From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat.

Agroecosystem diversification through increased crop genetic diversity could provide multiple services such as improved disease control or increased productivity. However, we still poorly understand how genetic diversity affects agronomic performance. We grew 179 inbred lines of durum wheat in pure stands and in 202 binary mixtures in field conditions. We then tested the effect of allelic richness between genotypes and genotype richness on grain yield and Septoria tritici blotch disease. Allelic richness was tested at 19K single nucleotide polymorphisms distributed along the durum wheat genome. Both genotype richness and allelic richness could be equal to 1 or 2. Mixtures were overall more productive and less diseased than their pure stand components. Yet, we identified one locus at which allelic richness between genotypes was associated with increased disease severity and decreased grain yield. The effect of allelic richness at this locus was stronger than the effect of genotype richness on grain yield (-7.6% vs +5.7%). Our results suggest that positive effects of crop diversity can be reversed by unfavourable allelic associations. This highlights the need to integrate genomic data into crop diversification strategies. More generally, investigating plant-plant interactions at the genomic level is promising to better understand biodiversity-ecosystem functioning relationships.

Shift in ecological strategy helps marginal populations of shepherd's purse (Capsella bursa-pastoris) to overcome a high genetic load.

The outcome of species range expansion depends on the interplay of demographic, environmental and genetic factors. Self-fertilizing species usually show a higher invasive ability than outcrossers but selfing and bottlenecks during colonization also lead to an increased genetic load. The relationship between genomic and phenotypic characteristics of expanding populations has, hitherto, rarely been tested experimentally. We analysed how accessions of the shepherd's purse, Capsella bursa-pastoris, from the colonization front or from the core of the natural range performed under increasing density of competitors. First, accessions from the front showed a lower fitness than those from the core. Second, for all accessions, competitor density impacted negatively both vegetative growth and fruit production. However, despite their higher genetic load and lower absolute performances, accessions from the front were less affected by competition than accessions from the core. This seems to be due to phenotypic trade-offs and a shift in phenology that allow accessions from the front to avoid competition.

GeCKO: user-friendly workflows for genotyping complex genomes using target enrichment capture. A use case on the large tetraploid durum wheat genome.

BACKGROUND: Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups. RESULTS: GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps. CONCLUSIONS: The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts.