Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives - Cortés, Andrés J.; López-Hernández, Felipe; Blair, Matthew W. (Frontiers in Genetics, 2022)
Legumes offer a key source of high dietary protein and micronutrient contents for the poorest. However, they are generally considered susceptible to drought and heat, stresses that are expected to become more frequent due to climate change. Therefore, leveraging innovative tools to speed-up the pre-breeding discovery of natural sources of adaptation from landraces, crop-wild relatives and orphan crops is a key prerequisite to accelerate the genetic gain of abiotic stress tolerance. In order to fill this scientific research gap, here we review modern inter-disciplinary approaches that combine ecological climate data with last generation evolutionary genomics under the paradigm of Genome–Environment Associations (GEA). We first exemplify how GEA utilizes in situ geo-referencing from genomically characterized gene bank accessions to pinpoint genomic signatures of natural selection while assessing its genetic basis. We later discuss the necessity to update current GEA models to predict both regional and microhabitat local adaptation with mechanistic eco-physiological climate indices and cutting-edge GWAS-type models. Furthermore, in order to account for polygenic evolutionary adaptation, we encourage the community to start gathering Genomic Estimated Adaptive Values (GEAVs) from Genomic Prediction (GP) and multi-dimensional Machine Learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates, and optimized for a scalable marker subset. We close envisioning avenues to make adaptation inferences more robust by merging high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, coupling evolutionary adaptive predictions with ecological genomics’ innovations will enable capturing hidden adaptations to abiotic stresses in expanded legume germplasm sources to assists responses to climate change.