Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore ...Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.展开更多
Sperm is essential for successful artificial insemination in dairy cattle,and its quality can be influenced by both epi-genetic modification and epigenetic inheritance.The bovine germline differentiation is characteri...Sperm is essential for successful artificial insemination in dairy cattle,and its quality can be influenced by both epi-genetic modification and epigenetic inheritance.The bovine germline differentiation is characterized by epigenetic reprogramming,while intergenerational and transgenerational epigenetic inheritance can influence the offspring’s development through the transmission of epigenetic features to the offspring via the germline.Therefore,the selec-tion of bulls with superior sperm quality for the production and fertility traits requires a better understanding of the epigenetic mechanism and more accurate identifications of epigenetic biomarkers.We have comprehensively reviewed the current progress in the studies of bovine sperm epigenome in terms of both resources and biological discovery in order to provide perspectives on how to harness this valuable information for genetic improvement in the cattle breeding industry.展开更多
Background: The objective of the present study was to estimate(co)variance components of female fertility traits in Chinese Holsteins, considering fertility traits in different parities as different traits. Data on 88...Background: The objective of the present study was to estimate(co)variance components of female fertility traits in Chinese Holsteins, considering fertility traits in different parities as different traits. Data on 88,647 females with 215,632 records(parities) were collected during 2000 to 2014 from 32 herds in the Sanyuan Lvhe Dairy Cattle Center, Beijing, China. The analyzed female fertility traits included interval from calving to first insemination, interval from first to last insemination, days open, conception rate at first insemination, number of inseminations per conception and non-return rates within 56 days after first insemination.Results: The descriptive statistics showed that the average fertility of heifers was superior to that of cows. Moreover,the genetic correlations between the performances of a trait in heifers and in cows were all moderate to high but far from one, which suggested that the performances of a trait in heifers and cows should be considered as different but genetically correlated traits in genetic evaluations. On the other hand, genetic correlations between performances of a trait in different parities of cows were greater than 0.87, with only a few exceptions, but variances were not homogeneous across parities for some traits. The estimated heritabilities of female fertility traits were low; all were below 0.049(except for interval from calving to first insemination). Additionally, the heritabilities of the heifer interval traits were lower than those of the corresponding cow interval traits. Moreover, the heritabilities of the interval traits were higher than those of the threshold traits when measuring similar fertility functions. In general, estimated genetic correlations between traits were highly consistent with the biological categories of the female fertility traits.Conclusions: Interval from calving to first insemination, interval from first to last insemination and non-return rates within 56 days after first insemination are recommended to be included in the selection index of the Chinese Holstein population. The parameters estimated in the present study will facilitate the development of a genetic evaluation system for female fertility traits to improve the reproduction efficiency of Chinese Holsteins.展开更多
Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Mi...Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.展开更多
Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered e...Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered equally significant to dimensional quality.Surface Roughness(Ra)is a widely recognized measure to evaluate and investigate the surface quality of machined parts.Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100%inspection and examination because of the time and efforts involved in performing the measurement.However,Machine vision has emerged as the innovative approach to executing the surface roughness measurement.It can provide economic,automated,quick,and reliable solutions.This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics,i.e.,surface roughness,waviness,flatness,surface texture,etc.,machine vision parameters.This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.展开更多
Background: Genotyping by sequencing(GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes,depende...Background: Genotyping by sequencing(GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results: Chip array(Chip) and four depths of GBS data was simulated. After quality control(call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS(GBSc), true genotypes for the GBS loci(GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively.Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.展开更多
In genomic selection, prediction accuracy is highly driven by the size of animals in the reference population(RP).Combining related populations from different countries and regions or using a related population with l...In genomic selection, prediction accuracy is highly driven by the size of animals in the reference population(RP).Combining related populations from different countries and regions or using a related population with large size of RP has been considered to be viable strategies in cattle breeding. The genetic relationship between related populations is important for improving the genomic predictive ability. In this study, we used 122 French bulls as test individuals. The genomic estimated breeding values(GEBVs) evaluated using French RP, America RP and Chinese RP were compared.The results showed that the GEBVs were in higher concordance using French RP and American RP compared with using Chinese population. The persistence analysis, kinship analysis and the principal component analysis(PCA) were performed for 270 French bulls, 270 American bulls and 270 Chinese bulls to interpret the results. All the analyses illustrated that the genetic relationship between French bulls and American bulls was closer compared with Chinese bulls. Another reason could be the size of RP in China was smaller than the other two RPs. In conclusion, using RP of a related population to predict GEBVs of the animals in a target population is feasible when these two populations have a close genetic relationship and the related population is large.展开更多
基金funded by the"Genetic improvement of pig survival"project from Danish Pig Levy Foundation (Aarhus,Denmark)The China Scholarship Council (CSC)for providing scholarship to the first author。
文摘Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.
基金funded by the National Key R&D Program of China(2021YFD1200903)Seed Fund(CAU),Shandong Provincial Natural Science Foundation(ZR2021MC070)+3 种基金the National Key R&D Program of China(2021YFF1000701-06)Shandong Provincial Natural Science Foundation(ZR2020MC165)the Earmarked Fund for CARS-36.X.W.is funded by the“Hundred Talents Program”project of Hebei Province(E2020100019)the research project of Zhongnongtongchuang(ZNTC)group(ZNTC2019A10 and ZNTC2021B12)in China.
文摘Sperm is essential for successful artificial insemination in dairy cattle,and its quality can be influenced by both epi-genetic modification and epigenetic inheritance.The bovine germline differentiation is characterized by epigenetic reprogramming,while intergenerational and transgenerational epigenetic inheritance can influence the offspring’s development through the transmission of epigenetic features to the offspring via the germline.Therefore,the selec-tion of bulls with superior sperm quality for the production and fertility traits requires a better understanding of the epigenetic mechanism and more accurate identifications of epigenetic biomarkers.We have comprehensively reviewed the current progress in the studies of bovine sperm epigenome in terms of both resources and biological discovery in order to provide perspectives on how to harness this valuable information for genetic improvement in the cattle breeding industry.
基金supported by the earmarked fund for the Modern Agro-industry Technology Research System(CARS-37)the Genomic Selection in Plants and Animals(Gen SAP)research project financed by the Danish Council of Strategic Research(Aarhus,Denmark)+1 种基金the Program for Changjiang Scholar and Innovation Research Team in University(IRT1191)provided by the China Scholarship Council(CSC)
文摘Background: The objective of the present study was to estimate(co)variance components of female fertility traits in Chinese Holsteins, considering fertility traits in different parities as different traits. Data on 88,647 females with 215,632 records(parities) were collected during 2000 to 2014 from 32 herds in the Sanyuan Lvhe Dairy Cattle Center, Beijing, China. The analyzed female fertility traits included interval from calving to first insemination, interval from first to last insemination, days open, conception rate at first insemination, number of inseminations per conception and non-return rates within 56 days after first insemination.Results: The descriptive statistics showed that the average fertility of heifers was superior to that of cows. Moreover,the genetic correlations between the performances of a trait in heifers and in cows were all moderate to high but far from one, which suggested that the performances of a trait in heifers and cows should be considered as different but genetically correlated traits in genetic evaluations. On the other hand, genetic correlations between performances of a trait in different parities of cows were greater than 0.87, with only a few exceptions, but variances were not homogeneous across parities for some traits. The estimated heritabilities of female fertility traits were low; all were below 0.049(except for interval from calving to first insemination). Additionally, the heritabilities of the heifer interval traits were lower than those of the corresponding cow interval traits. Moreover, the heritabilities of the interval traits were higher than those of the threshold traits when measuring similar fertility functions. In general, estimated genetic correlations between traits were highly consistent with the biological categories of the female fertility traits.Conclusions: Interval from calving to first insemination, interval from first to last insemination and non-return rates within 56 days after first insemination are recommended to be included in the selection index of the Chinese Holstein population. The parameters estimated in the present study will facilitate the development of a genetic evaluation system for female fertility traits to improve the reproduction efficiency of Chinese Holsteins.
基金This study was funded by the Genomic Selection in Animals and Plants(GenSAP)research project financed by the Danish Council of Strategic Research(Aarhus,Denmark).Xiao Wang received Ph.D.stipends from the Technical University of Denmark(DTU Bioinformatics and DTU Compute),Denmark,and the China Scholarship Council,China.
文摘Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.
基金the Science and Engineering Research Board,Department of Science and Technology,Government of India for supporting this work through the Grant DST-SERB EMR/2016/003372.
文摘Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered equally significant to dimensional quality.Surface Roughness(Ra)is a widely recognized measure to evaluate and investigate the surface quality of machined parts.Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100%inspection and examination because of the time and efforts involved in performing the measurement.However,Machine vision has emerged as the innovative approach to executing the surface roughness measurement.It can provide economic,automated,quick,and reliable solutions.This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics,i.e.,surface roughness,waviness,flatness,surface texture,etc.,machine vision parameters.This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.
基金supported by the Genomic Selection in PlantsAnimals(GenSAP)research project financed by the Danish Council of Strategic Research(Aarhus,Denmark)the scholarship provided by the China Scholarship Council(CSC)
文摘Background: Genotyping by sequencing(GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results: Chip array(Chip) and four depths of GBS data was simulated. After quality control(call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS(GBSc), true genotypes for the GBS loci(GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively.Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.
基金supported by the earmarked fund for China Agriculture Research System(CARS-36)the National Natural Science Foundation of China(31671327,31701077,31371258)+2 种基金the Program for Changjiang Scholar and Innovation Research Team in University(Grant No.IRT1191)Anhui Science and Technology Key Project(17030701008)Anhui Academy of Agricultural Sciences Key Laboratory Project(18S0404)
文摘In genomic selection, prediction accuracy is highly driven by the size of animals in the reference population(RP).Combining related populations from different countries and regions or using a related population with large size of RP has been considered to be viable strategies in cattle breeding. The genetic relationship between related populations is important for improving the genomic predictive ability. In this study, we used 122 French bulls as test individuals. The genomic estimated breeding values(GEBVs) evaluated using French RP, America RP and Chinese RP were compared.The results showed that the GEBVs were in higher concordance using French RP and American RP compared with using Chinese population. The persistence analysis, kinship analysis and the principal component analysis(PCA) were performed for 270 French bulls, 270 American bulls and 270 Chinese bulls to interpret the results. All the analyses illustrated that the genetic relationship between French bulls and American bulls was closer compared with Chinese bulls. Another reason could be the size of RP in China was smaller than the other two RPs. In conclusion, using RP of a related population to predict GEBVs of the animals in a target population is feasible when these two populations have a close genetic relationship and the related population is large.
