The Tibetan antelope （Pantholops hodgsonii）, indigenous to China, became an endangered species because of considerable reduction both in number and distribution during the 20th century. Presently, it is listed as an...The Tibetan antelope （Pantholops hodgsonii）, indigenous to China, became an endangered species because of considerable reduction both in number and distribution during the 20th century. Presently, it is listed as an Appendix Ⅰ species by CITES and as Category I by the Key Protected Wildlife List of China. Understanding the genetic diversity and population structure of the Tibetan antelope is significant for the development of effective conservation plans that will ensure the recovery and future persistence of this species. Twenty-five microsatellites were selected to obtain loci with sufficient levels of polymorphism that can provide information for the analysis of population structure. Among the 25 loci that were examined, nine of them showed high levels of genetic diversity. The nine variable loci （MCM38, MNS64, IOBT395, MCMAL TGLA68, BM1329, BMSI341, BM3501, and MB066） were used to examine the genetic diversity of the Tibetan antelope （n = 75） in Hoh Xil National Nature Reserve（HXNNR）, Qinghai, China. The results obtained by estimating the number of population suggested that all the 75 Tibetan antelope samples were from the same population. The mean number of alleles per locus was 9.4 ± 0.5300 （range, 7-12） and the mean effective number of alleles was 6.519± 0.5271 （range, 4.676-9.169）. The observed mean and expected heterozygosity were 0.844 ± 0.0133 （range, 0.791-0.897） and 0.838 ± 0.0132 （range, 0.786-0.891）, respectively. Mean Polymorphism Information Content （PIC） was 0.818 ± 0.0158 （range, 0.753-0.881）. The value of Fixation index （Fis） ranged from -0.269 to -0.097 with the mean of -0.163 ± 0.0197. Mean Shannon＇s information index was 1.990 ± 0.0719 among nine loci （range, 1.660-2.315）. These results provide baseline data for the evaluation of the level of genetic variation in Tibetan antelope, which will be important for the development of conservation strategies in future.展开更多
Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large n...Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.展开更多
基金Conservation Technology for Endangered Wildlife Program, Social Service Project of the Ministry of Science and Technology (No. 2001DIB100058)National Key Project of 10th Five-Year Plan (No. 2001BA510B10).
文摘The Tibetan antelope （Pantholops hodgsonii）, indigenous to China, became an endangered species because of considerable reduction both in number and distribution during the 20th century. Presently, it is listed as an Appendix Ⅰ species by CITES and as Category I by the Key Protected Wildlife List of China. Understanding the genetic diversity and population structure of the Tibetan antelope is significant for the development of effective conservation plans that will ensure the recovery and future persistence of this species. Twenty-five microsatellites were selected to obtain loci with sufficient levels of polymorphism that can provide information for the analysis of population structure. Among the 25 loci that were examined, nine of them showed high levels of genetic diversity. The nine variable loci （MCM38, MNS64, IOBT395, MCMAL TGLA68, BM1329, BMSI341, BM3501, and MB066） were used to examine the genetic diversity of the Tibetan antelope （n = 75） in Hoh Xil National Nature Reserve（HXNNR）, Qinghai, China. The results obtained by estimating the number of population suggested that all the 75 Tibetan antelope samples were from the same population. The mean number of alleles per locus was 9.4 ± 0.5300 （range, 7-12） and the mean effective number of alleles was 6.519± 0.5271 （range, 4.676-9.169）. The observed mean and expected heterozygosity were 0.844 ± 0.0133 （range, 0.791-0.897） and 0.838 ± 0.0132 （range, 0.786-0.891）, respectively. Mean Polymorphism Information Content （PIC） was 0.818 ± 0.0158 （range, 0.753-0.881）. The value of Fixation index （Fis） ranged from -0.269 to -0.097 with the mean of -0.163 ± 0.0197. Mean Shannon＇s information index was 1.990 ± 0.0719 among nine loci （range, 1.660-2.315）. These results provide baseline data for the evaluation of the level of genetic variation in Tibetan antelope, which will be important for the development of conservation strategies in future.
基金supported by the National Basic Research Program of China(2011CB100100)the Priority Academic Program Development of Jiangsu Higher Education Institutions+4 种基金the National Natural Science Foundations(31391632,31200943,and31171187)the National High-tech R&D Program(863 Program)(2014AA10A601-5)the Natural Science Foundations of Jiangsu Province(BK2012261)the Natural Science Foundation of the Jiangsu Higher Education Institutions(14KJA210005)the Innovative Research Team of Universities in Jiangsu Province
文摘Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.
