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1.
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
2.
旨在探究低密度液相芯片在生产实践中的实用性,降低育种成本。本试验选用了3 761头约160日龄,110 kg左右健康大白猪,随机抽取100头大白猪,根据10K芯片标记信息,从50K芯片中抽取标记生成10K芯片,作为填充群体。再从剩余群体中,分别随机抽取800、2 000、3 600个个体作为参考群体,使用Beagle 4.1软件对100头填充群体进行基因型填充至50K芯片,重复10次,以基因型一致性和基因型相关系数来评价基因型填充的准确性。结果表明,10K和50K芯片平均连锁不平衡（r2）程度为0.227和0.258,相差不大。最小等位基因频率（MAF）为0.05是基因型填充准确性的拐点,剔除掉MAF<0.05标记后,填充准确性明显升高。填充准确性随参考群体规模增大而上升,参考群由800头扩大到3 600头,填充准确性从0.90提高到0.95,10次重复的标准差也从0.006下降到0.002。对于较小的参考群体规模,染色体基因型填充准确性波动较大,随着参考群体规模增大,每条染色体填充准确性相差不大。本研究结果表明,猪液相芯片从10K填充到50K是可行的,可以大规模用于基因组选择,降低基因组选择育种成本。 相似文献
3.
Genomic data is more and more widely used in livestock breeding. Genotype imputation is an important tool to handle missing values in genotypic data, and the quality of imputation results directly affects the subsequent analysis. To obtain good imputation results, a comprehensive imputation strategy needs to be formulated. We studied on the effects of several factors on genotype imputation by simulation. The factors included reference population size, genetic relationship (distance) between the target population and the reference population, the number of target sites (proportion), the minimum allele frequency (MAF), and the imputation algorithm. The results showed that the number of target sites was the main factor affecting the genotype imputation, and it showed significantly positive correlation with the quality of imputation(P<0.05). The reference population size was the main factor affecting the imputation error rate in Beagle5.1. Correspondingly, the number of target sites was the main factor affecting the imputation error rate in Minimac4. Genetic distance between the target population and the reference population had a more significant effect on the imputation quality of Beagle5.1 than Minimac4. In general, the imputation error rate increased as the increases of MAF in a site. When the number of individuals in the reference population was small and the number of target sites was large, the speed of Minimac4 was superior to Beagle5.1, but there was a reverse trend as the reference population size increased. On the premise of ensuring the imputation quality, Beagle5.1 had relatively lower requirements for the above factors. In contrast, when the number of target sites was low and reference population size was large, the imputation effect of Beagle5.1 was better, while Minimac4 was more suitable for the imputation of a small reference population size and a higher number of target sites. In this study, different strategies were formulated for different imputation purposes, and the study results would provide a reference for genotype imputation. 相似文献
4.
基因型填充策略研究 总被引：1，自引：1，他引：0
基因组数据在畜禽遗传育种中的应用越来越广泛，基因型填充作为基因组数据处理的重要工具，填充结果的好坏直接影响后续分析，为了得到好的填充结果，需要制定完善的填充策略。本研究通过模拟数据探讨参考群体大小、目标群体与参考群体间遗传关系（距离）远近、目标位点数目（比例）、最小等位基因频率以及填充算法等因素对基因型填充效果的影响。结果表明，目标位点数目与填充效果呈显著的正相关（P<0.05），是影响基因型填充准确性的主要因素；参考群体大小是影响Beagle5.1填充错误率的主要因素，目标位点数目是影响Minimac4填充错误率的主要因素；目标群体和参考群体的遗传距离对Beagle5.1填充效果的影响较Minimac4更为显著；一般情况下，最小等位基因频率越高的位点填充错误率越高；在参考群体个体数量少且目标位点数目多的情况下，Minimac4的填充速度优于Beagle5.1，但随参考群体个体数目增加有逆趋势。在保证填充质量的前提下，Beagle5.1对本研究中几种因素的标准要求相对较低。相对地，当目标群体位点数目较低，参考群体个体数目较多时，Beagle5.1的填充效果更好，而Minimac4更适合参考群体个体数目较少，目标群体位点数目较高的填充中。本研究针对不同的填充目的制定了不同策略，为基因型填充标准提供了参考。 相似文献
5.
Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle 
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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
6.
P. Ma M.S. Lund X. Ding Q. Zhang G. Su 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2014,131(6):462-472
This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD‐genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density. 相似文献
7.
The objective of this paper was to investigate, for various scenarios at low and high marker density, the accuracy of imputing genotypes when using a multivariate mixed model framework using information from 2, 4, or 10 surrounding markers. This model predicts genotypes at a locus, using genotypes at nearby loci as correlated traits, and the additive genetic relationship matrix to use information from genotyped relatives. For 2 scenarios this method was compared with the population-based imputation algorithms FastPHASE and Beagle. Accuracies of imputation were obtained with Monte Carlo simulation and predicted with selection index theory, using input from the simulated data. Five different scenarios of missing genotypes were considered: 1) genotypes of some loci are missing due to genotyping errors, 2) juvenile selection candidates are genotyped using a smaller SNP panel, 3) some animals in the pedigree of a breeding population are not genotyped, 4) juvenile selection candidates are not genotyped, and 5) 1 generation of animals in the top of the pedigree are not genotyped. Surrounding marker information did not improve accuracy of imputation when animals whose genotypes were imputed were not genotyped for those surrounding markers. When those animals were genotyped for surrounding markers, results indicated a limited gain when linkage disequilibrium (LD) between SNP was low, but a substantial increase in accuracy when LD between SNP was high. For scenario 1, using 1 vs. 11 SNP, accuracy was respectively 0.75 and 0.81 at low, and 0.75 and 0.93 at high density. For scenario 2, using 1 vs. 11 SNP, accuracy was, respectively, 0.70 and 0.73 at low, and 0.71 and 0.84 at high density. Beagle outperformed the other methods at high SNP density, whereas the multivariate mixed model was clearly superior when SNP density was low and animals where genotyped with a reduced SNP panel. The results showed that extending the univariate gene content method to a multivariate BLUP model with inclusion of surrounding marker information only yields greater imputation accuracy when the animals with imputed loci are at least genotyped for some SNP that are in LD with the SNP to be imputed. The equation derived from selection index theory accurately predicted the accuracy of imputation using the multivariate mixed model framework. 相似文献
8.
D.P. Berry M.C. McClure M.P. Mullen 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2014,131(3):165-172
The objective of this study was to evaluate, using three different genotype density panels, the accuracy of imputation from lower‐ to higher‐density genotypes in dairy and beef cattle. High‐density genotypes consisting of 777 962 single‐nucleotide polymorphisms (SNP) were available on 3122 animals comprised of 269, 196, 710, 234, 719, 730 and 264 Angus, Belgian Blue, Charolais, Hereford, Holstein‐Friesian, Limousin and Simmental bulls, respectively. Three different genotype densities were generated: low density (LD; 6501 autosomal SNPs), medium density (50K; 47 770 autosomal SNPs) and high density (HD; 735 151 autosomal SNPs). Imputation from lower‐ to higher‐density genotype platforms was undertaken within and across breeds exploiting population‐wide linkage disequilibrium. The mean allele concordance rate per breed from LD to HD when undertaken using a single breed or multiple breed reference population varied from 0.956 to 0.974 and from 0.947 to 0.967, respectively. The mean allele concordance rate per breed from 50K to HD when undertaken using a single breed or multiple breed reference population varied from 0.987 to 0.994 and from 0.987 to 0.993, respectively. The accuracy of imputation was generally greater when the reference population was solely comprised of the breed to be imputed compared to when the reference population comprised of multiple breeds, although the impact was less when imputing from 50K to HD compared to imputing from LD. 相似文献
9.
A population-based imputation procedure was used to predict the most likely genotype of un-typed loci on low density SNP maker panels to improve data integrity before genetic association and selection studies when pedigree information is not available such as in feedlot applications. It is of practical importance to evaluate the accuracy effects of imputed genotypes. In our report, a population consisting of 2246 Angus bulls that were genotyped using both Illumina Bovine3k and Bovin50 BeadChip was used. Several scenarios with varying percentages of missing SNP genotypes under a random missing pattern were simulated. Additionally, several scenarios with varying percentages of animals genotyped using the 3 k and 50 k panels assuming a structured missing pattern were considered. With the random missing scenarios, SNP genotypes on the Bovine50 panel were masked at random until reaching the desired missing percentage. With the structured missing scenarios, all SNP genotypes in the Bovine50 chip were masked, with the exception of those corresponding to the Bovine3 panel. The missing rates considered in this study ranged from 70% to 94% across chromosomes. Population-based imputation software fastPHASE1.2 was used for the separate analysis of each of the 30 pairs of chromosomes in the bovine genome. The results of the imputation of the random-missing SNP genotypes were similar to previous reports and accuracy rates, defined as the percentage of correct prediction of the true missing genotypes, ranging from 68% to 97% were influenced primarily by the proportion of missing genotypes. Moreover, imputation performance using structured-missing-pattern panels was impacted by the amount of individuals in reference population and level of linkage disequilibrium (LD) on each chromosome. In order to further elucidate the potential effect of incorrect imputation on genomic selection, wrongly imputed genotypes were grouped into two groups as a function of the number of incorrectly imputed alleles. 相似文献
10.
旨在探究五指山猪和杜洛克猪免疫和脂质代谢相关基因的选择信号差异。本研究从海南国家级五指山猪保种场采集30头4月龄健康(10公,20母)五指山猪的耳组织进行全基因组重测序(whole genome sequencing,WGS),从NCBI数据库下载29头杜洛克猪全基因组重测序数据(SRA：PRJNA378496);通过生物信息学方法分析59个样本WGS数据的单核苷酸多态性(single nucleotide polymorphism,SNPs),进行SNPs过滤,定位SNPs在基因组的位置,分析其结构特征和基因型频率,并注释对应基因的功能;使用XP-CLR方法筛选五指山猪基因组受到强烈选择的区域,分析两个品种相关通路基因的功能差异。结果表明,五指山猪平均每个样本共筛选到36 961 902个SNPs位点,内含子区域分布最多,平均每个样本16 729 364个,约占45.26%,编码区(起始、终止密码子)平均有2 073个SNPs;五指山猪受选择的基因组区域主要集中在免疫、代谢和神经功能相关通路;免疫反应通路中,9个基因(TGFBR2、IL26、IL15、BMPR2、TNFSF15、TNFSF4、TNFSF8、ACKR4、TNFRSF11B)功能区域SNPs位点在五指山猪中只存在一种突变纯合基因型,而在杜洛克猪大部分个体中存在3种基因型(野生纯合基因型、杂合子、突变纯合基因型),其中野生纯合基因型比例最高;脂类代谢通路中,关键调节基因IRS2、PRKG1、ADCY5的基因型频率与免疫反应基因类似。在五指山猪中突变纯合基因型比例为100%,在杜洛克猪中野生纯合基因型所占比例为48%~93%(14/29-27/29)。本研究筛选出与五指山猪免疫性状相关的候选基因9个、与脂类代谢相关的候选基因3个,发现五指山猪免疫、代谢和神经功能相关基因的受选择程度强于杜洛克猪,为揭示五指山猪特色性状形成的分子机制提供参考。 相似文献
11.
Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits 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 Yoshikazu Sugimoto Hiroaki Iwaisaki 《Animal Science Journal》2016,87(9):1106-1113
The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP. 相似文献
12.
Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small. 相似文献
13.
An imputation‐based genome‐wide association study on traits related to male reproduction in a White Duroc × Erhualian F2 population 下载免费PDF全文
下载免费PDF全文 Jun Ren Feng Zhang Chao Jiang Yuan Hong Kai Jiang Qiang Yang Chengbin Wang Nengshui Ding Lusheng Huang Zhiyan Zhang Yuyun Xing 《Animal Science Journal》2016,87(5):646-654
Boar reproductive traits are economically important for the pig industry. Here we conducted a genome‐wide association study (GWAS) for 13 reproductive traits measured on 205 F2 boars at day 300 using 60 K single nucleotide polymorphism (SNP) data imputed from a reference panel of 1200 pigs in a White Duroc × Erhualian F2 intercross population. We identified 10 significant loci for seven traits on eight pig chromosomes (SSC). Two loci surpassed the genome‐wide significance level, including one for epididymal weight around 60.25 Mb on SSC7 and one for semen temperature around 43.69 Mb on SSC4. Four of the 10 significant loci that we identified were consistent with previously reported quantitative trait loci for boar reproduction traits. We highlighted several interesting candidate genes at these loci, including APN, TEP1, PARP2, SPINK1 and PDE1C. To evaluate the imputation accuracy, we further genotyped nine GWAS top SNPs using PCR restriction fragment length polymorphism or Sanger sequencing. We found an average of 91.44% of genotype concordance, 95.36% of allelic concordance and 0.85 of r2 correlation between imputed and real genotype data. This indicates that our GWAS mapping results based on imputed SNP data are reliable, providing insights into the genetic basis of boar reproductive traits. 相似文献
14.
全基因组测序在畜禽中应用的研究进展 总被引：1，自引：0，他引：1
在基因组研究方面,目前全基因组测序已由第一代测序技术发展到第三代测序技术,全基因组测序与传统方法相比具有更加全面、精准、高效等优势。随着测序技术的发展和费用的降低,全基因组测序（whole genome sequencing,WGS）技术逐渐成为基因组研究应用最广泛的技术。全基因组测序已经在畜禽起源进化、重要经济性状基因挖掘、分子育种等方面取得了诸多成果。通过全基因组重测序,能够发现拷贝数变异（copy number variation,CNV）及单核苷酸多态性（single nucleotide polymorphism,SNP）变异,丰富现有的CNV和SNP数据库,为抗病、生长、食欲、代谢调节、表型、环境适应机制及重要经济性状基因的分析提供重要数据。作者针对全基因组测序技术在主要畜禽上的研究进展,综述了全基因组测序在畜禽的品种遗传多样性、群体演变机制、功能基因挖掘等研究中的应用,并探讨了全基因组测序存在的问题,旨在为畜禽种质资源保护和分子育种实践提供参考。 相似文献
15.
Carl A. Gagnon Christian Lalonde Chantale Provost 《Journal of veterinary diagnostic investigation》2021,33(2):216
The genomic surveillance of porcine reproductive and respiratory syndrome virus (PRRSV) is based on sequencing of the ORF5 gene of the virus, which covers only 4% of the entire viral genome. It is expected that PRRSV whole-genome sequencing (WGS) will improve PRRSV genomic data and allow better understanding of clinical discrepancies observed in the field when using ORF5 sequencing. Our main objective was to implement an efficient method for WGS of PRRSV from clinical samples. The viral genome was purified using a poly(A)-tail viral genome purification method and sequenced using Illumina technology. We tested 149 PRRSV-positive samples: 80 sera, 33 lungs, 33 pools of tissues, 2 oral fluids, and 1 processing fluid (i.e., castration liquid). Overall, WGS of 67.1% of PRRSV-positive cases was successful. The viral load, in particular for tissues, had a major impact on the PRRSV WGS success rate. Serum was the most efficient type of sample to conduct PRRSV WGS poly(A)-tail assays, with a success rate of 76.3%, and this result can be explained by improved sequencing reads dispersion matching throughout the entire viral genome. WGS was unsuccessful for all pools of tissue and lung samples with Cq values > 26.5, whereas it could still be successful with sera at Cq ≤ 34.1. Evaluation of results of highly qualified personnel confirmed that laboratory skills could affect PRRSV WGS efficiency. Oral fluid samples seem very promising and merit further investigation because, with only 2 samples of low viral load (Cq = 28.8, 32.8), PRRSV WGS was successful. 相似文献
16.
Simon F Lashmar Donagh P Berry Rian Pierneef Farai C Muchadeyi Carina Visser 《Journal of animal science》2021,99(7)
A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria. 相似文献
17.
本试验采用PCR-RFLP和DNA测序等技术检测牛生长激素基因P3位点（growth hormone,GH-P3）在中国西门塔尔牛群体中的多态性,并利用最小二乘法拟合线性模型对该位点与牛经济性状进行关联分析。结果表明,BB基因型个体胴体重和净肉重极显著高于AA和AB基因型个体（P＜0.01）,屠宰率和净肉率显著高于AA和AB基因型（P＜0.05）,AA基因型背膘厚极显著低于AB、BB基因型（P＜0.01）。本研究结果显示,GH-P3位点对中国西门塔尔牛个体胴体重、屠宰率、净肉重、净肉率及背膘厚等经济性状有显著或极显著的影响,为中国西门塔尔牛重要经济性状的分子标记辅助选择（MAS）及品系培育提供理论依据。 相似文献
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Nasir Moghaddar Daniel J Brown Andrew A Swan Phillip M Gurman Li Li Julius H van der Werf 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2022,139(1):71-83
The objective of this study was to investigate the accuracy of genomic prediction of body weight and eating quality traits in a numerically small sheep population (Dorper sheep). Prediction was based on a large multi-breed/admixed reference population and using (a) 50k or 500k single nucleotide polymorphism (SNP) genotypes, (b) imputed whole-genome sequencing data (~31 million), (c) selected SNPs from whole genome sequence data and (d) 50k SNP genotypes plus selected SNPs from whole-genome sequence data. Furthermore, the impact of using a breed-adjusted genomic relationship matrix on accuracy of genomic breeding value was assessed. The selection of genetic variants was based on an association study performed on imputed whole-genome sequence data in an independent population, which was chosen either randomly from the base population or according to higher genetic proximity to the target population. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of genomic prediction was assessed according to the correlation between genomic breeding value and corrected phenotypes divided by the square root of trait heritability. The accuracy of genomic prediction was between 0.20 and 0.30 across different traits based on common 50k SNP genotypes, which improved on average by 0.06 (absolute value) on average based on using prioritized genetic markers from whole-genome sequence data. Using prioritized genetic markers from a genetically more related GWAS population resulted in slightly higher prediction accuracy (0.02 absolute value) compared to genetic markers derived from a random GWAS population. Using high-density SNP genotypes or imputed whole-genome sequence data in GBLUP showed almost no improvement in genomic prediction accuracy however, accounting for different marker allele frequencies in reference population according to a breed-adjusted GRM resulted to on average 0.024 (absolute value) increase in accuracy of genomic prediction. 相似文献