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74 results for “QTL mapping”
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R1 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R2 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
<p>Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include:</p> <ul> <li><strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2</li> <li><strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass</li> <li><strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass</li> <li><strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass</li> <li><strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass</li> </ul> <p>See <a href="https://qtltools.github.io/qtltools/">https://qtltools.github.io/qtltools/</a> for column descriptions of QTL mapping results.</p>
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]
<p>This is the main code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species <em>Brassica oleracea </em>var<em>. villosa".</em> The main code is availabe in separate html-files. DOI will be added if available.</p>
Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato
<p>Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato (1135884)</p>
Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences
<p>Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., <em>CWC27</em> and <em>CLUAP1</em>), ultraviole<em>t</em> protection (e.g., <em>ERCC8</em>), and pigmentation (e.g. <em>PDE4D</em>) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.</p>
Dissecting the genetic basis of variation in Drosophila sleep using a multiparental QTL mapping resource
There is considerable variation in sleep duration, timing and quality in human populations, and sleep dysregulation has been implicated as a risk factor for a range of health problems. Human sleep traits are known to be regulated by genetic factors, but also by an array of environmental and social factors. These uncontrolled, non-genetic effects complicate powerful identification of the loci contributing to sleep directly in humans. The model system, Drosophila melanogaster, exhibits a behavior that shows the hallmarks of mammalian sleep, and here we use a multitiered approach, encompassing high-resolution QTL mapping, expression QTL data, and functional validation with RNAi to investigate the genetic basis of sleep under highly controlled environmental conditions. We measured a battery of sleep phenotypes in >750 genotypes derived from a multiparental mapping panel and identified several, modest-effect QTL contributing to natural variation for sleep. Merging sleep QTL data with a large head transcriptome eQTL mapping dataset from the same population allowed us to refine the list of plausible candidate causative sleep loci. This set includes genes with previously characterized effects on sleep and circadian rhythms, in addition to novel candidates. Finally, we employed adult, nervous system-specific RNAi on the Dopa decarboxylase, dyschronic, and timeless genes, finding significant effects on sleep phenotypes for all three. The genes we resolve are strong candidates to harbor causative, regulatory variation contributing to sleep.
QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
<p><span>The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (<em>QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, </em>and<em> QYR-APR-4D.2</em>) for YR APR and four QTLs (<em>QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, </em>and<em> </em></span><em>QLR-APR-5A.2</em>) <span>for LR APR were identified. </span><span>In silico analysis revealed that the key putative candidate genes such as <em>Cytochrome P450</em></span><em><span>, Protein kinase-like domain superfamily</span><span>, Zinc-binding ribosomal protein</span><span>, SANT/Myb domain</span><span>, WRKY transcription factor</span><span>, Nucleotide-sugar transporter,</span></em><span> and <em>NAC</em> </span><em><span>domain superfamily</span></em><span> were in the QTL regions and involved in the regulation of host response towards the pathogen infection. </span><span>The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).</span></p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
<p>The mapping population comprises of 180 RILs developed from a cross between GW322 and KAUZ.</p> <p><strong>Phenotypic data</strong><br>Phenotypic evaluation was conducted across two consecutive crop seasons (2021-22 and 2022-23) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at ICAR-IARI, New Delhi. Various physiological and agronomic traits of importance were measured. The component traits of yield including days to heading (DH), normalized difference vegetation index (NDVI), SPAD chlorophyll content (SPAD), plant height (PH), spike length (SL), thousand-grain weight (TGW), grain weight per spike (GWPS), biomass (BM) and grain yield per plot (PY) were measured under heat and combined stress conditions.</p> <p><strong>Genotypic data</strong><br>DNA was isolated from 21-day-old seedlings using the CTAB method (Murray and Thompson, 1980). DNA quality check was done using 0.8% agarose gel electrophoresis. The Axiom Breeders' array containing 35K Single Nucleotide Polymorphism (SNP) was employed for the genotyping of RILs and parents.</p>
SNP markers used for QTL mapping in the inbred lines
<p><span>Young leaves of the 175 inbred lines and their seven parents were collected from seedlings grown in a greenhouse. </span><span>About 200 mg bulk leaf sample from three plants of a line was placed in 2 ml safe-lock </span><span>Eppendorf tube and stored at ‒80 </span><span>˚C for one night prior to crushing using a Mixer Mill (TissueLyser II, Qiagen, Germany). Genomic DNA was extracted using SIGMA DNA extraction kit (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer's instruction. DNA concentration and purity of the samples were assessed using a NanoDrop 2000c spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The samples were processed and sequenced using tunable genotyping-by-sequencing (tGBS®) method by Data2Bio (Ames, IW, USA). Genomic DNA was digested using two restriction enzymes NSpI (5′-RCATG^Y-3′) and BfuCI/Sau3AI (5′-^GATC-3′) which created 3´and 5´overhangs, respectively. Two single-stranded oligos, one containing a sample-specific internal barcode and the other a universal oligo, were ligated to the complementary 3´ and 5´ overhangs, respectively. </span>All 175 inbred lines' and seven parents' treated DNA was pooled for construction of the tGBS library and sequencing. The raw sequence data were demultiplexed by barcode, which was subsequently removed bioinformatically from each sequence. The barcode-trimmed sequence reads of genotype were further trimmed using the trimming software, Lucy (Chou & Holmes, 2001; Li & Chou 2004) to remove low-quality reads based on Phred quality scores of Q15.</p>
Phenotype and QTL mapping data from: Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover
<p>Local adaptation is common in plants, yet characterization of its underlying genetic basis is rare in herbaceous perennials. Moreover, while many plant species exhibit intraspecific chemical defense polymorphisms, their importance for local adaptation remains poorly understood. We examined the genetic architecture of local adaptation in a perennial, obligately-outcrossing herbaceous legume, white clover (<i>Trifolium repens</i>). This widespread species displays a well-studied chemical defense polymorphism for cyanogenesis (HCN release following tissue damage) and has evolved climate-associated cyanogenesis clines throughout its range. Two biparental F<sub>2</sub> mapping populations, derived from three parents collected in environments spanning the U.S. latitudinal species range (Duluth, MN, St. Louis, MO and Gainesville, FL), were grown in triplicate for two years in reciprocal common garden experiments in the parental environments (6,012 total plants). Vegetative growth and reproductive fitness traits displayed trade-offs across reciprocal environments, indicating local adaptation. Genetic mapping of fitness traits revealed a genetic architecture characterized by allelic trade-offs between environments, with 100% and 80% of fitness QTL in the two mapping populations showing significant QTL X E interactions, consistent with antagonistic pleiotropy. Across the genome there were three hotspots of QTL co-localization. Unexpectedly, we found little evidence that the cyanogenesis polymorphism contributes to local adaptation. Instead, divergent life history strategies in reciprocal environments were major fitness determinants: selection favored early investment in flowering at the cost of multi-year survival in the southernmost site vs. delayed flowering and multi-year persistence in the northern environments. Our findings demonstrate that multi-locus genetic tradeoffs contribute to contrasting life history characteristics that allow for local adaptation in this outcrossing herbaceous perennial.</p>
Coordinates of genetic variants used for QTL mapping
<p>Column names</p> <ol> <li>chr - chromosome name</li> <li>pos - position</li> <li>snp_id - variant id</li> <li>ref - reference allele</li> <li>alt - alternate allele</li> <li>type - variant type (SNP or INDEL)</li> <li>AC - alternate allele count</li> <li>AN - total allele count</li> </ol>
R/QTL datasets for fusiform rust resistance QTL mapping
<p>Fusiform rust disease, caused by the endemic fungus <i>Cronartium quercuum</i> f. sp. <i>fusiforme</i>, is the most damaging disease affecting economically important pine species in the southeast United States. In this report, we detail the genomic localization and sequence-level discovery of candidate race-nonspecific broad-spectrum fusiform rust resistance genes in <i>Pinus taeda </i>L. Two full-sib families, each with ~1000 progeny, were challenged with a complex inoculum consisting of over 150 pathogen isolates. High-density linkage mapping revealed three QTL distributed on two linkage groups. The two QTL on linkage group 2 were additive with respect to their effects on the probability of disease outcome. All three QTL were validated using a population of 2057 cloned pine genotypes in a six-year-old multi-environmental field trial. As a complement to the QTL mapping approach, bulked segregant RNAseq analysis revealed a small number of candidate nucleotide binding leucine rich repeat genes harboring SNP significantly associated with disease resistance. The results of this study demonstrate that single qualitative resistance genes can confer effective resistance against genetically diverse mixtures of an endemic pathogen.</p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
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QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations
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Dissecting the genetic basis of variation in Drosophila sleep using a multiparental QTL mapping resource
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Data from: Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: estimates of selection and QTL mapping
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SNP markers used for QTL mapping in the inbred lines
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Phenotype and QTL mapping data from: Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover
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