【目的】估计杜洛克猪(Duroc,DD)、长白猪(Landrace,LL)、大白猪(Yorkshire,YY)繁殖性状和生长性状的遗传参数,分析不同年份育种值变化的遗传趋势,为制定合理的育种方案提供理论依据。【方法】以杜洛克猪、长白猪、大白猪繁殖性状和生...【目的】估计杜洛克猪(Duroc,DD)、长白猪(Landrace,LL)、大白猪(Yorkshire,YY)繁殖性状和生长性状的遗传参数,分析不同年份育种值变化的遗传趋势,为制定合理的育种方案提供理论依据。【方法】以杜洛克猪、长白猪、大白猪繁殖性状和生长性状的性能测定数据为研究材料,其中繁殖性状数据10963条,包括总产仔数(total number born,TNB)、产活仔数(number born alive,NBA)、出生窝重(litter born weight,LBW)和21日龄窝重(litter weight at 21 days,LW21);生长性状数据25257条,包括达100 kg体重日龄(age at 100 kg live weight,AGE)和达100 kg体重背膘厚(backfat adjusted to 100 kg,BF)。采用基于动物模型的最佳线性无偏预测(best linear unbiased prediction,BLUP)方法,使用ASReml统计分析软件进行遗传力、遗传相关和育种值估计。【结果】TNB、NBA和LBW的遗传力在0.08~0.20之间,LW21的遗传力在0.02~0.05之间;AGE和BF的遗传力在0.22~0.37之间。繁殖性状TNB、NBA、LBW、LW21的遗传相关系数总体分布在0.20~0.97之间,呈中等偏上正相关;生长性状AGE和BF的遗传相关系数分布在-0.07~-0.03之间,呈微弱的负相关。杜洛克猪繁殖性状的遗传趋势上升幅度较大,长白猪、大白猪繁殖性状的遗传趋势上升幅度较小;生长性状中AGE的遗传趋势均呈下降趋势,且下降幅度较大,BF的遗传趋势变化幅度较小。【结论】本研究对杜洛克猪、长白猪、大白猪繁殖性状和生长性状的遗传参数和遗产趋势进行了准确的评估,结果可为该育种场的育种工作提供参考。展开更多
Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during t...Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.展开更多
Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLU...Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, Bayes A generated the highest accuracy almost for all six traits, and GBLUP performed as well as Bayes A for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three Bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three Bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of Bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.展开更多
文摘【目的】估计杜洛克猪(Duroc,DD)、长白猪(Landrace,LL)、大白猪(Yorkshire,YY)繁殖性状和生长性状的遗传参数,分析不同年份育种值变化的遗传趋势,为制定合理的育种方案提供理论依据。【方法】以杜洛克猪、长白猪、大白猪繁殖性状和生长性状的性能测定数据为研究材料,其中繁殖性状数据10963条,包括总产仔数(total number born,TNB)、产活仔数(number born alive,NBA)、出生窝重(litter born weight,LBW)和21日龄窝重(litter weight at 21 days,LW21);生长性状数据25257条,包括达100 kg体重日龄(age at 100 kg live weight,AGE)和达100 kg体重背膘厚(backfat adjusted to 100 kg,BF)。采用基于动物模型的最佳线性无偏预测(best linear unbiased prediction,BLUP)方法,使用ASReml统计分析软件进行遗传力、遗传相关和育种值估计。【结果】TNB、NBA和LBW的遗传力在0.08~0.20之间,LW21的遗传力在0.02~0.05之间;AGE和BF的遗传力在0.22~0.37之间。繁殖性状TNB、NBA、LBW、LW21的遗传相关系数总体分布在0.20~0.97之间,呈中等偏上正相关;生长性状AGE和BF的遗传相关系数分布在-0.07~-0.03之间,呈微弱的负相关。杜洛克猪繁殖性状的遗传趋势上升幅度较大,长白猪、大白猪繁殖性状的遗传趋势上升幅度较小;生长性状中AGE的遗传趋势均呈下降趋势,且下降幅度较大,BF的遗传趋势变化幅度较小。【结论】本研究对杜洛克猪、长白猪、大白猪繁殖性状和生长性状的遗传参数和遗产趋势进行了准确的评估,结果可为该育种场的育种工作提供参考。
文摘Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.
基金supported by the National Natural Science Foundation of China(3137125831272418)+10 种基金the Anhui International Technology Cooperation Plan Project(1503062014)the Anhui Academy of Agricultural Sciences President Innovation Fund Project for Outstanding Youth(13B0405)Beijing City Committee of Science and Technology Key Project(D151100004615004)the Program for Changjiang Scholar and Innovation Research Team in University(IRT1191)the Ministry of Agriculture 948 Program(2011-G2A)the National Swine Industry Technology System(CARS-36)the Anhui Swine Industry Technology System(AHCYTX-06-10)the Anhui Modern Agricultural Projectsthe Anhui Finance Project for Animal Husbandry Developmentthe Maanshan Science and Technology Plan Projects(NY-2015-01)the Anhui Academy of Agricultural Science and Technology Innovation Team Building Project(13C0405)
文摘Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, Bayes A generated the highest accuracy almost for all six traits, and GBLUP performed as well as Bayes A for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three Bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three Bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of Bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.
