Mapping quantitative trait loci (QTLs) associated with resistance to major pathotype-isolates of pearl millet downy mildew pathogen
2019
Chelpuri, Durgaraju | Sharma, Rajan | Durga, Kilaru Kanaka | Katiyar, Pooja | Mahendrakar, Mahesh D. | Singh, Ram Baran | Yadav, Rattan S. | Gupta, Rajiv | Srivastava, Rakesh K.
Downy mildew (DM) caused by Sclerospora graminicola is the most devastating disease of pearl millet. It may lead to annual grain yield losses of up to ~80% and substantial deterioration of forage quality and production. The present study reports construction of the linkage map integrating simple sequence repeat (SSR) markers, for detection of quantitative trait loci (QTLs) associated with DM resistance in pearl millet. A mapping population comprising of 187 F₈ recombinant inbred lines (RILs) was developed from the cross (ICMB 89111-P6 × ICMB 90111-P6). The RILs were evaluated for disease reaction at a juvenile stage in the greenhouse trials. Genotyping data was generated from 88 SSR markers on RILs and used to construct genetic linkage map comprising of 53 loci on seven linkage groups (LGs) spanning a total length of 903.8 cM with an average adjacent marker distance of 18.1 cM. Linkage group 1 (LG1; 241.1 cM) was found to be longest and LG3 the shortest (23.0 cM) in length. The constructed linkage map was used to detect five large effect QTLs for resistance to three different pathotype-isolates of S. graminicola from Gujarat (Sg445), Haryana (Sg519) and Rajasthan (Sg526) states of India. One QTL was detected for isolate Sg445 resistance, and two each for Sg519 and Sg526 resistance on LG4 with LOD scores ranging from 5.1 to 16.0, explaining a wide range (16.7% to 78.0%) of the phenotypic variation (R²). All the five co-localized QTLs on LG4 associated with the DM resistance to the three pathotype-isolates were contributed by the resistant parent ICMB 90111-P6. The QTLs reported here may be useful for the breeding programs aiming to develop DM resistant pearl millet cultivars with other desirable traits using genomic selection (GS) approaches.
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