植物分子育种系列专题报告
序号
| 时间
| 地点
| 报告题目
|
报告1
| 9月10日9:00-11:30
| 科技楼A610
| Marker-Assisted Selection: an Overview and Historical Perspective
|
报告2
| 9月11日9:00-11:30
| 科技楼A610
| OptimizingVitis aestivalis-derived ‘Norton’ Grape Breeding with Marker-Assisted Selection
|
报告3
| 9月12日9:00-11:30
| 科技楼A610
| Molecular Markers and Their Application in Black Walnut Improvement
|
报告4
| 9月14日9:00-11:30
| 科技楼A610
| Improving Grape Rootstock Breeding Using Molecular Genetic Approaches
|
报告人:Chin-Feng Hwang(黄晋豐) 美国密苏里州立大学农学院植物科学系主任、教授,植物分子育种专家。
报告时间:2018年9月10日 -9月14日
报告地点:宁夏大学贺兰山校区科技楼A610会议室
欢迎广大师生届时光临!
宁夏大学农学院
宁夏大学草业科学一流学科
宁夏优势特色作物现代分子育种重点实验室
2018年9月4日
专家简介:Dr. Chin-Feng Hwang is a Professor working on the development of molecular genetic tools for marker-assisted selection (MAS) to expedite grape and black walnut breeding programs in the Department of Environmental Plant Science and Natural Resources, Darr College of Agriculture at Missouri State University. The current research in the Hwang lab emphasizes the development and release of new Vitis aestivalis-derived ‘Norton’/V. vinifera hybrids with enhanced pathogen resistance, cold hardiness and improved fruit quality for winemaking. Norton is a unique grape. Although it is grown in U.S. regions where V. vinifera production requires extensive pesticide use, it has naturally evolved resistance to powdery mildew, downy mildew and Botrytis bunch rot. It is cold hardy and produces wine approaching the quality of V. vinifera-based wine. The integration of effective genetic resistance from Norton into V. vinifera cultivars would reduce growers’ dependence on chemical inputs and have significant environmental, health and financial benefits. The overall goals of Dr. Hwang’s research program are to use genetic markers to rapidly deploy favorable alleles, accelerate breeding cycles for new cultivar release and train a new generation of plant breeders. Laboratory activities include classical breeding and inheritance studies, plant-pathogen interactions, DNA marker analysis, linkage map construction and quantitative trait loci (QTL) detection.