Prediction and validation of putative candidate genes underlying drought‐tolerant QTLs through in silico and RT‐PCR approaches in rice (Oryza sativa)

Abstract

AbstractIn the present study, the aim was to predict and validate putative candidate genes underlying drought‐tolerant quantitative trait loci (QTLs) in rice crop using in silico approaches and real‐time polymerase chain reaction (RT‐PCR). The genes underlying major drought‐tolerant QTLs which have been reported by data mining, sequence variation, gene ontology analysis, quantitative traits gene finder and gene expression analysis were subjected to RiceVarmap software to design primers, and only a few variants gave the SNP/InDel primers; thus, finally, 15 primers were ultimately selected, which were used in identification of differentially expressed genes (DEGs) among contrasting rice genotypes IR 64 and N 22 for drought tolerance trait using quantitative RT‐PCR studies by providing drought stress treatment during panicle initiation stage. In this investigation, we predicted 11 genes as candidate genes underlying drought‐tolerant QTLs. Out of these, only four QTLs were found responsible for the major effect in drought tolerance regions such as QTL‐Qsn‐4b, QTL‐rn7a, QTL‐Qtgw‐2a and QTL‐phc4.1 and 11 prioritized candidates were identified that expressed in leaf tissues. Only four primers belong to two QTLs, primer vg0712623096 from QTL‐rn7a (LOC_Os07g22450) located on chromosome‐7 encoding NAC domain‐containing protein and the primers vg0431750843(LOC_Os04g53310) encoding soluble starch synthase 3‐ chloroplast precursor, vg0432626757 (LOC_Os04g54850) encoding pectin acetylesterase domain‐containing protein and vg0433031562 (LOC_Os04g55520) encoding AP2 domain‐containing protein, from QTL‐Qsn‐4b, located on chromosome‐4 found to have higher differential expression in N 22 in comparison with IR 64 during drought stress as per quantitative RT‐PCR 2–ΔΔCt values. Considering the overall study, these four primers/genes were identified as candidate genes underlying genomic regions governing drought tolerance. Therefore, these putative candidate genes could be focussed for further functional analysis to exploit in rice breeding.