dearth of qualified young scientists to effectively mine the genomes must immediately be addressed. Although the motivating factors may have changed, the need for high-quality animal breeding and genetics research and education has never been greater. 相似文献
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Combined use of principal component analysis and random forests identify population‐informative single nucleotide polymorphisms: application in cattle breeds 下载免费PDF全文
下载免费PDF全文 F. Bertolini G. Galimberti D.G. Calò G. Schiavo D. Matassino L. Fontanesi 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2015,132(5):346-356
The genetic identification of the population of origin of individuals, including animals, has several practical applications in forensics, evolution, conservation genetics, breeding and authentication of animal products. Commercial high‐density single nucleotide polymorphism (SNP) genotyping tools that have been recently developed in many species provide information from a large number of polymorphic sites that can be used to identify population‐/breed‐informative markers. In this study, starting from Illumina BovineSNP50 v1 BeadChip array genotyping data available from 3711 cattle of four breeds (2091 Italian Holstein, 738 Italian Brown, 475 Italian Simmental and 407 Marchigiana), principal component analysis (PCA) and random forests (RFs) were combined to identify informative SNP panels useful for cattle breed identification. From a PCA preselected list of 580 SNPs, RFs were computed using ranking methods (Mean Decrease in the Gini Index and Mean Accuracy Decrease) to identify the most informative 48 and 96 SNPs for breed assignment. The out‐of‐bag (OOB) error rate for both ranking methods and SNP densities ranged from 0.0 to 0.1% in the reference population. Application of this approach in a test population (10% of individuals pre‐extracted from the whole data set) achieved 100% of correct assignment with both classifiers. Linkage disequilibrium between selected SNPs was relevant (r2 > 0.6) only in few pairs of markers indicating that most of the selected SNPs captured different fractions of variance. Several informative SNPs were in genes/QTL regions that affect or are associated with phenotypes or production traits that might differentiate the investigated breeds. The combination of PCA and RF to perform SNP selection and breed assignment can be easily implemented and is able to identify subsets of informative SNPs useful for population assignment starting from a large number of markers derived by high‐throughput genotyping platforms. 相似文献
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