Genome-Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives

• Published in: Frontiers in genetics Vol. 13; p. 910386
• Main Authors: Cortés, Andrés J, López-Hernández, Felipe, Blair, Matthew W
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05-08-2022
• Subjects: gene banks; Genetics; genome-wide environmental scans; genome-wide selection scans (GWSS); genome–environment associations (GEA); germplasm collections; landraces; genome-wide selection scans (GWSS); landraces; genome–environment associations (GEA); genomic prediction (GP); germplasm collections; genome-wide environmental scans; gene banks; abiotic stress
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2022.910386

Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome-environment associations (GEAs). We first exemplify how GEA utilizes
georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat-based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by
GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change. "I shall endeavor to find out how nature's forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature" Alexander von Humboldt-
.