Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are se...Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.展开更多
Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock m...Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.展开更多
基金funded by National Key Research and Development Program of China(2021YFD1200404)the Yangzhou University Interdisciplinary Research Foundation for Animal Science Discipline of Targeted Support(yzuxk202016)the Project of Genetic Improvement for Agricultural Species(Dairy Cattle)of Shandong Province(2019LZGC011).
文摘Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.
基金supported by grants from Key R&D Program of Shandong Province(2022LZGC003)China Agriculture Research System of MOF and MARA,the National Key Research and Development Project(2023YFD1300200 and 2023YFF1001104)+1 种基金the Science and Technology Program of Sichuan Province(2024ZHCG0109)the 2115 Talent Development Program of China Agricultural University.
文摘Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.
