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306 results for “gene mapping”
Data from: Localizing FST outliers on a QTL map reveals evidence for large genomic regions of reduced gene exchange during speciation-with-gene-flow
Populations that maintain phenotypic divergence in sympatry typically show a mosaic pattern of genomic divergence, requiring a corresponding mosaic of genomic isolation (reduced gene flow). However, mechanisms that could produce the genomic isolation required for divergence-with-gene-flow have barely been explored, apart from the traditional localized effects of selection and reduced recombination near centromeres or inversions. By localizing FST outliers from a genome scan of wild pea aphid host races on a Quantitative Trait Locus (QTL) map of key traits, we test the hypothesis that between-population recombination and gene exchange are reduced over large 'divergence hitchhiking' (DH) regions. As expected under divergence hitchhiking, our map confirms that QTL and divergent markers cluster together in multiple large genomic regions. Under divergence hitchhiking, the nonoutlier markers within these regions should show signs of reduced gene exchange relative to nonoutlier markers in genomic regions where ongoing gene flow is expected. We use this predicted difference among nonoutliers to perform a critical test of divergence hitchhiking. Results show that nonoutlier markers within clusters of FST outliers and QTL resolve the genetic population structure of the two host races nearly as well as the outliers themselves, while nonoutliers outside DH regions reveal no population structure, as expected if they experience more gene flow. These results provide clear evidence for divergence hitchhiking, a mechanism that may dramatically facilitate the process of speciation-with-gene-flow. They also show the power of integrating genome scans with genetic analyses of the phenotypic traits involved in local adaptation and population divergence.
Data from: Deciphering the genomic architecture of the stickleback brain with a novel multi-locus gene-mapping approach
Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multi-locus QTL mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2-interpopulation cross with 239 individuals and 15 198 fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multi-locus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multi-locus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h2 ≈ 0.15-0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL mapping approach developed here has distinctive advantages over the traditional QTL mapping methods in analyses of dense marker panels.
Assembly-mapped metaproteomics gene annotations from Columbia River hyporheic sediments
<p>Annotations for the full set of genes that recruited unique peptides from the Columbia River hyporheic zone sediments manuscript.</p>
Supplementary data "Broad genomic workup including Optical Genome Mapping uncovers a DDX3X::MLLT10 gene fusion in Acute Myeloid Leukemia"
<pre>Supplementary data "Broad genomic workup including Optical Genome Mapping uncovers a DDX3X::MLLT10 gene fusion in Acute Myeloid Leukemia" - OGM Rare Variant Analysis for both time points extracted from Bionano Access RVP Analysis output folder: -> unfiltered annotated SV output -> unfiltered CNV output - Whole Exome Sequencing Gene Panel results as output by Varvis (Limbus) (CSV file) -> WES SNVs -> WES CNVs - Quality metrics + selected results: -> OGM; both time points -> Whole Exome Sequencing; time point 1</pre>
GWAS summary statistics pertaining to the publication "Mapping of the gene network that regulates glycan clock of ageing"
<p>Dataset pertaining to the publication "Mapping of the gene network that regulates IgG galactosylation". If you use this dataset, please cite the manuscript in order to fairly acknowledge the contribution of all participating studies and their sponsors.</p> <p>Data consists of three gzipped files corresponding to genome-wide association meta-analysis (GWAMA) of three IgG N-glycome traits describing the percentage of galactosylation: G0, G1 and G2. The files are tab-separated and contain genome-wide association meta-analysis data for the discovery studies. Summary data are given for the meta-analyses of over 15 million directly genotyped or imputed single variant polymorphisms corresponding to the HRC (Haplotype Reference Consortium) r1.1 reference panel. Meta-analysis estimates are corrected for inflation of test statistics using genomic control at the individual study level.</p>
FIGURE 98. ITS2 gene tree and distribution map for both G. armatus and G in Crickets of the genus Gryllus in the United States (Orthoptera: Gryllidae: Gryllinae)
FIGURE 98. ITS2 gene tree and distribution map for both G. armatus and G. integer showing both geographic separation and zone of possible hybridization. Collection stop numbers for G. armatus: S04-121 (G358, G359); S05-110 (G511, G512, G513, G515); S07-26 (G1165); S07-33 (G1077); S07-79 (G1228); S10-62 (G1918); S10-63 (G1899); S11-90 (G2172); S12- 36 (G2264); S12-104 (G2411); S13-18 (G2473); S13-46 (G2566); S15-54 (G3096, G3077, G3081); S15-71 (G3101, G3074); S15-73 (G3072); Albuquerque, NM (2003-175); Cordes Junction, AZ (2006-241); Agua Fria, AZ (2006-244). Collection stop numbers for G. integer: S03-100 (G58, G59); S04-36 (G243); S04-40 (G210); S0455 (G245, G246); S04-60 (G218); S04-128 (G370); S05-23 (G477); S05-99 (G499); S06-77 (G629); S09-109 (G1439); S09-114 (G1441, G1445); S11-72 (G2113, G2148); S11-73 (G2143); S12-116 (G2431); S15-80 (G3195, G3316); S15-91 (G3178, G3265); S15-95 (G3292); S16-21 (G3416); S17-6 (G3502, G3525); S16-5 (G3374, G3381); S16-12 (G3366); Hwy 276 mile 30 Sinclair Gas, Garfield Co., UT (2003-038, 2003-039, 2003-041); Winslow, AZ (2003-312, 2012-029, 2012-030, 2012-031, 2012-032).
FIGURE 122. 16S gene tree and distribution map showing two 16S Clades for both G. vernalis and G in Crickets of the genus Gryllus in the United States (Orthoptera: Gryllidae: Gryllinae)
FIGURE 122. 16S gene tree and distribution map showing two 16S Clades for both G. vernalis and G. fultoni (the latter situation is discussed, below, under G. fultoni). G. vernalis samples: S03-56 (G26, G27, G1740); S03-57 (G28, G1700, G1701, G1738); S03-62 (G31, G33, G440); S14-35 (G2754, G2755). G. fultoni samples: S01-47 (G1704); S03-56 (G38, G1703); S03- 62 (G32, G34); S03-64 (G35, G1712); S07-22 (G1020, G1138).
FIGURE 3. Heat map showing recovery efficiency for 333 in Delimitation of the new tribe Parartocarpeae (Moraceae) is supported by a 333- gene phylogeny and resolves tribal level Moraceae taxonomy
FIGURE 3. Heat map showing recovery efficiency for 333 genes. Each column is a gene, and each row is one sample. The intensity of color in each cell is determined by the length of sequence recovered divided by the length of the reference gene (maximum of 1.0).
Data from: Seeking signatures of reinforcement at the genetic level: a hitchhiking mapping and candidate gene approach in the house mouse
Reinforcement is the process by which prezygotic isolation is strengthened as a response to selection against hybridization. Most empirical support for reinforcement comes from the observation of its possible phenotypic signature: an accentuated degree of prezygotic isolation in the hybrid zone as compared to allopatry. Here, we implemented a novel approach to this question by seeking for the signature of reinforcement at the genetic level. In the house mouse, selection against hybrids and enhanced olfactory-based assortative mate preferences are observed in a hybrid zone between the two European subspecies Mus musculus musculus and M. m. domesticus, suggesting a possible recent reinforcement event. To test for the genetic signature of reinforcing selection and identify genes involved in sexual isolation, we adopted a hitchhiking mapping approach targeting genomic regions containing candidate genes for assortative mating in mice. We densely scanned these genomic regions in hybrid zone and allopatric samples using a large number of fast evolving microsatellite loci that allow the detection of recent selection events. We found a handful of loci showing the expected pattern of significant reduction in variability in populations close to the hybrid zone, showing assortative odour preference in mate choice experiments as compared to populations further away and displaying no such preference. These loci lie close to genes that we pinpoint as testable candidates for further investigation.
Systematic Gene Expression Mapping Clusters Nuclear Receptors According to Their Function in the Brain - Website save
<p>This is a copy of the website that was related to mousepat.ics-mci.fr</p>
Optical genome mapping reveals and characterizes recurrent aberrations and new fusion genes in adult ALL
<p>Optical genome mapping results files on single patient level:</p> <p>SV (Structural Variants)</p> <p>CNV (Copy number Variants)</p> <p>Aneuploidies </p> <p>OGM raw data sets a are available from the corresponding author upon request.</p>
Data from: An integrative approach for mapping differentially expressed genes and network components using novel parameters to elucidate key regulatory genes in colorectal cancer
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Data from: A novel resistance gene for bacterial blight in rice, Xa43(t) identified by GWAS and confirmed by QTL mapping using a bi-parental population
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Data from: Localizing FST outliers on a QTL map reveals evidence for large genomic regions of reduced gene exchange during speciation-with-gene-flow
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Data from: Seeking signatures of reinforcement at the genetic level: a hitchhiking mapping and candidate gene approach in the house mouse
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Data from: Deciphering the genomic architecture of the stickleback brain with a novel multi-locus gene-mapping approach
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Construction of genetic linkage map based on SNP markers, QTL mapping and detection of candidate genes of growth-related traits in Pacific abalone using genotyping-by-sequencing
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Data from: A gene for genetic background in Zea mays: fine-mapping enhancer of teosinte branched1.2 to a YABBY class transcription factor
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Data from: The coupling hypothesis: why genome scans may fail to map local adaptation genes
Genomic scans of multiple populations often reveal marker loci with greatly increased differentiation between populations. Often this differentiation coincides in space with contrasts in ecological factors, forming a genetic–environment association (GEA). GEAs imply a role for local adaptation, and so it is tempting to conclude that the strongly differentiated markers are themselves under ecologically based divergent selection, or are closely linked to loci under such selection. Here, we highlight an alternative and neglected explanation: intrinsic (i.e. environment-independent) pre- or post-zygotic genetic incompatibilities rather than local adaptation can be responsible for increased differentiation. Intrinsic genetic incompatibilities create endogenous barriers to gene flow, also known as tension zones, whose location can shift over time. However, tension zones have a tendency to become trapped by, and therefore to coincide with, exogenous barriers due to ecological selection. This coupling of endogenous and exogenous barriers can occur easily in spatially subdivided populations, even if the loci involved are unlinked. The result is that local adaptation explains where genetic breaks are positioned, but not necessarily their existence, which can be best explained by endogenous incompatibilities. More precisely, we show that (i) the coupling of endogenous and exogenous barriers can easily occur even when ecological selection is weak; (ii) when environmental heterogeneity is fine-grained, GEAs can emerge at incompatibility loci, but only locally, in places where habitats and gene pools are sufficiently intermingled to maintain linkage disequilibria between genetic incompatibilities, local-adaptation genes and neutral loci. Furthermore, the association between the locally adapted and intrinsically incompatible alleles (i.e. the sign of linkage disequilibrium between endogenous and exogenous loci) is arbitrary and can form in either direction. Reviewing results from the literature, we find that many predictions of our model are supported, including endogenous genetic barriers that coincide with environmental boundaries, local GEA in mosaic hybrid zones, and inverted or modified GEAs at distant locations. We argue that endogenous genetic barriers are often more likely than local adaptation to explain the majority of Fst-outlying loci observed in genome scan approaches – even when these are correlated to environmental variables.
Data from: Mapping the fitness landscape of gene expression uncovers the cause of antagonism and sign epistasis between adaptive mutations
How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels. We found strong antagonism and sign epistasis between these beneficial mutations. Mutations with the largest individual benefit interacted the most antagonistically with other mutations, a trend we also uncovered through analyses of datasets from other model systems. However, these beneficial mutations interacted multiplicatively (i.e., no epistasis) at the level of enzyme expression. By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs. Knowledge of the phenotypic landscape also illuminated that, although the fitness peak was phenotypically far from the ancestral state, it was not genetically distant. Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture. Given that adaptation in nature often results from optimizing gene expression, these conclusions can be widely applicable to other organisms and selective conditions. Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.