Biology subjects

Faivre-Rampant, P.

Publications and source records attributed to Faivre-Rampant, P..

6 recordsLinked to original sources

bioRxiv2026

Linkage and association mapping coupled with pan-genome analyses of Vat homologs reveal QTLs and alleles for aphid resistance in melon

Aphids threaten crop productivity through phloem feeding and the transmission of plant viruses. Aphis gossypii, in particular, is a widespread and damaging pest of worldwide cultivated melon. Resistance to its emerging CUC1 clone in Europe remains poorly characterized. Here, we dissected the genetic architecture of melon resistance to CUC1 using complementary traits that capture multiple stages of the aphid-melon interaction: plant attractiveness to aphids, acceptance, aphid colonization, and multiplication. Genome-wide association studies (GWAS) in a diversity panel of 174 accessions identified a quantitative trait locus (QTL) for attractiveness on chromosome 6, while analyses in a complementary panel of 212 accessions revealed QTLs for plant acceptance by aphids on chromosomes 3, 8, and 12. Colonization and multiplication traits further highlighted resistance QTLs on chromosomes 5 and 12, the latter supported by both SNP-based GWAS and bulk-segregant analysis. Pan-NLRome k-mer- and graph-based GWAS, together with Vat presence/absence association analyses, provided allele-level resolution of the QTL on chromosome 5, corresponding to the Vat region. Leveraging allelic diversity at this locus, we functionally characterized 20 Vat homologs with four R65aa motifs within their leucine-rich repeat (LRR) domain and demonstrated the capacity of 4R65aa-type Vat alleles to confer clone-specific resistance. Resistance-conferring alleles limited virus multiplication, such as Cucumber mosaic virus (CMV), when transmitted by five A. gossypii clones, including CUC1. Together, our results revealed multiple genetic determinants underlying quantitative resistance to the A. gossypii CUC1 clone in melon and highlighted the central role of Vat homologs in resistance to both A. gossypii and the viruses it vectors. These findings provide strategic targets for pyramiding resistance loci acting at different stages of the pest life cycle to enhance durable protection against these biotic threats.

genetics↗

bioRxiv2025

Intra-specific NLR allelic diversity and genomic landscape for plant resistance association studies

The identification of nucleotide-binding domain leucine-rich repeat receptor genes (NLRs) across multiple accessions is essential to capture their genetic diversity within a plant species. Using Nanopore adaptive sampling and de novo assembly, we accurately reconstructed the NLR regions across 143 Cucumis melo accessions representing diverse botanical groups and geographical origins. NLR annotation evidenced diverse cluster architectures and unexpected variation in NLR content across accessions, leading to unsaturated allelic diversity curves. Using this diversity, we further proposed pan-NLRome graph- and k-mer-based genome-wide association studies (GWAS), which, using Fusarium wilt races 1 and 2 severity data, accurately identified Fom-1, Fom-2, and novel non-NLR candidates. Furthermore, we extended these approaches for the identification of a candidate gene for flaccid necrosis caused by zucchini yellow mosaic virus. Our study offers a comprehensive view of NLR diversity in melon, overcoming the limitations of single-reference analyses, and supporting future efforts in NLR-focused GWAS and resistance breeding.

genomics↗

bioRxiv2023

Synchronous effective population size changes and genetic stability of forest trees through glacial cycles

The effect of past environmental changes on the demography and genetic diversity of natural populations remains a contentious issue and has rarely been investigated across multiple, phylogenetically distant species. Here, we performed comparative population genomic analyses and demographic inferences for seven widely distributed and ecologically contrasted European forest tree species based on concerted sampling of 164 populations across their natural ranges. For all seven species, the effective population size, Ne, increased or remained stable over many glacial cycles and up to 15 million years in the most extreme cases. Hence, surprisingly, the drastic environmental changes associated with the Pleistocene glacial cycles have had little impact on the level of genetic diversity of dominant forest tree species, despite major shifts in their geographic ranges. Based on their trajectories of Ne over time, the seven tree species can be divided into three major groups, highlighting the importance of life history and range size in determining synchronous variation in genetic diversity, over time. Altogether, our results indicate that forest trees were able to retain their evolutionary potential over very long periods of time despite strong environmental changes. One-Sentence SummaryPopulation genomic analyses reveal that forest trees retained their evolutionary potential over the Quaternary.

evolutionary biology↗

bioRxiv2021

Oxford Nanopore and Bionano Genomics technologies evaluation for plant structural variation detection.

BackgroundStructural Variations (SVs) are very diverse genomic rearrangements. In the past, their detection was restricted to cytological approaches, then to NGS read size and partitionned assemblies. Due to the current capabilities of technologies such as long read sequencing and optical mapping, larger SVs detection are becoming more and more accessible. This study proposes a comparison in SVs detection and characterization from long-read sequencing obtained with the MinION device developed by Oxford Nanopore Technologies and from optical mapping produced by the Saphyr device commercialized by Bionano Genomics. The genomes of the two Arabidopsis thaliana ecotypes Columbia-0 (Col-0) and Landsberg erecta 1 (Ler-1) were chosen to guide the use of one or the other technology. ResultsWe described the SVs detected from the alignment of the best ONT assembly and DLE-1 optical maps of A. thaliana Ler-1 on the public reference Col-0 TAIR10.1. After filtering, 1 184 and 591 Ler-1 SVs were retained from ONT and BioNano technologies respectively. A total of 948 Ler-1 ONT SVs (80.1%) corresponded to 563 Bionano SVs (95.3%) leading to 563 common locations in both technologies. The specific locations were scrutinized to assess improvement in SV detection by either technology. The ONT SVs were mostly detected near TE and gene features, and resistance genes seemed particularly impacted. ConclusionsStructural variations linked to ONT sequencing error were removed and false positives limited, with high quality Bionano SVs being conserved. When compared with the Col-0 TAIR10.1 reference, most of detected SVs were found in same locations. ONT assembly sequence leads to more specific SVs than Bionano one, the later being more efficient to characterize large SVs. Even if both technologies are obvious complementary approaches, ONT data appears to be more adapted to large scale populations study, while Bionano performs better in improving assembly and describing specificity of a genome compared to a reference.

genomics↗

bioRxiv2018

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

BackgroundGenomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative. Our study is one of the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees corresponding to a subsample of the French breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding mostly to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.\n\nResultsDuring the imputation process, we used 7,540 SNPs from the chip to impute 1,438,827 SNPs from sequences along the 19 Chromosomes. At the individual level, imputation accuracy was very high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality strongly depended on genotyped SNP density and to a lesser extent on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.\n\nConclusionsThis study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.

genomics↗

bioRxiv2018

Phenomic selection: a low-cost and high-throughput alternative to genomic selection

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits and coined this new approach \"phenomic selection\" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.\n\nARTICLE SUMMARYDespite its widely adopted interest in breeding, genomic selection - the prediction of breeding values using DNA polymorphisms - remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose \"phenomic selection\" (PS) as the use of low-cost and high-throughput phenotypic records to reconstruct similarities between genotypes and predict their performances. As a proof of concept of PS, we made use of near infrared spectroscopy applied to different tissues in poplar and wheat to predict various key traits and showed that PS could reach predictions as accurate as with molecular markers.

genetics↗