19 research outputs found

    Analysis of case-control study type I error rates from 3 simulated SNPs within <i>BDNF</i>.

    No full text
    <p>The three SNPs show allele frequency differences between CEU and YRI of 0.066 (rs11030108), 0.102 (rs10767658), and 0.233 (rs1013402). The y-axis is estimated type I error rate versus the simulated CEU proportion (x-axis). Panels on the left show data with a difference in disease prevalence ratio of 1.25 while a ratio of 1.5 is shown on the right.</p

    Estimated odds ratios (OR) from case-control analysis of 3 simulated SNPs within <i>BDNF</i>.

    No full text
    <p>Conventions are the same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a> except the y-axis is the average estimated OR from the same analysis as presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a>.</p

    Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.

    No full text
    <p>Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.</p

    Chronological Changes in MicroRNA Expression in the Developing Human Brain

    Get PDF
    <div><p>Objective</p><p>MicroRNAs (miRNAs) are endogenously expressed noncoding RNA molecules that are believed to regulate multiple neurobiological processes. Expression studies have revealed distinct temporal expression patterns in the developing rodent and porcine brain, but comprehensive profiling in the developing human brain has not been previously reported.</p><p>Methods</p><p>We performed microarray and TaqMan-based expression analysis of all annotated mature miRNAs (miRBase 10.0) as well as 373 novel, predicted miRNAs. Expression levels were measured in 48 post-mortem brain tissue samples, representing gestational ages 14–24 weeks, as well as early postnatal and adult time points.</p><p>Results</p><p>Expression levels of 312 miRNAs changed significantly between at least two of the broad age categories, defined as fetal, young, and adult.</p><p>Conclusions</p><p>We have constructed a miRNA expression atlas of the developing human brain, and we propose a classification scheme to guide future studies of neurobiological function.</p></div

    Detection of population structure in four HapMap populations.

    No full text
    <p>The first two principal components from EIGENSTRAT are plotted for all 3 SNP panels (A, SNP panel 93; B, SNP panel 52; C, SNP panel 19). As more AIMs are used in the analysis, the resolution improves. The 52 SNP panel appears to have some overlap between CEU and CHB+JPT though it should be noted that these datapoints are more clearly differentiated by considering the third and fourth principal components (not shown).</p

    Demographic information of brain tissue donors.

    No full text
    <p>samples excluded from expression analysis.</p><p>UMB# – Sample identifier, NICHD Brain and Tissues Bank for Developmental Disorders.</p><p>GA – gestational age.</p><p>Pool – indicates sample pooling for TaqMan arrays.</p><p>PMI – post-mortem interval (hours).</p

    Temporal expression analysis using real-time quantitative PCR.

    No full text
    <p>The number of miRNAs that exceed the indicated fold difference are tabulated for each pair-wise sample type comparison.</p

    Autism Associated Gene, <i>ENGRAILED2</i>, and Flanking Gene Levels Are Altered in Post-Mortem Cerebellum

    Get PDF
    <div><p>Background</p><p>Previous genetic studies demonstrated association between the transcription factor <i>ENGRAILED2</i> (<i>EN2</i>) and Autism Spectrum Disorder (ASD). Subsequent molecular analysis determined that the <i>EN2</i> ASD-associated haplotype (<i>rs1861972</i>-<i>rs1861973</i> A-C) functions as a transcriptional activator to increase gene expression. <i>EN2</i> is flanked by 5 genes, <i>SEROTONIN RECEPTOR5A (HTR5A), INSULIN INDUCED GENE1 (INSIG1)</i>, <i>CANOPY1 HOMOLOG (CNPY1), RNA BINDING MOTIF PROTEIN33 (RBM33)</i>, and <i>SONIC HEDGEHOG (SHH)</i>. These flanking genes are co-expressed with <i>EN2</i> during development and coordinate similar developmental processes. To investigate if mRNA levels for these genes are altered in individuals with autism, post-mortem analysis was performed.</p><p>Methods</p><p>qRT-PCR quantified mRNA levels for <i>EN2</i> and the 5 flanking genes in 78 post-mortem cerebellar samples. mRNA levels were correlated with both affection status and <i>rs1861972-rs1861973</i> genotype. Molecular analysis investigated whether <i>EN2</i> regulates flanking gene expression.</p><p>Results</p><p><i>EN2</i> levels are increased in affected A-C/G-T individuals (pβ€Š=β€Š.0077). Affected individuals also display a significant increase in <i>SHH</i> and a decrease in <i>INSIG1</i> levels. <i>Rs1861972</i>-<i>rs1861973</i> genotype is correlated with significant increases for <i>SHH</i> (A-C/G-T) and <i>CNPY1</i> (G-T/G-T) levels. Human cell line over-expression and knock-down as well as mouse knock-out analysis are consistent with <i>EN2</i> and <i>SHH</i> being co-regulated, which provides a possible mechanism for increased <i>SHH</i> post-mortem levels.</p><p>Conclusions</p><p><i>EN2</i> levels are increased in affected individuals with an A-C/G-T genotype, supporting <i>EN2</i> as an ASD susceptibility gene. <i>SHH</i>, <i>CNPY1</i>, and <i>INSIG1</i> levels are also significantly altered depending upon affection status or <i>rs1861972</i>-<i>rs1861973</i> genotype. Increased <i>EN2</i> levels likely contribute to elevated <i>SHH</i> expression observed in the post-mortem samples</p></div

    Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

    No full text
    <div><p>Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.</p></div
    corecore