Linking DD CNV genes to model phenotypes

Identifying individual genes whose copy number change has substantially contributed to heterogeneous DD phenotypes is greatly hindered by the large numbers of genes (median 40.5, mean 60.6) located within these DD-associated CNVs. To identify one or few candidates, among these dozens of genes, that are likely to contribute to DD phenotypes we adopted a two-stage strategy. First, by applying rigorous statistical tests, we sought evidence that specific classes of genes (those with particular phenotype annotations) are significantly over-represented within these DD-associated copy number variation regions (CNVRs). We then derived a set of candidate genes drawn from all loci that are: (i) present within these CNVRs, and (ii) annotated with at least one of these over-represented classes.

To identify classes of genes enriched in these DD-associated CNVs, we investigated whether they randomly sample genes that, when disrupted in mice, result in specific nervous system phenotypes. We chose to consider only nervous system phenotypes because of their obvious relevance to DD patients who all exhibit neurological deficits. We first assembled 98 DD-associated CNVs into 65 distinct CNVRs (Table 1). Several overlapping CNVs were observed in opposite directions of copy number change. To investigate whether the direction of copy change might reveal distinct pathoetiologies with equally distinct genetic causes, we divided by direction of change and separately assembled 24 Gain CNVRs and 51 Loss CNVRs (Table 1). For each set of Gain, Loss or All CNVRs, we considered only those genes that were overlapped by their CNVRs and that were not overlapped by ‘benign’ CNVs in apparently healthy control individuals (see Materials and Methods). For each set, we then examined whether the mouse orthologs of these genes were significantly enriched in any of 147 nervous system phenotypic terms after controlling for false-discovery associations (FDR <5%).

Four model phenotypes were identified that were each substantially and significantly enriched among All DD-associated CNVRs (Table 2 and Fig. 1). The first three of these phenotypes (abnormal tract, abnormal brain white matter morphology and abnormal brain commissure morphology) are closely related within the hierarchy of mouse phenotypes. The ∼2-fold increase in the numbers of genes in these CNVRs whose orthologs, when disrupted, present these anatomical anomalies strongly implicates them as being a distinguishing feature of these DD patients’ genotypes compared with the general population. Twenty-six genes contribute to the significant associations between DD-associated CNVRs and all three model phenotypes, while a further six genes contribute only to either abnormal tract and/or abnormal brain white matter morphology terms (Table 3).

The fourth phenotype (reduced long-term potentiation; rLTP; Table 2) appears to have been identified largely independently of the other phenotypes, as only four of the 24 contributing DD-associated CNV genes that possess the rLTP model phenotype also exhibit any of the three other model phenotypes (Table 3). The association between these patients’ CNVR genes and the rLTP model phenotype strongly implicates deficits in synaptic plasticity and long-term memory as contributing strongly to human DD phenotypes. It is important to note that, as expected, genes associated with each of these four model phenotypes are substantially depleted among the set of 4576 genes overlapped by apparent benign CNVs (Fig. 1).

Model phenotypes for secondary symptoms of DD individuals

The success in associating model phenotypes to, and identifying candidate genes for, the primary DD phenotype then prompted us to apply our statistical approach to these patients’ secondary phenotypes. For 74 of the 87 DD patients, there were clinical data available that indicated one or more secondary clinical phenotypes in addition to DD (Supplementary Material, Table S2). Twelve major secondary phenotype categories were defined in the cohort, namely behavioral abnormalities, brain malformations, cardiac defects, cleft lip, cleft palate, facial dysmorphia, eye abnormalities, sensorineural hearing loss, limb abnormalities, seizures, short stature and urogenital symptoms (further explained in Materials and Methods). To test for associations between these secondary phenotypes and CNV genes, we first grouped patients’ CNVs non-exclusively according to the 12 phenotypes and assembled them into CNVRs (see Materials and Methods). Next, we tested for enrichments of human genes in these CNVRs whose mouse orthologs, when disrupted, exhibit phenotypes drawn from only the most relevant categories of mouse phenotypes (Supplementary Material, Table S3). For example, genes in CNVs from patients exhibiting brain malformations were tested for association with nervous system, but not pigmentation, phenotypes. Categories that were tested each contained between 129 and 220 terms and, as before, we applied a stringent correction for multiple tests (FDR <5%).

Significant associations were identified for three secondary symptom classes for these patients, namely behavioral abnormalities, seizures and eye abnormalities (Figs 2–4, respectively). Importantly, and as expected, these enrichments are observed to be specific only to those CNVRs associated with the particular secondary symptom and not to CNVRs from patients not presenting with that particular secondary symptom (Figs 2–4). For example, patients not presenting with a behavioral phenotype were not associated with a behavioral phenotype in mouse knockout experiments (brown, black and tan bars in Fig. 2). The specificity of these enrichments to those patients presenting with a particular secondary symptom also implies that under-ascertainment of these secondary symptoms within the DD cohort is not apparent.

Many of these model phenotype associations for secondary symptoms obtain significance among either Gain or Loss CNVRs but not among All CNVRs. Indeed, model phenotype associations with the patients’ seizures phenotype are found only among Gain CNVRs, while associations with eye abnormalities are observed only among Loss CNVRs (Figs 3 and 4). CNVRs in the opposite copy number change direction (i.e. Loss and Gain, respectively) appear little different from CNVRs not associated with these secondary symptoms. These Gain- or Loss-specific enrichments appear unlikely to have resulted from diminished power within particular CNV subsets (seizures-associated CNVs: 256 Gain genes, Loss 962 genes; eye symptoms-associated CNVs: 482 Loss genes, 501 Gain genes). The seizures model phenotype thus appears to be specifically associated with Gain CNVs, while eye model phenotypes are specifically associated with Loss CNVs.

Model phenotypes identify candidate genes

Our findings reflect the non-random concentration of genes with particular annotations (specifically, mouse model phenotypes) that overlap DD-associated CNVRs. Such genes thus become strong candidates for causative elements whose copy number change underlies the DD disorder for individual patients (Table 2 and Supplementary Material, Table S4). The four significantly enriched model phenotypes associated with the primary DD phenotype (abnormal brain white matter morphology, abnormal brain commissure morphology, abnormal tract and reduced long-term potentiation) provide 52 candidate genes that are overlapped by DD-associated CNVs (Table 3 and Supplementary Material, Table S4). By randomly sampling 1000 gene sets of equal number (see Materials and Methods), we find that this represents a large 86% increase over the number expected by chance (random samples’ distribution median 28, SD 5). In addition, we identify 72 genes associated with model phenotypes for secondary symptoms, of which 21 are also associated with the primary DD presentation. All but one (namely, DCC) of these 21 genes associated with both primary and secondary presentations represent neurological phenotypes. This is consistent with abnormal neural development often resulting in multiple developmental effects.

Candidate genes for DD are relevant to 42 of 98 (43%) DD-associated CNVs and 39 of 87 (45%) DD patients (Supplementary Material, Table S4). Among all CNVs harboring one or more DD-associated candidate gene, the median number of such genes per CNV is 1 (mean 1.6, SD 1.0), with a maximum of 6. However, Gain CNVs contain significantly more such genes than Loss CNVs (Mann–Whitney U test, P = 0.003; candidate genes per Gain CNV: median 2, mean 2.2, SD 1.4; candidate genes per Loss CNV: median 1, mean 1.3, SD 0.4; Supplementary Material, Table S4) despite Gain CNVs tending to be physically smaller than Loss CNVs (median 4.38 versus 5.40 Mb, respectively; Table 1). Although we must sound a note of caution resulting from the incomplete coverage of human genes by mouse knockout phenotypes (see Discussion), this result suggests that for DD to be revealed in the clinic, Gain DD-associated CNVs will tend to involve more causative genes than Loss DD-associated CNVs. This is expected if gene deletions are more deleterious than gene duplications.

The model phenotypes identified for seizures, behavior or eye abnormality secondary symptoms of DD patients yield candidate genes for 60–78% of associated CNVs and 59–80% of associated patients (Supplementary Material, Table S4). Single CNVs often contain more than one secondary symptom-associated candidate gene (seizure-associated CNVs: median 2 genes, mean 3.1, SD 2.0; behavior abnormality-associated CNVs: median 2 genes, mean 1.8, SD 0.9; and eye abnormality-associated CNVs: median 2.5 genes, mean 3.0, SD 1.4). For both seizures and eye-related secondary symptoms, there are significantly more candidate genes per CNV than for the primary DD phenotype (Mann–Whitney U tests, P< 0.002). This implies that for these secondary symptoms to be manifested, multiple dosage-sensitive genes may often need to be affected.

As 11 of 87 DD patients harbor two, as opposed to one, large rare CNVs, we then considered whether multiple CNVs within an individual might contribute to the same enrichment. For patients presenting with seizures in this study this is indeed the case: the median number of seizure-associated candidate genes is 3.5 per patient versus 2 per CNV. These patients’ susceptibility to suffer seizures may thus result from gene copy number changes at more than one genomic location.

Comparison of DD- and LD-associated CNVs

DD is defined as the significant delay in reaching early developmental (both physical and mental) milestones, and thus often is considered to be etiologically similar to LD. Indeed, DD children are frequently diagnosed with LD at a later age. Consequently, we were interested in comparing the candidate genes and enriched model phenotypes identified for the DD cohort with those we identified previously for a separate LD cohort (12). Neither of the two model phenotypes found from 148 LD-associated CNVs was found to be significantly enriched in the DD-associated CNV gene set (data not shown). This may be interpreted as implying that the etiologies of DD and LD are divergent, or more likely that both disorders are etiologically heterogeneous resulting in findings not being replicated for different patient cohorts of small-to-medium size. Nevertheless, among the four model phenotypes identified here from DD-associated CNVs, genes whose orthologs are associated with abnormal tract are also significantly over-represented among genes overlapped by LD CNVs (+49% enrichment, P= 0.04; single test). For LD-associated CNVs, the abnormal tract enrichment segregates strongly with Loss CNVs (+73% enrichment, P= 0.009) where the enrichment is very similar to that observed within the DD-associated CNV genes (Fig. 1). Furthermore, among LD-associated Loss CNVs, the two related mouse model phenotypes for DD (Fig. 1) are also significantly enriched: abnormal brain white matter morphology (+70.8% enrichment, P= 0.01) and abnormal brain commissure morphology (+59.4%, P = 0.04). Consequently, there is evidence that DD and LD share a congruent etiology. Of 21 and 30 candidate genes identified from these two model phenotypes that are associated with LD and DD, respectively, five are seen in both patient cohorts, namely AKT3, HTT, SIM1, CLIP2 and SEMA3F. Variants of these five genes thus may contribute to both DD and LD disorders, thereby identifying a common molecular etiology for patients in both cohorts.

Concordance between model and human phenotypes

Significant association between DD CNV genes and the brain commissure model phenotype is consistent with previous observations of brain commissure abnormalities in patients with neurodevelopmental disorders (20). The mouse phenotype Abnormal brain commissure morphology covers several commissure abnormalities often seen in DD patients, involving the corpus callosum (21,22) (CC), the anterior commissure (22,23) and the hippocampal commissure (22,24). Indeed, CC abnormalities have frequently been associated with chromosomal aberrations such as CNVs (22,25). Furthermore, many genes whose mutations give rise to an abnormal CC have been observed to be haploinsufficient, with the severity of the abnormality often correlating with the reduction in copy number (20). Commissure abnormalities have been previously implicated in 3–5% of patients with neurodevelopmental disorders (20), while we observed that 25% (22/87) of DD patients have CNVs that overlap commissure-associated genes. This suggests that commissure abnormalities might be substantially more prevalent among DD patients than is readily apparent from non-invasive imaging, particularly for those often more subtle CC abnormalities arising from heterozygous mutations (20). Furthermore, many CC abnormality-associated genes appear to give variable phenotypic manifestations depending on the presence of other genetic modifiers (20,26,27), of which there are potentially many in these large DD CNVs.

We were also encouraged by the significant association of DD CNV genes with an rLTP model phenotype, as this result accords with previous descriptions of human DD phenotypes (28). More specifically, the roles of LTP in synaptic plasticity and in neural development have long been recognized (29). Reduced LTP, for example, is a prominent feature of mice that have been engineered to carry copy number gains that model mental retardation in Down Syndrome (30). In this respect, it may be relevant that the rLTP signal we identify in DD-associated CNVs is most enriched among Gain CNVs (Fig. 1).

The candidate genes identified for DD in this study include two transcription factors from the nuclear factor 1 gene family, NFIA and NFIB. The NFI gene family controls several important processes in CNS development including axon guidance, glial and neuronal cell differentiation, and neuronal migration (31). Furthermore, since the NFI gene family are transcription factors, they regulate the expression of several other neuronal and glial genes. Interestingly, expression of another DD candidate gene, TNC, is regulated by NFI transcription factors, as is EPHB1 (32,33), one of seven members of the Ephrin families of receptor protein-tyrosine kinases and their ligands involved in synapse formation and plasticity in the central nervous system (34) all identified as DD candidate genes in this study (EFNB2, EFNB3, EPHA4, EPHA8, EPHB1 and EPHB2). Similarly, multiple members of other gene families, like fibroblast growth factors and their receptors (namely FGF2, FGF12, FGF14, FGFR3 and FGFRL1), glutamate receptors (GRM5 and GRIK2) and members of the mitogen-activated protein kinase family (MAPK1 and MAPK3) were also identified as candidate genes for related phenotypes (Table 3). These observations indicate functional convergence and common signaling pathways underlying the neurological phenotypes observed in DD patients. Furthermore, several candidate genes are known to have functions relevant to DD and associated phenotypes, such as MAPK1 and MAPK3, both of which belong to the ERK MAP kinase signaling cascade which contributes to brain development, learning, memory and cognition (35). Among all these gene families with members implicated in causing DD in our study, only the Ephrin family has multiple candidate genes overlapped by the same CNV (EPHA8 and EPHB2, Supplementary Material, Table S4).

For all but seven of the 103 candidate genes (namely, NFIA, SCN1A, OPA1, OPRM1, DCLK1, MMP14 and DCC), the phenotype that contributes to the enrichments detected here describes the homozygous disruption of their mouse ortholog. Given that the DD-associated CNVs reported here are all apparently heterozygous, relating the homozygous loss in mouse to the hemizygous loss in human for the remaining 96 candidate genes might appear, at first, to be a difficulty. However, for all 32 (29%) of these genes for which hemizygous knockout phenotypes have been reported, none exhibit normal phenotypes and thus all 32 candidate genes can be considered as being haploinsufficient. Neither can it be assumed that where a hemizygous mouse phenotype has not been reported, the hemizygous state gives no phenotype. For example, whereas only the homozygous mouse knockout of Fgf14 is reported, hemizygous disruption of FGF14 in humans is demonstrably pathogenic (19,36).

Forty-three percent of DD patients whose CNVs we have been able to suggest candidate genes show a copy number change in more than one candidate gene, with some patients possessing five or more such genes (Supplementary Material, Table S4). Thus, where multiple dosage-sensitive genes are affected by CNVs, each may contribute additively, by eliciting specific aspects of the phenotype. Alternatively, multiple gene alleles may act combinatorially through shared pathways to yield emergent consequences. This alternative explanation may account for nine genes whose phenotypes are only revealed in compound knockouts that were identified in this study as candidates for DD or secondary phenotypes. Of these nine, all but one (ST8SIA2) lie within CNVs harboring multiple candidate genes (Table 2 and Supplementary Material, Table S4). This provides evidence for epistasis among multiple CNV alleles controlling DD and associated phenotypes.

Secondary phenotypes

Model phenotypes that we identified for these DD patients appear to readily explain their secondary phenotypes. For example, mouse retina abnormalities were associated with human eye abnormalities, and mouse abnormal conditioning behavior was associated with human behavioral abnormalities. However, the specific association of four mouse model phenotypes (abnormal voluntary movement, abnormal stationary movement, abnormal emotion/affect behavior and abnormal response to novelty) with patients with seizures might appear less obvious. These associations appear not to be chance observations as two of these model phenotypes (abnormal stationary movement and abnormal voluntary movement) are replicated in our previously described LD cohort (+174% enrichment, P = 3 × 10⁻⁴; and, +59% enrichment, P = 2 × 10⁻³, respectively), and are also found to be segregating with Gain CNVs (+517% and +273% enrichments, respectively). One possibility we considered is that copy number changes involving multiple genes jointly perturb a shared neurological pathway so as to produce a phenotype, which is distinct from that resulting due to disruptions of each single gene. Indeed, our observation that the median number of seizure candidate genes per seizure patient is 3.5 is consistent with this scenario of mass action and emergent properties (30,37).

Patient samples

All patient and normal samples used in this study were collected after obtaining informed consent under protocols approved by the Children's Hospital of Philadelphia (CHOP) Institutional Review Board. Genomic DNA was prepared either from peripheral blood lymphocytes (PBLs) or from subject-derived cell lines using the Puregene^TM DNA isolation kit (Gentra Systems Inc. Minneapolis, MN, USA). In six out of the 87 patients reported here, genomic DNA extracted from lymphoblastoid cell lines was used for array analysis; the remaining 81 genomic DNA samples were extracted directly from PBLs.

CNV detection, validation and data analysis

Microarray experiments were performed using Affymetrix GeneChip 500K or SNP 6.0 arrays (Affymetrix, Santa Clara, CA, USA) or Illumina Infinium™ II HumanHap550 BeadChip (Illumina, San Diego, CA, USA). Genomic DNA from the subject was processed and labeled using reagents and protocols supplied by the manufacturers. Affymetrix arrays were analyzed with the Partek Genomics Suite (Partek Inc, St. Louis, MO, USA) for CNV detection, and Illumina arrays were analyzed using BeadStudio 3.0 software package (Illumina) for CNV detection. Detected CNVs were further analyzed and annotated using CNV Workshop (40). All abnormalities detected by microarray analysis were confirmed and visualized either by metaphase fluorescence in situ hybridization (FISH) or real-time quantitative PCR (q-PCR) as described previously (41,42). For the six patient samples in which genomic DNA used for array analysis was obtained from lymphoblastoid cell lines, CNV validation was performed by metaphase FISH on cell pellets that were prepared from PBLs, thus ruling out cell line artifacts. Parental analysis was performed on both parents for each of the patients reported and was used to confirm de novo status of the observed CNVs. The parental analysis was either performed in our laboratory or the parental data were available from the reports of analysis performed in clinical diagnostic laboratories. Parental samples were analyzed using locus-specific assays, either FISH or q-PCR (as above) to validate CNVs observed in patients. Observed CNVs were compared with the available CNV databases including the Database of Genomic Variants (http://projects.tcag.ca/variation/) and a control CNV database generated from over 2000 controls at CHOP (http://cnv.chop.edu/) in order to eliminate those that appear to be common in the healthy general population.

Furthermore, we also removed from consideration those regions overlapped by, and exhibiting the same direction of copy change as, 26 452 control CNVs that we had employed as a control set in a previous analysis (12). This control CNV set was formed from 25 196 CNVs identified in 240 individuals from Redon et al. (9) combined with 1276 inherited CNVs described in Nguyen et al. (10). Together, these apparently benign CNVs represent 429 Mb of unique sequence (14.0% of the NCBI36 human genome assembly; Table 1 and Supplementary Material, Table S1). Genes within these ‘benign’ CNVs were also analyzed as control enrichments in a manner similar to the genes observed in pathogenic CNVs.

CNV intervals were merged into CNVRs when they overlapped by more than 1 bp. CNV sets were also subdivided according to the direction of copy number change (i.e. Gain or Loss; Table 1). Overlapping CNVs were also merged within each of these subdivisions.

Mouse Genome Informatics phenotypes

Human protein-coding genes were assigned to a CNV if they were completely overlapped by the CNV according to information from Entrez genes (43). Phenotype annotations for disruptions of mouse orthologs of these genes were obtained from the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org, version 3.54) (44–46). Specifically, phenotypic associations listed in the MGI file ‘MGI_PhenoGenoMP.rpt’ were mapped on to the human Entrez genes listed in ‘HMD_HumanPhenotype.rpt’. Annotations assigned to genes by the MGI resource represent (i) only the most specific phenotypes that have been reported within a published experiment, and (ii) the over-arching phenotypic category under which those phenotypes fall (Paul Szauter [MGI], personal communication). Thus, the MGI resource might report a highly specific (fine level) term (e.g. abnormal pars anterior morphology) and a general (coarse level) phenotypic category (i.e. nervous system) but not intervening terms (e.g. abnormal tract) that are linked through parent–child relationships within the Mammalian Phenotype Ontology (11). Consequently, and in contrast to our previous analysis (12), we developed the method to allow it to consider not only phenotypes supplied by the MGI resource but also the imputed linking terms between them within the ontology.

Using this approach and taking advantage of simple, unambiguous, 1:1 gene orthology relationships from the MGI resource, 5329 distinct MGI phenotypic terms were mapped to 5011 human genes. Thereafter, we considered only those phenotypic terms present within the well-populated nervous system phenotypes, defined as those terms associated with at least 1% of all genes annotated with any nervous system phenotype. This resulted in the investigation of 146 phenotypic terms that were associated with 1804 mouse–human orthologs. This reduction of phenotypic terms under consideration limits poorly populated and therefore uninformative results, and reduces the number of tests performed thereby improving the method's power. We then tested, using the hypergeometric test, the null hypothesis that a (mouse) phenotype associated with (human) Entrez genes overlapping a set of DD-associated genomic intervals occurs at a frequency that is no different from that obtained by random sampling of all 5011 human genes whose mouse orthologs have a documented phenotypes when disrupted. False discovery observations were controlled by applying an FDR threshold of <5% (47) (see below). Our approach makes the reasonable assumption that MGI mouse phenotypes have been annotated independently of whether their associated human genes lie inside or outside of the DD-associated CNVs. Given that a large number of phenotypic terms were being tested, and that the assumption of independence between terms when applying an FDR correction is unrealistic, application of this significance threshold is likely to be highly conservative.

Linking model phenotypes to patient secondary phenotypes

Many of the patients considered in this study present clinical features in addition to DD. For each additional clinical feature, such as seizures or brain malformations, we were interested in testing for significant enrichments of mouse model phenotypes associated with these patients’ CNV genes. Patients were grouped on the basis of 12 secondary clinical features (Supplementary Material, Table S2).

Phenotypic data were collected from clinical reports based on examination by a team of highly experienced geneticists, dysmorphologists, neurologists and other pediatric specialists at CHOP. Secondary phenotypes such as dysmorphic facial features, cleft lip, cleft palate, sensorineural hearing loss, seizure disorders and short stature were based on these clinical assessments and observations. Behavioral abnormalities recorded in our patient cohort included autistic spectrum disorders, Asperger's syndrome, Attention-deficit/hyperactivity disorder, tantrums, mood disorder, anger, aggression, head banging and biting. Brain malformations recorded in our patient cohort included craniosynostosis, microcephaly, plagiocephaly, brachycephaly, agenesis of corpus callosum, absent right cerebellar hemisphere, abnormal magnetic resonance imaging findings, immature brain formation and delayed myelination. Cardiac defects recorded in our patient cohort included atrial septal defects, ventricular septal defects, truncus arteriosus, tetralogy of fallot, non-compaction cardiomyopathy, coarctation of the aorta, pulmonary stenosis and patent ductus arteriosus. Eye abnormalities included retinoblastoma, hamartoma, strabismus and nystagmus. Limb abnormalities included digital abnormalities and small hands and feet. Urogenital abnormalities included abnormal genital development, XY karyotype with female genitalia, webbed penis, ureteropelvic junction obstruction, urinary reflux and unilateral hydronephrosis.

All CNVs from patients exhibiting a particular clinical feature were then merged into CNVRs as before. CNVRs were also assembled separately from Loss or Gain CNVs into Loss-only and Gain-only CNVRs, respectively. In a similar manner, the CNVs of patients that did not exhibit the particular secondary clinical feature were assembled to form control CNVR sets. Tests were limited to only those categories of secondary model phenotypes that were considered a priori as being relevant to each of these 12 secondary clinical features (Supplementary Material, Table S2). As before, we considered only those phenotypes populated with >1% of all genes annotated within the phenotypic category. Genes overlapped by these CNVRs were then examined for enrichments of these phenotypic terms. An FDR upper threshold of 5% was applied to all P-values, as before, in order to control the rate of false discoveries.

Statistical tests

The significance of enrichments or deficits of genes associated with particular mouse model phenotypes was evaluated using the hypergeometric test that describes the number of successes in a sequence of x draws from a finite population without replacement. More specifically, considering only those 5011 human genes with a mouse ortholog whose disruption had been phenotyped and given the proportion of these that possessed a particular phenotype (for example, see Table 2), we calculated the likelihood of obtaining the observed number of genes with that particular phenotype (for example, see Table 2) simply by chance among those genes overlapped by a given set of CNVs (Table 1). For example, given the total population of 5011 human genes with disrupted and phenotyped mouse orthologs, of which 71 non-exclusively yield a reduced long-term potentiation phenotype (Table 2), the likelihood of a random sample of 808 genes containing 24 genes whose disrupted mouse ortholog yields a reduced long-term potentiation phenotype is 1.8 × 10⁻⁴ (Table 2). Where multiple tests were performed, the application of an FDR multiple testing correction was applied to ensure a less than 5% likelihood of any significant term being a false-positive (47).

Calculation of the fold-enrichment within the DD-associated CNVs for the final set of 52 DD-associated candidate genes was performed by random sampling. One thousand gene sets, matched in gene number to that within the DD-associated CNVRs, were obtained by random sampling and the median expected number of genes, 28, annotated with one or more of the significantly enriched terms (Fig. 1) were recorded. Given the 52 candidate genes within the DD-associated CNVRs, we thus estimate an ∼1.9-fold enrichment over the number expected by chance.