18 research outputs found
Episignature analysis of moderate effects and mosaics
DNA methylation classifiers (episignatures) help to determine the pathogenicity of variants of uncertain significance (VUS). However, their sensitivity is limited due to their training on unambiguous cases with strong-effect variants so that the classification of variants with reduced effect size or in mosaic state may fail. Moreover, episignature evaluation of mosaics as a function of their degree of mosaicism has not been developed so far. We improved episignatures with respect to three categories. Applying (i) minimum-redundancy-maximum-relevance feature selection we reduced their length by up to one order of magnitude without loss of accuracy. Performing (ii) repeated re-training of a support vector machine classifier by step-wise inclusion of cases in the training set that reached probability scores larger than 0.5, we increased the sensitivity of the episignature-classifiers by 30%. In the newly diagnosed patients we confirmed the association between DNA methylation aberration and age at onset of KMT2B-deficient dystonia. Moreover, we found evidence for allelic series, including KMT2B-variants with moderate effects and comparatively mild phenotypes such as late-onset focal dystonia. Retrained classifiers also can detect mosaics that previously remained below the 0.5-threshold, as we showed for KMT2D-associated Kabuki syndrome. Conversely, episignature-classifiers are able to revoke erroneous exome calls of mosaicism, as we demonstrated by (iii) comparing presumed mosaic cases with a distribution of artificial in silico-mosaics that represented all the possible variation in degree of mosaicism, variant read sampling and methylation analysis
Phenotypic and genome-wide studies on dicarbonyls: major associations to glomerular filtration rate and gamma-glutamyltransferase activity
Connecting Anxiety and Genomic Copy Number Variation: A Genome-Wide Analysis in CD-1 Mice.
Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice
Identification of Restless Legs Syndrome Genes by Mutational Load Analysis
Objective Restless legs syndrome is a frequent neurological disorder with substantial burden on individual well-being and public health. Genetic risk loci have been identified, but the causatives genes at these loci are largely unknown, so that functional investigation and clinical translation of molecular research data are still inhibited. To identify putatively causative genes, we searched for highly significant mutational burden in candidate genes. Methods We analyzed 84 candidate genes in 4,649 patients and 4,982 controls by next generation sequencing using molecular inversion probes that targeted mainly coding regions. The burden of low-frequency and rare variants was assessed, and in addition, an algorithm (binomial performance deviation analysis) was established to estimate independently the sequence variation in the probe binding regions from the variation in sequencing depth. Results Highly significant results (considering the number of genes in the genome) of the conventional burden test and the binomial performance deviation analysis overlapped significantly. Fourteen genes were highly significant by one method and confirmed with Bonferroni-corrected significance by the other to show a differential burden of low-frequency and rare variants in restless legs syndrome. Nine of them (AAGAB, ATP2C1, CNTN4, COL6A6, CRBN, GLO1, NTNG1, STEAP4, VAV3) resided in the vicinity of known restless legs syndrome loci, whereas 5 (BBS7, CADM1, CREB5, NRG3, SUN1) have not previously been associated with restless legs syndrome. Burden test and binomial performance deviation analysis also converged significantly in fine-mapping potentially causative domains within these genes. Interpretation Differential burden with intragenic low-frequency variants reveals putatively causative genes in restless legs syndrome. ANN NEUROL 201
Genomic positions of CNVs on chromosome 3.
<p>The chromosome is indicated by a thick horizontal line (grey). Depending on the detection method, CNVs in HAB/LAB mice are depicted in orange (aCGH), dark red (JaxMDGA) and red (NGS), respectively. Data displayed above the grey line represent a copy number gain in HAB vs. LAB animals, data below a copy number loss. Data printed on the grey line show CNVs in 64 CD-1 mice, with those highlighted in color that could be associated with anxiety-related behavior (time on the open arm of EPM) with a nominal <i>p</i>-value less than 0.1 (light blue) or less than 0.05 (blue). Start points of CNVs are marked by dots and lines are drawn to the end points.</p
Distribution of CNVs in CD-1 mice.
<p>Chromosomes are indicated by grey horizontal lines. Start points of CNVs are marked by dots and lines are drawn to the end points. Due to limitations in resolution, a small CNV might appear as dot only. CNVs highlighted in blue or red were associated with anxiety-related behavior (time on the open arm of the EPM) with a nominal <i>p</i>-value less than 0.1 or 0.05, respectively.</p
Protein coding genes in genomic regions of CNVs detected in HAB/LAB and CD-1 mice.
<p>All genes listed overlap both CNVs in HAB/LAB mice detected with aCGH, JaxMDGA and NGS, and CNVs in CD-1 mice which were best associated with the time the animals spent on the open arm of the EPM (nominal <i>p</i>-value < 0.1).</p><p>Protein coding genes in genomic regions of CNVs detected in HAB/LAB and CD-1 mice.</p
Association of copy number with anxiety-related behavior in CD-1 mice.
<p>Exemplarily, data of three associations resulting in nominal <i>p</i>-values reaching significance (<i>p</i> < 0.05), a trend (<i>p</i> < 0.1), and not reaching significance (<i>p</i> > 0.05), respectively, are shown. Each dot represents data of a single animal (N = 64). The relative copy number is represented by the mean normalized intensities of JaxMDGA probes within the respective CNV. <b>(A)</b> CNV no. 498; <i>P</i><sub>nom</sub> = 0.0009; regression line: y = 0.0091x + 9.4389. <b>(B)</b> CNV no. 164; <i>P</i><sub>nom</sub> = 0.0554; regression line: y = 0.0061x + 10.201. <b>(C)</b> CNV no. 453; <i>P</i><sub>nom</sub> = 0.9791; regression line: y = 0.0008x + 9.6225.</p
Docetaxel/doxorubicin chemotherapy of MCF10CA1a is unaffected by <i>EGFR</i> mutation or in combination with afatinib.
<p>Cells were cultured in the presence of EGF and treated with docetaxel/doxorubicin in a 5:1 ratio (DD) alone or in combination with the half IC<sub>50</sub> dose of afatinib (DDA) for seven days. Cell survival was assessed using the MTS assay and the IC<sub>50</sub> dose was reported using the docetaxel concentrations. CA: MCF10CA1a-EV, WT: MCF10CA1a-<i>EGFR</i>-WT, GS: MCF10CA1a-<i>EGFR</i>-GS, DEL: MCF10CA1a-<i>EGFR</i>-DEL.</p
Overexpression of wild type or mutant <i>EGFR</i> increases the growth of MCF10CA1a mammary fat pad xeongrafts.
<p>Female BALB/c nude mice were injected in the mammary fat pad with 5x10<sup>6</sup> cells from the indicated cell line and tumour formation was monitored with bioluminescent imaging. CA: MCF10CA1a-EV, WT: MCF10CA1a -<i>EGFR</i>-WT, GS: MCF10CA1a -<i>EGFR</i>-GS, DEL: MCF10CA1a -<i>EGFR</i>-DEL <b>A.</b> Representative bioluminescent images of individual mice taken on day 14 and day 43. <b>B.</b> Plot of the increase in luciferase signal in each group of mice (*p<0.05, **p<0.01, ****p<0.0001, n = 5, vs CA control, One-Way ANOVA). <b>C.</b> Representative bioluminescent images of individual mice taken on day 49 post injection in an independent cohort of mice. <b>D.</b> Plot of the magnitude of luciferase signal in each group of mice at day 49 post injection (**p<0.01, n = 5).</p