Genetic Architecture and Genomic Prediction of Cooking Time in Common Bean (Phaseolus vulgaris L.)

Abstract

Cooking time of the common bean is an important trait for consumer preference, with implications for nutrition, health, and environment. For efficient germplasm improvement, breeders need more information on the genetics to identify fast cooking sources with good agronomic properties and molecular breeding tools. In this study, we investigated a broad genetic variation among tropical germplasm from both Andean and Mesoamerican genepools. Four populations were evaluated for cooking time (CKT), water absorption capacity (WAC), and seed weight (SdW): a bi-parental RIL population (DxG), an eight-parental Mesoamerican MAGIC population, an Andean (VEF), and a Mesoamerican (MIP) breeding line panel. A total of 922 lines were evaluated in this study. Significant genetic variation was found in all populations with high heritabilities, ranging from 0.64 to 0.89 for CKT. CKT was related to the color of the seed coat, with the white colored seeds being the ones that cooked the fastest. Marker trait associations were investigated by QTL analysis and GWAS, resulting in the identification of 10 QTL. In populations with Andean germplasm, an inverse correlation of CKT and WAC, and also a QTL on Pv03 that inversely controls CKT and WAC (CKT3.2/WAC3.1) were observed. WAC7.1 was found in both Mesoamerican populations. QTL only explained a small part of the variance, and phenotypic distributions support a more quantitative mode of inheritance. For this reason, we evaluated how genomic prediction (GP) models can capture the genetic variation. GP accuracies for CKT varied, ranging from good results for the MAGIC population (0.55) to lower accuracies in the MIP panel (0.22). The phenotypic characterization of parental material will allow for the cooking time trait to be implemented in the active germplasm improvement programs. Molecular breeding tools can be developed to employ marker-assisted selection or genomic selection, which looks to be a promising tool in some populations to increase the efficiency of breeding activities.

Associated SNPs, Heritabilities, Trait Correlations, and Genomic Breeding Values for Resistance in Snap Beans (Phaseolus vulgaris L.) to Root Rot Caused by Fusarium solani (Mart.) f. sp. phaseoli (Burkholder)

“…Optimum ratio of training to testing populations for achieving the highest repeatable predictive ability was 70:30% training:validation for most traits. This level is within the range of what has been found for other studies of genomic prediction in common bean (Keller et al, 2020;Diaz L. M. et al, 2021;Diaz S. et al, 2021;Shi et al, 2021). At 90% training population, the highest average predictabilities as measured by r were achieved, but standard deviations were much larger, leading to less certainty in whether a prediction was accurate.…”

“…GS models generally use many markers distributed across the genome, and as a result, are more effective than traditional MAS in selection for traits with many genes with small effect. GS has been applied to common bean for root rot ( Diaz L. M. et al, 2021 ) as well as to agronomic traits ( Keller et al, 2020 ), cooking time ( Diaz S. et al, 2021 ), and nematode resistance ( Wen et al, 2019 ; Shi et al, 2021 ) to discover genotypes with the best breeding values for recombination schemes, but deployment in breeding programs is only beginning.…”

A Review on the Cooking Attributes of African Yam Bean (Sphenostylis stenocarpa)

“…Past research investigations have explained the relationship between seed properties, variety type, seed storage conditions, and cooking time [54,55]. Table 1 presents the physical properties reported in AYB seeds.…”

“…In addition, several studies have shown that nutrients such as minerals and proteins are conserved when grains are cooked over a short period. In contrast grains requiring long cooking hours usually lose a significant amount of nutrients [55,86]. Cooking methods reported in AYB include boiling, steaming, roasting, and frying.…”

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

“…Genomic information is an important breeding tool to improve selection accuracy and to accelerate genetic gain in beans (Delfini et al., 2021; Diaz et al., 2021; Keller et al., 2020). However, selection for desirable traits also results in erosion of genetic diversity (Esquinas‐Alcázar, 2005; Trucchi et al., 2021) and inefficient selection procedures in crop breeding may lead to premature plateauing of genetic gain (Cowling et al., 2017).…”