Jump to Main Content
Candidate gene prediction and expression profiling of near isogenic lines (NILs) carrying stay-green QTLs in rabi sorghum
- Chaudhari, Gaurav N., Fakrudin, B.
- Journal of plant biochemistry and biotechnology 2017 v.26 no.1 pp. 64-72
- NAD (coenzyme), Phytoplasma, algorithms, chromosomes, drought tolerance, environmental factors, genes, indole acetic acid, introgression, isogenic lines, leaves, prediction, quantitative polymerase chain reaction, quantitative trait loci, reverse transcriptase polymerase chain reaction, statistical analysis, tissues
- In sorghum, stay-green is an important target trait considered for improvement of post-flowering drought tolerance. Prediction and expression profiling of genes present in the introgressed QTL regions of the genome paveway for identification of candidate genes and proof of genetic basis. Physical location for stay-green QTLs on different chromosomes in sorghum genome was mapped and the length of the QTL intervals ranged from 1.8 to 2.5 Mbp. The stable QTLs were commonly detected at two different locations and across three different consecutive seasons in two different mapping populations. Using the ab initio approach three algorithms viz., FGENESH, GENSCAN and GENMARK and their combinations, a total of five genes viz., NSP, NAD, PHD, MADS, MLO in QTL qSTG1A (1.82 Mbp), ten genes viz., IAA, SORBIDRAFT, CYP450, GAG/POL, PK, GENE X, UGTS, MTC, AGP16, VP25 in qSTG2 (2.54 Mbp) and one gene, SF CC1 in qSTG3 (2.18 Mbp) was predicted as common ones on chromosomes 3 and 1. Structural features and protein annotations indicated their involvement in drought tolerance pathways as compared with genbank information at NCBI. Expression profiling of NILs based on the functional annotations, qRT-PCR assays indicated upheavals of predicted genes at 30 and 45 DAF in leaf tissues of qSTG introgressed lines and significant difference between transcript levels of genes in different DAF as revealed by statistical analyses. Results indicated the potential of QTL pyramiding for the improving plant performance under adverse environmental conditions.