The genetic overlap between Intellectual Disability and Attention-Deficit/Hyperactivity Disorder
Anne van Pens
Master thesis, written in October and November 2014 in Nijmegen, the Netherlands
Supervisors: Marieke Klein, Alejandro Arias-Vasquez, Barbara Franke
Affiliations: 1: Radboudumc, Nijmegen, The Netherlands 2; Donders Institute, Nijmegen, The Netherlands
Corresponding author: Anne van Pens, [email protected]
Abstract Objective: Attention-Deficit/Hyperactivity Disorder(ADHD) and Intellectual Disability(ID) co-occur more often than expected by chance, suggesting some genetic overlap. In four subprojects we investigated whether genes, affected by rare genetic variations in patients with ID, contribute to multifactorial ADHD. Methods: (1) Single nucleotide polymorphisms (SNPs) in 392 autosomal ID-related genes and (2) a subset involved in neurite outgrowth were tested for association with multifactorial ADHD risk, both on gene-set and gene-wide level, using data from the meta-analysis of the ADHD working group of the Psychiatric Genomic Consortium (PGC; 5, 621 cases and 13, 589 controls). (3) Twelve genes selected on frequent occurrence in copy number variants (CNVs) in patients with ADHD and ID and/orcongenital anomalies {PRODH, RBFOX1, PTPRD, CNTNAP2, NRXN1, XYLT1, PRIM2, FAM110C, SKI, NRG3, GRIN2A, NRG3and ERBB4}and(4) two genes selected because of suggested involvement in ID and ADHD in the literature {CHRNA7and NRXN1}were tested for gene-wideassociations with multifactorial ADHD risk using the ADHD PGC meta-analysis data and with symptom counts using data from the International Multicenter ADHD Genetics project (IMAGE; 930 cases). Single-SNP and gene-wideassociation analyses for CHRNA7and NRXN1were performed with regional brain volumes in 1302 healthy participants of the Brain ImagingGenetics (BIG) cohort. Single-SNPassociation analyses were also performed forvoxel-wide structural connectivity measurements. Results: SNPs in all autosomal ID-related genes, but not in the subset of neurite outgrowth genes, were significantly linked to ADHDas a group. The MEF2Cgeneshowed gene-wideassociation with ADHD risk. Other gene-wideand SNP-specificanalyses did not yield significantassociations. Conclusion: SNPs in 392 genes, and specifically the MEF2Cgene, affected by rare genetic variations in patients with ID, contribute to multifactorial ADHD risk as a group. This contribution to ADHD risk does not seem to be driven by neurite outgrowth genes.
Keywords: Genetic Overlap, Attention-Deficit/Hyperactivity Disorder, Intellectual disability, Gene-set analysis, MEF2C, Brain Imaging Introduction
Neurodevelopmental continuum Neurodevelopmental disorders are often accompanied by developmental or psychiatric comorbidities. For example, 68-87% ofAttention-Deficit/Hyperactivity Disorder (ADHD) patients have at least one co-morbid disorder (Ghanizadeh, 2009; Jensen & Steinhausen, 2014; Kadesjo & Gillberg, 2001; Kraut et al., 2013; Larson, Russ, Kahn, & Halfon, 2011). Because neurodevelopmental disorders share a common genetic etiology (Pettersson, Anckarsater, Gillberg, & Lichtenstein, 2013), it has been hypothesized that the same 'risk genes' often contribute to different neurodevelopmental disorders (Moreno-De-Lucaet al., 2013). Therefore, all neurodevelopmental disorders have been hypothesized to lie on a 'continuum', with a largely shared causality (Moreno- De-Lucaet al., 2013; Owen, 2012). The model of developmental brain dysfunction by Moreno-de- Luca et al. (2013) predicts that each particular genetic cause can manifest as a spectrum of impairments of varying severity in the cognitive, neurobehavioral and neuromotor domain. What specific impairments reach the threshold for clinical diagnosis, however, depends on the genetic background of the individual and on environmental factors. This explains why patients with neurodevelopmental disorders often fulfill also part of the criteria for other neurodevelopmental disorders. For example, many studies support the idea that 30-50% of all children with autism spectrum disorder (ASD) also fulfill the criteria for ADHD (de Bruin, Ferdinand, Meester, de Nijs, & Verheij, 2007; Gadow, DeVincent, & Pomeroy, 2006; Leyferet al., 2006; Schwenck& Freitag, 2014; Simonoffet al., 2008; Sinzig, Morsch, & Lehmkuhl, 2008; van Steenset, Bogels, & de Bruin, 2013). Martin et al. (2014) showed that there is substantial genetic overlap between the biological processes ofASD and ADHD, as for example large rare copy number variations (CNVs) contributing to the disorders disrupt the same biological processes. This supports the neurodevelopmental continuum hypothesis.
Overlap between ADHD and Intellectual Disability Lesswell-studied than the overlap between ADHDand ASD, is the overlap between ADHDand intellectual disability (ID). The prevalence of ADHD in patients with ID seems to be twice as high as in the general population (Franke et al., 2012; Maulik, 2010). The real prevalence ofADHD in ID- patients may be much higher, though, because ADHDcan be difficult to diagnose in children with ID. This is because ID-patients may not understand the questions of a psychiatric interview meant for people with a normal intelligence quotient (IQ) (Turygin, Matson, & Adams, 2014). In addition, one must take into account the patient's developmental age, rather than the biological age to assess if the observed hyperactive, impulsive and/or inattentive behavior is aberrant (Buitelaar, Kan, & Asherson, 2011). Apart from that, parents of a child diagnosed with ID, may easily assign ADHD-like symptoms of their child to ID, and never report them to a medical professional. In addition, the low IQ in ID-patients may cause inability to understand schoolwork, games, etcetera, and may therefore lead to decreased motivation to pay attention to these things. On the other hand, inattention problems in ADHD may lead to lower scores on IQtests (Styck & Watkins, 2014). All in all, research is needed to investigate whetherthe larger-than-expected shared prevalence is due to genetic factors, non-genetic factors or a combination of both. Attention-Deficit/Hyperactivity Disorder (ADHD) ADHD is a psychiatric disorder characterized by inattention and/or hyperactivity and impulsivity (American Psychiatric Association, 2013). The prevalence ofADHD is 5-6% in children and 2. 5-5% in adults (Franke et at., 2012). Although the disorderstarts in early childhood, it often persists into adulthood (Demian, 2011; Franke et al., 2012). According to the Diagnostic and Statistical Manual of Mental DisordersV (DSM-V), a first diagnosis of ADHDcan also be made in adulthood, provided that the symptoms have been visible since age 12 when assessed in retrospect (American Psychiatric Association, 2013). In both children and adults, ADHD is a heterogeneous disorder, since it is diagnosed by having six out of nine symptoms in at least one or two domains of the disorder (American Psychiatric Association, 2013). In addition, the majority of patients with ADHD has co-morbid disorders (Ghanizadeh, 2009; Jensen & Steinhausen, 2014; Kadesjo & Gillberg, 2001; Kraut et al., 2013; Larson et al., 2011), such as conduct/oppositional defiant disorders, language development disorders, motor development disorders, ASD and/or ID (Jensen & Steinhausen, 2014). When ADHD remains untreated, it can also lead to drop-out of school (Barbaresi, Katusic, Colligan, Weaver, & Jacobsen, 2007), mood disorders (Chen et al., 2014; Cubero-Millan et al., 2014) and drug abuse (Levy et al., 2014). Fortunately, early diagnosis and adequate treatment of ADHD can improve the quality of life of patients significantly. Insight in the etiology of ADHDand its co-morbidities can improve diagnosisand treatment of patients with ADHD. Since the heritability of ADHD is high, with an estimated 70 - 80% of the phenotypic variance explained by genetic factors (Franke et al., 2012), identification of the genes involved in ADHDcan help us elucidate the pathogenesis ofADHD. IdentifyingADHD-genes appears challengingthough, since most cases ofADHD have a multifactorial etiology. This means that they are caused by the combination of multiple variants in many genes and by environmental factors, which all have a small effect on the risk for ADHD. Most of these gene variants are 'common', which means that they have a prevalence of at least 1% in the general population, as is the case for single nucleotide polymorphisms (SNPs), but also for certain CNVs. Interestingly, halfofthetop-ranked ADHD candidate genes, as found in five genome-wide association studies (GWASs) that were studied by Poelmans et al. (2011), contribute to a single biological process, i. e. neurite outgrowth. In addition to multifactorial ADHD, for an unknown percentage of ADHDcases the underlying genetic background is likely to be of a mono- or oligogenic nature. In these forms of the disorder, one or a few severe gene defects are sufficientto cause ADHD in an individual patient (Franke et al., 2012). However, less severe defects in the same genes may contribute to multifactorial forms ofADHD. The genetic heterogeneity of ADHD, i. e. the different combinations of risk genes in different patients with ADHD, may explain why symptoms and co-morbidities of patients with ADHD vary so widely ('phenotypic heterogeneity'). Genes may be associated with part of the symptoms or brain phenotypes of ADHD, rather than with all of them. It is assumed that abnormality of a certain behavioral, functional brain or structural brain domain lies in between the risk gene and the resulting disease (Hoogman, Buitelaar, Franke, Cools, & Arias-Vasquez, 2012).
Intellectual disability (ID) ID is defined by deficits in intellectual functioning and adaptive functioning that have become apparent during the developmental period (American Psychiatric Association, 2013). The deficit in intellectual functioning is defined as scoring lower than 97.5% of people with the same age and same culture on Intelligence Quotient (IQ) tests (American PsychiatricAssociation, 2013). The prevalence of IDvaries widely in different studies and different countries, mainly due to differencesin diagnosticdefinitions and methods (Greydanus & Pratt, 2005). Accordingto ten large population-based studies reported by the International Encyclopedia of Rehabilitation (Maulik, 2010), the prevalence of ID varies widely due to differences in definitions, but ties around 3% in the United States, when ID is defined as an IQ below 70 (without other criteria). Many of these children and adults also suffer from co-morbidities, especially from hearing impairments or seizure disorders (Maulik, 2010), but also from other physical problems. In The Netherlands, the rate of physical health problems in Dutch adults with ID is twice as high as that in adults without ID (van Schrojenstein Lantman-DeValk, Metsemakers, Haveman, & Crebolder, 2000). In addition, the prevalence of psychiatric problems in patients with ID is four to five times as high as in the general population (Harris, 2006). Although it may be hard to diagnose co-morbid psychiatric problems in patients with ID, this is very important, since treatment of the co-morbid psychiatric problems may still improve the well-being of the patient (Turygin, Matson, Adams, & Williams, 2014). For example, methylphenidate, the most widely used drug to relieve ADHD symptoms, works also well in patients with both ADHDand ID (Lipkin, 2013). ID is usually a monogenic or oligogenic disorder, which means that it is caused by one or a few mutations in one or a few genes (Chelly & Mandel, 2001; van Bokhoven, 2011). These genetic variations usually have large effect sizes and they occur with a low frequency in the general population.
Research questions and approach We hypothesized that genes being affected by rare genetic variations in patients with ID, can also contribute to multifactorial ADHD risk. In case of ADHD, these genes might harbor common variations (e. g. SNPs). In this study, we investigated the genetic overlap between ID and ADHD by using four different approaches. First, an explorative pathway analysis was used to investigate whether 392 ID-related genes are associated with ADHD risk. Subsequently, gene-wide association analyses were performed to identify associations of the individual genes with ADHD risk. Second, a pathway analysis, now limited to ID-related genes known to be involved in the process of neurite outgrowth, was performed to test for association with ADHD. Third, we selected 12 genes, based on frequent occurrence in CNVs of patients with ID and ADHD (symptoms) and/or on their relation to ID and ADHD described in the literature. For all 12 genes, gene-wide association analyses were performed to test for associations with ADHD risk, symptom counts and symptom severity for inattention and hyperactivity/impulsivity. Fourth, two genes were selected based on suggestionsfor associations with both ADHDand ID in the literature. Gene-wideassociation analyses were performed for several ADHD-related brain phenotypes, such as the volumes of the total brain, gray matter, white matter, prefrontal cortex (PFC), caudate nucleus, hippocampus and nucleus accumbens. Genotype effects of selected SNPs on gray and white matter differences were investigated by using voxel-based morphometry (VBM) and voxel-wise analysis of structural connectivity, respectively. Materials and Methods
Description of the samples ADHD meta-analysis of the Psychiatric Genomic Consortium (PGC) Meta-analytic data from 9 study cohorts containing 5, 621 cases ofADHD and 13, 589 controls were available for analysis (Neale et al., 2012, unpublished). This is the largest dataset for association with ADHD that is currently available (Neale et al., 2012, unpublished). The meta-analytic data, from a case-control analysis, were available as summary statistics, includinggenome-wide SNPdata (imputed with 1,000 Genomes (Abecasis et al., 2012)) with corresponding p-values, minor allele frequencies (MAFs), odds ratios and imputation quality scores (INFO). ADHD was diagnosed according to the DSM-IV criteria (American Psychiatry Association, 2000). Data were obtained through the PGCADHDworking group after an official proposal for the data analysis had been approved by the contributing sites.
International Multicenter ADHD Genetics (IMAGE) Project The IMAGE dataset contains genome-wide SNP data, that is 2, 182, 904 SNPs imputed with Hapmap II release 22 (Li, Wilier, Ding, Scheet, & Abecasis, 2010), for 930 Caucasian children with ADHD (diagnosed according to DSM-IV criteria). Of these children, 88% were male, the average age was 10 years (range 5-18 years) and the average IQwas 101 (range 55-160). Symptom counts were assessed for inattention and hyperactivity/impulsivity separately by using the Parental account of childhood symptoms (PACS), a semi-structured, standardized, investigator-based interview (Brookes et al., 2006). Data were complete for 871 patients. The Parent and Teacher Conners' long version rating scales (Conners, Sitarenios, Parker, & Epstein, 1998a, 1998b) were used to obtain continuous measures of ADHD severity. Teacher Conners' scores were available for 916 patients and parental Conners' scores for 930 patients.
CNVdatabase of the Radboudumc The CNV database of the Genome Diagnosticsdivision of the department of Human Genetics of the Radboudumc includes information about all CNVs found in patients Radboudumc receiving genetic testing between 2005 and 2013 for ID, congenital anomalies or suspicion of genetic syndromes (Hehir-Kwa, Pfundt, Veltman, & de Leeuw, 2013). Coded information on CNVs of patients from a subset of this database, comprising 252 patients (with 3, 255 CNVs) who had a diagnosisof ID and increased ADHD symptoms or a diagnosis of ADHD (77. 4%) or an unclear diagnosis including ADHD (13. 9%) was obtained from the Clinical Genetics division of the Human Genetics department in September 2013 and used in this study. Patients had a mean age of 15 years (range 5 - 69 years) and 180 patients were male.
The Cognomics Resource BIG The sample of the Brain Imaging Genetics (BIG) cohort included in the current study consisted of healthy individuals of Caucasianorigin, for which both genome-widegenotyping data (imputed with 1, 000 Genomes (Abecasis et al., 2012)) and structural magnetic resonance imaging (MRI) scans were available (Cousijn et al., 2014). Participants had no self-reported psychiatric or neurological history, had never abused drugs and did not use medication other than oral anti-conceptives. Thirteen hundred and two participants (mean age 22 years, age range 18-40years, of which 557 males) were scanned with either a 1.5 Tesla or a 3.0 Tesla Siemens MRI scanner (Guadalupe et al., 2014). All of them underwent a structural MRI scan. After that, the structural MRI data was processed and segmented as described by Guadalupe et al. (2014). After that, the volumes of the individual brain structures were calculated. For this, the images were re-oriented to the MN1152 standard in FSL software (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012) using the parameters of the Enhancing Neuro-lmaging Genetics Through Meta-analysis (ENIGMA) protocol (http://enigma.ini. usc.edu/protocols/) (Thompson et al., 2014)). Subsequently, the cortical volumes were segmented using FreeSurfer (Fischl, 2012), while the subcortical volumes were segmented using FSL software (Jenkinson et al., 2012). Apart from that, a VBM analysis was performed using SPM8 (Ashburner et al., 2013). In this analysis, the structural MR images were smoothed and used in multiple regression analysis in order to test for volumetric differences of white matter or gray matter associated with SNPgenotypes, as has been described by Hoogman et al. (2014).
Association of an ID-related gene-set with ADHD Gene selection For the selection of the ID-related gene-set, we downloaded the 'Intellectual DisabilityGene Panel' that was published by the Radboudumcdepartment of Human Genetics' Genome Diagnostics division (downloaded from https://www.radboudumc.nl/lnformatievoorverwijzers/ Genoomdiagnostiek/Documents/ngs-intellectual_disability_panel_181213. pdfon March 27 , 2014). This gene panel listed 490 candidategenes for ID (shown in Supplementary Table 1), based on findings of de novo mutations in patients with ID visiting the Radboudumcand collaborating institutes and based on the literature including the online database Online Mendelian Inheritance in Man (OMIM) (Hamosh, Scott, Amberger, Bocchini, & McKusick, 2005). This list forms the basisfor diagnostic testing using exome sequencing at the department of Human Genetics of the Radboudumc.
Data extraction For association analyses, we extracted SNPsand association p-values from the ADHD PGC meta- analysis. Since this meta-analysis only covered autosomal genes, we excluded the X-chromosomal genes and were left with 396 autosomal genes. All SNPs tying within these genes (accordingto base pair positions in UCSC HG19; (Kent et al., 2002)) were extracted. Flanking regions of 25 kilobase (kb) were used to capture regulatory regions. A total of 687, 602 SNPs with a MAF of at least 0.01 were considered for further analysis.
Gene-set analysis We used the Knowledge-basedmining system for Genome-wideGenetic studies version 3. 5 (KGG 3. 5) software (Li, P. C. Sham, S. S. Cherny, & Y. Q. Song, 2010) in order to test whether the group of ID-related genes is associated with ADHD. Within this software package we chose the Hybrid set- based test (HYST) (Li, Kwan, & Sham, 2012) for association testing. A text file listing all 396 autosomal ID-related genes and a text file listing all SNPs that were extracted from the ADHD PGC meta-analysis (as described in the previous paragraph), were used as input for KGG. The genome-wide genotyping data of BIG (Guadalupe et al., 2014) was used as a reference to define the underlying linkage disequilibrium (LD) structure. The LDupper limit was set to an r of 0.8, whilethe lower limit wasset to 0.2. SNPswith an INFOscore below 0.3 were ignored. Apart from that, default settings of KGG were used. KGG makes use of UCSC Hgl9 Refgene (Kent et al., 2002) to assign base pair positions to genes. Next, a gene-based association scan was run with HYST, based on a hybrid test of Gates and a scaled Chi-squaretest, in which SNPswithout LD information were ignored. Finally, a pathway-based association scan was run, to investigate the association between ADHDand our customized pathway, consisting of all 396 ID-related genes. SNPswithout LD information were again ignored. The actual analysis in KGG could only include 392 genes, since 4 genes (GRIK2, GSS, INPP54and NEU1] had too few SNPsavailable. Genes were considered significant if their gene-wide p-value was below 0.05 after Bonferroni correction for testing 392 genes. Significantlyassociated genes were considered for further analysis. The pathway was considered significant if the pathway p-value calculated by the HYST was below 0. 05 after Bonferroni correction for testing two pathways (see also below).
Gene-wide analyses We made use of the International Multicenter ADHDGenetics Project (IMAGE) to further investigate if SNPsin the MEF2Clocus, found to be significant in the KGG analysis, are associated with symptom counts for inattention and/or hyperactivity/impuisivity in patients with ADHD (Bralten et al., 2013). SNPs lying within the MEF2Cgene (accordingto base pair positions in UCSC Hgl8 (Kent et al., 2002)) and its 25 kb flanking regions were extracted from the IMAGE dataset. SNPdata were pruned prior to analyses (using the command 'indep-pairwise50 5 0.8' in PUNK), resulting in 35 SNPson the MEF2C locus. Gene-based association analysis was performed using the 'linear' and 'mperm' commands PLINK(S. Purcell et al., 2007), accordingto the protocol described in (Bralten et al., 2013). Thousand permutations were used to compute empirical p-values. We tested whether the MEF2C locus was associated with the Blom-transformed (Blom, Ludwig, & Gunnar, 1958) symptom counts for inattention and hyperactivity/impulsivity, respectively. MEF2C associations were considered significant if the p-value was below 0. 05 after Bonferroni correction for testing two symptom domains.
Association of ID genes involved in neurite outgrowth with ADHD
Gene selection and association testing We aimed to investigate whether genes involved in neurite outgrowth as well as in ID show an association with ADHD as a group. For this, we followed the same procedure as above, but then with a different gene selection. First, the Intellectual Disability Gene Panel of the Radboudumc (see above for description) was downloaded. Second, a list of 788 genes involved in the GO-term 'neuron projection development', accordingto AmiGOZ (http://amigo.geneontology.org (Carbon et al., 2009) was created. Third, we selected 62 genes that were included both in the ID-gene list and in the 'neuron projection development' GO-term. After exclusion of X-chromosomal genes, we were left with 46 genes and 87,241 SNPsfor further analyses. We used KGG 3. 5 for gene-set association analysis as described above. Genes were considered significant if their gene-wide p-value was below 0.05 after Bonferroni correction for testing 46 genes.
Association of genes affected by rare CNVs in ID-patients with ADHD
Gene selection
Here, we set out to investigate if genes affected by rare CNVs (i. e. CNVs having a prevalence below 1% in the general population) in patients with ID from our own department were also associated withADHD. For the selection of genes, we used the CNVdatabase of the department of Human Genetics of the Radboudumc (see above). All of the 252 patients had a double diagnosisof ID and ADHD. Genes within the CNVswere annotated accordingto Hgl9 RefSeq (Pruitt, Brown, & Tatusova, 2002). Subsequently, a selection was made of genes that a) occurred at least twice in CNVs with a prevalence below 1% in the general population (preferably single-gene CNVs), b) were related to ID accordingto the literature and c) had been suggested to play a role in ADHDaccording to the literature (especially through CNVstudies in patients with ADHD(Elia et al., 2010; Elia et al., 2011; Lesch et al., 2011; Stergiakouli et al., 2012; Williams et al., 2012; Williams et al., 2010)). This procedure resulted in the selection of 19 genes.
Data extraction Since the PGC meta-analysis results only contained data on autosomal genes, we had to exclude seven X-chromosomal genes. All SNPslying in the selected gene loci (according to UCSC Hgl9 (Kent et al., 2002)), includingflanking regions of 50 kb, were extracted from the summary statistics of the ADHD PGC meta-analysis, as described above for approach 1. We included SNPs that had a MAF > 0. 01 and INFO > 0. 6 for further analyses.
Gene-wide analyses with the PGC data Gene-wideanalyses were run for each of the 12 genes using the offline version of the Versatile Gene- based Association Study (VEGAS) software, a tool that combines p-values of all SNPs within a gene into one gene-wide p-value by making use of Monte Carlo simulations and permutations (Liu et al., 2010). A 1,000 Genomes reference population was used for LD correction. The default settings of VEGAS, i. e. flanking regions of 50 kb and maximal one million permutations were used. The input files contained all SNPs within gene loci, including flanking regions. Results were considered significant if the p-value was below0. 05 after Bonferroni correction for testing 12 genes.
Gene-wide analyses with the IMAGE data We also tested whetherthe 12 genes were associated with Blom-transformed (Blom et al., 1958) symptom counts and Conners' scores for hyperactivity/impulsivity or inattention using the data from the IMAGE study (see above). Data were analyzed as described for approach 1. We used 1, 000 permutations to calculate empirical p-vatues. For results with borderline significant p-values, analyses were re-run with 10, 000 permutations to acquire a more precise estimation of the p-value. Analyses were performed with pruning (using the command 'indep-pairwise 50 5 0. 8' in PUNK). Associations were considered significant if the p-value was < 0.05 after Bonferroni correction for performing 72 tests. This correction was applied in addition to the permutation testing for individual findings.
Association of genes for ID and ADHD with brain phenotypes
Gene selection One of the ways by which genes may influence disease risk is by altering brain structure or function. Here we aimed to investigate whether genes involved in both ID and ADHDaccording to previous studies can be linked to structural brain volumes that often show differences between patients with ADHDand healthy individuals. For the selection of genes, we used four criteria: 1) indicationsfor involvement in both ADHDand ID accordingto the literature in PubMed; 2) location on an autosomal chromosome; 3) preferably involved in neuron projection development according to AmiG02 (Carbon et al., 2009); 4) being part of the ID gene panel of the Radboudumc. This resulted in the selection ofCHRNA7and NRXN1. CHRNA7was not involved in neuron projection development, but has been reported frequently in single- or two-gene CNVs in ADHD (Stergiakouli et al., 2012; Williams et al., 2012; Williams et al., 2010), and was therefore chosen. A third gene satisfying selection criteria, CNTNAP2, could not be considered given overlapping projects of others involving this gene.
Data extraction SNPs in the CHRNA7 (27 SNPs) and NRXN1 (712 SNPs) gene loci, according to UCSC Hgl9 (Kent et al., 2002) and including 25 kb flanking regions relative to the largest isoforms, were extracted from the genome-wide genotype data of the BIG cohort (Cousijn et al., 2014).
Gene-wide analyses The SNPs in CHRNA7and NRXN1, respectively, were tested for associations with ADHD status by using the ADHD PGC meta-analysis data and for associations with ADHD symptom scores using the IMAGE project data. Gene-based association analyseswere performed as described above for the previous approach. In addition, gene-wideanalyses were performed to investigate if the SNPswithin CHRNA7or NRXN1were associated with regional brain volumes. The following regional brain volumes were selected, based on reports of differences in volumes in patients with ADHDwith ADHD and healthy individuals according to the literature: total brain volume, total gray matter, total white matter, PFC, caudate nucleus, hippocampus, and nucleus accumbens. For the latter four structures, averaged volumes of left and right hemisphere were considered. Total brain volume is the sum of total gray matter and white matter. PFCvolume was computed by summing all PFC substructures (lateral orbitofrontal cortex, medial orbitofrontal cortex, pars opercularis, pars orbitalis, pars triangularis, rostral middle frontal cortex, superiorfrontal cortex and frontat pole) as described by Destrieux et al. (2010). Gene-wide association analyses were performed by using the commands 'linear' and 'mperm' in PUNK, according to the protocol of Bralten et al. (2013). Thousand permutations were used to calculate empirical p-values. Gender, age, and magnetic field strength were used as covariates for all analyses. Total gray matter was used as a covariate for the analysis of total white matter and vice versa, and total brain volume was used as covariate for analysis of the PFC, caudate nucleus, hippocampus, and nucleus accumbens. Analyses were performed with pruning (using the command 'indep-pairwise50 5 0.8' in PLINK).Associations with p-values below 0.05 after Bonferroni correction for doing 14 tests were considered significant. For genes that showed borderline significant p-values when uncorrected for multiple testing, the analysis was repeated with 10,000 permutations. For PFC, where borderline significant p-values were observed, association tests were also performed for all the substructures (left and right separately). P-values below 0.05 were considered significantafter Bonferroni correction for testing 16 PFC substructures.
Selection of SNPs For single-SNPanalyses, we selected SNPs in NRXN1and CHRNA7genes that were most likely to increase the risk for ADHD.SNPs were selected based on a) low p-values in the ADHD PGC meta- analysis (Neale et al., 2012, unpublished), b) low p-values in the PGC meta-analysesfor the psychiatric disorders schizophrenia (Schizophrenia Psychiatric Genome-WideAssociation Study (GWAS) Consortium, 2011), major depression disorder (Ripke et al., 2013), bipolar disorder (Psychiatric GWAS Consortium Bipolar Disorder Working Group, 2011) and/or low p-values in the PGCCross-disorder study (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013) as assessed through Ricopili (May 2014; http://www.broadinstitute.org/mpg/ricopili/), c) functional effects of these SNPsdescribed in the literature (affecting gene expression, brain functioning, brain activation, etc. ), and/or d) functional effects of proxy-SNPs that lie in high LD (> 0. 8 according to SNAP(Johnson et al., 2008)) with the SNPsof interest. A total of five SNPswere selected for analysis through these procedures.
SNP-specificanalyses P-values for associations between selected SNPsand total brain volume, total gray matter, total white matter, PFC, caudate nucleus, hippocampus, and nucleus accumbens were retrieved from previous genome-wideanalyses using the BIG cohort. SNPswith p-values below 0.05 after Bonferroni correction for performing 35 tests were considered significant. A VBM analysis was done using SPM8 (Ashburner et al., 2013) using the BIG data analysis in order to test for volumetric differences of white matter or gray matter associated with SNPgenotypes, as has been described by Hoogman et al. (2014). SNP genotypes were coded as a linear additive effect. Scanner field strength, age, and sex were used as covariates. In addition, we tested whether SNPgenotypes were associated with fractional anisotropy (FA) and mean diffusivity (MD) (parameters measuring structural connectivity). Again, the used regression models assumed an additive effect ofSNP genotypes and used scanner field strength, age, and sex as covariates. False discovery rate (FDR) control was used to correct for the testing of multiple voxels. Associationswere considered significant if their FDR-corrected peak level had a p-value below 0.05 after Bonferroni correction for testing four measures (gray matter, white matter, FA and MD) for five SNPs.
Results
Association of an ID-related gene-set with ADHD The ID-related gene-set of 392 genes (see Supplementary Table 1) was significantly associated with ADHD (P = 0.0000656). P-valuesfor the ten top-ranked findings are shown in Table 1. Gene-wide p- values of all 392 genes are displayed in Supplementary Figure 1. MEF2C was the only gene that was
10 significant (uncorrected P = 0. 0000461) after correction for multiple testing (correcting for 392 genes). A region plot of the uncorrected p-values for the association with ADHD in the ADHD PGC meta-analysis for the individual SNPsin MEF2Cand its flanking regions is shown in Figure 1. MEF2C was not significantly associated with Blom-transformed symptom count for inattention (P= 0. 517) or hyperactivity/impulsivity (P= 0. 078) in the IMAGE childhood ADHD sample. Two additional genes were borderline significant after Bonferroni correction: ST3GAL3 (P=0. 000142) and TUSC3 (P= 0. 000329). In total, 41 of the 392 (10. 5%) were nominally associated with ADHD (P < 0. 05).
Table 1. Top ten genes from pathway analysis of ID-related genes. Gene P-value Chr. Start Length ffSNPs
MEF2C 0.0000461 5 88183208 138266 1198
ST3GAL3 0. 000142 1 44201933 193961 1478
TUSC3 0.000329 8 15397791 133555 3011
TRAPPC9 0.00105 8 140999030 19659 5084
ATR 0. 00117 3 142279180 107171 1073
BBS7 0.00234 4 122779757 3078 512 LIG4 0.00341 13 108859793 8096 427
ALG2 0.00535 9 101979651 3679 284 PNP 0. 00633 14 20937537 5194 491
AGA 0.00649 4 178358221 2558 413 ARFGEF2 0. 00665 20 47538426 114805 1012 MLYCD 0.00705 16 83932730 17058 684
ASL 0.00731 7 65554225 3717 380 NDUFS3 0. 00782 11 47603603 1209 225
KCNK9 0. 00863 8 140624803 90497 856 Gene-wide association p-values of the top ten genes from the pathway analysis oflD-related genes in KGG. Bold font = significantly associated with ADHD after Bonferroni correction. The Bonferroni p- value cut-off for significance was 0. 000128. Chr. = chromosome number; Start = start position of the gene in bp; Length = length of the gene in bp; ff SNPs= number ofSNPsused for this analysis.
11
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rs190982 h 100 0.8 -0.6 80 -0.4 ... !- 0.2 c- 4 i- . . 0 0 .1 I 60 a (0 . 01 3^ 0 (5 Os . . s ^' ' .' ...... <%% ...' f-40^ .;r ''" -^r-^- y*^-^ l»i 1 - 'u'
-UNC004B1 -MEF2C
88. 15 88.2 Position on chr5 (Mb)
Figure 1. Region plot of association p-values for SNPs on MEF2Clocus. The region plot was made using Locuszoom (Pruim et al., 2010). Association p-values are shownfor SNPs located within the largest isoform ofMEF2C(according to UCSC Hgl9) and its 25 kbflanking regions using datafrom the ADHD PGC meta-analysis. The purple SNP, rsl90982, is the reference SNP. Colors of other SNPs indicate the correlation (linkage disequilibrium coefficient [f1)) with the reference SNP.
Association of ID genes involved in neurite outgrowth with ADHD
The gene-set of 46 autosomal ID genes involved in neurite outgrowth was not associated ADHD (P=0. 363). Gene-wide p-values are displayed in Supplementary Figure 2. None of the individual genes was significantly associated with ADHDafter Bonferroni-correction. Two out offorty-six genes (4. 3%) had a nominally significant p-value (P < 0. 05).
Association of genes affected by rare CNVs in ID-patients with ADHD None of the 12 selected autosomal genes was significantly associated with ADHD in the ADHD PGC meta-analysis data (Table 2). The lowest p-values were found for PRODH (uncorrected P= 0. 0283) and for RBFOX1 (uncorrected P=0. 0845). PRODH and RBFOX1 were neither associated with Blom- transformed symptom counts, nor with parental or teacher Conners' scores for inattention or hyperactivity/impulsivity (Table 3).
12 Table 2. P-values ofgene-wide associations with ADHD in the ADHD PGC meta-analysis data. Gene P-value Chr. Start Length ffSNPs
PRODH 0.028 22 18900286 23780 45
RBFOX1 0.085 16 6069095 1694245 2432
PTPRD 0. 176 9 8314246 2298477 2132
CNTNAP2 0. 252 7 145813453 2304637 554
NRXN1 0. 283 2 50145643 1114031 3954
XYLT1 0. 480 16 17195626 369112 1244
PRIM2 0.492 6 57179603 333772 177
FAM110C 0.621 2 38814 8056 13
SKI 0.681 1 2160134 81424 189
GRIN2A 0.826 16 9852376 424235 808
NRG3 0.858 10 83635070 1111865 727
ERBB4 0. 919 2 212240446 1163119 4634
Gene-wideassociation p-valuesfor ADHD. Chr. = chromosome number; Start = start position of the gene in bp; Length = length of the gene in bp; ffSNPs= number ofSNPs usedfor this analysis.
Table 3. P-values for associations of Blom-transformed symptom counts and Conners' scores with PRODH and RBFOX1 Gene ff SNPs Symptom Association with Association with Association domain Blom-transformed parental with teacher symptom counts Connors'score Conners' score RBRFOX1 794 Inattention 0. 434 0.827 0.687 Hyperactivity 0.419 0.658 0. 439 /impulsivity PRODH Inattention 0. 132 0. 555 0.986 Hyperactivity 0. 128 0. 012 0. 681 /impulsivity Gene-wideassociation p-valuesfor RBFOX1and PRODHwith Blom-transformed symptom counts and Conners'scores. Bold font = nominally significant. # SNPs = number ofSNPsused for this analysis.
Association of genes for ID and ADHD with brain phenotypes NRXN1 and CHRNA7were selected as genes of interests. Neither of the genes was associated with any of the tested brain volumes (total brain, total gray matter, total white matter, PFC, caudate nucleus, hippocampus, and nucleus accumbens) in the BIG dataset (Table 4). Because CHRNA7was
13 nominally associated with the PFC, we tested whether CHRNA7 is associated with the substructures of the PFC(left and right separately). None of the associationswas significant (Supplementary Table 2). Three SNPs were selected for CHRNA7[rs2337980, rs904952, and rs6494223) and two for NRXN1 (rsl0191989 and rsll891766} for SNP-specificanalyses. None of these SNPswas significantly associated with total brain volume, total gray matter, total white matter, PFC, caudate nucleus, hippocampus, or nucleus accumbens (Supplementary Table 3). Neitherofthevoxel-wiseanalyses did reveal any effect of SNPgenotypes on brain structure (total gray matter or total white matter) or connectivity (fractional anisotropy or mean diffusivity) (Supplementary Table 4).
Table 4. Gene-wide association for CHRNA7 and NRXN1 with brain volumes ff Total Total Total Prefront Nucleus Hippo- Nucleus SNPs brain gray white alcortex caudate campus accum- volume matter matter (PFC) bens CHRNA7 27 0.265 0.883 0.893 0. 044 0.759 0. 175 0. 844
NRXN1 712 0. 194 0. 22 0. 747 0. 658 0. 785 0. 895 0.63
Gene-wide association p-valuesfor CHRNA7 and NRXN1 with different brain volumes. Bold font = nominally significance.
Discussion
Summary of findings SNPs in the ID-related gene-set of 392 genes were significantly associated with ADHD, whereasSNPs in the neurite outgrowth subset were not. The MEF2Cgene was significantly associated with ADHD risk, but did not show a clear relation with domain-specific symptom counts for inattention or hyperactivity/impulsivity. None of the other ID-related genes tested were significantly associated with ADHD risk when tested individually, although ST3GAL3and TUSC3 showed borderline significant associationswith ADHD risk after Bonferroni correction. The 12 individual genes that were selected because of their frequent occurrence in rare CNVs in patients with both ADHD (symptoms) and ID, PRODH, RBFOX1, PTPRD, CNTNAP2, NRXN1, XYLT1, PRIM2, FAM110C, SKI, GRIN2A, NRG3, and ERBB4, were not significantly associated with ADHD risk based on common genetic variants, nor with ADHD symptom count. Similarly, the two additional genes selected based on evidence from the literature for involvement in both ADHDand ID, CHRNA7ar\d NRXN1,were not significantly associated with ADHD risk, nor with selected brain volumes that might be thought of as endophenotypes for ADHD, i. e. the volumes of the total brain, total gray matter, total white matter, PFC, caudate nucleus, hippocampus, or nucleus accumbens. None of the individual SNPs in CHRNA7 (rs2337980, rs904952 and rs6494223), or in NRXN1 [rsl 0191989 and rsll891766) was associated with the aforementioned regional brain volumes either. These SNPs were also not associated with voxel-wise measures of gray- or white matter or of structural connectivity.
Interpretation of results in relation to the literature In our first two approaches, we used gene-set association analyses, also known as 'pathway analyses'. There are two important reasons to perform gene-set analyses rather than gene-wideor SNP-specific analyses. Firstly, for polygenic disorders such as ADHD, combining the effects of multiple
14 genes in one analysis can increase the explained phenotypic variance and improve the power of the study by allowing allelic heterogeneity (Bralten et al., 2013). Secondly, gene-set analyses can answer broadly oriented questions that give insight into the mechanisms and nature of disorders. Our results show that ID and multifactorial ADHD share an overlapping genetic background. While neurite outgrowth is a process involved in ADHD and ID (Poelmans et al., 2011; Trazzi et al., 2013; Van Maldergem et al., 2013; Wang, Moore, Adelmant, Marto, & Silver, 2013), SNPs in the ID-related genes involved in the process of neurite outgrowth could not be linked to ADHD risk, suggesting that the overlap in ADHDand ID is not driven (primarily) by genes involved in this process (at least not those that we tested). To our knowledge, we are the first to have tried to find the overlapping genes between the rare variants in {monogenic or oligogenic) ID and the (common) SNPs in multifactorial ADHD. Most previous studies have focused on either rare genetic variations in monogenic or oligogenic disorders or on common variations in multifactorial disorders, but not on a combination of both. For example, Lee et al. (2013) studied the correlation ofSNPs in five multifactorial psychiatric disorders and found that the SNPs in ADHD correlate moderately with those in major depressive disorder. In addition, Doherty and Owen (2014) reported in a review article that SNPatleles overrepresented in patients with ADHDwere also overrepresented in patients with schizophreniaor bipolar disorder. Apart from that, Doherty and Owen (2014) also focused on rare, large CNVsin monogenic or oligogenic disorders. Their report included seven large CNVs associated with both ID and ADHD, suggesting involvement of certain genes in both ID and ADHD. Since these CNVs are rare, usually cause severe physical problems and have a high penetrance for ADHD, they are likely to be involved in monogenic or oligogenic ADHD, but unlikely to play a role in multifactorial ADHD, which is the most common form ofADHD. However, assuming that genes harboring rare variants involved in oligogenic ADHD can also contribute to multifactorial ADHD through common genetic variations, we think that the arguments of Doherty and Owen (2014) are in accordance with our finding of genetic overlap between ID and multifactorial ADHD. In addition, previous studies have reported individual genes involved in both ID and ADHD (Hunter, Epstein, Tinker, Abramowitz, & Sherman, 2012; Jolly, Homan, Jacob, Barry, & Gecz, 2013). We have shown that the ID-related gene MEF2Cis significantly associated with the risk for multifactorial ADHD. However, it does not seem to be associated with symptom count for inattention (P=0.517). An association of /W£F2Cwithsymptom count for hyperactivity/impulsivity might exist (P=0. 078), even though our study was not able to prove this. To our knowledge, MEF2C has not been associated with ADHD before, even though it has been studied extensively and has been linked to several disorders and characteristics. For instance, MEF2C, located on chromosome 5, has been previously found mutated in severe ID, and associated with Alzheimer's disease and epilepsy (Beecham et at., 2014; Novara et al., 2010; Novara et al., 2013; Nowakowska et al., 2010; Paciorkowski et al., 2013). In addition, MEF2C haploinsufficiency has been linked to 1) autistic traits such as lack of eye contact, absence of speech, impaired engagement with other people, and stereotypic movements and to 2) severe motor problems, such as hypotonia, severely impaired fine motor coordination and the inability to walk freely (Novara et al., 2010; Novara et al., 2013; Nowakowska et al., 2010; Paciorkowski et al., 2013). The latter is interesting, because ADHD correlates strongly with autistic traits and moderately with motor coordination problems (Rommelse et al., 2009), even though the motor problems in ADHDare usually far less severe than the motor problems in patients with MEF2C haploinsufficiency (Fliers et al., 2008). Interestingly, MEF2C does not play a role in neurite outgrowth according to AmiG02 (Carbon et al.,
15 2009). Moreover, none of the individual ID-related genes involved in neurite outgrowth, nor the entire set of ID-related neurite outgrowth genes, could be linked to ADHD risk in our study. The tack of a significant association of the set of ID-related neurite outgrowth genes and ADHD was unexpected, because this gene-set seemed to have a higher o prior/ chance of being associated with ADHD risk than the set of all ID-related genes, since half of the ADHD candidate genes as found by GWASs were reported to be involved in neurite outgrowth in the study by Poelmans et al. (2011). Importantly, none of the ADHD candidate from the identified neurite outgrowth network was present in the ID-related gene-set that we tested. When Bralten et al. (2013) performed a gene-set analysis combiningthe dopamine/noradrenaline pathway, the serotonin pathway and the set of neurite outgrowth genes involved in ADHD as reported by Poelmans et al. (2011), they found a strong association with symptom count of hyperactivity/impulsivity, but not with symptom count of inattention. Since we already know from twin modeling that the genetic overlap of hyperactivity/impulsivitywith inattention is incomplete (Moruzzi, Rijsdijk, & Battaglia, 2014), this emphasizes again the importance of testing hyperactivity/impulsivity separately from inattention. Apart from MEF2C, the SNPs of none of the other genes we tested showed significant gene-wide associations with ADHD risk. For most genes, this was in line with the literature, since no association withADHDhas been previously reported for the majority of the genes that we tested. Although case reports ofADHD have been described for a minority of the tested genes, they describe CNVs or rare genetic variations, while our study only tested SNPs. For example, CNVs of CHRNA7(Hoppman- Chaney, Wain, Seger, Superneau, & Hodge, 2013; Stergiakouli et al., 2012) and deletions of (exons of) the NRXN1 gene (Bradley et al., 2010; Curran, Ahn, Grayton, Collier, & Ogilvie, 2013) have been linked to ADHD risk. However, our study did not show gene-wide associations of the SNPswithin CHRNA7or NRXN1with multifactorial ADHD risk, possibly because SNPsare likely to have smaller effects on gene functioning than CNVs or deletions of (a few exons of) the gene. Even if some SNPs in some of the tested genes were associated with multifactorial ADHD risk, their effects might have been too small to give a gene-wide effect that survives Bonferroni correction for testing multiple genes. CHRNA7 and NRXN1were also not associated with any of the regional brain volumes that we tested in healthy individuals. The five SNPs in CHRNA7 and NRXN1 that we selected based on the literature describing psychiatric disorders and functional effects linked to these SNPs or their proxy-SNPs, were neither associated with the tested regional brain volumes, nor with the measures of structural connectivity in healthy individuals. For CHRNA7, this is in line with the literature. As far as we know, no previous studies have linked genetic variations in CHRNA7to regional brain volumes. SNPs in NRXN1, however, have been linked to frontal white matter volume in healthy adults (Voineskos et al., 2011), which is likely to be related with two of the parameters we tested: total white matter volume and PFC volume. Importantly, even though the SNPs in CHRNA7 and NRXN1 could neither be linked to structural connectivity nor to any of the tested brain volumes in healthy individuals, they might still affect structural connectivity or regional brain volumes in patients with ADHD. This can occur if certain variations in other ADHD-retated genes are needed to establish an effect of CHRNA7 and/or NRXN1on the brain.
Strengths and limitations This study should be seen in light of several strengths and limitations. By our comprehensive project of four different approaches, we investigated whether ID-related genes are involved in ADHD. Importantly, we investigated the association of ID-related genes with ADHD on different levels: on a
16 gene-set, gene-wide, and single-SNP level. In addition, we used different types of criteria to select the candidate ID genes, such as 1) involvement in neurite outgrowth, 2) frequent occurrence in CNVs in patients with ADHD (symptoms) and ID, and 3) indications for involvement in ADHD and ID in the literature. Finally, we did not only look at ADHD diagnosticstatus, but also at symptom counts and different brain volume and structural connectivity measures. Another strength of our research is that we used the largest meta-analysis containing genome-wide data on ADHD that is currently available, the ADHD PGC meta-analysis available (Neale et al., 2012, unpublished). This is crucial when investigating multifactorial disorders, in order to be able to detect genetic associations with small effect sizes. The resemblance between all our approaches is that they all focus only on SNPs, only on autosomal genes and only on multifactorial ADHD. Therefore, we are not able to say anything about genetic variations other than SNPs, about X-chromosomal genes, or about monogenic/oligogenic forms of ADHD. Our first two approaches used gene-set analyses testing the SNPs in the entire group of ID-related genes and the subset of ID-related genes involved in neurite outgrowth, respectively, for an associationwith multifactorial ADHD.To select the ID-related genes, we used an ID gene panel composed for diagnostictesting purposes by the Radboudumc. Although the genes in this panel have been carefully selected based on findings ofcfe novo mutations in patients with ID visiting the Radboudumc and based on the literature by experienced geneticists, this list is no gold standard and differs substantially from other lists of ID-related genes (S. M. Purcell et al., 2014; van Bokhoven, 2011). In addition, the list is probably far from complete, since research continues to find new ID candidategenes. Similarly, research on gene ontology continues to find new genes involved in neurite outgrowth, and the list of neurite outgrowth genes used in the study was therefore also likely to be incomplete. The gene ontology browser that we used, AmiGOZ (Carbon et al., 2009) is updated every week. About a quarter of genes present in the ID gene panel had to be excluded from analysis because they were located on the X-chromosome, which is not covered in the ADHD PGC meta-analysis. Because of this and because of the stringent Bonferroni correction we applied when testing all genes individually, we might have missed important genes involved in both ID and ADHD in our gene-wide analyses. Our third and fourth approach focused on individual genes. In the third, 12 genes were selected based on frequent occurrence in CNVsof patients with ADHDand ID and/or congenital anomalies. This third study might have been limited by the small sample size, because the CNV database included CNVsof only 252 patients. In addition, we might have selected genes that occurred often in CNVs of patients with ID with ADHD, because of their association with ID (or congenital anomalies) and not due to their association with ADHD.To control for this, it would have been necessary to compare the frequency of the genes in CNVsin patients with ID with ADHDto that in patients with ID without ADHD. However, such a dataset was not available for this study, since the patients in the CNV database were not routinely tested for ADHD. In addition to that, ADHD can be difficult to diagnose or exclude in patients with ID (Buitelaar et al., 2011). In our fourth approach, the SNPs in CHRNA7 and NRXN1, two genes selected based on indications for involvement in ID and ADHD in the literature, could not be associated with any of the tested brain volumes or structural connectivity measures in healthy adults without ADHD from the BIG cohort. Testing in patients with ADHD was not possible because of time constraints.
17 Future research It would be worthwhile to verify the association of ID-related genes with ADHD using data that includes X-chromosomal genes, in order to get a more complete picture. Also, it would be nice to see what part of the ID-related gene-setcan be linked toADHDwhen looking at CNV studies ofADHD. Investigating with what regional brain volumes the subset of ID-related genes most strongly associated with ADHD risk (e.g. having an uncorrected p-value below 0.05) can be associated, has more power than testing this for individual genes, and could lead to more insight into the nature of the shared mechanisms of ADHD and ID. Moreover, it would be interesting to know what pathways are enriched in this subset, for instance by making use of a SNP-based pathway enrichment analysis (Weng et al., 2011). Finally, investigating if this subset can be associated with any other neurodevelopmental disorder, can give us more insight into the mechanisms behind the suggested existence of a 'neurodevelopmental continuum' (Moreno-De-Luca et al., 2013). As for MEF2C, future research could focus on how SNPs in MEF2C affect brain imaging measures in individuals with and without ADHD. Many studies have shown that MEF2C haploinsufficiency affects brain structure, such as causing thinning of the corpus callosum (Nowakowska et al., 2010) and enlargement of the lateral ventricles (Novara et al., 2013). It would be interesting to see if these effects are also seen for certain SNP genotypes in MEF2C. In addition, one could investigate how variations in MEF2C affect performance on neuropsychological tasks (measuring attention, impulsivity, short-term memory, etcetera), for example in the patients with ADHD and controls in the International Multicentre persistent ADHD CollaboraTion (IMpACT) study (Franke et al., 2010).
Final conclusion Our gene-set analyses contribute to the understanding of the genetic overlap between ID and ADHD by showing that 1) the SNPs in at least part of the genes affected by rare genetic variations in ID patients, contribute to ADHD, and 2) this genetic overlap between ID and ADHDseems to be not primarily driven by the neurite outgrowth genes we tested. More generally, they contribute to insight in the 'neurodeveiopmental continuum', by showing that multifactorial disorders might be caused by different variations in the same risk genes as monogenic or oligogenic disorders. Apart from that, one of our gene-wide analyses has identified MEF2C as a novel candidate for multifactorial ADHD, which to our knowledge has not been linked to ADHD before. Because the association of MEF2C with multifactorial ADHD that we found was highly significant and because MEF2C haploinsufficiency has been linked to common co-morbidities of ADHD, we think MEF2C is a promising candidate gene for ADHD.
Acknowledgements I would like to thank Barbara Franke and especially Marieke Klein, who supervised me greatly. Apart from that, I want to thank alt people of the department Multifactorial Diseases of the RadboudUMC in Nijmegen for assisting in my project. In addition, I am grateful to the SURF Foundation support team, since the gene-set analyses in KGG were carried out on the Dutch national e-infrastructure with the support of SURF Foundation. Also, I am thankful to all people who contributed to the development of the databases I used: the PGC ADHD working group, the people contributing to IMAGE, the Genome Diagnostics division of the department of Human Genetics of the Radboudumc
18 (especially Nicole de Leeuw) and the people contributing to the Cognomics Resource BIG. Finally, I want to thank the developers of the programs KGG, VEGASand PLINK,which I used to perform gene- set and gene-wide analyses.
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26 Supplementary material
Supplementary Table 1. All genes in the 'Intellectual Disability Gene Panel'
Gene P-value MEF2C 0. 0000461 ST3GAL3 0.000142 TUSC3 0.000329 TRAPPC9 0.00105 ATR 0.00117 BBS? 0. 00234 LIG4 0. 00341 ALG2 0.00535 PNP 0.00633 AGA 0.00649 ARFGEF2 0. 00665 MLYCD 0. 00705 ASL 0.00731 NDUFS3 0.00782 KCNK9 0.00863 MKKS 0.0115 BSCL2 0.0116 GUSB 0.0152 MAT1A 0.0162 GPRS6 0. 0208 RFT1 0. 0211 SETBP1 0.0255 CDK5RAP2 0.0257 NLRP3 0. 0259 ISPD 0. 0267 IGF1 0.0287 KCTD7 0.0288 SMARCA2 0. 0291 ERLIN2 0.03 AN010 0.0316 DIP2B 0. 0335 NRXN1 0.0337 ATP1A2 0.037 ANKH 0. 0373 HPD 0. 0385 DHCR7 0.0387 GTF2HS 0. 0404 HDAC4 0.0423 CTDP1 0.0437 AKT3 0.0467
27 PNKP 0.0476 MOCS2 0.0501 NKX2-1 0.0567 NDUFS4 0.0573 ADAR 0. 0615 CACNA1C 0. 0617 ABHD5 0. 0673 LARP7 0.0673 EFTUD2 0.0694 ARID1B 0.0725 SLC35C1 0.0731 SCN2A 0. 076 PPOX 0. 0792 CEP290 0.0817 ANKRD11 0.0858 GRIN1 0.0867 ABCC9 0.0873 NF1 0. 093 SYNGAP1 0. 0931 CRBN 0.096 ELOVL4 0.0986 BBS12 0. 102 UBE3A 0. 103 PEX10 0. 105 SLC2SA15 0. 111 NDUFA11 0. 112 ACTG1 0. 117 NDUFV1 0. 118 BRAF 0. 123 RELN 0. 123 CEP1S2 0. 123 LRPPRC 0.126 KIAA1279 0.13 MCOLN1 0. 131 AP4E1 0. 133 SOBP 0.134 FOXG1 0. 137 HRAS 0. 138 RMND1 0.139 SIL1 0. 15 COX15 0. 153 ROGDI 0. 153 PEX11B 0. 156 GNPAT 0. 157 MANBA 0. 158 ALG1 0. 158
28 VPS13B 0. 162 DHCR24 0. 165 IER3IP1 0. 166 LARGE 0. 167 MGAT2 0. 168 GRIN2B 0. 172 MAP2K2 0. 175 HAX1 0. 176 ARID1A 0. 179 SCN8A 0. 183 UPB1 0.189 TUBA1A 0. 19 ASXL1 0. 19 TSC2 0.191 BBS2 0. 192 PRODH 0.193 KIF7 0.195 SMOC1 0.198 RNASEH2A 0. 198 CEP41 0. 201 GATM 0. 205 ERCC6 0.206 KRAS 0.211 DPAGT1 0. 218 PUS1 0.218 DPYD 0.219 BCKDHB 0.22 GFAP 0.224 SERAC1 0.234 PLCB1 0.24 PTEN 0. 243 SDHA 0.244 SRCAP 0.244 ALG9 0.246 AMT 0. 248 DNAJC19 0.249 SOX2-OT 0.252 SLC12A6 0. 257 ALG6 0. 259 HLCS 0.26 FKRP 0. 262 DARS2 0. 263 MBD5 0.267 DYNC1H1 0.272 FOXP1 0. 273 AHCY 0. 274
29 BCKDHA 0. 279 L2HGDH 0. 287 TUBB2B 0. 288 C5orf42 0. 29 GJC2 0.291 TECR 0.294 PDSS2 0.298 APTX 0.3 CNTNAP2 0. 305 ALG3 0. 31 TGFBR2 0. 31 CC2D1A 0.311 PTPN11 0.312 CCBE1 0.312 GLI2 0. 314 TBCE 0. 319 CCDC78 0. 32 SOS1 0.322 NDUFS2 0.33 CACNG2 0.347 BBS10 0. 353 PIK3R2 0. 353 AP3B1 0.354 EHMT1 0. 357 MTR 0. 361 MTRR 0.362 SLC25A22 0.364 GALT 0. 371 KIF11 0. 372 DMPK 0.376 PDSS1 0.383 SHH 0. 385 HESX1 0. 39 POLR3A 0.394 SRDSA3 0.406 TTC8 0. 409 SC02 0.411 CTNNB1 0.414 SYT14 0.422 SMAD4 0.425 POMGNT1 0.43 D2HGDH 0. 434 FKTN 0.437 RBM28 0.44 TMC01 0.445 GCH1 0. 445
30 TREX1 0.446 AGPAT2 0.449 CHKB-CPT1B 0.451 CEP135 0.454 FH 0.456 ZNFS92 0. 462 ATP6VOA2 0.463 GPHN 0.472 BBS4 0.472 AP4S1 0.477 RAB27A 0.483 B3GALTL 0.485 ZIC2 0. 485 NAGA 0.492 PIGV 0.496 PHGDH 0. 505 MYCN 0. 507 OCLN 0. 515 RAB3GAP1 0.517 KIRREL3 0. 518 COQ2 0. 52 PRSS12 0. 52 MMACHC 0.526 DHTKD1 0.531 CREBBP 0.533 RPGRIP1L 0.536 NTRK1 0.546 ERCC3 0.552 THRB 0.557 MOCS1 0.559 PEPD 0.561 PEX26 0.568 ATP2A2 0.572 FBN1 0.576 COG8 0.576 BCS1L 0.58 UBR1 0. 58 MAP2K1 0.584 SALL1 0.586 MMADHC 0. 587 ERCC8 0. 594 DPM1 0. 594 TGFBR1 0. 598 ADSL 0.603 XPA 0.621 POLR3B 0.626
31 ERCC2 0.626 FANCD2 0.628 NDUFS7 0.629 WDR62 0. 633 MPDU1 0.65 PVRL1 0. 652 SLC33A1 0. 655 suox 0. 657 SPRED1 0.66 GAMT 0.661 MMAA 0.665 FRAS1 0.667 HOXA1 0. 668 MY05A 0. 668 PACS1 0.669 LAMA2 0.671 RNASEH2B 0.671 RNASEH2C 0.673 POC1A 0. 674 MUT 0.678 FGFR3 0.679 GNS 0.689 MCCC1 0. 691 NAGLU 0.692 AK1 0.693 RAD21 0.695 IDUA 0. 696 PEXS 0. 699 POMT1 0. 702 ACVR1 0. 704 NDUFA12 0. 704 ACTB 0. 708 ABCD4 0. 712 PAFAH1B1 0. 712 EMX2 0. 713 DOCKS 0.714 ADCK3 0. 72 GRW2A 0. 723 COL4A1 0. 724 DDHD2 0. 725 MED17 0. 729 ALG12 0. 73 NBN 0. 731 SMARCA4 0. 731 DNMT3B 0. 734 NIPBL 0. 738
32 ^ c/5 ^. ^ ^ ? s s ^ 5^3 R 1§S h-3 F II to 55 I 1111 II ^ I I Is!! tsi I S to I IIh" co I sl KM
LU UU CENPJ 0.88 PC 0.884 DYM 0.884 EIF2AK3 0.887 SMPD1 0.889 KCNQ2 0. 89 FGFR2 0. 892 BIVM-ERCC5 0. 892 MAN1B1 0.893 COG7 0.894 VLDLR 0.895 ZEB2 0.896 NDUFS8 0. 898 FTO 0.9 PCNT 0.9 SLC17A5 0.901 SOX10 0.901 GNAS 0.903 SMARCB1 0. 904 ORC1 0. 905 ARL6 0.906 BBSS 0.907 NDE1 0. 908 PTCH1 0.91 I JAMS 0.91 B4GALT1 0.924 ACOX1 0. 926 PEX7 0.927 CBS 0.928 DHFR 0.93 PAX6 0. 93 PIGO 0.935 SHOC2 0.935 ETHE1 0. 936 DBT 0.941 TSC1 0.942 ACSF3 0.942 COL4A2 0. 943 EPB41L1 0.956 COLEC11 0.958 AUH 0. 958 TMEM237 0.959 TMEM165 0.959 RARS2 0. 962 CDON 0.965 PMM2 0.965
34 GAD1 0. 971 COG1 0. 975 SNAP29 0. 976 ASPM 0.978 TAT 0.98 AMI 0.981 PEX1 0.983 SATB2 0.984 ARL13B 0. 988 SCN1A 0. 989 SMC3 0. 99 GLI3 0.991 ALDH5A1 0.993 PANK2 0.994 B4GALT7 0. 995 LAMC3 0. 995 RAB18 0. 995 ESC02 0.996 BLM 0.996 NSUN2 0.997 SIX3 0. 998 MVK 0.998 RAF1 0.999 AIMP1 0. 999 i CHD7 0. 999 BBS9 1 MCPH1 1 PIGN 1 \PYCR1 1 GRIK2 unknown NEU1 unknown INPP5E unknown TCF4 unknown GSS unknown ABCD1 unknown ACSL4 unknown AFF2 unknown AGTR2 unknown AIFM1 unknown AP1S2 unknown ARHGEF6 unknown ARHGEF9 unknown ARX unknown ATP6AP2 unknown ATP7A unknown ATRX unknown
35 BCOR unknown BRWD3 unknown CASK unknown CDKL5 unknown CUL4B unknown DCX unknown DKC1 unknown DLG3 unknown DMD unknown FGD1 unknown FLNA unknown FMR1 unknown FTSJ1 unknown GDI1 unknown GK unknown GPC3 unknown GRIA3 unknown HCCS unknown HCFC1 unknown HDAC8 unknown HPRT1 unknown HSD17B10 unknown IDS unknown IKBKG unknown IL1RAPL1 unknown IQSEC2 unknown KDM5C unknown KDM6A unknown LICAM unknown LAMP2 unknown MAGT1 unknown MAOA unknown MECP2 unknown MED12 unknown MIDI unknown NAA10 unknown NDP unknown NDUFA1 unknown NHS unknown NLGN4X unknown NSDHL unknown OCRL unknown OFD1 unknown OPHN1 unknown PAK3 unknown PCDH19 unknown
36 PDHA1 unknown PGK1 unknown PHF6 unknown PHF8 unknown PLP1 unknown PORCN unknown PQBP1 unknown PRPS1 unknown PTCHD1 unknown RAB39B unknown RAB40AL unknown RPS6KA3 unknown SHROOM4 unknown SLC16A2 unknown SLC6A8 unknown SLC9A6 unknown SMC1A unknown SMS unknown SOX3 unknown SRPX2 unknown SYN1 unknown SYP unknown TIMM8A unknown TSPAN7 unknown UBE2A unknown UPF3B unknown ZDHHC9 unknown ZNF41 unknown ZNF674 unknown ZNF711 unknown ZNF81 unknown KANSL1 unknown KMT2D unknown KRBOX4 unknown MPLKIP unknown SC5D unknown The Intellectual Disability Gene panel was published by the department ofGenome Diagnostics of the Radboudumc (downloaded from https://www. radboudumc. nl/lnformatievoorverwijzers/ Genoomdiagnostiek/Documents/ngs-intellectual_disability_panel_181213. pdf on March 27th, 2014). P-value = gene-basedp-value according to KGG (M. X. Li, P. C. Sham, S. S. Cherny, & Y. Q. Song, 2010).
37 Supplementary Table 2. Gene-wideassociation p-values of CHRNA7w\t[\ PFCsubstructure volumes
Brain structure Left Right
Lateral orbitofrontal cortex 0.506 0.105 Medial orbitofrontal cortex 0. 524 0.426 Pars opercularis 0. 072 0.982
Pars orbitalis 0.530 0.616
Pars triangularis 0. 713 0. 180 Rostral middle frontal cortex 0. 709 0. 173 Superiorfrontal cortex 0. 323 0. 259 Frontal pole 0.731 0.416
Gene-wide association p-values of CHRNA7 locus (including a 25 kb flanking region, 27 SNPs were analyzed).
Supplementary Table 3. Single-SNPassociation P-values of CHRNA7and NRXN1candidate SNPs with brain volume measures
Total Total Total brain gray white Prefrontal Nucleus Hippo- Nucleus SNP volume matter matter cortex caudate campus accumbens CHRNA7 rs2337980 0. 1449 0. 7265 0. 09026 0. 5204 0. 368 0. 8704 0. 192 rs904952 0. 8607 0. 2418 0. 3137 0. 09853 0. 5283 0. 2836 0.6402 rs6494223 0. 07973 0. 09207 0. 9469 0. 3777 0. 8921 0.07458 0.8682
NRXN1 rsl0191989 0.3851 0. 1526 0.5261 0. 7229 0. 8533 0. 8478 0. 5343 rsll891766 0.8281 0.8824 0.7289 0. 5418 0.2506 0.5828 0.4095 Single-SNPassociation p-values between structural volumes and five candidateSNPs ofCHRNA7 and NRXN1.
38 Supplementary Table 4. P-values for FDR-corrected peak levels with lowest p-value for SNPs in CHRNA7 and NRXN1 for total gray- and white matter and structural connectivity measures.
Total gray Total white Fractional Mean SNP matter matter anisotropy diffusivity CHRNA7 rs2337980 0. 802 0.740 0.484 * rs904952 0.062 0.915 0. 892 0.771
rs6494223 0. 774 0.844 * *
NRXN1 rsl0191989 0. 897 0. 372 0.890 rsll891766 0. 980 0. 689 FDR-correctedpeak-level p-values are shown. The associationsfor total gray matter and total white matter were acquired in VBM analyses; the associations for fractional anisotropy and mean diffusivity in voxel-wise analyses. * No suprathreshold clusters.
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