bioRxivEvolutionary Biology
Genomic evidence of genuine wild versus admixed olive populations evolving in the same natural environments in western Mediterranean Basin
L. Zunino, P. Cubry, G. Sarah, P. Mournet, A. El Bakkali, L. Aqbouch, E. Costes, B. Khadari
Abstract
Admixtures between wild animals and plants and their domesticated relatives are widely documented. This can have positive or negative impacts on the evolution of admixed populations in natural environments, yet the phenomenon is still misunderstood in long-lived woody species, contrary to short-lived crops. Wild olive Olea europaea L. occurs in the same eco-geographical range as domesticated olive, i.e. the Mediterranean Basin (MB). Moreover, it is an allogamous and anemophilous species whose seeds are disseminated by birds, i.e. factors that drive gene flow between crops and their wild relatives. Here we investigated the genetic structure of western MB wild olive populations in natural environments assuming a homogenous gene pool with limited impact of cultivated alleles, as previously suggested. We used a target sequencing method based on annotated genes from the Farga reference genome to analyze 27 western MB olive tree populations sampled in natural environments in France, Spain and Morocco. We also target sequenced cultivated olive tree accessions from the Worldwide Olive Germplasm Bank of Marrakech and Porquerolles and from an eastern MB wild olive tree population. We combined PCA, sNMF, pairwise Fst and TreeMix and clearly identified genuine wild olive trees throughout their natural distribution range along a north-south gradient including, for the first time, in southern France. However, contrary to our assumption, we highlighted more admixed than genuine wild olive trees. Our results raise questions regarding the admixed population evolution pattern in this environment, which might be facilitated by crop-to-wild gene flow.
- organism: plant (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: variant calling
- approach: statistical model
- approach: matrix factorization
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