Genetic mapping for agronomic traits in a MAGIC population of common bean (Phaseolus vulgaris L.) under drought conditions.

BACKGROUND: Common bean is an important staple crop in the tropics of Africa, Asia and the Americas. Particularly smallholder farmers rely on bean as a source for calories, protein and micronutrients. Drought is a major production constraint for common bean, a situation that will be aggravated with current climate change scenarios. In this context, new tools designed to understand the genetic basis governing the phenotypic responses to abiotic stress are required to improve transfer of desirable traits into cultivated beans. RESULTS: A multiparent advanced generation intercross (MAGIC) population of common bean was generated from eight Mesoamerican breeding lines representing the phenotypic and genotypic diversity of the CIAT Mesoamerican breeding program. This population was assessed under drought conditions in two field trials for yield, 100 seed weight, iron and zinc accumulation, phenology and pod harvest index. Transgressive segregation was observed for most of these traits. Yield was positively correlated with yield components and pod harvest index (PHI), and negative correlations were found with phenology traits and micromineral contents. Founder haplotypes in the population were identified using Genotyping by Sequencing (GBS). No major population structure was observed in the population. Whole Genome Sequencing (WGS) data from the founder lines was used to impute genotyping data for GWAS. Genetic mapping was carried out with two methods, using association mapping with GWAS, and linkage mapping with haplotype-based interval screening. Thirteen high confidence QTL were identified using both methods and several QTL hotspots were found controlling multiple traits. A major QTL hotspot located on chromosome Pv01 for phenology traits and yield was identified. Further hotspots affecting several traits were observed on chromosomes Pv03 and Pv08. A major QTL for seed Fe content was contributed by MIB778, the founder line with highest micromineral accumulation. Based on imputed WGS data, candidate genes are reported for the identified major QTL, and sequence changes were identified that could cause the phenotypic variation. CONCLUSIONS: This work demonstrates the importance of this common bean MAGIC population for genetic mapping of agronomic traits, to identify trait associations for molecular breeding tool design and as a new genetic resource for the bean research community.

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP.

BACKGROUND: Therecent development and availability of different genotype by sequencing (GBS) protocols provided a cost-effective approach to perform high-resolution genomic analysis of entire populations in different species. The central component of all these protocols is the digestion of the initial DNA with known restriction enzymes, to generate sequencing fragments at predictable and reproducible sites. This allows to genotype thousands of genetic markers on populations with hundreds of individuals. Because GBS protocols achieve parallel genotyping through high throughput sequencing (HTS), every GBS protocol must include a bioinformatics pipeline for analysis of HTS data. Our bioinformatics group recently developed the Next Generation Sequencing Eclipse Plugin (NGSEP) for accurate, efficient, and user-friendly analysis of HTS data. RESULTS: Here we present the latest functionalities implemented in NGSEP in the context of the analysis of GBS data. We implemented a one step wizard to perform parallel read alignment, variants identification and genotyping from HTS reads sequenced from entire populations. We added different filters for variants, samples and genotype calls as well as calculation of summary statistics overall and per sample, and diversity statistics per site. NGSEP includes a module to translate genotype calls to some of the most widely used input formats for integration with several tools to perform downstream analyses such as population structure analysis, construction of genetic maps, genetic mapping of complex traits and phenotype prediction for genomic selection. We assessed the accuracy of NGSEP on two highly heterozygous F1 cassava populations and on an inbred common bean population, and we showed that NGSEP provides similar or better accuracy compared to other widely used software packages for variants detection such as GATK, Samtools and Tassel. CONCLUSIONS: NGSEP is a powerful, accurate and efficient bioinformatics software tool for analysis of HTS data, and also one of the best bioinformatic packages to facilitate the analysis and to maximize the genomic variability information that can be obtained from GBS experiments for population genomics.

Use of the advanced backcross-QTL method to transfer seed mineral accumulation nutrition traits from wild to Andean cultivated common beans.

Iron deficiency anemia and zinc deficiency are major health concerns across the world and can be addressed by biofortification breeding of higher mineral concentration in staple crops, such as common bean. Wild common beans have for the most part had higher average seed mineral concentration than cultivars of this species but have small un-commercial seeds. A logical approach for the transfer of the seed mineral trait from wild beans to cultivated beans is through the advanced backcross breeding approach. The goal of this study was to analyze a population of 138 BC(2)F(3:5) introgression lines derived from the very high iron wild genotype G10022 backcrossed into the genetic background of the commercial-type variety 'Cerinza', a large-red seeded bush bean cultivar of the Andean genepool. In addition to measuring seed mineral accumulation traits and the quantitative trait loci (QTL) controlling these traits we were interested in simultaneously testing the adaptation of the introgression lines in two replicated yield trials. We found the cross to have high polymorphism and constructed an anchored microsatellite map for the population that was 1,554-cM long and covered all 11 linkage groups of the common bean genome. Through composite interval mapping (CIM) and single point analysis (SPA), we identified associations of markers and mineral traits on b01, b06, b07, b08, b10 and b11 for seed iron concentration, and markers on b01, b04 and b10 for seed zinc concentration. The b07 and b08 QTL aligned with previous QTL for iron concentration. A large number of QTL were found for seed weight (9 with CIM and 36 with SPA analysis) and correlations between seed size and mineral content affected the identification of iron and zinc contents' QTL on many linkage groups. Segregation distortion around domestication genes made some areas difficult to introgress. However, in conclusion, the advanced backcross program produced some introgression lines with high mineral accumulation traits using a wild donor parent.