Identification of Pathogenicity Loci in Magnaporthe oryzae Using GWAS with Neck Blast Phenotypic Data.
Nyein Nyein Aye MyintSiripar KorinsakCattleya ChutteangKularb LaosatitBurin ThunnomTheerayut ToojindaJonaliza L SiangliwPublished in: Genes (2022)
Magnaporthae oryzae (M. oryzae) is the most destructive disease of rice worldwide. In this study, one hundred and two isolates of M . oryzae were collected from rice ( Oryzae sativa L.) from 2001 to 2017, and six rice varieties with resistance genes Pizt , Pish , Pik , Pib, and Pi2 were used in a genome-wide association study to identify pathogenicity loci in M . oryzae . Genome-wide association analysis was performed using 5338 single nucleotide polymorphism (SNPs) and phenotypic data of neck blast screening by TASSEL software together with haplotype block and SNP effect analysis. Twenty-seven significant SNPs were identified on chromosomes 1, 2, 3, 4, 5, 6, and 7. Many predicted genes (820 genes) were found in the target regions of six rice varieties. Most of these genes are described as putative uncharacterized proteins, however, some genes were reported related to virulence in M. oryzae . Moreover, this study revealed that R genes, Pik , Pish , and Pi2 , were broad-spectrum resistant against neck blast disease caused by Thai blast isolate. Haplotype analysis revealed that the combination of the favorable alleles causing reduced virulence of isolates against IRBLz5-CA carrying Pi2 gene contributes 69% of the phenotypic variation in pathogenicity. The target regions and information are useful to develop marker-specific genes to classify blast fungal isolates and select appropriate resistance genes for rice cultivation and improvement.
Keyphrases
- genome wide
- bioinformatics analysis
- genome wide identification
- dna methylation
- genome wide association
- genome wide association study
- biofilm formation
- genome wide analysis
- pseudomonas aeruginosa
- gene expression
- healthcare
- computed tomography
- data analysis
- electronic health record
- pet imaging
- genetic diversity
- big data
- artificial intelligence