Boote, K. J. | bioRxiv Science

bioRxiv2025

Identification of genetic variation associated with high-temperature tolerance in cowpea

Heat tolerance is an important trait in cowpea, a crop that constitutes the primary protein source for a large portion of the human population in sub-Saharan Africa. Cowpea grows across this region, with cultivated, landrace, semi-wild, and wild cowpeas germplasm growing across diverse climatic conditions. This study used environmental association (envGWAS) and allele frequency outlier approaches in a panel of 580 gene bank accessions to identify genomic regions associated with heat and limited precipitation. Because allele frequency outliers are detected independent of potential selection factors driving differentiation, we used a ranking-based approach to identify the climate variables most associated with variants among outliers. Precipitation-related variables dominated the signals we identified for envGWAS and allele frequency outliers. We found variants on all eleven chromosomes putatively associated with the adaptation of cowpea to higher-temperature environments. The considerable overlap between variants associated with low precipitation and high temperature suggests that these traits may be inextricably linked in cowpea. The Sahel region is the source of many accessions with derived variants associated with high temperature, suggesting the potential for accessions from this region to contribute to heat tolerance alleles for cowpea improvement.

evolutionary biology↗

bioRxiv2021

INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as "genetic coefficients." These GSPs are estimated using phenotypic observations and may not represent "true" genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTLxE interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and GxE effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

genomics↗