Biology subjects

Belzile, F.

Publications and source records attributed to Belzile, F..

7 recordsLinked to original sources

bioRxiv2025

A rare recombination allows the redefinition of a major avirulence gene in the Phytophthora sojae genome

Significant yield losses in soybean production are imputable to Phytophthora root and stem rot (PRR) caused by Phytophthora sojae. Soybean resistance to the pathogen depends on the presence of resistance genes (Rps) that recognize key effectors from P. sojae, which are encoded by avirulence genes (Avr). A unique molecular signature associated with these genes allows the prediction of the outcome of infection with great accuracy making the interaction Rps-Avr central to reduce disease incidence. In this study, we reassessed the identity of the avirulence gene whose protein product is recognized by Rps6. Following extensive soil sampling and single-spore isolation of a large population of P. sojae, we found two salient isolates carrying a rare recombination between two effectors, Avr3c and Avr4/6. Using a PCR assay and molecular markers, we showed that only alleles at the Avr3c locus were in perfect association with the phenotypes of the isolates. Furthermore, whole-genome resequencing and de novo assembly of the two isolates revealed the full extent of this genomic rearrangement. These results bring to light an unsuspected connection between Avr3c and Rps6 and offer a more reliable target for the pathotyping of P. sojae, which ultimately leads to a better use of resistant soybean material.

plant biology↗

bioRxiv2024

Whole-Genome Sequencing of the Wild Barley Diversity Collection: A Resource for Identifying and Exploiting Genetic Variation for Cultivated Barley Improvement

To exploit allelic variation in Hordeum vulgare subsp. spontaneum, the Wild Barley Diversity Collection was evaluated for several agronomic traits and subjected to paired-end Illumina sequencing at [~]9X depth, generating 109.5 million single nucleotide polymorphisms after alignment to the Morex V3 assembly. A genome-wide association study of lemma color identified one marker-trait association (MTA) on chromosome 1HL close to HvBlp, the cloned gene controlling black lemma. Four MTAs were identified for stem rust resistance: one co-locating to the complex RMRL1-RMRL2 locus on 5HL, and three novel loci on 1HS, 1HL, and 5HL. Six MTAs for days to heading (DTH) on vernalized plants were identified on all chromosomes except 1H and 6H. Two MTAs for DTH on non-vernalized plants were identified on chromosomes 1HL and 2HS. All MTAs for DTH were novel. The whole genome sequence data described herein will facilitate the identification and utilization of new alleles for barley improvement.

genomics↗

bioRxiv2023

Machine Learning-GWAS reveals the role of WSD1 gene for cuticular wax ester biosynthesis and key genomic regions controlling early maturity in bread wheat

This study employed Machine Learning-Genome-Wide Association Study (ML-GWAS) to identify genomic regions linked to cuticular wax ester biosynthesis (SW) and early maturity (DM) in wheat. Using a dataset with 170 wheat accessions and 74K SNPs, four GWAS tools (MLM, CMLM, FarmCPU, and BLINK) and five machine learning techniques (RF, ANN, SVR, CNN, and SVM) were applied. A highly significant SW association was found on chromosome 1A, with the peak SNP (chr1A:556842331) explaining 50% of the phenotypic variation. A promising candidate gene, TraesCS1A01G385500, was identified as an ortholog of Arabidopsis thalianas WSD1 gene, which plays a crucial role in very long-chain (VLC) wax ester biosynthesis. For DM, four QTLs were detected on chromosomes 4B (two QTLs), 2A, and 5A. Haplotype analysis revealed that alleles TT significantly contribute to cuticular wax ester biosynthesis and early maturity in wheat varieties. The study underscores the superior performance of ML models, especially when combined with advanced multi-locus GWAS models like BLINK and FarmCPU, with significantly lower p-values for identifying relevant QTLs compared to traditional methods. ML approaches hold potential for revolutionizing the study of complex genetic traits, offering insights to enhance wheat crops resilience and quality. ML-GWAS emerges as a compelling tool for genomic-based breeding, enabling breeders to develop improved wheat varieties with greater precision and efficiency.

genomics↗

bioRxiv2023

Performance analysis of conventional and AI-based variant callers using short and long reads

The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use. In this study, we evaluated the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies for the same set of samples from the Genome In A Bottle (GIAB) project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons. This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

bioinformatics↗

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↗

bioRxiv2021

Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology

BackgroundStructural variant (SV) discovery based on short reads is challenging due to their complex signatures and tendency to occur in repeated regions. The increasing availability of long-read technologies has greatly facilitated SV discovery, however these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars. ResultsWe used Oxford Nanopore sequencing data ([~]12X mean coverage) for 17 samples to both benchmark SV calls made from the Illumina data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped SVs for population structure analysis and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of SVs are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency SVs are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the SVs and report evidence of the recent activity of a Stowaway MITE. ConclusionsOur results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

genomics↗

bioRxiv2019

Soybean Haplotype Map (GmHapMap): A Universal Resource for Soybean Translational and Functional Genomics

Here we describe the first worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1,007 Glycine max accessions and yielding 15 million variants. The number of unique haplotypes plateaued within this collection (4.3 million tag SNPs) suggesting extensive coverage of diversity within the cultivated germplasm. We imputed GmHapMap variants onto 21,618 previously genotyped (50K array/210K GBS) accessions with up to 96% success for common alleles. A GWAS performed with imputed data enabled us to identify a causal SNP residing in the NPC1 gene and to demonstrate its role in controlling seed oil content. We identified 405,101 haplotypes for the 55,589 genes and show that such haplotypes can help define alleles. Finally, we predicted 18,031 putative loss-of-function (LOF) mutations in 10,662 genes and illustrate how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for soybean genomics and breeding.

plant biology↗