Biology subjects

Didelot, F.

Publications and source records attributed to Didelot, F..

4 recordsLinked to original sources

bioRxiv2026

Genomic forecasting links climate-associated variation to spring phenology in the European wild apple, a crop wild relative of the cultivated apple

Climate-associated genomic variation is often used as a proxy for adaptive potential, yet whether it reflects adaptation with phenotypic consequences, rather than demography alone, is rarely tested in long-lived species. Genomic offsets forecast climate maladaptation from genotype-environment models but are seldom validated against phenotypes. We combined whole-genome resequencing, SNP and transposable element insertion polymorphism (TIP) variation, landscape genomics, genome-wide association, genomic offset, and common-garden phenotyping in the European wild apple (Malus sylvestris Mill.), a crop wild relative with a complex demographic history, to test this. Results: Of 148 resequenced accessions (135 wild, 13 cultivated), 89 wild individuals formed the core dataset. Population structure was associated with Last Glacial Maximum precipitation rather than contemporary climate, and genetic diversity increased from west to east. Genome-environment associations across 9.75 million SNPs identified climate-associated loci enriched in photoperiod-related flowering-time genes, with limited overlap with phenotype-associated loci. SNP-based genomic offset was associated with bud-break timing in the Saclay common garden (marginal R2 = 0.31), with higher offsets corresponding to earlier bud break, but not with growth or ecophysiological traits. This association depended on Austrian and Romanian populations and was clearest in non-admixed wild individuals. Offsets based on TIPs or glacial climate did not predict measured phenotypes. Conclusions: Climate association, phenotypic relevance, and predictive power of genomic variation are separable. In a long-lived tree, genomic offset predicted spring phenology, a key adaptive trait, but this signal varied across provenances and ancestries and was absent for transposable elements. Genomic offsets can therefore forecast potential phenological maladaptation, provided they are validated against phenotypes such as bud break.

ecology↗

bioRxiv2025

Gene flow from the European wild apple and selection shaped the domesticated apple (Malus domestica Borkh.) genome

How selection and demography shape genomes of long-lived crops remains largely unresolved. Using apple (Malus domestica) as a model, we integrate 218 whole genomes (68 cultivated dessert/cider; 150 wild: M. sieversii, M. orientalis, M. sylvestris), RNA-seq, and a flowering-time GWAS to resolve how these forces forged the cultivated apple genome. Despite weak neutral differentiation and widespread admixture, dessert and cider apples form distinct gene pools that derive primarily from M. sieversii-M. orientalis rather than European M. sylvestris. We find no evidence of a strong domestication bottleneck, as expected in perennials. Demography-aware selection scans reveal largely non-overlapping targets: dessert shows more hard sweeps at genes linked to fruit quality, disease resistance, and flowering, whereas cider shows proportionally more soft sweeps and balancing selection; RNA-seq differential expression supports these candidates. Wild-to-crop introgression from M. sylvestris is extensive but heterogeneous by context: some introgressed tracts concentrate in hard-sweep regions and approach fixation (consistent with rapid, targeted uptake), whereas others persist at intermediate frequencies with soft-sweep signatures (consistent with diffuse, recurrent introgression of adaptive alleles). Extending to the phenotype, the lead chromosome 9 flowering-time association lies within an introgressed segment near a transposable element and is separated from sweep peaks, consistent with regulatory/polygenic control. Cultivated apples carry a lower predicted deleterious load than wild relatives. Together, these results provide one of the most comprehensive population genomic portraits of a perennial fruit tree domestication, clarifying how selection and adaptive introgression jointly shaped the cultivated apple genome architecture and yielding actionable targets for breeding and conservation. Significance statementPerennial crops are underexplored compared to annuals, leaving open the question of how selection, gene flow, and demography shape their genomes. Using the apple tree (Malus domestica), we analyzed 218 genomes, along with expression and trait data. Despite weak genome-wide differences, dessert and cider apples form distinct gene pools. Widespread gene flow from the European wild apple supplied adaptive alleles, with contrasting dynamics: in cider, a few introgressed DNA segments rose rapidly; in dessert, many variants shifted gradually. Cultivated apples also carry a lower predicted burden of harmful mutations than wild relatives. Together, these results redefine perennial domestication and pinpoint genomic targets to accelerate breeding and conservation.

genetics↗

bioRxiv2024

Integrative multi-environmental genomic prediction in apple

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, non-additive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard G-BLUP. Including non-additive effects slightly improved predictive ability by up to 0.03 for two traits, but enviromic-based effects resulted in no improvement. The deep learning approach achieved the highest predictive ability for three traits with simpler genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

plant biology↗

bioRxiv2021

Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (GxE). Here we show contrasting genetic architecture and genomic prediction accuracies for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic prediction accuracies of 0.18-0.88 were estimated using single-environment univariate, single-environment multivariate, multi-environment univariate, and multi-environment multivariate models. The GxE accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

genomics↗