Manhattan plot for the FBAT-CNV P-values.

<p>The y-axis shows the distribution of –log₁₀(p) where p is the FBAT-CNV test association test P-value for all CNV loci passing quality control filters (Methods). The x-axis shows chromosomes numbered from 1 (left) to X (right).</p

Top ten T1D associated CNVs after removing known loci and technical artifacts.

<p>The last column lists the genes for which at least one exon overlaps the defined CNV region. P-value refers to the FBAT association test for autosomal CNVs, and to the FBAT-X association test otherwise.</p

Decomposition of multi-probe CNV data at the <i>INS</i> VNTR locus into first two principal components PC1 and PC2.

<p>Principal components PC1 and PC2 summarize the multi-probe CNV data at the <i>INS</i> VNTR locus. Colors (green/red/black) were chosen based on the genotypes of the SNP rs689 (AA/AT/TT), which captures the class I-class III separation.</p

Quantile-quantile plot comparing the expected versus the observed distribution of the FBAT-CNV P-values.

<p>These plots show the distribution of -2log₁₀(p), which is, under the null, distributed as chi-square with 2 degrees-of-freedom. IgG/TCR loci are discussed elsewhere and not included in these plots. A – N = 3,286 CNVs that passed quality controls and were tested for association. Loci overlapping the MHC region are marked in blue. Loci mapping to, or in strong LD with, the <i>INS</i> VNTR region are marked in red. B – N = 3,214 CNVs passed quality controls and did not overlap or tagged the <i>INS</i> VNTR and the MHC region. C – N = 448 VNTRs targeted by the CGH array that passed quality controls. <i>INS</i> VNTR CNV regions are marked in red as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen-1004367-g003" target="_blank">Figure 3A</a>.</p

Differences between case-control and FBAT-CNV association tests.

<p>A- In a case-control analysis, technical variability may affect the CNV intensity data between cases and controls. Therefore, it is necessary to call the discrete genotypes, potentially allowing for genotype uncertainty in the association tests. Mixture models are typically used for calling, as illustrated by the colored lines on top of the histograms. Intensity data must therefore be sufficiently separated to make these discrete calls (CNV data in this example obtained from both control groups in the WTCCC study <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367-WTCCC1" target="_blank">[28]</a>). B- With the FBAT-CNV framework, one compares the average parental CNV signal with the signal for affected offspring. Consistent deviation of affected offspring intensity data compared to parental average indicates biased transmission of CNV alleles. As the test is solely based on the intensity data, and no systematic bias is expected between parents and offspring, it is not necessary to make discrete calls (CNV data obtained from INS VNTR first principal component).</p

Summary of the CNVs included in the array design and tested for T1D association using FBAT-CNV.

<p>CNVs originate from two main sources: the GSV map of common CNVs <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367-Conrad2" target="_blank">[27]</a> and the 1,000 Genomes sequence data. Tested CNVs also include 365 novel insertion CNVs obtained from the Venter genome. Detailed description of the array design is provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367.s016" target="_blank">Text S1</a>.</p

Number of transmissions between parents and affected T1D offspring, before/after applying QC steps.

<p>Number of transmissions between parents and affected T1D offspring, before/after applying QC steps.</p

Spurious associations at TCR and IGH loci.

<p>Age at sampling (x-axis) versus CNV intensity signal (y-axis) for the three most associated Immunoglobin Heavy (IGH) and T cell receptor (TCR) loci CNVs. Each point represents an individual in the study (irrespective of familial/T1D status). Blue crosses indicate DNA extracted from LCLs (N = 551) and red crosses DNA extracted from blood (N = 2,981). Red and blue lines have been fitted to the LCL/blood data using cubic splines. A - CNVR6085.1 (chr14:21977832-21987926) mapping to TCR alpha and TCR delta locus on chr14, FBAT-CNV P = 3.6 10⁻⁶³. The plot shows correlation between age at sampling and probe intensity for DNA extracted from blood samples. B - CNVR3590.1 (chr7:142194021-142204412) mapping to TCR beta locus on chr7, FBAT-CNV P = 4.4 10⁻³¹. The plot shows correlation between age at sampling and probe intensity for DNA extracted from blood samples. C - CNVR6294.22 (chr14:105433837-105441555) mapping to Ig heavy chain locus on chr14, FBAT-CNV P = 6.5 10⁻⁵. No age-dependent effect was detected at this locus.</p

Downregulated Wnt/β-catenin signalling in the Down syndrome hippocampus.

Pathological mechanisms underlying Down syndrome (DS)/Trisomy 21, including dysregulation of essential signalling processes remain poorly understood. Combining bioinformatics with RNA and protein analysis, we identified downregulation of the Wnt/β-catenin pathway in the hippocampus of adult DS individuals with Alzheimer's disease and the 'Tc1' DS mouse model. Providing a potential underlying molecular pathway, we demonstrate that the chromosome 21 kinase DYRK1A regulates Wnt signalling via a novel bimodal mechanism. Under basal conditions, DYRK1A is a negative regulator of Wnt/β-catenin. Following pathway activation, however, DYRK1A exerts the opposite effect, increasing signalling activity. In summary, we identified downregulation of hippocampal Wnt/β-catenin signalling in DS, possibly mediated by a dose dependent effect of the chromosome 21-encoded kinase DYRK1A. Overall, we propose that dosage imbalance of the Hsa21 gene DYRK1A affects downstream Wnt target genes. Therefore, modulation of Wnt signalling may open unexplored avenues for DS and Alzheimer's disease treatment