Gene-based single nucleotide polymorphism markers for genetic and association mapping in common bean.

BACKGROUND: In common bean, expressed sequence tags (ESTs) are an underestimated source of gene-based markers such as insertion-deletions (Indels) or single-nucleotide polymorphisms (SNPs). However, due to the nature of these conserved sequences, detection of markers is difficult and portrays low levels of polymorphism. Therefore, development of intron-spanning EST-SNP markers can be a valuable resource for genetic experiments such as genetic mapping and association studies. RESULTS: In this study, a total of 313 new gene-based markers were developed at target genes. Intronic variation was deeply explored in order to capture more polymorphism. Introns were putatively identified after comparing the common bean ESTs with the soybean genome, and the primers were designed over intron-flanking regions. The intronic regions were evaluated for parental polymorphisms using the single strand conformational polymorphism (SSCP) technique and Sequenom MassARRAY system. A total of 53 new marker loci were placed on an integrated molecular map in the DOR364 × G19833 recombinant inbred line (RIL) population. The new linkage map was used to build a consensus map, merging the linkage maps of the BAT93 × JALO EEP558 and DOR364 × BAT477 populations. A total of 1,060 markers were mapped, with a total map length of 2,041 cM across 11 linkage groups. As a second application of the generated resource, a diversity panel with 93 genotypes was evaluated with 173 SNP markers using the MassARRAY-platform and KASPar technology. These results were coupled with previous SSR evaluations and drought tolerance assays carried out on the same individuals. This agglomerative dataset was examined, in order to discover marker-trait associations, using general linear model (GLM) and mixed linear model (MLM). Some significant associations with yield components were identified, and were consistent with previous findings. CONCLUSIONS: In short, this study illustrates the power of intron-based markers for linkage and association mapping in common bean. The utility of these markers is discussed in relation with the usefulness of microsatellites, the molecular markers by excellence in this crop.

Saturation of an intra-gene pool linkage map: towards a unified consensus linkage map for fine mapping and synteny analysis in common bean.

Map-based cloning and fine mapping to find genes of interest and marker assisted selection (MAS) requires good genetic maps with reproducible markers. In this study, we saturated the linkage map of the intra-gene pool population of common bean DOR364 × BAT477 (DB) by evaluating 2,706 molecular markers including SSR, SNP, and gene-based markers. On average the polymorphism rate was 7.7% due to the narrow genetic base between the parents. The DB linkage map consisted of 291 markers with a total map length of 1,788 cM. A consensus map was built using the core mapping populations derived from inter-gene pool crosses: DOR364 × G19833 (DG) and BAT93 × JALO EEP558 (BJ). The consensus map consisted of a total of 1,010 markers mapped, with a total map length of 2,041 cM across 11 linkage groups. On average, each linkage group on the consensus map contained 91 markers of which 83% were single copy markers. Finally, a synteny analysis was carried out using our highly saturated consensus maps compared with the soybean pseudo-chromosome assembly. A total of 772 marker sequences were compared with the soybean genome. A total of 44 syntenic blocks were identified. The linkage group Pv6 presented the most diverse pattern of synteny with seven syntenic blocks, and Pv9 showed the most consistent relations with soybean with just two syntenic blocks. Additionally, a co-linear analysis using common bean transcript map information against soybean coding sequences (CDS) revealed the relationship with 787 soybean genes. The common bean consensus map has allowed us to map a larger number of markers, to obtain a more complete coverage of the common bean genome. Our results, combined with synteny relationships provide tools to increase marker density in selected genomic regions to identify closely linked polymorphic markers for indirect selection, fine mapping or for positional cloning.