High-resolution mapping of the quantitative trait locus (QTLs) conferring resistance to false smut disease in rice

Autor: kumari neelam, Kishor Kumar, Amandeep Kaur, Amit Kishore, Pavneet Kaur, Ankita Babbar, Gurwinder Kaur, Ishwinder Kamboj, Jagjeet Singh Lore, Yogesh Vikal, G.S. Mangat, Rupinder Kaur, Renu Khanna, Kuldeep Singh
Rok vydání: 2021
Předmět:
DOI: 10.21203/rs.3.rs-325960/v1
Popis: Decoding the genetic mechanisms underlying disease resistance is of great importance for crop improvement. Rice false smut (RFS) is a major fungal disease caused by Ustilaginoidea virens that hampers the grain quality and yield of rice worldwide. It causes 2.8-49% global yield loss depending upon disease severity and varieties grown. In India, the severity of yield loss ranged from 2-75%. Keeping the economic importance of this disease, identification of the genes/QTLs governing disease resistance is of prime importance for the development of the linked markers and cloning of the genes. Here, we report mapping of QTLs using a recombinant inbred line (RIL) population derived from a cross between resistant line, RYT2668, and a highly susceptible variety, PR116. The population was evaluated for rice false smut disease under field conditions for three cropping seasons 2013, 2015, and 2016. A total of seven QTLs were mapped on rice chromosomes 2, 4, 5, 7, and 9 of rice using 2326 single nucleotide polymorphism (SNP) markers. Among them, a novel QTL qRFSr9.1 affecting total smut ball (TSB)/panicle on chromosome 9 exhibited the largest phenotypic effect. The prediction of putative candidate genes within the qRFSr9.1 spanned in 994.1Kb revealed four NBS-LRR domain-containing disease resistance proteins. We identified SNPs/Indels associated with the disease resistance which could be used for accelerating breeding programs using marker-assisted selection. In summary, our findings mark the ‘hot-spot’ region on rice chromosomes along with the identification of disease resistance genes in conferring resistance to the rice false smut disease.
Databáze: OpenAIRE