<i>De Novo</i> Mutations in Moderate or Severe Intellectual Disability

<div><p>Genetics is believed to have an important role in intellectual disability (ID). Recent studies have emphasized the involvement of <i>de novo</i> mutations (DNMs) in ID but the extent to which they contribute to its pathogenesis and the identity of the corresponding genes remain largely unknown. Here, we report a screen for DNMs in subjects with moderate or severe ID. We sequenced the exomes of 41 probands and their parents, and confirmed 81 DNMs affecting the coding sequence or consensus splice sites (1.98 DNMs/proband). We observed a significant excess of <i>de novo</i> single nucleotide substitutions and loss-of-function mutations in these cases compared to control subjects, suggesting that at least a subset of these variations are pathogenic. A total of 12 likely pathogenic DNMs were identified in genes previously associated with ID (<i>ARID1B, CHD2, FOXG1, GABRB3, GATAD2B, GRIN2B, MBD5, MED13L, SETBP1, TBR1, TCF4, WDR45</i>), resulting in a diagnostic yield of ∼29%. We also identified 12 possibly pathogenic DNMs in genes (<i>HNRNPU, WAC</i>, <i>RYR2, SET, EGR1, MYH10</i>, <i>EIF2C1</i>, <i>COL4A3BP, CHMP2A, PPP1CB, VPS4A, PPP2R2B</i>) that have not previously been causally linked to ID. Interestingly, no case was explained by inherited mutations. Protein network analysis indicated that the products of many of these known and candidate genes interact with each other or with products of other ID-associated genes further supporting their involvement in ID. We conclude that DNMs represent a major cause of moderate or severe ID.</p></div

Global characterization of copy number variants in epilepsy patients from whole genome sequencing

<div><p>Epilepsy will affect nearly 3% of people at some point during their lifetime. Previous copy number variants (CNVs) studies of epilepsy have used array-based technology and were restricted to the detection of large or exonic events. In contrast, whole-genome sequencing (WGS) has the potential to more comprehensively profile CNVs but existing analytic methods suffer from limited accuracy. We show that this is in part due to the non-uniformity of read coverage, even after intra-sample normalization. To improve on this, we developed PopSV, an algorithm that uses multiple samples to control for technical variation and enables the robust detection of CNVs. Using WGS and PopSV, we performed a comprehensive characterization of CNVs in 198 individuals affected with epilepsy and 301 controls. For both large and small variants, we found an enrichment of rare exonic events in epilepsy patients, especially in genes with predicted loss-of-function intolerance. Notably, this genome-wide survey also revealed an enrichment of rare non-coding CNVs near previously known epilepsy genes. This enrichment was strongest for non-coding CNVs located within 100 Kbp of an epilepsy gene and in regions associated with changes in the gene expression, such as expression QTLs or DNase I hypersensitive sites. Finally, we report on 21 potentially damaging events that could be associated with known or new candidate epilepsy genes. Our results suggest that comprehensive sequence-based profiling of CNVs could help explain a larger fraction of epilepsy cases.</p></div

Exonic CNVs in <i>CHD2</i> detected by PopSV.

<p>The ‘CNV’ panel shows the exonic deletions (blue) and duplications (red) called by PopSV. The ‘Coverage’ panel shows the read depth signal in the affected individuals (colored points/lines) and the coverage distribution in the reference samples (boxplot and grey point).</p

Distribution of the DNMs identified in this study and in controls.

<p>*canonical splice site variants not included.</p><p>**Consensus splice site variant not included.</p><p>NA, not applicable. LoF SNVs, nonsense and canonical splice site. Nominally significant <i>P</i> values (<0.05) calculated using an <i>R</i> exact binomial test.</p><p>Distribution of the DNMs identified in this study and in controls.</p

Pathogenic profiles in known epilepsy genes.

<p>Pathogenic profiles in known epilepsy genes.</p

PopSV approach.

<p>a) Technical bias across the genome remains after stringent correction and filtering. The distribution of the bin inter-sample mean coverage in the epilepsy cohort (red) is compared to null distributions (blue: bins shuffled, green: simulated normal distribution). b) PopSV approach. First the genome is fragmented and reads mapping in each bin are counted for each sample and GC corrected (1). Next, coverage of the sample is normalized (2) and each bin is tested by computing a Z-score (3), estimating p-values (4) and identifying abnormal regions (5). c) Number and proportion of calls from a twin that was replicated in the other monozygotic twin.</p

Number of DNMs per affected individual in each trio.

<p>Number of DNMs per affected individual in each trio.</p

CNVs in the epilepsy and control cohorts.

<p>a) Regions with a CNV in each epilepsy patient. b) Each CNV in the CNV catalog of the epilepsy and control cohorts was annotated with its maximum frequency in five CNV databases. c) Enrichment in exonic sequence for all CNVs (left) and rare CNVs (right), larger than 50 Kbp (top) or smaller than 50 Kbp (bottom). The fold-enrichment (y-axis) represents how many CNVs overlap coding sequences compared to control regions randomly distributed in the genome.</p

CNVs and epilepsy genes.

<p>a) Number of rare CNVs in or close to exons of protein-coding genes (top) or epilepsy genes (bottom), in the epilepsy cohort. b) Number of epilepsy genes hit by exonic deletions in the epilepsy cohort and never seen in the public and internal databases (dotted line), compared to the expected distribution in all genes and size-matched genes (histograms). c) Rare non-coding CNVs in functional regions near epilepsy genes. The graph shows the cumulative number of individuals (y-axis) with a rare non-coding CNV located at X Kbp or less (x-axis) from the exonic sequence of a known epilepsy gene. We used CNVs overlapping regions functionally associated with the epilepsy gene (eQTL or promoter-associated DNase site).</p

Physical protein-protein interaction network generated by GeneMANIA (http://www.GeneMANIA.org/; Gene Ontology molecular function based weighting).

<p>The Query genes included those listed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004772#pgen-1004772-t003" target="_blank">Table 3</a> from this study (in bold) and known and candidate ID genes reported with predicted-damaging DNMs from other studies (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004772#pgen.1004772.s003" target="_blank">Table S2</a>). Known ID genes are in red. The resulting network of 38 interconnected proteins was found to be enriched for proteins whose Gene Ontology molecular functions are implicated in the glutamate receptor signalling pathway (GRIN1, GRIN2A, GRIN2B, GRIA1, CACNG2, SHANK3; <i>FDR q</i>-value = 7.04e-6).</p