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44 results for “qTLS”
[Data from:] Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa
<p><strong>Background</strong></p> <p>Cabbage white butterflies (<em>Pieris</em> spp.) can be severe pests of <em>Brassica</em> crops such as Chinese cabbage, Pak choi (<em>Brassica rapa</em>) or cabbages (<em>B. oleracea</em>). Eggs of <em>Pieris</em> spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (<em>B. nigra</em>). Unravelling the genetic basis of this egg-killing trait in <em>Brassica</em> crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by <em>P. brassicae</em> eggs in <em>B. rapa.</em></p> <p><strong>Results</strong></p> <p>A germplasm screening of <em>B. rapa</em> 56 accessions, representing the genetic and geographical diversity of a <em>B. rapa</em> core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for P<em>ieris</em> b<em>rassicae-</em>induced cell death on chromosomes A02 (<em>Pbc1</em>), A03 (<em>Pbc2</em>), and A06 (<em>Pbc3</em>). The three QTLs <em>Pbc1-3</em> contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with <em>A. thaliana</em> suggested that <em>Pbc1</em> and <em>Pbc2</em> are novel QTLs associated with this trait, while <em>Pbc3</em> contains also LecRK-I.1, a gene of <em>A. thaliana</em> previously associated with cell death induced by a <em>P. brassicae</em> egg extract.</p> <p><strong>Conclusions</strong></p> <p>This study provides the first genomic regions associated with the <em>Pieris</em> egg-induced HR-like cell death in a <em>Brassica</em> crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.</p>
Data from: Unravelling cucumber resistance to several viruses via genome-wide association studies highlighted resistance hotspots and new QTLs
<p>The mapping and introduction of sustainable resistance to viruses in crops is a major challenge in modern breeding, especially regarding vegetables. We hence assembled a panel of cucumber elite lines and landraces from different horticultural groups for testing with six virus species. We mapped 18 quantitative trait loci (QTL) with a multiloci genome wide association studies (GWAS), some of which have already been described in the literature. We detected two resistance hotspots, one on chromosome 5 for resistance to the cucumber mosaic virus (CMV), cucumber vein yellowing virus (CVYV), cucumber green mottle mosaic virus (CGMMV) and watermelon mosaic virus (WMV), colocalizing with the RDR1 gene, and another on chromosome 6 for resistance to the zucchini yellowing mosaic virus (ZYMV) and papaya ringspot virus (PRSV) close to the putative VPS4 gene location. We observed clear structuring of resistance among horticultural groups due to plant virus coevolution and modern breeding which have impacted linkage disequilibrium (LD) in resistance QTLs. The inclusion of genetic structure in GWAS models enhanced the GWAS accuracy in this study. The dissection of resistance hotspots by local LD and haplotype construction helped gain insight into the panel’s resistance introduction history. ZYMV and CMV resistance were both introduced from different donors in the panel, resulting in multiple resistant haplotypes at same locus for ZYMV, and in multiple resistant QTLs for CMV.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
Review of QTLs found in studies aimed at finding QTLs for bread wheat root traits (from 2005 to mid-2020)
<p>This list contains a number of articles that have been reviewed for QTLS for bread wheat root traits from 2005 to mid-2020 publication dates.</p>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 2)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 1)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
Data for: "Chromosome One QTLs Associated with Beta Vulgaris Response to Bacterial Leaf Spot"
<p>Listed here are data used in analysis for publication "Chromosome One QTLs Associated with Beta Vulgaris Response to Bacterial Leaf Spot".</p> <p>Data included: filtered VCF files of SNP marker data for full Wisconsin Beta Diversity Panel (WBDP, n=219) and table beet subset (n=152). Deposited also are the raw phenotypic values, the final weighted BLS score per pot, the BLUE value for each accession, and the leaf color covariate data.</p>
Re-analysis of chromatin accessibility QTLs from the Kumasaka et al, 2018 study
<p>ATAC-seq data from <a href="https://doi.org/10.1038/s41588-018-0278-6">Kumasaka et al, 2018</a> was processed with the <a href="https://github.com/nf-core/atacseq/tree/2.1.2">nf-core/atacseq</a> v2.1.2 pipeline using Nextflow v23.09.3. We aligned raw ATAC-seq reads to the GRCh38 reference genome (Homo_sapiens.GRCh38.dna.primary_assembly.fa downloaded from Ensembl) with BWA v0.7.17. We called broad peaks with MACS2 v2.2.7.1 and defined consensus peaks as the union of all peaks that were present in at least 5% of the samples. We then quantified read overlaps with the set of consensus peaks with featureCounts v2.0.1. Finally, we normalised the read counts (counts per million) and then used the inverse normal transformation to standardise the data distribution.</p> <p>Genotype data for the 91 overlapping samples were downloaded from 1000 Genomes 30x on GRCh38 <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">website</a>. Finally, we used the <a href="https://github.com/eQTL-Catalogue/qtlmap">eQTL-Catalogue/qtlmap</a> v24.01.1 workflow to perform chromatin accessibility QTL analysis. We set cis window size to 200,000 bp and excluded peaks that had less than 25 variants within that window. More details of the association testing workflow can be found <a href="https://doi.org/10.1371/journal.pgen.1010932">here</a>.</p>
Private QTLs underlie the genetic architecture of hierarchical size traits in <em>Drosophila</em>
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Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach
Background <p>Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (<i>Prunus persica</i> (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time.</p> Results <p>Phenotypic data of seven F<sub>1</sub> low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40–46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (<i>D</i>-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles.</p> Conclusions <p>A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.</p>
Data from: Genes and QTLs controlling inflorescence and stem branch architecture in Leymus (Poaceae: Triticeae) wildrye
Grass inflorescence and stem branches show recognizable architectural differences among species. The inflorescence branches of Triticeae cereals and grasses, including wheat, barley, and 400–500 wild species, are usually contracted into a spike formation, with the number of flowering branches (spikelets) per node conserved within species and genera. Perennial Triticeae grasses of genus Leymus are unusual in that the number of spikelets per node varies, inflorescences may have panicle branches, and vegetative stems may form subterranean rhizomes. Leymus cinereus and L. triticoides show discrete differences in inflorescence length, branching architecture, node number, and density; number of spikelets per node and florets per spikelet; culm length and width; and perimeter of rhizomatous spreading. Quantitative trait loci controlling these traits were detected in 2 pseudo-backcross populations derived from the interspecific hybrids using a linkage map with 360 expressed gene sequence markers from Leymus tiller and rhizome branch meristems. Alignments of genes, mutations, and quantitative trait loci controlling similar traits in other grass species were identified using the Brachypodium genome reference sequence. Evidence suggests that loci controlling inflorescence and stem branch architecture in Leymus are conserved among the grasses, are governed by natural selection, and can serve as possible gene targets for improving seed, forage, and grain production.
Data from: A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: testing co-localization among outlier markers, candidate genes, and QTLs for production traits
Comparative genome scans can be used to identify chromosome regions, but not traits, that are putatively under selection. Identification of targeted traits may be more likely in recently domesticated populations under strong artificial selection for increased production. We used a North American Atlantic salmon 6K SNP dataset to locate genome regions of an aquaculture strain (Saint John River) that were highly diverged from that of its putative wild founder population (Tobique River). First, admixed individuals with partial European ancestry were detected using STRUCTURE and removed from the dataset. Outlier loci were then identified as those showing extreme differentiation between the aquaculture population and the founder population. All Arlequin methods identified an overlapping subset of 17 outlier loci, 3 of which were also identified by BayeScan. Many outlier loci were near candidate genes and some were near published quantitative trait loci (QTLs) for growth, appetite, maturity, or disease-resistance. Parallel comparisons using a wild, non-founder population (Stewiacke River) yielded only one overlapping outlier locus as well as a known maturity QTL. We conclude that genome scans comparing a recently domesticated strain with its wild founder population can facilitate identification of candidate genes for traits known to have been under strong artificial selection.
A Web-resource for Nutrient Use Efficiency related Genes, QTLs, and microRNA in important cereals and model plants
<p>Cereals are key contributors to global food security. Genes involved in the uptake (transport), assimilation and utilization of macro- and micronutrients are responsible for the presence of these nutrients in grain and straw. Although many genomic databases for cereals are available, there is currently no cohesive web resource of manually curated nutrient use efficiency (NtUE)-related genes and quantitative trait loci (QTLs). In this study, we present a <a href="http://bioclues.org/NtUE/index.php">web- resource</a> containing information on NtUE-related genes/QTLs and the corresponding available microRNAs for some of these genes in four major cereal crops (wheat (<em>Triticum aestivum</em>), rice (<em>Oryza sativa</em>), maize (<em>Zea mays</em>), barley (<em>Hordeum vulgare</em>)), two alien species related to wheat (<em>Triticum urartu</em> and<em> Aegilops tauschii</em>), and two model species (<em>Brachypodium distachyon </em>and <em>Arabidopsis thaliana</em>). Gene annotations integrated in the current web resource were manually curated from the existing databases and the available literature. The primary goal of developing this web resource is to provide descriptions of the NtUE-related genes and their functional annotation. MicroRNAs targeting some of the NtUE related genes and the QTLs for NtUE-related traits are also included. The genomic information embedded in the web resource should help users to search for the desired information.</p>
Colocalisation between QTLs and summary statistics from complex traits
<p>Colocalisation between QTLs and summary statistics from complex traits</p>
Regionalpcs ROSMAP Fine-mapped QTLs
<p>This dataset contains DAP-G fine-mapped QTL results for the following study:</p> <p><strong><em>"regionalpcs improve discovery of DNA methylation associations with complex traits"</em></strong></p> <p>Tiffany Eulalio*<sup>1</sup>, Min Woo Sun<sup>1</sup>, Olivier Gevaert<sup>1</sup>, Michael D. Greicius<sup>2</sup>, Thomas J. Montine<sup>3</sup>, Daniel Nachun*‡<sup>3</sup>, Stephen B. Montgomery*‡<sup>1,3</sup></p> <p>‡ These authors contributed equally as senior authors</p> <p>* Corresponding authors: Tiffany Eulalio (<a href="mailto:eulalio@alumn.stanford.edu">eulalio@alumn.stanford.edu</a>), Daniel Nachun (<a href="mailto:dnachun@stanford.edu">dnachun@stanford.edu</a>), Stephen B. Montgomery (<a href="mailto:smontgom@stanford.edu">smontgom@stanford.edu</a>)</p> <p> Author affiliations:</p> <p>1. Department of Biomedical Data Science, Stanford University, Stanford, CA</p> <p>2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA</p> <p>3. Department of Pathology, Stanford University, Stanford, CA</p> <p> </p> <p><strong>Dataset description</strong>:</p> <p>The DAP-G results are organized by region type (full gene, gene body, preTSS, and promoters), cell type (astrocytes, endothelial cells, neurons, oligodendrocytes, and bulk), and summary type (averages and regionalpcs).</p>
Data from: Chromosome-scale reference genome and RAD-based genetic map of yellow starthistle (Centaurea solstitialis) reveal putative structural variation and QTLs associated with invader traits
<p>The data directory here includes all of the data and scripts necessary to recreate the results and plots for the manuscript titled "Chromosome-scale reference genome and RAD-based genetic map of yellow starthistle (Centaurea solstitialis) reveal putative structural variation and QTLs associated with invader traits". These data include a genetic map, QTL analysis, paleolog analysis, gene synteny analysis, and assembly validation for yellow starthistle (Centaurea solstitialis).</p>
Data from: A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: testing co-localization among outlier markers, candidate genes, and QTLs for production traits
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Data from: Life-history QTLs and natural selection on flowering time in Boechera stricta, a perennial relative of Arabidopsis
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Data from: Genes and QTLs controlling inflorescence and stem branch architecture in Leymus (Poaceae: Triticeae) wildrye
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