旨在开发整合基因组信息、表型信息和功能注释信息筛选遗传位点的不同方法,比较评估8种在复杂性状上的基因组选择(genomic selection,GS)方法的预测准确性。本研究将542只北京鸭和2548只杜洛克猪基因型-表型数据通过3个维度,基于基因组...旨在开发整合基因组信息、表型信息和功能注释信息筛选遗传位点的不同方法,比较评估8种在复杂性状上的基因组选择(genomic selection,GS)方法的预测准确性。本研究将542只北京鸭和2548只杜洛克猪基因型-表型数据通过3个维度,基于基因组信息(连锁不平衡结合主成分分析(linkage disequilibrium and principal component analysis,LD_PCA),结合其他主成分位点(LD and other principal component analysis,LD_outPCA),结合皮尔逊相关系数分析(LD and Pearson correlation coefficient,LD_PCC))、表型信息(连锁不平衡结合全基因组关联分析(LD and genome-wide association study,LD_GWAS),结合互作分析(LD and EPISNP,LD_EPI))和功能注释信息(权重分配法(weight distribution method,WDM)),共8种不同筛选方法处理构建基因型矩阵,比较不同方法的基因组育种值估计准确性。结果表明,比较发现LD_PCC结合6种GS方法(GBLUP、BayesA、BayesB、BayesC、Bayes LASSO和RRGBLUP),在鸭龙骨长、猪百千克日龄、猪背膘厚和乳头数表型获得最高平均预测准确性(对比初始基因型矩阵提高约4%~13.9%)分别达到了0.7093、0.4400、0.4974和0.4401;权重分配法(WDM)在猪背膘厚和乳头数表型对比未加权获得最高预测准确性(高约10%)。综上,本研究发现WDM和LD_PCC可以有效提升GS的预测准确性,为深入研究位点筛选方法影响GS准确性和在育种实践中的应用提供了方向和借鉴。展开更多
Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medi...Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medicine. High-purity oligodendrocyte progenitor cells(OPCs) and neural progenitor cells(NPCs) have been derived from PSCs recently due to the advancements in understanding the developmental signaling pathways. Extracellular matrices(ECM) have been shown to play important roles in regulating the survival, proliferation, and differentiation of neural cells. To improve the function and maturation of the derived neural cells from PSCs, understanding the effects of ECM over the course of neural differentiation of PSCs is critical. During neural differentiation of PSCs, the cells are sensitive to the properties of natural or synthetic ECMs, including biochemical composition, biomechanical properties, and structural/topographical features. This review summarizes recent advances in neural differentiation of humanPSCs into OPCs and NPCs, focusing on the role of ECM in modulating the composition and function of the differentiated cells. Especially, the importance of using three-dimensional ECM scaffolds to simulate the in vivo microenvironment for neural differentiation of PSCs is highlighted. Future perspectives including the immediate applications of PSC-derived neural cells in drug screening and disease modeling are also discussed.展开更多
文摘旨在开发整合基因组信息、表型信息和功能注释信息筛选遗传位点的不同方法,比较评估8种在复杂性状上的基因组选择(genomic selection,GS)方法的预测准确性。本研究将542只北京鸭和2548只杜洛克猪基因型-表型数据通过3个维度,基于基因组信息(连锁不平衡结合主成分分析(linkage disequilibrium and principal component analysis,LD_PCA),结合其他主成分位点(LD and other principal component analysis,LD_outPCA),结合皮尔逊相关系数分析(LD and Pearson correlation coefficient,LD_PCC))、表型信息(连锁不平衡结合全基因组关联分析(LD and genome-wide association study,LD_GWAS),结合互作分析(LD and EPISNP,LD_EPI))和功能注释信息(权重分配法(weight distribution method,WDM)),共8种不同筛选方法处理构建基因型矩阵,比较不同方法的基因组育种值估计准确性。结果表明,比较发现LD_PCC结合6种GS方法(GBLUP、BayesA、BayesB、BayesC、Bayes LASSO和RRGBLUP),在鸭龙骨长、猪百千克日龄、猪背膘厚和乳头数表型获得最高平均预测准确性(对比初始基因型矩阵提高约4%~13.9%)分别达到了0.7093、0.4400、0.4974和0.4401;权重分配法(WDM)在猪背膘厚和乳头数表型对比未加权获得最高预测准确性(高约10%)。综上,本研究发现WDM和LD_PCC可以有效提升GS的预测准确性,为深入研究位点筛选方法影响GS准确性和在育种实践中的应用提供了方向和借鉴。
基金Supported by FSU start up fund and FSU Research Foundation GAP awardpartial support from National Science Foundation,No.1342192
文摘Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medicine. High-purity oligodendrocyte progenitor cells(OPCs) and neural progenitor cells(NPCs) have been derived from PSCs recently due to the advancements in understanding the developmental signaling pathways. Extracellular matrices(ECM) have been shown to play important roles in regulating the survival, proliferation, and differentiation of neural cells. To improve the function and maturation of the derived neural cells from PSCs, understanding the effects of ECM over the course of neural differentiation of PSCs is critical. During neural differentiation of PSCs, the cells are sensitive to the properties of natural or synthetic ECMs, including biochemical composition, biomechanical properties, and structural/topographical features. This review summarizes recent advances in neural differentiation of humanPSCs into OPCs and NPCs, focusing on the role of ECM in modulating the composition and function of the differentiated cells. Especially, the importance of using three-dimensional ECM scaffolds to simulate the in vivo microenvironment for neural differentiation of PSCs is highlighted. Future perspectives including the immediate applications of PSC-derived neural cells in drug screening and disease modeling are also discussed.
