1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
New insights into the fusarium wilt resistance in chickpea using genomics and transcriptomics meta-analysis
Paper ID : 1477-ICB10
Authors:
Jahad Soorni, Fatemeh Loni, Parisa Daryani, Nazanin Amirbakhtiar, Zahra-Sadat Shobbar *
Agricultural Biotechnology Research Institute of Iran (ABRII), Department of Systems Biology, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Abstract:
Chickpea (Cicer arietinum L.) is one of the most important grain legumes providing a major source of dietry protein. Fusarium wilt (FW) caused by eight Fusarium oxysporum f. sp. ciceri pathogenic races affect chickpeas causing a great yield loss wordwide. In the present study, an integrated meta-analysis approach was used to find genomic regions and candidate genes involved in resistance to fusarium wilt. All the reported quantitative trait loci (QTLs) and transcroptomics data including RNA-seq and SAGE studies related to FW resistance were collected. A meta-analysis was performed using 29 initial QTLs associated with different races of FW resistance reported so far through nine independent experiments by Biomercator which led to indentification of seven meta-QTLs. The genes located in these hotspot regions were retrieved through Ensembl Plants and NCBI databases. Moreover, the differentially expressed genes in chickpea were found through analysis of the related transcroptomics data. Conclusively, 981 FW-resistance related genes were identified using meta-QTL analysis and compared with 291 and 5183 FW-responsive genes detected through RNAseq and SAGE techniques. The integrated genome and transcriptome analyses revealed a number of candidate genes underlying resistance to FW races, which included some genes from MAP kinase, serine threonine kinase, WRKY transcription factors, antioxidant enzymes and NBS-LRR gene families. These candidate genes/regions can be used for development of FW-resistant cultivars through genetics engineering or molecular breeding.
Keywords:
Meta-QTL, RNAseq; pathogen; consensus map; gene finding, biotic stress
Status : Paper Accepted (Poster Presentation)