The 2010 ASHS Annual Conference
4476:
LD Mapping of Melon Traits
4476:
LD Mapping of Melon Traits
Wednesday, August 4, 2010: 4:00 PM
Springs K & L
East European melon varieties are distributed in versions known as adana were grouped under the convar- europeus. Current research is to identify QTLs for various traits through association mapping strategies and cultivars with breeding value using model based predictions. We aim to test LD pattern across the melon genome by using mapped markers from the published literature (Gonzalo et al., 2005 and Fukino et al., 2008), EST based SSRs and AFLPs. A total of 369 alleles amplified by 44 SSR primers and 2938 AFLPs were used in the study. LD (Linkage disequilibrium) was estimated separately for various linkage groups as well as genome-wide markers. LD pattern and extent in melon varies from one linkage group to the other. Our study concluded that there is high LD across the melon genome with reference to Ukrainian collections. We performed association mapping using General Linear Model (GLM) and Mixed Linear Model (MLM) with shared ancestry (Q-matrix for both GLM and MLM and Kinship for MLM alone) using both sets of data (mapped and unmapped) across the five growing seasons (2003-2007) using the program TASSEL 2.1. In the current study, a majority of QTLs showed that they are significant through the multiple years of evaluation. Common markers are identified for fruit yield and soluble solids that can be used for marker assisted selection to simultaneous improve yield and high quality. Seven markers were identified to be linked with the resistance of powdery mildew. This is the first QTL identification study using association mapping approach in melons. Best Linear Unbiased Prediction (BLUP) is a standard method for estimating random effects of a mixed model (Piepho et al., 2008). We used BLUP to predict breeding values of melon collections taking into consideration of QTL genotype X environmental interaction. We conclude that in melon improvement programs, application of mixed models with random genetic effects can be very useful to estimate heritable genetic variance for various traits.