IncreBean: Genomic based improvement of climbing beans

Common bean (Phaseolus vulgaris L.) is the most important grain legume and protein source for direct human consumption. Beans are of particular relevance for the diet and income of smallholder farmers in the tropics. Climbing beans, a distinct subgroup of common beans, produce significantly higher yields compared to bush types and showed positive effects on soil fertility. The International Center for Tropical Agriculture (CIAT) in collaboration with the ETH Zürich reinforces breeding activities in this neglected climbing type. In the current project, statistical models are developed in order to quickly predict performance of climbing bean genotypes based on genetic marker data. The model-based selection of suitable parental genotypes allows to create superior new breeding lines in a precise manner. The implementation of this recent bioinformatic approaches enables efficient improvement of climbing beans regarding yield and nutrition quality

Using bean populations derived from P. acutifolius to advance toward generation of new bean varieties and discerning the traits and genetic base associated to heat tolerance

This dataset contains phenotypic and genotypic data of an interspecific genetic mapping population between P. vulgaris and P. acutifolius using the bridge genotype VAP 1. The population is composed by 14 crosses including five Mesoamerican elite common bean (SMR 155, SEF 10, SMC 214, ICTA Ligero, and SEN 118) and two wild P. acutifolius parents (G40056 and G40287). The main goal of this population is understand the effect of introgressions from P. acutifolius in the heat tolerance of Common Bean. The raw phenotypic data comes from one trial carried out at CIAT HQ in Palmira(Colombia) where the interspecific population was tested in two greenhouses with night temperatures regulated at 25C and a control environment. In this trial several agronomic traits were measured including yield per hectare (YdHa), pod harvest index (PHI), seed weight (SW), seeds per pod (PSN) and pod number (PN). The agronomic performance of the population was modeled using linear mixed models with spatial correction. From these models, best linear unbiased estimators / predictors were obtained (BLUEs/BLUPs). The genotypic file is a VCF that includes 36,839 SNPs for 261 interspecific families and 8 founders