Biology subjects

de Ronne, M.

Publications and source records attributed to de Ronne, M..

4 recordsLinked to original sources

bioRxiv2023

Genetic Insights into Agronomic and Morphological Traits of Drug-Type Cannabis Revealed by Genome-Wide Association Studies

Cannabis sativa L., previously concealed by prohibition, is now a versatile and promising plant, thanks to recent legalization, opening doors for medical research and industry growth. However, years of prohibition have left the cannabis research community underdeveloped and lacking knowledge about cannabis genetics and trait inheritance. To bridge this gap, we conducted a comprehensive genome-wide association study (GWAS), using a panel of 176 drug-type cannabis accessions, curated to represent the Canadian legal market. This pioneering GWAS harnessed the power of high-density genotyping-by-sequencing (HD-GBS), resulting in an exhaustive catalog of 800K genetic variants. These variants served as the bedrock for a GWAS designed to dissect the genetic foundations of nine key traits. To identify the most robust markers associated with these traits, two sophisticated statistical methodologies were used (SUPER and BLINK), ultimately identifying 33 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to a rich trove of putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These markers show great promise for revolutionizing cannabis breeding to meet diverse needs. In doing so, they lay the solid foundation for an innovative cannabis industry poised to reshape the future.

plant biology↗

bioRxiv2023

SNPLift: Fast and accurate conversion of genetic variant coordinates across genome assemblies

MotivationThe advent of high-throughput sequencing technologies and the availability of reference genomes have provided an unprecedented opportunity to discover and genotype millions of genetic variants in hundreds or even thousands of samples. Variant calling, the identification of genetic variants from raw sequencing data, is both time-consuming and computationally demanding. Currently, reference genomes are evolving very rapidly and new assembly versions come out more and more frequently. To take advantage of new or improved reference genomes, raw reads alignments, genotype calling, and filtration must typically all be redone. This is a costly and time consuming operation that is not always viable when projects are under time constraints. ResultsHere, we introduce SNPLift, a bioinformatic pipeline that can quickly transfer the coordinate of nucleotide variants (SNPs and Indels) between different versions of reference genomes. We tested SNPLift on nine SNP datasets in VCF format from different species (Homo sapiens, Arabidopsis thaliana, Coregonus clupeaformis, Medicato truncatula, Oriza sativa, Salvelinus namaycush, Solanum lycopersicum, Zea mays, and Glycine max). Depending on the species, we achieved accurate lifting of variants ranging from 92.92% to 99.69%. Importantly, SNPLift significantly reduces the computational resources and time required for variant analysis compared to performing a complete re-analysis using a new reference genome. SNPLift offers a fast and efficient solution to leverage the benefits of updated or improved reference genomes. Availability and implementationSNPLift is available at https://github.com/enormandeau/snplift with its documentation. It contains a script that runs an automated test on a small dataset, composed of 190,443 SNPs in chromosome 1 of Medicago truncatula. SNPLift uses only common tools that are easy to install and works under Linux and MacOS.

genomics↗

bioRxiv2023

Exploring Ethylene-Related Genes in Cannabis sativa: Implications for Sexual Plasticity

Sexual plasticity is a phenomenon wherein organisms possess the ability to alter their phenotypic sex in response to environmental and physiological stimuli, without modifying their sex chromosomes. Cannabis sativa L., a medically valuable plant species, exhibits sexual plasticity when subjected to specific chemicals that influence ethylene biosynthesis and signaling. Nevertheless, the precise contribution of ethylene-related genes (ERGs) to sexual plasticity in cannabis remains unexplored. The current study employed Arabidopsis thaliana L. as a model organism to conduct gene orthology analysis and reconstruct the Yang Cycle, ethylene biosynthesis, and ethylene signaling pathways in C. sativa. Additionally, two transcriptomic datasets comprising male, female, and chemically induced male flowers were examined to identify expression patterns in ERGs associated with sexual determination and sexual plasticity. These ERGs involved in sexual plasticity were categorized into two distinct expression patterns: floral organ concordant (FOC) and unique (uERG). Furthermore, a third expression pattern, termed karyotype concordant (KC) expression, was proposed, which plays a role in sex determination. The study revealed that CsERGs associated with sexual plasticity are dispersed throughout the genome and are not limited to the sex chromosomes, indicating a widespread regulation of sexual plasticity in C. sativa. Key MessagePresented here are model Yang cycle, ethylene biosynthesis and signaling pathways in Cannabis sativa. C. sativa floral transcriptomes were used to predict putative ethylene-related genes involved in sexual plasticity in the species.

plant biology↗

bioRxiv2023

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a trait at a given locus in crop species. Assessing genetic variants beyond single-nucleotide polymorphisms (SNPs) could alleviate this problem, for example by including structural variants (SVs). In this study, we tested the potential of SV-and k-mer-based GWAS in soybean by applying these methods to 13 traits. We also performed conventional GWAS analysis based on SNPs and small indels for comparison. We assessed the performance of each GWAS approach based on results at loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes based on the most significantly associated k-mers. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to the oomycete Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources required to genotype SVs at population scale. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Together, our results suggest that coupling k-mer-and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

genomics↗