López-Hernández Felipe, Villanueva-Mejía Diego F, Tofiño-Rivera Adriana Patricia, Cortés Andrés J
Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA)-CI La Selva, Km 7 vía Rionegro-Las Palmas, Rionegro 054048, Colombia.
Applied Sciences and Engineering School, EAFIT University, Medellín 050022, Colombia.
Int J Mol Sci. 2025 Jul 30;26(15):7370. doi: 10.3390/ijms26157370.
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean ( L.) × tolerant Tepary bean ( A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation.
气候变化正危及全球粮食安全，至少7.13亿人面临饥饿。为应对这一挑战，作为菜豆的豆类可提供基于自然的解决方案，提供营养和膳食纤维，特别是对拉丁美洲和非洲的农村社区而言。然而，由于菜豆通常易受热害和干旱影响，必须加快其分子渐渗适应性育种，以便能在受极端天气影响的地区种植。因此，本研究旨在将一组先进的菜豆（L.）×耐逆的 tepary 豆（A. Gray）种间品系与贝叶斯回归算法相结合，以预测对哥伦比亚加勒比海岸湿润和干燥次区域的适应性，在该地区菜豆通常对极端热浪表现出适应不良。总共成功培育出87个具有杂交谱系的先进品系，克服了种间不亲和性。通过测序（GBS）对该杂交群体进行基因分型，发现了15645个单核苷酸多态性（SNP）标记。记录了每个基因型的三个产量构成因素（单株产量、种子数和荚数）和两个生物量变量（营养生物量和种子生物量），并将其输入到几个贝叶斯回归模型中，以确定在哥伦比亚海岸三个地点具有最佳遗传育种值的顶级基因型。我们比较分析了几种回归方法，对所有性状和地点表现最佳的模型是 BayesC。此外，我们比较了所有标记以及仅那些由先验全基因组关联研究（GWAS）模型确定为相关标记的使用情况。使用完整的SNP集具有更好的预测能力，这表明作为GWAS重建一部分的遗传力缺失。此外，根据其回归效应，根据前500个最具解释力的标记确定每个性状和地点的最佳SNP集。这些500个SNP在各地点平均重叠率为5.24%，这强化了多基因适应的地点依赖性。最后作为针对加勒比地区窄适应性的推荐方案的一部分，我们检索了基因组估计育种值（GEBV），并为每个性状和地点选择了前10个基因型。在田间条件下进行验证并筛选稳定性后，候选基因型和SNP可用于进一步的渐渗育种周期以实现适应。
