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17 results for “quantitative trait locus”
Root penetration index 3, a major quantitative trait locus (QTL) associated with root system penetrability in Arabidopsis.
<p><span>Soil mechanical impedance precludes root penetration, confining root system development to shallow soil horizons where mobile nutrients are scarce. Using a two-phase-agar system, we characterized <em>Arabidopsis thaliana</em> responses to low and high mechanical impedance at three root penetration stages. We found that seedlings whose roots fail to penetrate agar barriers show a significant reduction in leaf area, root length and elongation zone and an increment in root diameter, while those capable of penetrating show only minor morphological effects. Analyses using different auxin-responsive reporter lines, exogenous auxins and inhibitor treatments suggest that </span><span>auxin responsiveness and PIN-mediated auxin distribution play an important role in regulating root responses to mechanical impedance. </span><span>The assessment of 21 Arabidopsis accessions revealed that primary root penetrability (PRP) varies widely among accessions. To search for quantitative trait loci (QTLs) associated to root system penetrability, we evaluated a recombinant inbred population (RIL) derived from Landsberg erecta (Ler-0, with a high PRP) and Shahdara (Sha, with a low PRP) accessions. QTL analysis revealed a major-effect QTL localized in chromosome 3 (<em>q</em>-<em>RPI3</em>), which accounted for 29.98% (LOD = 8.82) of the total phenotypic variation. Employing an introgression line (IL-321), with a homozygous <em>q</em>-<em>RPI3</em>region from Sha in the Ler-0 genetic background, we demonstrated that <em>q-RPI3</em> plays a crucial role in root penetrability. This multiscale study revels new insights into root plasticity during the penetration process in hard agar layers, natural variation and genetic architecture behind primary root penetrability in Arabidopsis.</span></p>
Quantitative trait locus mapping reveals an independent genetic basis for joint divergence in leaf function, life-history, and floral traits between scarlet monkeyflower (Mimulus cardinalis) populations
<p><b>PREMISE </b></p> <p>Across taxa, vegetative and floral traits that vary along a fast-slow life-history axis are often correlated with leaf functional traits arrayed along the leaf economics spectrum, suggesting a constrained set of adaptive trait combinations. Such broad-scale convergence may arise from genetic constraints imposed by pleiotropy (or tight linkage) within species, or from natural selection alone. Understanding the genetic basis of trait syndromes and their components is key to distinguishing these alternatives and predicting evolution in novel environments.</p> <p><b>METHODS </b></p> <p>We used a line-cross approach and quantitative trait locus (QTL) mapping to characterize the genetic basis of twenty leaf functional/physiological, life history, and floral traits in hybrids between annualized and perennial populations of scarlet monkeyflower (<i>Mimulus cardinalis</i>).</p> <p><b>RESULTS </b></p> <p>We mapped both single and multi-trait QTLs for life history, leaf function and reproductive traits, but found no evidence of genetic co-ordination across categories. A major QTL for three leaf functional traits (thickness, photosynthetic rate, and stomatal resistance) suggests that a simple shift in leaf anatomy may be key to adaptation to seasonally dry habitats.</p> <p><b>CONCLUSIONS </b></p> <p>Our results suggest that the co-ordination of resource-acquisitive leaf physiological traits with a fast life history and more selfing mating system results from environmental selection rather than functional or genetic constraint. Independent assortment of distinct trait modules, as well as a simple genetic basis to leaf physiological traits associated with drought escape, may facilitate adaptation to changing climates. </p>
Root penetration index 3, a major quantitative trait locus (QTL) associated with root system penetrability in Arabidopsis.
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Quantitative trait locus mapping reveals an independent genetic basis for joint divergence in leaf function, life-history, and floral traits between scarlet monkeyflower (Mimulus cardinalis) populations
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Quantitative trait locus analysis of parasitoid counteradaptation to symbiont-conferred resistance
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Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping
GEO Series GSE3330. Mus musculus. 120 samples. Type: Expression profiling by array.
Heterochromatin is a quantitative trait locus associated with spontaneous epiallele formation
GEO Series GSE171157. Arabidopsis thaliana. 1 samples. Type: Methylation profiling by high throughput sequencing.
Molecular Quantitative Trait Locus Mapping In Human Endothelial Cells Identifies Regulatory SNPs Underlying Gene Expression and Complex Disease Traits
GEO Series GSE139377. Homo sapiens. 504 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
C57BL/6 substrain differences in inflammatory and neuropathic nociception and genetic mapping of a major quantitative trait locus underlying acute thermal nociception
GEO Series GSE119719. Mus musculus. 115 samples. Type: Expression profiling by high throughput sequencing.
Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
In multiple quantitative trait locus (QTL) mapping, a high-dimensional sparse regression model is usually employed to account for possible multiple linked QTLs. The QTL model may include closely linked and thus highly correlated genetic markers, especially when high-density marker maps are used in QTL mapping because of the advancement in sequencing technology. Although existing algorithms, such as Lasso, empirical Bayesian Lasso (EBlasso) and elastic net (EN) are available to infer such QTL models, more powerful methods are highly desirable to detect more QTLs in the presence of correlated QTLs. We developed a novel empirical Bayesian EN (EBEN) algorithm for multiple QTL mapping that inherits the efficiency of our previously developed EBlasso algorithm. Simulation results demonstrated that EBEN provided higher power of detection and almost the same false discovery rate compared with EN and EBlasso. Particularly, EBEN can identify correlated QTLs that the other two algorithms may fail to identify. When analyzing a real dataset, EBEN detected more effects than EN and EBlasso. EBEN provides a useful tool for inferring high-dimensional sparse model in multiple QTL mapping and other applications. An R software package 'EBEN' implementing the EBEN algorithm is available on the Comprehensive R Archive Network (CRAN).
Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
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Quantitative Trait Locus and Brain Expression of HLA-DPA1 And Shared Immune Alterations in Psychiatric Disorders
GEO Series GSE78246. Homo sapiens. 40 samples. Type: Expression profiling by array.
DEFINING A RAT BLOOD PRESSURE QUANTITATIVE TRAIT LOCUS TO A <81.8KB CONGENIC SEGMENT: COMPREHENSIVE SEQUENCING AND RENAL TRANSCRIPTOME ANALYSIS.
GEO Series GSE22515. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
Expression Quantitative Trait Locus Mapping Studies in Mid-Secretory Phase Endometrial Cells Identifies HLA-F and TAP2 as Fecundability-Associated Genes
GEO Series GSE77688. Homo sapiens. 88 samples. Type: Expression profiling by array.
The novel quantitative trait locus qTGW3 encodes a GSK3-like kinase and negatively regulates grain size and weight in rice
GEO Series GSE98924. Oryza sativa Japonica Group. 9 samples. Type: Expression profiling by high throughput sequencing.
Expression quantitative trait locus (eQTL) mapping with a cross between Tetranychus urticae inbred strains MR-VPi and ROS-ITi
GEO Series GSE221677. Tetranychus urticae. 506 samples. Type: Expression profiling by high throughput sequencing.
Expression Quantitative Trait Locus (eQTL) Mapping of Stage-specific Gene Expression in Progeny from a type I X III Genetic Cross of Toxoplasma gondii
GEO Series GSE26558. Toxoplasma gondii. 80 samples. Type: Expression profiling by array.
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OpenNeuro
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