Biology subjects

Torkamaneh, D.

Publications and source records attributed to Torkamaneh, D..

23 records · Page 2Linked to original sources

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↗

bioRxiv2021

Machine Learning based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and its Components

Genome-wide association study (GWAS) is currently one of the important approaches for discovering quantitative trait loci (QTL) associated with traits of interest. However, insufficient statistical power is the limiting factor in current conventional GWAS methods for characterizing quantitative traits, especially in narrow genetic bases plants such as soybean. In this study, we evaluated the potential use of machine learning (ML) algorithms such as support vector machine (SVR) and random forest (RF) in GWAS, compared with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying QTL associated with soybean yield components. In this study, important soybean yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity were assessed using 227 soybean genotypes evaluated across four environments. Our results indicated SVR-mediated GWAS outperformed RF, MLM and FarmCPU in discovering the most relevant QTL associated with the traits, supported by the functional annotation of candidate gene analyses. This study for the first time demonstrated the potential benefit of using sophisticated mathematical approaches such as ML algorithms in GWAS for identifying QTL suitable for genomic-based breeding programs.

plant biology↗

bioRxiv2021

Transcriptomics of improved fruit retention by hexanal in 'Honeycrisp' reveals hormonal crosstalk and reduced cell-wall remodelling in the fruit-abscission zone

Apples (Malus domestica Borkh) are prone to pre-harvest fruit drop which is more pronounced in Honeycrisp. Using a transcriptomic approach, we analyzed the molecular mechanisms of fruit retention in Honeycrisp. A total of 726 differentially expressed genes (DEGs) were identified in the abscission zone of hexanal-treated and untreated fruit (FAZ). Hexanal down-regulated the genes involved in ethylene biosynthesis, such as S-adenosylmethionine synthase (SAM2) and 1-aminocyclopropane-1carboxylic acid oxidases (ACO3, ACO4 and ACO4-like). Genes related to ABA biosynthesis (FDPS and CLE25) were also down-regulated. On the contrary, gibberellic acid (GA) biosynthesis genes, gibberellin 20 oxidase1-like (GA20OX-like) and ent-kaurene oxidase (KO) were up-regulated. Further, hexanal down-regulated the expression of genes related to cell-wall remodelling enzymes such as polygalacturonase (PG1), glucanases (endo-{beta}-1,4-glucanase; EG) and expansins (EXPA1-like, EXPA6, EXPA8, EXPA10-like, EXPA16-like). Hexanal also reduced ethylene, and abscisic acid (ABA) production at commercial harvest stage. Hexanal reduced ethylene production in fruits and thus reduced the sensitivity of FAZ cells to ethylene and ABA. Simultaneously, hexanal maintained the cell-wall integrity of FAZ cells by regulating genes involved in cell-wall modifications. Our findings show that fruit abscission is delayed by hexanal, by down regulating ABA through an ethylene-dependent mechanism. HighlightHexanal, a naturally occurring plant compound, increased fruit retention in apples by decreasing ethylene and ABA production and maintaining the cell-wall integrity in the fruit abscission zone.

genomics↗

bioRxiv2021

Accumulation of somatic mutations leads to genetic mosaicism in Cannabis

Cannabis is typically propagated using stem cuttings taken from mother plants to produce genetically uniform propagules. However, producers anecdotally report that clonal lines deteriorate over time and eventually produce clones with less vigour and lower cannabinoid levels than the original mother plant. While the cause of this deterioration has not been investigated, one potential contributor is the accumulation of somatic mutations within the plant. To test this, we used deep sequencing of whole genomes (>50x) to compare the variability within an individual Cannabis sativa cv. "Honey Banana" plant sampled at the bottom, middle and top. We called over 6 million sequence variants based on a reference genome and found that the top had the most by a sizable amount. Comparing the variants among the samples uncovered that nearly 600K (34%) were unique to the top while the bottom only contained 148K (12%) and middle with 77K (9%) unique variants. Bioinformatics tools were used to identify mutations in critical cannabinoid/terpene biosynthesis pathways. While none were identified as high impact, four genes contained more than double the average level of nucleotide diversity ({pi}) in or near the gene. Two genes code for essential enzymes required for the cannabinoid pathway while the other two are in the terpene pathways, demonstrating that mutations were accumulating within these pathways and could influence their function. Overall, a measurable number of intra-plant genetic diversity was discovered that could impact long-term genetic fidelity of clonal lines and potentially contribute to the observed decline in vigour and cannabinoid content.

plant biology↗

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↗