• bioinformatics
• GWAS
• landscape genomics
• local adaptation
• orphan crops
• population genomics

The utilization of locally adapted crop genetic resources is essential to address food insecurity, climate change vulnerability and economic instability in smallholder farming systems. Neglected or Underutilized crop Species (NUS) such as teff (Eragrostis tef), a staple crop in Ethiopia and Eritrea, offer promising opportunities to build resilient agricultural ecosystems and ensure sustainable consumption and production patterns in marginal environments. Advancements in genomics and crop genetic improvement now provide powerful tools to bridge the gap between NUS and 21st century agriculture. This study focuses on the genomic characterization of a core collection of 370 Ethiopian teff accessions held at the Ethiopian Biodiversity Institute (EBI, Addis Abeba, Ethiopia). A bioinformatics workflow was first implemented to map pre-sequenced and demultiplexed Double-Digest Restriction-site Associated DNA Sequencing (ddRAD-Seq) reads on the teff V3 genome and to identify Single Nucleotide Polymorphisms (SNPs). The 31K high quality genome-wide loci identified were implemented in an unsupervised clustering algorithm, a Bayesian inference framework and a neighbor-joining phylogeny to unfold the genetic diversity and the evolutionary history of the teff core collection. Diversity analyses were combined with landscape genomics and environmental-association analysis to uncover spatio-environmental patterns of genetic variation and to identify Quantitative Trait Loci (QTLs) driving local adaptation. Furthermore, genotype information was coupled to previously collected phenotypic and Participatory Variety Selection (PVS) data and integrated in a Mixed Linear Model (MLM)-based Genome Wide Association Study (GWAS) framework to identify polymorphisms associated with adaptive traits. The analyses reveal a subdivision of the population into nine genetic clusters and allow us to infer spatio-environmental genetic patterns across temperature, soil acidity and precipitation seasonality clines. Local adaptation to bioclimatic conditions was contributed by three QTLs, namely S2_2138473, S15_19236753 and S7_382207, which was found to be in linkage disequilibrium with a gene encoding for a LEA-2 domain-containing protein. Significant differences in phenotypic performances and in their relative scores provided by farmers were observed among genetic clusters at the two study sites, corroborating the hypothesis that the broad variation in phenology, plant architecture and, consequently, yield displayed by teff landraces is actively maintained by local adaptation and, interestingly, by farmer cultural patterns. Several significant marker-trait associations (MTAs) and marker-farmer preference associations (MFAs) underpinning phenology, panicle architecture, yield and farmers’ phenotypic performance scores and likely controlling teff adaptive variation, are identified. The multidimensional genomics approach developed in this study, integrating landscape genomics, association mapping and participatory variety selection, paves the way towards integrative germplasm management practices and genomics-assisted breeding efforts.