Climate scenarios predict that extreme climate events will become more frequent by the end of the century (IPCC, 2018), alongside an expected increase in global surface temperature from 1.5 to 4.8°C (IPCC, 2018). In such a context of climate warming, global food security is at risk, with crop yields threatened by both the direct effect of increased temperature on plant development (

The quest for genome-wide signatures of selection in populations using SNP data has proven efficient to uncover genes involved in conserved or adaptive molecular functions, but none of the statistical methods were designed to identify interacting genes as targets of selective processes. Here, we propose a straightforward statistical test aimed at detecting epistatic selection, based on a linkage disequilibrium (LD) measure accounting for population structure and heterogeneous relatedness between individuals. SNP-based ( ) and windowbased ( 1 ) statistics fit a Student distribution, allowing to easily and quickly test the significance of correlation coefficients in the frame of Genome-Wide Epistatic Selection Scans (GWESS) using candidate genes as baits. As a proof of concept, use of SNP data from the Medicago truncatula symbiotic legume plant uncovered a previously unknown gene coadaptation between the MtSUNN (Super Numeric Nodule) receptor and the MtCLE02 (CLAVATA3-Like) signalling peptide, and experimental evidence accordingly supported a MtSUNN-dependent negative role of MtCLE02 in symbiotic root nodulation. Using human HGDP-CEPH SNP data, our new statistical test uncovered strong LD between SLC24A5 andEDAR worldwide, which persists after correction for population structure and relatedness in Central South Asian populations. This result suggests adaptive genetic interaction or coselection between skin pigmentation and the ectodysplasin pathway involved in the development of ectodermal organs (hairs, teeth, sweat glands), in some human populations.Applying this approach to genome-wide SNP data will foster the identification of evolutionary coadapted gene networks.