Genomic selection has been demonstrated as a powerful technology to revolutionize animal breeding. However, marker density and minor allele frequency can affect the predictive ability of genomic estimated breeding val...Genomic selection has been demonstrated as a powerful technology to revolutionize animal breeding. However, marker density and minor allele frequency can affect the predictive ability of genomic estimated breeding values （GEBVs）. To investigate the impact of marker density and minor allele frequency on predictive ability, we estimated GEBVs by constructing the different subsets of single nucleotide polymorphisms （SNPs） based on varying markers densities and minor allele frequency （MAF） for average daily gain （ADG）, live weight （LW） and carcass weight （CW） in 1 059 Chinese Simmental beef cattle. Two strategies were proposed for SNP selection to construct different marker densities： 1） select evenly-spaced SNPs （Strategy 1 ）, and 2） select SNPs with large effects estimated from BayesB （Strategy 2）. Furthermore, predictive ability was assessed in terms of the correlation between predicted genomic values and corrected phenotypes from 10-fold cross-validation. Predictive ability for ADG, LW and CW using autosomal SNPs were 0.13＋0.002, 0.21＋0.003 and 0.25＋0.003, respectively. In our study, the predictive ability increased dramatically as more SNPs were included in analysis until 200K for Strategy 1. Under Strategy 2, we found the predictive ability slightly increased when marker densities increased from 5K to 20K, which indicated the predictive ability of 20K （3% of 770K） SNPs with large effects was equal to the predictive ability of using all SNPs. For different MAF bins, we obtained the highest predictive ability for three traits with MAF bin 0.01-0.1. Our result suggested that designing a low-density chip by selecting low frequency markers with large SNP effects sizes should be helpful for commercial application in Chinese Simmental cattle.展开更多
Growth differentiation factor 11 （GDF11） is an important circulating factor that regulates aging. However, the role of GDF11 in bone metabolism remains unclear. The present study was undertaken to investigate the re...Growth differentiation factor 11 （GDF11） is an important circulating factor that regulates aging. However, the role of GDF11 in bone metabolism remains unclear. The present study was undertaken to investigate the relationship between serum GDF11 level, bone mass, and bone turnover markers in postmenopausal Chinese women. Serum GDF11 level, bone turnover biochemical markers, and bone mineral density （BMD） were determined in 169 postmenopausal Chinese women （47-78 years old）. GDF11 serum levels increased with aging. There were negative correlations between GDF11 and BMD at the various skeletal sites. After adjusting for age and body mass index （BMI）, the correlations remained statistically significant. In the multiple linear stepwise regression analysis, age or years since menopause, BMI, GDF11, and estradiol were independent predictors of BMD. A significant negative correlation between GDF11 and bone alkaline phosphatase （BAP） was identified and remained significant after adjusting for age and BMI. No significant correlation was noted between cross-linked N-telopeptides of type I collagen （NTX） and GDF11. In conclusion, GDF11 is an independent negative predictor of BMD and correlates with a biomarker of bone formation, BAP, in postmenopausal Chinese women. GDF11 potentially exerts a negative effect on bone mass by regulating bone formation.展开更多
Genomic selection(GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time an...Genomic selection(GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy(rMG) in GS were evaluated systematically, using six agronomic traits(plant height, ear height, ear length, ear diameter,grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability,statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rMGshowed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction(RR-BLUP), genomic BLUP(GBLUP), Bayes A, Bayes B, Bayes C, Bayesian least absolute shrinkage and selection operator(Bayesian LASSO), and reproducing kernel Hilbert space(RKHS), the RKHS and additive-dominance model(Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.展开更多
Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although seque...Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.展开更多
Background The objective of this study is to determine the role of tumor marker density(TMD)values such as alpha-fetoprotein tumor volume ratio(ATVR),beta-human chorionic gonadotropin tumor volume ratio(βTVR),alpha-f...Background The objective of this study is to determine the role of tumor marker density(TMD)values such as alpha-fetoprotein tumor volume ratio(ATVR),beta-human chorionic gonadotropin tumor volume ratio(βTVR),alpha-fetoprotein testicle size ratio(ATSR),beta-human chorionic gonadotropin testicle size ratio(βTSR),lactate dehydrogenase tumor volume ratio(LTVR),and lactate dehydrogenase testicle size ratio(LTSR)in the determination of progression-free survival(PFS)in patients with testicular cancer.Materials and methods A retrospective study was conducted of 95 patients followed-up in our clinic with a diagnosis of testicular cancer between January 2015 and August 2022.Patients were grouped according to clinical stage,as either early stage(n=50)or advanced stage(n=45).Clinical and pathological data and TMD values for all patients were recorded.Results The median age of patients was 35 years(21–63 years).All TMDs except LTVR in advanced stage patients were found to be significantly higher than those of early stage patients(p<0.05).Median ATVR(2.58 vs.0.0),ATSR(0.63 vs.0.03),βTVR(0.9 vs.0.009),andβTSR(0.18 vs.0.007)of the nonseminoma patients were found to be significantly higher than those of the seminoma patients,respectively(p<0.001).Progression-free survival(months)was decreased in seminoma patients with high values ofβTVR(11.3±1.9 vs.35.2±0.7),βTSR(16.2±3.4 vs.35.2±0.75),LTVR(17.7±3.4 vs.35.2±0.7),and LTSR(21.5±3.13 vs.35.09±0.8)(p<0.001).Decreased PFS(months)was associated with higher values of ATVR(5.37±0.7 vs.35.05±0.93),βTVR(7.4±1.5 vs.34.6±1.3),ATSR(5.37±0.75 vs.35.05±0.9),βTSR(7±1.5 vs.34.6±1.3),and LTSR(7.9±1.2 vs.34.3±1.5)in nonseminoma patients(p<0.001).Based on multivariate analysis,βTVR-LTVR and ATVR-ATSR were determined to be independent risk factors for reduced PFS in seminoma and nonseminoma patients,respectively(p<0.05).Conclusions The results of this study suggest that the calculation of TMDs could be a promising and simple method for prediction of PFS among testicular cancer patients.展开更多
基金supported by the National Natural Science Foundation of China(31201782,31672384 and 31372294)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(ASTIPIAS03)+3 种基金the Cattle Breeding Innovative Research Team of Chinese Academy of Agricultural Sciences(cxgc-ias-03)the Key Technology R&D Program of China during the 12th Five-Year Plan period(2011BAD28B04)the National High Technology Research and Development Program of China(863 Program 2013AA102505-4)the Beijing Natural Science Foundation,China(6154032)
文摘Genomic selection has been demonstrated as a powerful technology to revolutionize animal breeding. However, marker density and minor allele frequency can affect the predictive ability of genomic estimated breeding values （GEBVs）. To investigate the impact of marker density and minor allele frequency on predictive ability, we estimated GEBVs by constructing the different subsets of single nucleotide polymorphisms （SNPs） based on varying markers densities and minor allele frequency （MAF） for average daily gain （ADG）, live weight （LW） and carcass weight （CW） in 1 059 Chinese Simmental beef cattle. Two strategies were proposed for SNP selection to construct different marker densities： 1） select evenly-spaced SNPs （Strategy 1 ）, and 2） select SNPs with large effects estimated from BayesB （Strategy 2）. Furthermore, predictive ability was assessed in terms of the correlation between predicted genomic values and corrected phenotypes from 10-fold cross-validation. Predictive ability for ADG, LW and CW using autosomal SNPs were 0.13＋0.002, 0.21＋0.003 and 0.25＋0.003, respectively. In our study, the predictive ability increased dramatically as more SNPs were included in analysis until 200K for Strategy 1. Under Strategy 2, we found the predictive ability slightly increased when marker densities increased from 5K to 20K, which indicated the predictive ability of 20K （3% of 770K） SNPs with large effects was equal to the predictive ability of using all SNPs. For different MAF bins, we obtained the highest predictive ability for three traits with MAF bin 0.01-0.1. Our result suggested that designing a low-density chip by selecting low frequency markers with large SNP effects sizes should be helpful for commercial application in Chinese Simmental cattle.
基金supported by Grant 81570806 from the National Natural Science Foundation of China
文摘Growth differentiation factor 11 （GDF11） is an important circulating factor that regulates aging. However, the role of GDF11 in bone metabolism remains unclear. The present study was undertaken to investigate the relationship between serum GDF11 level, bone mass, and bone turnover markers in postmenopausal Chinese women. Serum GDF11 level, bone turnover biochemical markers, and bone mineral density （BMD） were determined in 169 postmenopausal Chinese women （47-78 years old）. GDF11 serum levels increased with aging. There were negative correlations between GDF11 and BMD at the various skeletal sites. After adjusting for age and body mass index （BMI）, the correlations remained statistically significant. In the multiple linear stepwise regression analysis, age or years since menopause, BMI, GDF11, and estradiol were independent predictors of BMD. A significant negative correlation between GDF11 and bone alkaline phosphatase （BAP） was identified and remained significant after adjusting for age and BMI. No significant correlation was noted between cross-linked N-telopeptides of type I collagen （NTX） and GDF11. In conclusion, GDF11 is an independent negative predictor of BMD and correlates with a biomarker of bone formation, BAP, in postmenopausal Chinese women. GDF11 potentially exerts a negative effect on bone mass by regulating bone formation.
基金supported by the National Basic Research Program of China(2014 CB138206)National Key Research and Development Program of China(2016YFD0101803)+3 种基金the National Natural Science Foundation of China-CGIAR International Collaborative Program(31361140364)the Agricultural Science and Technology Innovation Program(ASTIP)of CAASFundamental Research Funds for Central Non-Profit of Institute of Crop Sciences,CAAS(1610092016124)supported by the Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE
文摘Genomic selection(GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy(rMG) in GS were evaluated systematically, using six agronomic traits(plant height, ear height, ear length, ear diameter,grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability,statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rMGshowed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction(RR-BLUP), genomic BLUP(GBLUP), Bayes A, Bayes B, Bayes C, Bayesian least absolute shrinkage and selection operator(Bayesian LASSO), and reproducing kernel Hilbert space(RKHS), the RKHS and additive-dominance model(Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.
基金supported by grants from the National Natural Science Foundation of China(32061143030,32170636,32100448)the Key Research and Development Program of Jiangsu Province(BE2022343)+6 种基金the Seed Industry Revitalization Project of Jiangsu Province(JBGS[2021]009)Project of Hainan Yazhou Bay Seed Lab(B21HJ0223)the State Key Laboratory of North China Crop Improvement and Regulation(NCCIR2021KF-5,NCCIR2021ZZ-4)Jiangsu Province Agricultural Science and Technology Independent Innovation(CX(21)1003)the Independent Scientific Research Project of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PLR202102)the Open Funds of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PL202005)Yangzhou University High-end Talent Support Program,and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.
文摘Background The objective of this study is to determine the role of tumor marker density(TMD)values such as alpha-fetoprotein tumor volume ratio(ATVR),beta-human chorionic gonadotropin tumor volume ratio(βTVR),alpha-fetoprotein testicle size ratio(ATSR),beta-human chorionic gonadotropin testicle size ratio(βTSR),lactate dehydrogenase tumor volume ratio(LTVR),and lactate dehydrogenase testicle size ratio(LTSR)in the determination of progression-free survival(PFS)in patients with testicular cancer.Materials and methods A retrospective study was conducted of 95 patients followed-up in our clinic with a diagnosis of testicular cancer between January 2015 and August 2022.Patients were grouped according to clinical stage,as either early stage(n=50)or advanced stage(n=45).Clinical and pathological data and TMD values for all patients were recorded.Results The median age of patients was 35 years(21–63 years).All TMDs except LTVR in advanced stage patients were found to be significantly higher than those of early stage patients(p<0.05).Median ATVR(2.58 vs.0.0),ATSR(0.63 vs.0.03),βTVR(0.9 vs.0.009),andβTSR(0.18 vs.0.007)of the nonseminoma patients were found to be significantly higher than those of the seminoma patients,respectively(p<0.001).Progression-free survival(months)was decreased in seminoma patients with high values ofβTVR(11.3±1.9 vs.35.2±0.7),βTSR(16.2±3.4 vs.35.2±0.75),LTVR(17.7±3.4 vs.35.2±0.7),and LTSR(21.5±3.13 vs.35.09±0.8)(p<0.001).Decreased PFS(months)was associated with higher values of ATVR(5.37±0.7 vs.35.05±0.93),βTVR(7.4±1.5 vs.34.6±1.3),ATSR(5.37±0.75 vs.35.05±0.9),βTSR(7±1.5 vs.34.6±1.3),and LTSR(7.9±1.2 vs.34.3±1.5)in nonseminoma patients(p<0.001).Based on multivariate analysis,βTVR-LTVR and ATVR-ATSR were determined to be independent risk factors for reduced PFS in seminoma and nonseminoma patients,respectively(p<0.05).Conclusions The results of this study suggest that the calculation of TMDs could be a promising and simple method for prediction of PFS among testicular cancer patients.
