Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of mos...Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of most variant function assessment methods.In this study,we gather 1030 raw epigenomic datasets from 10 animal species and systematically annotate 7 types of key regulatory regions,creating a comprehensive functional annotation map of animal genomic variants.Our findings demonstrate that integrating variants with regulatory annotations can identify tissues and cell types underlying economic traits,underscoring the utility of these annotations in functional variant discovery.Using our functional annotations,we rank the functional potential of genetic variants and classify over 127 million candidate variants into 5 functional confidence categories,with high-confidence variants significantly enriched in eQTLs and trait-associated SNPs.Incorporating these variants into genomic prediction models can improve estimated breeding value accuracy,demonstrating their practical utility in breeding programs.To facilitate the use of our results,we develop the Integrated Functional Mutation(IFmut:http://www.ifmutants.com:8212)platform,enabling researchers to explore regulatory annotations and assess the functional potential of animal variants efficiently.Our study provides a robust framework for functional genomic annotations in farm animals,enhancing variant function assessment and breeding precision.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
Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic...Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic males with female genotypes, neo-males are harmful in C. semilaevis aquaculture because they reduce overall production. The present study evaluated the difference in the growth-related traits： total length （TL）, body weight （BW） and square root of body weight （SQ_BW） at the age of 570 days between normal and neo-male offspring （neo-males used as male parents）. The difference in the proportion of females between normal and neo-male offspring was also assessed. Based on the linear mixed model, restricted maximum likelihood （REML） and best linear unbiased prediction （BLUP） were used to estimate various （co）variance components and estimated breeding values （EBVs） of growth-related traits. As a result, all the mean values of the three studied traits were significantly larger in normal offspring than in neo-male offspring. Additionally, the female proportion was significantly larger in normal offspring than in neo-male offspring. Heritability was 0.128＋0.066 2 for TL, 0.128-4-0.065 5 for BW and 0.132~0.062 9 for SQBW, all of which were low level heritabilities. The correlation coefficients of EBVs and phenotypic values of the target traits were 0.516 for TL, 0.524 for BW and 0.506 for SQ_BW, all of which were highly significant （P〈0.01）. Genetic correlations among TL, BW and SQ_BW were positive high （0.921-0.969） and higher than those of phenotype （0.711-0.748）, both of which had low standard errors （0.063-0.123 for genotype, and 0.010-0.018 for phenotype）. Compared with normal offspring, neo-male offspring have lower breeding values for each studied trait through EBVs comparison. Therefore, neo-male offspring should not be used as broodstock in a C. semilaevis breeding programs.展开更多
Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods a...Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.展开更多
基金supported by the National Natural Science Foundation of China(32341051)the grant from Department of Agriculture and Rural Affairs of Hubei Province(HBZY2023B006-02)+2 种基金the National Funding(2023ZD04050)the National Natural Science Foundation of China Outstanding Youth(32125035)the National Key R&D Young Scientists Project(2022YFD1302000).
文摘Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of most variant function assessment methods.In this study,we gather 1030 raw epigenomic datasets from 10 animal species and systematically annotate 7 types of key regulatory regions,creating a comprehensive functional annotation map of animal genomic variants.Our findings demonstrate that integrating variants with regulatory annotations can identify tissues and cell types underlying economic traits,underscoring the utility of these annotations in functional variant discovery.Using our functional annotations,we rank the functional potential of genetic variants and classify over 127 million candidate variants into 5 functional confidence categories,with high-confidence variants significantly enriched in eQTLs and trait-associated SNPs.Incorporating these variants into genomic prediction models can improve estimated breeding value accuracy,demonstrating their practical utility in breeding programs.To facilitate the use of our results,we develop the Integrated Functional Mutation(IFmut:http://www.ifmutants.com:8212)platform,enabling researchers to explore regulatory annotations and assess the functional potential of animal variants efficiently.Our study provides a robust framework for functional genomic annotations in farm animals,enhancing variant function assessment and breeding precision.
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.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA10A403-2)the Taishan Scholar Project of Shandong Province of China
文摘Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic males with female genotypes, neo-males are harmful in C. semilaevis aquaculture because they reduce overall production. The present study evaluated the difference in the growth-related traits： total length （TL）, body weight （BW） and square root of body weight （SQ_BW） at the age of 570 days between normal and neo-male offspring （neo-males used as male parents）. The difference in the proportion of females between normal and neo-male offspring was also assessed. Based on the linear mixed model, restricted maximum likelihood （REML） and best linear unbiased prediction （BLUP） were used to estimate various （co）variance components and estimated breeding values （EBVs） of growth-related traits. As a result, all the mean values of the three studied traits were significantly larger in normal offspring than in neo-male offspring. Additionally, the female proportion was significantly larger in normal offspring than in neo-male offspring. Heritability was 0.128＋0.066 2 for TL, 0.128-4-0.065 5 for BW and 0.132~0.062 9 for SQBW, all of which were low level heritabilities. The correlation coefficients of EBVs and phenotypic values of the target traits were 0.516 for TL, 0.524 for BW and 0.506 for SQ_BW, all of which were highly significant （P〈0.01）. Genetic correlations among TL, BW and SQ_BW were positive high （0.921-0.969） and higher than those of phenotype （0.711-0.748）, both of which had low standard errors （0.063-0.123 for genotype, and 0.010-0.018 for phenotype）. Compared with normal offspring, neo-male offspring have lower breeding values for each studied trait through EBVs comparison. Therefore, neo-male offspring should not be used as broodstock in a C. semilaevis breeding programs.
基金supported by the National Natural Science Foundations of China (31272419, 31661143013)the National High Technology Research and Development Program of China (2013AA102503)+1 种基金China Agriculture Research System (CARS-36)the Program for Changjiang Scholar and Innovation Research Team in University (IRT_15R62)
文摘Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.
