Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits (Days to first flower—DFF; Days for flowering—DTF; Days to end of flowering—DEF) in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.

-The purpose of this study was to analyze the effect of genotype-environment interaction (GE) on common bean cultivars with

The purpose of this study was to predict the genetic progress in the selection for common bean agronomic traits based on the trait expression, using two indices of adaptive selection. The existence of correlation between various traits in common bean breeding is a major restriction, but some tools that allow breeders to predict the expected gains could optimize results. The following traits were evaluated:(1) plant cycle (days), (2) plant height (in cm), (3) stem diameter (cm), (4) insertion of the first pod (cm); (5) number of pods per plant; (6) number of grains per pod; (7) pod length (cm). Results show the possibility of selecting accessions for several agronomically important traits evaluated together. The only genotype selected by both indices was UDESC 03, confirming the possibility of selecting plants with superior agronomic traits among genotypes of common bean landraces.

ABSTRACT. We aimed to evaluate 40 common bean cultivars recommended by various Brazilian research institutions between 1970 and 2013 and estimate the genetic progress obtained for grain yield and other agronomic traits. Additionally, we proposed a bi-segmented nonlinear regression model to infer the year in which breeding began to show significant gains in Brazil. The experiment was carried out in Viçosa/MG and Coimbra/MG, in the dry and winter seasons of 2013. For this, a randomized complete block design with three replications was employed. The following traits were evaluated: number of pods per plant (NPP); number of seeds per pod (NSP); 1000-seed weight (W1000); grain yield (Yield); plant architecture (Arch); and grain aspect (GA). Genotypic means were estimated over years using linear mixed models, and genetic gains were estimated using bi-segmented nonlinear regression models. In summary, the methodology proposed in the present study indicated that bean breeding programs in Brazil began to influence Yield beginning in 1990, resulting in a gain of 6.74% per year (68.15 kg/ha per year). The years from which estimated genetic progress for NPP (5.62% per year), NSP (4.59% per year), W1000 (2.08% per year), and GA (1.36% per year) began to increase were 1994, 1990, 1989, and 1986, respectively.

This study aimed to identify which is the main component of grain yield of bean that that shows less sensitivity effect of the environment, and provides greater consistency in the expression of