Association mapping in sunflower

LIA VV; FUSARI CM; MORENO MV; DI RIENZO JA; FILIPPI CV; ZUBRZYCKI J; NISHINAKAMASU V; PUEBLA AF; MALIGNE A; MARINGOLO C; QUIROZ F; TROGLIA C; ALVAREZ D; HOPP E; ESCANDE A; HEINZ RA; PANIEGO NB

Davis

Congreso; Second Compositae Whitepaper Meeting; 2011

Compositae Genome Project

The Sunflower Genomics Laboratory at INTA (Instituto Nacional de Tecnología Agropecuaria, Argentina) seeks to apply and integrate the concepts and technologies of genomics to assist sunflower breeding. Our main activities include genome sequence analysis, analysis of gene-expression patterns related to plant responses to biotic and abiotic stresses, and evaluation of germplasm diversity. As part of our current research portfolio we have started to develop an association mapping platform based on public and proprietary inbred lines focusing on two complex traits: Sclerotinia Head Rot resistance (SHR) and drought tolerance. To this end, we have performed SNP discovery in ESTs and candidate genes, assessed nucleotide diversity and linkage disequilibrium (LD), optimized SNP genotyping technologies such as CEL1 cleavage of heteroduplex and denaturing high performance liquid chromatography (dHPLC), and designed a genotyping assay for the Illumina Golden Gate-VeraCode technology. To investigate quantitative resistance to Sclerotinia, a first association study was conducted using a candidate gene approach. A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napusinfected with S. sclerotiorum. Associations among haplotypes in 16 candidate genes and SHR incidence were tested using Mixed Linear Models that account for the population structure and kinship relationships inferred through the analysis of SSR loci. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P