23 research outputs found

    Additional file 2: of Recently evolved human-specific methylated regions are enriched in schizophrenia signals

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    Annotation of all DMRs with schizophrenia-associated SNPs. This file contains annotation of all the human-lineage specific DMRs that are associated with schizophrenia markers. Details of the various markers present within each DMR is provided, along with the marker with most significant p-value. (XLSX 263 kb

    Description of the samples.

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    <p><sup></sup> BPD, bipolar disorder; SCZ, schizophrenia; NCNG, Norwegian Cognitive NeuroGenetics; TOP, Norwegian Thematically Organized Psychosis; WTCCC, British Wellcome Trust Case Control Consortium; Danish, Danish sub-sample of the Scandinavian Collaboration on Psychiatric Etiology; PGC, Psychiatric Genomics Consortium.</p><p><sup></sup> indicates the cases and controls in the single-centre samples that are also included in the PGC multi-centre sample.</p

    Testing gene sets associated with normal neurocognitive variation for enrichment of association with bipolar disorder.

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    <p><i>q</i>-value, obtained from 3 GSEA runs with 1,000 permutations each). The maximum standard deviation from the average <i>q</i>-value was 0.07. Sets that passed the enrichment threshold (<i>p</i>-value≤0.05, FDR <i>q</i>-value≤0.25) were tested for validation using random mimic sets (see Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081052#pone.0081052.s001" target="_blank">File S1</a>).<sup></sup> For each GWAS dataset, the 5 most enriched candidate sets are shown. For the German dataset, the 14 most enriched sets are presented to show the overlap with the other datasets. The rank position (R) of the gene set within the total number of gene sets tested is determined by the average false discovery rate (</p><p>% of the random sets (i.e. validated sets).<sup>a</sup> indicates sets that were more enriched than 98</p><p><sup>b</sup> indicates sets that did not pass the enrichment threshold but were among the 5 most enriched in the corresponding sample.</p><p>“n.e.”. Visuospatial attention.1 – Visuospatial attention task with valid cue to the location of the visual target; Visuospatial attention.3 – Visuospatial attention task with neutral cue to the location of the visual target. The number after each gene set name represents the number of genes within that set (e.g. the Colour-word interference −25 set contains the top 25 genes within the colour-word interference ranking list of genes).<sup></sup> Sets that did not pass the enrichment threshold and ranked outside the top 5 are indicated by </p><p><i>p-</i>value of zero (0.0) indicates an actual <i>p</i>-value of less than 1/number-of-permutations.<sup></sup> A reported </p

    Testing gene sets associated with normal neurocognitive variation for enrichment of association with schizophrenia.

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    <p><i>q</i>-value, obtained from 3 GSEA runs with 1,000 permutations each). The maximum standard deviation from the average <i>q</i>-value was 0.06. Sets that passed the enrichment threshold (<i>p</i>-value≤0.05, FDR <i>q</i>-value≤0.25) were tested for validation using random mimic sets (see Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081052#pone.0081052.s001" target="_blank">File S1</a>).<sup></sup> For each GWAS dataset the 5 most enriched candidate sets are shown. The rank position (R) of the gene set within the total number of gene sets tested was determined by the average false discovery rate (</p><p>% of the random sets (i.e. validated sets).<sup>a</sup> indicates sets that were more enriched than 98</p><p><sup>b</sup> indicates sets that did not pass the enrichment threshold but were among the 5 most enriched in the corresponding sample.</p><p>“n.e.”. Visuospatial attention.1 – Visuospatial attention task with valid cue to the location of the visual target; Visuospatial attention.3 – Visuospatial attention task with neutral cue to the location of the visual target. The number after each gene set name represents the number of genes within that set (e.g. the Colour-word interference −25 set contains the top 25 genes within the colour-word interference ranking list of genes).<sup></sup> Sets that did not pass the enrichment threshold and ranked outside the top 5 are indicated by </p

    Gene expression data of the Allen Human Brain Atlas were mapped onto the 12 genetically based cortical regions in the MR space.

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    <p>A) Resulting volume registration between FreeSurfer surface (fsaverage) and Allen brain MNI coordinates displayed as a point cloud, with a slice of the MRI imaging at the bottom (colin27). B) After the volume registration, gene expression data points are mapped to FreeSurfer surface vertices by assigning each surface vertex the gene expression of the closest (Euclidean distance) Allen brain data point using nearest neighbor interpolation. If two vertices have the same closest Allen brain data point, they belong to the same patch and the patch id is displayed as color. Thus, the color patches illustrate the local density of data points. The color patches with similar sizes across the cortex represent an even distribution of Allen brain data points and their surface correspondences. Colors of the dots in both (A) and (B) panels represent cortical regions to which they were assigned, corresponding to the color schemes in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g001" target="_blank">Fig 1B</a>.</p

    Region-specific gene expression profiles in each lobe.

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    <p>A) The majority of genes were ubiquitously expressed in the cortical surface areas of all four lobes of the brain. A small percentage of the genes were either distinctively expressed in one lobe or co-expressed in multiple but not all four lobes of the brain. The frontal lobe exhibits the most distinctively expressed genes. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s006" target="_blank">S5 Table</a> for the lists of genes and locations. B) The distribution of functional annotations of the transcripts distinctively expressed in the frontal lobe. “All” indicates the distribution of all transcripts included in our analysis, irrespective of their expression levels and anatomical locations. There are a higher proportion of intergenic transcripts in the frontal lobe (22% compared to 14%). C) A gene network analysis for the frontal lobe (excluding intergenic transcripts). The yellow-colored genes belong to the most significantly associated pathway: interferon-gamma-mediated signaling pathway, related to immunity (FDR = 3.2 x10<sup>-4</sup>). Half of the genes were originally from the transcripts distinctively expressed in the frontal lobe. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s008" target="_blank">S7 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s009" target="_blank">S8 Table</a> for the complete list of associated pathways.</p

    Applying the genetically based cortical parcellations to independent data.

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    <p>A) The phenotypic correlation matrix of VETSA twin cohort versus the phenotypic correlation matrix of combined-5-cohort (C5C). The Mantel test confirmed that the similarity between them was highly significant (<i>p</i> = 0.0001). B) Cortical brain phenotypes—surface area measures of 12 cortical regions after controlling for total surface area. The cortex was parceled into 12 genetically based regions of maximal shared genetic influence derived from the VETSA sample [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.ref016" target="_blank">16</a>]. 1. motor & premotor; 2. dorsolateral prefrontal; 3. dorsomedial frontal; 4. orbitofrontal; 5. pars opercularis & subcentral; 6. superior temporal; 7. posterolateral temporal; 8. anteromedial temporal; 9. inferior parietal; 10. superior parietal; 11. precuneus; 12. occipital. C) The phenotypic correlation versus the genetic correlations (<i>r</i><sub><i>g</i></sub>) matrices of VETSA. The correlation of the two matrices was also highly significant (<i>p</i> < 0.0001), suggesting high genetic contributions to the cortical patterning. Correlation coefficients are listed in Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s002" target="_blank">S1 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s003" target="_blank">S2 Table</a>.</p

    Significant associations among correlation matrices.

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    <p>Correlation matrices among cortical surface areas derived from a variety of measures are highly consistent with each other as quantified by the Mantel test coefficients with 95% confidence intervals. Each pair of bars represents two correlation methods used in the Mantel test: linear regression with errors-in-both-variables (correlation with EIV) in gray color on the left versus Pearson’s correlation in light-gray color on the right. Variables were standardized in regression analysis. *<i>p</i>≤0.05, **<i>p</i>≤0.01, ***<i>p</i>≤0.001, ****<i>p</i>≤0.0001. Twin refers to VETSA cohort, and twin-based method was used to derive genetic correlations. Genotype-based method was used to derive genetic correlations for ombined-5-cohort. The corresponding matrices are visualized in Figs <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g001" target="_blank">1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g002" target="_blank">2</a>. See also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s002" target="_blank">S1 Fig</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s003" target="_blank">S2 Table</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s004" target="_blank">S3 Table</a>, and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s005" target="_blank">S4 Table</a>.</p

    Schematic overview of the method.

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    <p>SNP markers from GWAS data were assigned to single genes in a process termed “gene binning”, by implementing a novel LD-based tool (LDsnpR, Christoforou <i>et al.</i> under revision). Modified Sidak's <i>P</i>-values were extracted for each gene (“gene bin”) in the GWAS data sets. Single gene-based analysis of the differentially expressed cortical genes was performed by extracting the modified Sidak's <i>P</i>-values for the candidate genes from the NCNG GWAS. Gene set-based analysis of the differentially expressed cortical genes was performed by extraction of the modified Sidak's <i>P</i>-values, followed by GSEA of GWAS data on cognition, psychiatric disorders and non-psychiatric phenotypes. GSEA: Gene set enrichment analysis, GWAS: Genome-wide association study.</p
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