Genotyping by target sequencing(GBTS)integrates the advantages of silicon-based technology(high stability and reliability)and genotyping by sequencing(high flexibility and cost-effectiveness).However,GBTS panels are n...Genotyping by target sequencing(GBTS)integrates the advantages of silicon-based technology(high stability and reliability)and genotyping by sequencing(high flexibility and cost-effectiveness).However,GBTS panels are not currently available in pigs.In this study,based on GBTS technology,we first developed a 50K panel,including 52,000 single-nucleotide polymorphisms(SNPs),in pigs,designated GBTS50K.A total of 6,032 individuals of Large White,Landrace,and Duroc pigs from 10 breeding farms were used to assess the newly developed GBTS50K.Our results showed that GBTS50K obtained a high genotyping ability,the SNP and individual call rates of GBTS50K were 0.997–0.998,and the average consistency rate and genotyping correlation coefficient were 0.997 and 0.993,respectively,in replicate samples.We also evaluated the efficiencies of GBTS50K in the application of population genetic structure analysis,selection signature detection,genome-wide association studies(GWAS),genotyped imputation,genetic selection(GS),etc.The results indicate that GBTS50K is plausible and powerful in genetic analysis and molecular breeding.For example,GBTS50K could gain higher accuracies than the current popular GGP-Porcine bead chip in genomic selection on 2 important traits of backfat thickness at 100 kg and days to 100 kg in pigs.Particularly,due to the multiple SNPs(mSNPs),GBTS50K generated 100K qualified SNPs without increasing genotyping cost,and our results showed that the haplotype-based method can further improve the accuracies of genomic selection on growth and reproduction traits by 2 to 6%.Our study showed that GBTS50K could be a powerful tool for underlying genetic architecture and molecular breeding in pigs,and it is also helpful for developing SNP panels for other farm animals.展开更多
Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investig...Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investigated.Results:In this study,2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels.Four ML methods,including support vector regression(SVR),kernel ridge regression(KRR),random forest(RF)and Adaboost.R2 were implemented.Through 20 replicates of fivefold cross-validation(CV)and one prediction for younger individuals,the utility of ML methods in genomic prediction was explored.In CV,compared with genomic BLUP(GBLUP),single-step GBLUP(ssGBLUP)and the Bayesian method BayesHE,ML methods significantly outperformed these conventional methods.ML methods improved the genomic prediction accuracy of GBLUP,ssGBLUP,and BayesHE by 19.3%,15.0% and 20.8%,respectively.In addition,ML methods yielded smaller mean squared error(MSE)and mean absolute error(MAE)in all scenarios.ssGBLUP yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP,and the accuracy of BayesHE was close to that of GBLUP.In genomic prediction of younger individuals,RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE,while ssGBLUP performed comparably with RF,and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born,while for number of piglets born alive,Adaboost.R2_KRR performed significantly better than ssGBLUP.Among ML methods,Adaboost.R2_KRR consistently performed well in our study.Our findings also demonstrated that optimal hyperparameters are useful for ML methods.After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals,the average improvement was 14.3% and 21.8% over those using default hyperparameters,respectively.Conclusion:Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods,and could be new options for genomic prediction.Among ML methods,Adaboost.R2_KRR consistently performed well in our study,and tuning hyperparameters is necessary for ML methods.The optimal hyperparameters depend on the character of traits,datasets etc.展开更多
Modern scintillator-based radiation detectors require silicon photomultipliers(Si PMs)with photon detection efficiency higher than 40%at 420 nm,possibly extended to the vacuum ultraviolet(VUV)region,single-photon time...Modern scintillator-based radiation detectors require silicon photomultipliers(Si PMs)with photon detection efficiency higher than 40%at 420 nm,possibly extended to the vacuum ultraviolet(VUV)region,single-photon time resolution(SPTR)<100 ps,and dark count rate(DCR)<150 kcps/mm^(2).To enable single-photon time stamping,digital electronics and sensitive microcells need to be integrated in the same CMOS substrate,with a readout frame rate higher than 5 MHz for arrays extending over a total area up to 4 mm×4 mm.This is challenging due to the increasing doping concentrations at low CMOS scales,deep-level carrier generation in shallow trench isolation fabrication,and power consumption,among others.The advances at 350 and 110 nm CMOS nodes are benchmarked against available Si PMs obtained in CMOS and commercial customized technologies.The concept of digital multithreshold Si PMs with a single microcell readout is finally reported,proposing a possible direction toward fully digital scintillator-based radiation detectors.展开更多
基金supported by the grants from the Key R&D Program of Shandong Province,China(2022LZGC003)the China Agriculture Research System of MOF and MARA(CARS-35)+1 种基金the National Key Research and Development Project of China(2019YFE0106800)the 2115 Talent Development Program of China Agricultural University。
文摘Genotyping by target sequencing(GBTS)integrates the advantages of silicon-based technology(high stability and reliability)and genotyping by sequencing(high flexibility and cost-effectiveness).However,GBTS panels are not currently available in pigs.In this study,based on GBTS technology,we first developed a 50K panel,including 52,000 single-nucleotide polymorphisms(SNPs),in pigs,designated GBTS50K.A total of 6,032 individuals of Large White,Landrace,and Duroc pigs from 10 breeding farms were used to assess the newly developed GBTS50K.Our results showed that GBTS50K obtained a high genotyping ability,the SNP and individual call rates of GBTS50K were 0.997–0.998,and the average consistency rate and genotyping correlation coefficient were 0.997 and 0.993,respectively,in replicate samples.We also evaluated the efficiencies of GBTS50K in the application of population genetic structure analysis,selection signature detection,genome-wide association studies(GWAS),genotyped imputation,genetic selection(GS),etc.The results indicate that GBTS50K is plausible and powerful in genetic analysis and molecular breeding.For example,GBTS50K could gain higher accuracies than the current popular GGP-Porcine bead chip in genomic selection on 2 important traits of backfat thickness at 100 kg and days to 100 kg in pigs.Particularly,due to the multiple SNPs(mSNPs),GBTS50K generated 100K qualified SNPs without increasing genotyping cost,and our results showed that the haplotype-based method can further improve the accuracies of genomic selection on growth and reproduction traits by 2 to 6%.Our study showed that GBTS50K could be a powerful tool for underlying genetic architecture and molecular breeding in pigs,and it is also helpful for developing SNP panels for other farm animals.
基金supported by grants from the National Key Research and Development Project(2019YFE0106800)Modern Agriculture Science and Technology Key Project of Hebei Province(19226376D)China Agriculture Research System of MOF and MARA.
文摘Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investigated.Results:In this study,2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels.Four ML methods,including support vector regression(SVR),kernel ridge regression(KRR),random forest(RF)and Adaboost.R2 were implemented.Through 20 replicates of fivefold cross-validation(CV)and one prediction for younger individuals,the utility of ML methods in genomic prediction was explored.In CV,compared with genomic BLUP(GBLUP),single-step GBLUP(ssGBLUP)and the Bayesian method BayesHE,ML methods significantly outperformed these conventional methods.ML methods improved the genomic prediction accuracy of GBLUP,ssGBLUP,and BayesHE by 19.3%,15.0% and 20.8%,respectively.In addition,ML methods yielded smaller mean squared error(MSE)and mean absolute error(MAE)in all scenarios.ssGBLUP yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP,and the accuracy of BayesHE was close to that of GBLUP.In genomic prediction of younger individuals,RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE,while ssGBLUP performed comparably with RF,and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born,while for number of piglets born alive,Adaboost.R2_KRR performed significantly better than ssGBLUP.Among ML methods,Adaboost.R2_KRR consistently performed well in our study.Our findings also demonstrated that optimal hyperparameters are useful for ML methods.After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals,the average improvement was 14.3% and 21.8% over those using default hyperparameters,respectively.Conclusion:Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods,and could be new options for genomic prediction.Among ML methods,Adaboost.R2_KRR consistently performed well in our study,and tuning hyperparameters is necessary for ML methods.The optimal hyperparameters depend on the character of traits,datasets etc.
基金supported by the National Natural Science Foundation of China(Nos.62250002,62027808,and 62027801)the Sino-German Mobility Programme(No.M-0387)。
文摘Modern scintillator-based radiation detectors require silicon photomultipliers(Si PMs)with photon detection efficiency higher than 40%at 420 nm,possibly extended to the vacuum ultraviolet(VUV)region,single-photon time resolution(SPTR)<100 ps,and dark count rate(DCR)<150 kcps/mm^(2).To enable single-photon time stamping,digital electronics and sensitive microcells need to be integrated in the same CMOS substrate,with a readout frame rate higher than 5 MHz for arrays extending over a total area up to 4 mm×4 mm.This is challenging due to the increasing doping concentrations at low CMOS scales,deep-level carrier generation in shallow trench isolation fabrication,and power consumption,among others.The advances at 350 and 110 nm CMOS nodes are benchmarked against available Si PMs obtained in CMOS and commercial customized technologies.The concept of digital multithreshold Si PMs with a single microcell readout is finally reported,proposing a possible direction toward fully digital scintillator-based radiation detectors.
