作物学报2018，Vol.44Issue(1)：1-14,14.DOI:10.3724/SP.J.1006.2018.00001
利用SNP芯片和BSA分析规模化定位小麦抗白粉病基因
Large Scale Detection of Powdery Mildew Resistance Genes in Wheat via SNP and Bulked Segregate Analysis
摘要
Abstract
Large-scale detection of powdery mildew resistance genes is necessary for wheat germplasm innovation and breeding, especially via marker assisted selection. Illumina 90k iSelect SNP chip and Bulked Segregate Analysis (BSA) were applied to identify powdery mildew resistance gene in 36 wheat varieties (lines) from Henan province. SNP genotyping between 36 resistant bulks and 36 susceptible bulks revealed that single polymorphic SNP peaks were identified between 24 of the 36 bulk pairs, indi-cating single powdery mildew resistance gene may present in the 24 varieties (lines). Multiple polymorphic SNP peaks were found between other 12 resistant and susceptible bulks, indicating more than one powdery mildew resistance gene might be in these varieties (lines). Among the 36 bulk pairs, 26 showed the largest number of SNP enriched on chromosome 2AL, indicating the powdery mildew resistance genes, most likely on Pm4 locus, were in these 26 varieties (lines). A new marker Xwggc116 was developed and proved to be effective for detecting the powdery mildew resistance gene on 2AL. Overall, the combination of BSA and high-throughput SNP genotyping platform is highly effective for large scale powdery mildew resistance gene detection in wheat germplasm. There are a limited number of powdery mildew resistance genes (Pm2, Pm4, Pm21, and new 1BL/1RS trans-location) in wheat varieties (lines) of Henan province, indicating very narrow genetic diversity of the powdery mildew resistance genes in wheat breeding program. Exploring and utilization of new diversified disease resistance genes are urgent for breedingnew varieties with disease resistance.关键词
小麦品系/抗白粉病基因/BSA/SNPKey words
wheat varieties/powdery mildew resistance gene/BSA/SNP引用本文复制引用
吴秋红,刘志勇,陈永兴,李丹,王振忠,张艳,袁成国,王西成,赵虹,曹廷杰..利用SNP芯片和BSA分析规模化定位小麦抗白粉病基因[J].作物学报,2018,44(1):1-14,14.基金项目
本研究由国家重点研发计划项目(2017YFD0101004)资助.This study was supported by the National Key Research and Development Program of China (2017YFD0101004) （2017YFD0101004）
