The Molecular Genetic Architecture of Attention Deficit Hyperactivity Disorder

EXPERT REVIEW The molecular genetic architecture of attention deﬁcit hyperactivity disorder

Z Hawi1, TDR Cummins1, J Tong1, B Johnson1, R Lau1, W Samarrai2 and MA Bellgrove1

Attention deficit hyperactivity disorder (ADHD) is a common childhood behavioral condition which affects 2–10% of school age children worldwide. Although the underlying molecular mechanism for the disorder is poorly understood, familial, twin and adoption studies suggest a strong genetic component. Here we provide a state-of-the-art review of the molecular genetics of ADHD incorporating evidence from candidate gene and linkage designs, as well as genome-wide association (GWA) studies of common single-nucleotide polymorphisms (SNPs) and rare copy number variations (CNVs). Bioinformatic methods such as functional enrichment analysis and protein–protein network analysis are used to highlight biological processes of likely relevance to the aetiology of ADHD. Candidate gene associations of minor effect size have been replicated across a number of genes including SLC6A3, DRD5, DRD4, SLC6A4, LPHN3, SNAP-25, HTR1B, NOS1 and GIT1. Although case-control SNP-GWAS have had limited success in identifying common genetic variants for ADHD that surpass critical significance thresholds, quantitative trait designs suggest promising associations with Cadherin13 and glucose–fructose oxidoreductase domain 1 genes. Further, CNVs mapped to glutamate receptor genes (GRM1, GRM5, GRM7 and GRM8) have been implicated in the aetiology of the disorder and overlap with bioinformatic predictions based on ADHD GWAS SNP data regarding enriched pathways. Although increases in sample size across multi-center cohorts will likely yield important new results, we advocate that this must occur in parallel with a shift away from categorical case-control approaches that view ADHD as a unitary construct, towards dimensional approaches that incorporate endophenotypes and statistical classification methods.

Molecular Psychiatry (2015) 20, 289–297; doi:10.1038/mp.2014.183; published online 20 January 2015

INTRODUCTION endophenotypes and data-driven classification techniques, must Attention deficit hyperactivity disorder (ADHD) is the most now be used to advance the field. prevalent psychiatric condition of childhood, affecting 2–10% of school age children worldwide. Its features include extreme levels THE MOLECULAR GENETICS OF ADHD of motor activity, impulsivity and inattention. Individuals with ADHD may present with predominantly inattentive or hyperactive The last two decades of molecular genetic research in complex symptoms or, more commonly, a combination of both (ADHD- diseases including psychiatric conditions has been fuelled by the combined type). These symptoms are chronic and persist into common disease common variant (CDCV) hypothesis. The CDCV adulthood in ~ 30–60% of cases and are associated with lowered hypothesis argues that common genetic variations (allele fre- 4 academic functioning, increased risk for drug abuse and negative quency 5%) of low penetrance in the population are the major consequences for family and peer relations.1,2 Although environ- contributors to genetic susceptibility to common diseases. Although there are examples where the CDCV hypothesis has mental influences (such as low birth weight, delivery complica- proven useful for mapping genes underlying complex diseases tions, toxin exposure and food additives) have been identified, such as Crohn's disease and Alzheimer's disease,4,5 most of the genetic factors are recognised as the critical etiological compo- reported associations are of minor/modest effect size and account nent of ADHD. Large twin studies have consistently shown higher for a small proportion of the heritability of the associated disease/ monozygotic than dizygotic concordance rates with heritability trait.6 An alternative hypothesis is the common disease rare – 3 estimates ~ 75 90%. Although the genetic architecture of ADHD variant (CDRV) hypothesis which predicts that multiple rare is not known, a multi-factorial model is consistent with the high variations (⩽5% frequency) have a cumulative effect that accounts prevalence of ADHD in the general population and the high for a significant proportion of the genetic risk for common – concordance rate in monozygotic twins (68 81%) but modest risk conditions7 and that much of the genetic association signals to first-degree relatives (~20%). This article provides a state-of-the- reported under the CDCV approach actually represent diluted risk art review of the molecular genetics of ADHD. Findings from signals of rare, highly penetrant causal variants.8 candidate gene and genome-wide association studies (GWAS) are Earlier psychiatric genetic association studies pursued the CDCV integrated using bioinformatics and complex network analysis. hypothesis with a candidate gene approach (pre-specified gene Whereas the vast majority of genetic studies have treated ADHD of interest), using a single or limited number of genetic markers, as a unitary construct, we argue that a shift towards hetero- to examine the relationship between a gene and a disease geneity reduction, including the use of empirically derived condition. Advances in microarray technologies (high throughput

1School of Psychological Sciences, Monash University, Melbourne, VIC, Australia and 2New York City College of Technology, City University of New York, New York, NY, USA. Correspondence: Dr Z Hawi, School of Psychological Sciences, Monash University, Building 17, Clayton Campus, Wellington Road, Melbourne,VIC 3800, Australia. E-mail: [email protected] Received 18 April 2014; revised 14 November 2014; accepted 19 November 2014; published online 20 January 2015 Attention deﬁcit hyperactivity disorder Z Hawi et al 290

Table 1. Candidate genes showing replicated evidence of association with ADHD

Gene Associated variant Location Biological function References

SLC6A3 40 bp VNTR 3′ UTR Regulator of extracellular dopamine and mediates the reuptake of Cook et al.91a; Gizer dopamine from the synapse. et al.92b DRD4 48 bp VNTR Exon GPCR activated by the neurotransmitter dopamine. La Hoste et al.93a; Gizer et al.92b DRD5 148 bp 5ʹ flanking Transduces extracellular signals in the form of dopamine into several Daly et al.94a; dinucleotide intracellular responses, including effects on adenylate cyclase, Ca2+ Gizer et al.92b repeats levels and K+ conductance. SLC6A4 40 bp indel 5ʹ flanking A member of a transporter family that is Na+ and Cl dependent. Manor et al.95a; Mediates the reuptake of serotonin from synapses. Gizer et al.92b HTR1B rs6296 Exon1 GPCR for serotonin. A prime target for antidepressant drugs and Hawi et al.96a; psychoactive substances Gizer et al.92b SNAP25 rs3746544 3ʹ UTR Plasma membrane protein essential for synaptic vesicle fusion and Brophy et al.97a; neurotransmitter release Gizer et al.92b SLC9A9 Inversion Region A member of large solute carrier family 9. Acts in electroneutral de Silva et al.98a; breakpoints 3p14—q21 exchange of hydrogen/sodium ions across membranes. Lasky-Su et al.21c; Mick et al.23c LPHN3 Haplotype Exon 4–19 Encodes a member of the latrophilin subfamily of GPCR. May act in Arcos-Burgos encompassing signal transduction and cell adhesion. et al.99a; Ribases exons et al.100d GIT1 rs550818 Intron GPCR kinase. Thought to be involved in vesicle trafficking, cell Won et al.101a adhesion and increasing the speed of cell migration. Overexpression of GIT1 is known to regulate the beta2-adrenergic receptor. NOS1 180–210 bp CA Exon Mediates several biological processes including neurotransmission Reif et al.102a; repeat and is reported to associate with neurodegenerative conditions. Franke et al.103c Abbreviations: ADHD, attention deficit hyperactivity disorder; GPCR, G-protein-coupled receptors; GWAS, genome wide association studies; UTR, untranslated region; VNTR, variable number tandem repeat. aFirst reported by. bMeta-analysis article. cGWAS finding. dAssociation in large sample or validation using animal model.

genotyping) have now provided a powerful tool to investigate DAT1 VNTR influences neurocognitive measures in both ADHD genome-wide differences between patients and controls in and non-clinical samples.17,18 hypothesis-free designs. Like many candidate gene studies, the GWAS approach uses single-nucleotide polymorphisms (SNPs) to pursue the CDCV hypothesis. In contrast, the common disease rare GWAS IN ADHD variant approach has been interrogated at genome-wide level SNP-GWAS using copy number of variations (CNV) and only a limited number In childhood ADHD, four case-control GWAS19–22, two family- of single-nucleotide variant analyses have been performed across based GWAS23,24 and a quantitative trait loci GWAS25 have been 9,10 all psychiatric disorders. performed. One ADHD case-control GWAS has been performed in adults, while a further quantitative trait loci GWAS has been performed in a population-based cohort of adolescents and GENETIC ASSOCIATION STUDIES OF ADHD IN THE PRE-GWAS 26,27 ERA adults. In addition, a meta-analysis has been performed on the child studies28 however, neither the childhood or adult GWAS nor Dysregulation in biogenic neurotransmission has traditionally the subsequent meta-analysis have yielded genome-wide sig- been implicated in the aetiology of ADHD. The clinical effective- nificance (P ⩽ 5×10− 8). In contrast, a family-based association ness of stimulant medications (such as methylphenidate), which analysis of the childhood quantitative trait loci GWAS based on six 11 act on both dopamine and noradrenaline and the neurochem- traits derived from ADHD clinical and symptom measures 12–14 istry of animal models provide firm support for dysregulation identified significant associations mapped to the Cadherin13 of key neurotransmitters in ADHD. Table 1 lists candidate genes (CDH13) and glucose–fructose oxidoreductase domain 1 (GFOD1) from association or linkage studies that have been implicated in genes.25 In addition, trends towards association with CDH13 were ADHD and includes references to the initial report and subsequent reported in a case-control GWAS and a meta-analysis of genome- confirmations. Selection of these genes was based on (1) wide linkage scans.19,29 CDH13 has been reported to act as a confirmation of original reports of association with ADHD via negative regulator of neural cell growth and to associate with either meta-analysis of candidate gene studies or independent reduced brain volumes in ADHD individuals.30,31 Thus when taken GWAS or (2) linkage evidence substantiated with association together, these findings support a role for the CDH13 gene in findings in large sample sizes and/or subsequent validation using ADHD. Likewise, the association of GFOD1 with ADHD has received animal models. In line with biological models of ADHD, these further support in a recently published family-based study, yet its findings have revealed several ADHD risk loci mapped to biogenic aetiological role in ADHD remains unknown.32 neurotransmission and/or functionally related genes or pathways, Overall ADHD-GWA studies have had limited success in such as SLC6A3, DRD4, DRD5, SLC6A4, HTR1B, SNAP-25, LPHN3 and identifying associations at the critical significance level NOS1. In many cases preliminary evidence for the functionality of (P ⩽ 5×10− 8), however, strong trends towards association (arbi- the associated gene variants also exists. For example, the 10- trarily set at P ⩽ 1×10− 5) have been observed. Supplementary repeat allele of the DAT1 VNTR has been linked to altered Table 1 comprehensively catalogues each of the leading ADHD- expression using both in vitro gene reporter and quantitative PCR GWAS signals (P ⩽ 1×10− 5) alongside its biological function (as assays and in vivo human molecular imaging.15,16 Further, specified by the PANTHER, Gene card, UniProtKB and OMIM numerous studies have documented that allelic variation at the databases). As these ADHD-GWAS association signals largely

Molecular Psychiatry (2015), 289 – 297 © 2015 Macmillan Publishers Limited Attention deficit hyperactivity disorder Z Hawi et al 291 represent near-significant results, not all of them will represent This finding is interesting given evidence from behavioural real risk loci (many, for example, are likely to be type 1 errors). pharmacology of a potential role for the alpha-7 nicotinic receptor Nevertheless, while results for any individual genetic marker may in attention.44 More recently, Elia et al.32 investigated the not quite reach GWAS significance, a strong clustering of near- contribution of CNVs to the aetiology of ADHD using large significant results in a particular biological process/pathway, may discovery and replication samples of European ancestry. They itself have significance. The membership of any given gene within reported that CNVs affecting the metabotropic glutamate receptor a particular biological process/pathway can be determined by genes (GRM1, GRM5, GRM7 and GRM8) were enriched across all consulting biological classification systems (such as g:Profiler) that cohorts. These rare variant findings overlap with strong leads from categorise genes into functional categories. Under a null hypoth- GWAS (see Supplementary Table 1 highlighting, for example, esis of no enrichment in particular biological processes/pathways, GRM5) and our functional enrichment analysis presented above, the genes that are associated with a disorder will fall randomly thereby highlighting the relevance of the glutamatergic synapse/ within the different functional categories. Functional profiling receptor signaling pathway to ADHD and the potential overlap examines the alternative hypothesis, determining those categories between common and rare variant approaches to ADHD.21 It is in which the genes of interest are over-represented and providing also worth noting that CNVs reported to associate with ADHD for each category a corrected P-value that reflects the departure of across a number of studies were significantly enriched in genes/ the enrichment rate from the null distribution. loci (such AUTS2, CNTNAP2, Neurexin 1 gene (NRXN1) and Chr15q13) which have previously been linked to autism spectrum To assess whether the genes listed in Supplementary Table 1 – cluster within specific biological processes, we used g:Profiler, a disorder (ASD) and SZ38 40 reinforcing the notion that genetic freely available collection of web tools, which characterise and association may cut across psychiatric diagnostic boundaries. interpret a given list of disease-associated genes in the context of Although the ADHD CNV findings reviewed above provide their biomedical ontologies and pathways. g:Profiler examines intriguing evidence for a role for these large structural variations in every functional category/term in the gene ontology(GO), KEGG ADHD, several points are worth noting. First, the majority of CNVs (Kyoto Encyclopedia of Genes and Genomes) pathway and implicated thus far in ADHD are evidently not highly penetrant BioGRID protein–protein interaction databases and outputs those (that is, not causally linked to ADHD) as they were also detected functional terms with a significant enrichment. The enriched (albeit less frequently) in control samples. Second, although functional terms are then ranked by their corrected statistical evidence for overlap between common variant and CNV associa- 39 significance value.33 Table 2 lists those biological processes that tions can be found (for example, CNVs reported by Lionel et al. are enriched for the ADHD-associated genes defined above. encompass both the DRD5 locus (Table 1) and an ADHD linkage Notably, the most significantly enriched functional categories for peak (15q13)), in general there is minimal overlap and further the ADHD-GWAS association signals are ‘nervous system devel- studies are now required. Third, most of the reported CNVs show opment’ (GO:0007399), ‘neuron projection morphogenesis’ (GO: limited intersection between individual patients, meaning that any 0048858) and ‘oxogenesis’ (GO:0007409). Further, ‘cell–cell com- one rare variant identified in a particular individual with ADHD munication’, ‘glutamatergic synapse/receptor signaling’ may have limited explanatory value for the broader ADHD (KEGG:04724) and ‘multicellular organismal development’ population. Notwithstanding these limitations, CNV associations fi (GO:0007275) were represented at less significant levels. A similar identi ed even in small sub-sets of individuals with ADHD may enrichment analysis conducted on the top 85 ADHD GWAS provide important signposts to biological pathways which if associations (identified prior to 2011) using Ingenuity pathway perturbed, may confer risk to the development of ADHD. Given software demonstrated a highly significant enrichment of the the rarity of these variations, clearly large sample sizes and/or functional gene category known as ‘neurological disease’. This meta-analyses are now required to establish the role of CNVs in category involves genes that have previously been associated with psychiatric conditions such as ADHD. a diverse range of neurological conditions. Further, using BiNGO 34 bioinformatics, Poelmans et al. observed enrichment of the GO NETWORK ANALYSES OF ADHD GENES processes ‘calcium ion binding’ and ‘hexokinase activity’ both of which have important roles in neurite migration. Functional As with other psychiatric disorders, the monogenic concept of enrichment analysis can therefore amalgamate seemingly dis- ADHD has now been supplanted by a more plausible polygenic parate gene findings and point to biological processes that may hypothesis where multiple risk genes (each of minor/modest have an important role in the aetiology of ADHD. effect) contribute to the aetiology of the disorder. As detailed previously, ADHD-associated genes (and those showing trends towards association) are scattered through the genome but tend CNV-GWAS to be enriched within specific functional categories. This suggests CNVs are large rare chromosomal structural abnormalities that that the emphasis on any individual candidate gene should be account for about 13% of the human genome.35 These variations shifted to consider a broader network view of biological pathways which are the result of recombination- or replication-based events35 involving ADHD-implicated genes. Recently, Cristino et al.45 have been implicated in the aetiology of several psychiatric performed a detailed complex network analysis based on the conditions.36 The major disease mechanism involves gene dosage protein–protein interactions found in the two most complete effects, whereby CNV rearrangement may result in complete loss databases, Biogrid46 and HPRD,47 which together archive over (due to deletion) or overexpression (due to duplication) of genes. 360 000 genetic and protein interactions.45 Analysis of the To date eight CNV-GWAS have been published for childhood constructed network revealed that genes involved in biological – ADHD.22,32,37 42 No excess of CNVs was observed in ADHD in a processes such as synaptic transmission, catecholamine metabolic Caucasian sample from the USA investigated by Elia et al.37 processes, G-protein signaling pathways and cell migration were However, Jarick et al.41 showed that children with ADHD have a over-represented in ADHD. Further, many of these genes showed significantly increased frequency of CNVs at the PARK2 gene, a considerable interactions with genes identified as trending gene that has also been implicated in schizophrenia (SZ).43 towards significance in GWAS. For example, Neurexin 1 (a cell Further, two studies by Williams et al.38,40 reported a significant adhesion molecule) and Inositol 1,4,5-trisphosphate receptor excess of CNVs ⩾ 100 kbp in ADHD cases and a significantly (both trending towards significance in ADHD-GWAS) interact with increased burden and enrichment of duplicated CNVs ⩾ 100 kb SNAP25 (candidate gene) via synaptotagmin 1 (involved in that spanned genes (1.2-fold). Notably, CNV duplications spanning neurotransmitter release) and protein kinase cAMP-dependent a nicotine receptor gene (CHRNA7) were associated with ADHD.40 catalytic alpha (PRKACA), a signaling molecule important for a

Table 2. Gene enrichment analysis of the leading ADHD-GWAS SNP associations (P ⩽ 1×10− 5)

GO/KEGG ID P-value Biological process Genes

GO:0007399 5.06E − 04 Nervous system development MOBP, AK8, PTCH1, BCL11A, CPLX2, MAP1B, GRM5, RHOC, TGFB2, ZNF423, DNMT3B, GRIK1, UNC5B, NDN, NR4A2, FOXP1, HOXB1, TRIO, MYT1L, MAGI2, ATXN2, CLASP2, CTNNA2, ARSB GO:0048812α 2.56E − 03 Neuron projection morphogenesis BCL11A, MAP1B, RHOC, TGFB2, UNC5B, NDN, NR4A2, FOXP1, TRIO, ATXN2, CLASP2, CTNNA2 GO:0007409β 3.80E − 03 Axonogenesis BCL11A, MAP1B, RHOC, TGFB2, UNC5B, NDN, NR4A2, FOXP1, TRIO, CLASP2, CTNNA2 GO:0048731 4.80E − 03 System development CDH13, MOBP, AK8, ITGA11, PTCH1, BCL11A, PTHLH, CPLX2, MAP1B, GRM5, RDH10, RHOC, TGFB2, TFEB, ZNF423, DNMT3B, GRIK1, UNC5B, MEIS2, CREB5, DMRT2, NDN, NR4A2, FOXP1, HOXB1, TRIO, MYT1L, CRYGC, EREG, MAGI2, ATXN2, CLASP2, CTNNA2, ARSB GO:0060560 6.26E − 03 Developmental growth involved in BCL11A, PTHLH, MAP1B, RDH10, NDN, MAGI2 morphogenesis GO:0007275Ψ 6.67E − 03 Multicellular organismal development CDH13, MOBP, AK8, ITGA11, PTCH1, BCL11A, PTHLH, CPLX2, MAP1B, GRM5, TLL2, RDH10, RHOC, TGFB2, TFEB, ZNF423, DNMT3B, GRIK1, UNC5B, MEIS2, CREB5, DMRT2, NDN, NR4A2, FOXP1, HOXB1, TRIO, MYT1L, TSHZ2, CRYGC, EREG, MAGI2, ATXN2, PSMC3, CLASP2, CTNNA2, ARSB GO:0031175Φ 6.73E − 03 Neuron projection development BCL11A, MAP1B, RHOC, TGFB2, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2 GO:0048589 7.57E − 03 Developmental growth BCL11A, PTHLH, MAP1B, RDH10, NDN, FOXP1, EREG, MAGI2 GO:0048699Ψ 1.40E − 02 Generation of neurons PTCH1, BCL11A, MAP1B, GRM5, RHOC, TGFB2, DNMT3B, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2 GO:0040007 1.54E − 02 Growth CDH13, PTCH1, BCL11A, PTHLH, MAP1B, RDH10, TGFB2, NDN, PPM1F, FOXP1, EREG, MAGI2, ATXN2 GO:0030182 1.96E − 02 Neuron differentiation PTCH1, BCL11A, MAP1B, RHOC, TGFB2, DNMT3B, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2 KEGG:04724 2.71E − 02 Glutamatergic synapse GRM5, GRIK1, GRIK4 GO:0043616 3.06E − 02 Keratinocyte proliferation CDH13, PTCH1, EREG KEGG:03050 3.87E − 02 Proteasome SHFM1, PSMC3 GO:0030030 4.31E − 02 Cell projection organization CDH13, BCL11A, MAP1B, RHOC, TGFB2, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2 Abbreviations: ADHD, attention deficit hyperactivity disorder; GO, gene ontology; GWAS, genome wide association studies; KEGG, Kyoto Encyclopedia of Genes and Genomes; SNP, single-nucleotide polymorphisms. α = the same set of genes was also significantly enriched for cell projection morphogenesis (GO: 0048858, P = 1.90E − 02) and cell part morphogenesis(GO:0032990, P = 2.30E − 02). β = the same set of genes was also significantly enriched for axon development (GO: 0061564, P = 5.20E − 03) and cell morphogenesis involved in neuron differentiation (GO: 0048667, P = 1.04E − 02), Φ = the same set of genes was also significantly enriched for neuron development (GO: 0048666, P = 2.61E − 02). Ψ = the same set of genes was also significantly enriched for neurogenesis (GO:0022008, P = 2.66E − 02) and with a few exceptions for single-organism developmental processes (GO:0048856, P = 4.85E − 02), anatomical structure development (GO:0048856, P = 4.85E − 02) and single-multicellular organism processes (GO:0044707, P = 5.00E − 02). As the functions of the genetic loci LOC100505836, LOC100287010, LOC100506534, LOC392232, LOC101059934 and LOC643542 are not characterised, these loci were not included in the analysis. Further, the genes PDCP1, AK094352, LINC01183, SPATA13, BAALCOS and TSHZ2 were not included in the gene profiling analysis as they were not recognized by g:profiler.

variety of cellular functions. Analyses such as this can be used to regulatory mechanisms rather than perturbation of a single predict novel candidate genes that interact strongly with, or form function by one (or a few) highly penetrant mutation(s). The important network connections between, ADHD-associated genes. validity of this hypothesis has been questioned by the finding that For example, the network analysis of Cristino et al.45 demonstrated DNA variants, which influence gene expression show limited that the protein STX1A is an excellent new candidate as it interacts overlap with candidate or GWAS hits.48 Further, common SNP- with six ADHD-associated targets including: SNAP25, the gaba- disease associations observed under GWAS explain only a small nergic (SLC6A1), noradrenergic (SLC6A2), dopaminergic (SLC6A3) proportion of the heritability of complex phenotypes. For and serotonergic (SLC6A4) biogenic transporters and SYP example, GWAS identified 32 common SNP associations with (a glycoprotein participating in synaptic transmission). A role for Crohn's disease, yet these only explained ~ 20% of the overall STX1A is biologically very plausible as it is a member of a gene disease variance.4 In ADHD, SNP heritability for common variants family (including SNAP25) that is essential for docking of synaptic (examining the total contribution of SNPs to ADHD liability) was vesicles and the control of neurotransmitter exocytosis. estimated at 0.42 in a Han Chinese ADHD sample. This was found to be higher than that for a sample of European ancestry (0.28) although the two were significantly correlated.22 Despite the INTEGRATING COMMON DISEASE COMMON VARIANT AND limited number of significant GWAS findings for ADHD thus far, a RARE VARIANT ACCOUNTS OF ADHD role for common DNA variants in the aetiology of ADHD seems The CDCV hypothesis postulates that the aetiological effect of likely. Yet it is clear that genetic mapping using the CDCV common variants is largely driven by the disruption of complex hypothesis has captured only a small proportion of variation in

Molecular Psychiatry (2015), 289 – 297 © 2015 Macmillan Publishers Limited Attention deficit hyperactivity disorder Z Hawi et al 293 many diseases including ADHD and, that it fails to allow for the combination with ADHD rating scales, to derive latent classes that contribution of other factors including rare variants. Further, represent ADHD individuals with and without ASD features. notwithstanding the reproducibility of candidate gene findings Strategies such as this could both serve to reduce heterogeneity and the fact that GWAS have been completed for large number of (see below) as well as identifying unique and overlapping genetic genetic conditions/traits (as of November 2014, the catalog of signatures. GWAS includes 2041 publications reporting 14 778 SNP associations; http://www.genome.gov/gwastudies/), neither the underlying causal variants nor the pathological mechanisms of the FUNCTIONAL CHARACTERIZATION OF ADHD-ASSOCIATED majority of disease-associated variants have been defined. GENES In ADHD, Stergiakouli et al.21 examined whether SNPs trending Despite advances in our ability to detect DNA variants that confer towards GWAS significance influenced the same biological path- risk to complex genetic conditions, our knowledge of the functional ways as associated CNVs. They observed that the pathways effects of these variants is limited. Specifically, reproducible genetic enriched for GWAS-SNPs significantly overlapped with those associations between ADHD and the SLC6A3, DRD4, DRD5, SLC6A4, enriched for rare CNVs, indicating that common and rare variants SNAP25, LPHN3, CDH13, GIT1 and NOS1 genes have been reported, are likely to inform processes of relevance to the aetiology of yet neither the functional variant nor the exact pathological ADHD. Further, a whole-exome analysis performed on ADHD mechanism and pharmacological importance of these findings are nuclear family (father, two siblings and unaffected mother) known. Systematic screening of some complex disease-associated identified non-synonymous rare mutations in four brain- genes has revealed allelic variations that affect gene expression and expressed genes that have been implicated in other neuropsy- modify disease risk.58 10 chiatric conditions (ATP7B, CSTF2T, ALDH1L1 and METTL3). Measurement of allelic expression differences has been used as However, none of the mutations seemed to be highly penetrant a quantitative method for analyzing cis-acting polymorphisms and and ADHD causative. More recently, excess of rare variants epigenetic factors affecting gene expression and messenger RNA (synonymous and non-synonymous substitutions) were reported processing, and cis-acting elements have been found to explain to be carried on the seven-repeat allele of the DRD4-VNTR, a 35–54% of inter individual differences in gene expression.59,60 common allele of the VNTR, which has been reliably associated 49,50 Notably many regulatory polymorphisms are located in and with ADHD. However, as yet none of the investigated samples around gene promoter regions and function by altering transcrip- fi fi has suf cient power to yield rm conclusions regarding the role of tion. Further, some research has demonstrated the importance of rare variants in ADHD or any other genetic complex phenotype. genetic variations mapped to intronic regions, exon/intron boundaries and the 3′ untranslated regions in regulating gene SHARED GENETIC COMPONENTS ACROSS PSYCHIATRIC expression. The protein–protein interaction network analysis of 45 CONDITIONS Cristino et al. involving primary candidate genes for ADHD, ASD, SZ and X-linked intellectual disability demonstrated the impor- Large population based twin and epidemiological studies51,52 have tance of gene expression control in the aetiology of psychiatric led to increased recognition of symptom overlap (comorbidity) disorders.45 Specifically, genes showing evidence of association among psychiatric disorders. This in turn has been reflected in with these phenotypes were enriched for motifs important for changes to the Diagnostic and Statistical Manual of Mental transcription binding factors and/or micro-RNAs (mi-RNA) in their Disorders (DSM) with the fifth edition now allowing for dual upstream control regions (containing cis-regulatory sequences) diagnosis of conditions such as ADHD and ASD. It is now also and downstream untranslated regions (3′ untranslated region), widely acknowledged that the high rate of comorbidities and the 45 co-segregation amongst psychiatric phenotypes may suggest a respectively. Critically, an extremely limited number of func- shared genetic architecture. For instance, part of the shared genetic tional genomic studies have been performed in ADHD. Given the liability for ADHD and bipolar disorder may be explained by DNA availability of promising genetic targets such as CDH13, future variations in intron 8 of the dopamine transporter gene.53 Similarly, research should focus on characterizing the functional importance ADHD19,ASD54 and SZ55 share a susceptibility risk loci in the form of these variants and the mechanisms by which they may fl of the NRXN1 a member of the cell adhesion pathway, which is in uence the development of ADHD. known to function in neuronal cell adhesion (a critical property for synaptic formation and cell signaling pathways). SAMPLE SIZE AND DETECTION OF RISK GENES Further evidence for shared genetic liability comes from a large GWAS that involved 27 888 controls and 33 332 ethnically Seven years of GWAS in complex psychiatric conditions (including matched cases with psychiatric disorders including ADHD, ASD, ADHD) has demonstrated that the statistical power to detect 56 associations generally falls well short of acceptable levels (⩾80% SZ, bipolar disorder and unipolar disorders. This study found 61 cross-disorder SNP associations in L-type voltage-gated calcium power). Further, identifying and or replicating common risk fi channel subunits of the genes CACNA1C and CACNB2.56 Pathway variants in ADHD has proven particularly dif cult in comparison to and network analysis using GWAS data in all experimentally other psychiatric conditions such as schizophrenia. The limited validated pathways (KEGG pathways and Genomes database) success of nine ADHD-GWAS and a meta-analysis to detect shows significant association of five pathways (including synaptic significant associations may, in large part, be attributed to the neurotransmission) that are common to both schizophrenia and small effect sizes of individual risk loci in combination with small bipolar disorder.57 The abundant overlap of SNP, gene and samples and large correction for multiple comparisons. Although pathway associations reported using candidate genes and GWAS one cannot guarantee that GWAS significant hits will emerge with may explain some of the shared clinical overlap (comorbidity) increased samples sizes in ADHD, this has been the case for among psychiatric conditions. It also suggests the presence of schizophrenia where a large combined multi-stage schizophrenia common vulnerability mechanisms that contribute to conditions GWAS of 36 989 cases and 113 075 controls identified 128 such as ADHD. These findings may contribute to the identification independent associations spanning 108 loci, 83 of which had of shared pathological mechanisms for shared clinical presenta- not been previously reported.62 These findings for schizophrenia tions. Clearly, the next wave of genetic association studies of are instructive for ADHD as the sample size for the ADHD ADHD will need to pay greater attention to cross-disorder psychiatric genetic consortium (N = 4163) is smaller than that of associations for common comorbid conditions. For example, well the individual samples of other psychiatric disorders/traits validated quantitative rating scales for ASD traits could be used in including schizophrenia.

© 2015 Macmillan Publishers Limited Molecular Psychiatry (2015), 289 – 297 Attention deficit hyperactivity disorder Z Hawi et al 294 As statistical power depends on the number of causal variants, lying intermediate between that of ADHD probands and unrelated their effect sizes and their frequency distribution, large samples controls. Neurocognitive measures also have the added advan- (tens of thousands of subjects) of homogeneous ethnic back- tage of being readily scalable to the large sample sizes that are ground will be required to examine the role of rare variants in required for gene discovery. A number of physiological measures, ADHD.63,64 However, it should be noted that the need for such derived either from human electroencephalography (for example, large samples can be obviated (with some loss of information) by low frequency EEG oscillations)82 or functional brain imaging (for using collapsing methods that aggregate and analyse data at a example, resting-state functional MRI (rsfMRI))83,84 may relate to high order level, such as at the gene or pathway level. these cognitive measures and show tremendous promise for indexing liability to ADHD. CAN ALTERNATIVE PHENOTYPING STRATEGIES AID GENE Despite the promise of the endophenotype approach to ADHD, DISCOVERY FOR ADHD? relatively few replicated molecular genetic associations have emerged thus far. For example, a number of studies have reported It is now widely acknowledged that clinical and aetiological isolated genetic associations between both dopaminergic and heterogeneity poses a major obstacle to mapping the genetic 65–68 noradrenergic candidate genes, such as SLC6A3, DRD4 and SLC6A2 architecture of psychiatric disorders. Despite this broad and response time variability in ADHD samples.85–87 In addition, a acknowledgement each of the case-control SNP or CNV-GWAS quantitative trait analysis using this phenotype in 238 ADHD and studies of ADHD performed thus far, has adopted a unitary view of 147 control revealed suggestive linkage to chromosomes 12, 13 the disorder, comparing large heterogeneous samples of indivi- and 17, although these linkage signals showed little overlap with duals with ADHD to control samples. Although this approach is fi 88 pragmatic as neither individual ADHD nor consortia cohorts have ndings from linkage scans of ADHD. Nevertheless, a recent study by Cummins et al.89 highlights the the sample size to parse by DSM subtype or the presence of comorbidity, for example, it undoubtedly introduces noise and utility of novel data analysis strategies in combination with an places an upper limit on the gene discovery potential of these endophenotype approach. Working with a sample of 402 non- case-control studies. Although latent classes derived from clinical adults, response time variability measures were derived quantitative symptom counts, for example, have been shown to across a range of cognitive tasks assessing aspects of executive be heritable and may offer greater gene discovery potential than function and attention. Principal components analysis was used to DSM-defined subtypes, in general there has been little application reduce the dimensionality of the data and yielded two distinct of these symptom-based approaches within ADHD GWAS.69 The response time variability factors. Genetic association across 22 study by Lasky-Su et al.25 is a notable exception that suggests that catecholamine genes was then performed separately for each gains in power may be afforded by adopting a quantitative trait, principal component. Significant associations with SNPs of the as opposed to case-control methodology.65,70 ADRA2A gene and a response time variability factor were found Quantitative symptom-based approaches have also been that survived corrections for multiple comparisons both at the employed in both child and adult population-based cohorts.27,71 level of genotype and phenotype. Further, scores on this response Groen-Blokhuis et al.71 recently tested the hypothesis that a time variability factor mediated the relationship between DNA polygenic risk score derived from GWAS meta-analysis of clinically variation in ADRA2A and self-reported ADHD symptoms. Thus by defined childhood ADHD could predict continuous scores of using a relatively large sample size and reduced data dimension- inattention and hyperactivity rated using the Attention Problems ality, a relationship was established between a noradrenergic scale (APS) of the Child Behavior Checklist (CBCL) in a population- gene, response time variability and ADHD-like behaviours. This based child cohort (N = 2437). Polygenic risk scores predicted both association was partially replicated in a recent population based maternal (pre-school and school age) and teacher (school age) study (Bastiaansen et al., personal communication). ratings of AP, highlighting both the fact that APs exist on a Other studies have used novel analytic strategies to determine continuum in the normal population and the potential value of whether ADHD samples can be decomposed into distinct subgroups dimensional ratings for genetic studies of ADHD behavior. using neurocognitive indices. These approaches represent the Although a shift from categorical diagnoses to quantitative antithesis of the unitary model that has been used predominantly symptom measures is encouraging, we argue that the use of in genetic studies of ADHD. For example, Fair et al.90 obtained data intermediate traits, or endophenotypes, may hold greater promise 66,72,73 from 498 children (213 typically developing, 285 ADHD) on 20 for gene discovery in ADHD. While the study of susceptibility neuropsychological measures from a wide domain of cognitive genes is a valid and reliable method for improving biological functions implicated in the aetiology of ADHD. A factor analysis on understanding, its power is limited by several factors: the small size this data identified seven factors, including one designated as of likely individual gene effects; the heterogeneity of genetic response variability that could classify individuals (ADHD versus effects; reduced gene penetrance and the presence of phenocopies control) with 65% accuracy when used in a supervised classification within the sample. For these reasons, studies of susceptibility genes algorithm. Community detection was then used to determine for psychiatric disorders have emphasized the utility of quantitative 65,68 unique neuropsychological subgroups separately in typically devel- indices of disease risk or liability, termed endophenotypes. — — oping children and those with ADHD. Four distinct cognitive Endophenotypes are traits cognitive or physiological for exam- fi ple, that may be closer to dysfunction in discrete neural systems subtypes were identi ed in the typically developing children, than in the broad phenotype. Since the endophenotype is thought suggesting that multiple aetiological processes may underpin to be less removed from the relevant gene action than diagnosis, cognition even in normative samples. Community detection in the the genetic architecture of the endophenotype may be simpler ADHD group yielded the equivalent four subgroups plus two than that for a complex disorder such as ADHD.66,68,72 additional subtypes. Implementing a supervised classification Arguably the best evidence for an endophenotype for ADHD algorithm within each subgroup improved diagnostic accuracy, exists for a number of neurocognitive measures, for example, indicating that heterogeneity of individuals with ADHD appears to response time variability74,75, response inhibition76–78 and tem- be nested in normal variation. An important but as yet untested poral processing.79,80 Detailed reviews of the endophenotype implication of the approach of Fair et al.90 is that such heterogeneity approach in ADHD can be found elsewhere.66,72,73,81 Broadly reduction techniques may aid gene discovery in ADHD. Of course, speaking, studies have now demonstrated that these cognitive this will necessitate the collection of well-powered collaborative measures are heritable, associated with ADHD and often exhibit a samples of individuals with and without ADHD and a commitment familial risk profile with the performance of unaffected siblings to a set of empirically based endophenotypes.

Molecular Psychiatry (2015), 289 – 297 © 2015 Macmillan Publishers Limited Attention deficit hyperactivity disorder Z Hawi et al 295 CONCLUDING REMARKS 7 El-Fishawy P, State MW. The genetics of autism: key issues, recent findings, and The last 20 years has seen significant advances in our under- clinical implications. Psychiatr Clin North Am 2010; 33:83–105. standing of the genetic correlates of complex diseases, spurred on 8 International HapMap Consortium. The International HapMap Project. Nature 426 – in part by technological advances in high throughput genotyping. 2003; :789 796. fi 9 Kenny EM, Cormican P, Furlong S, Heron E, Kenny G, Fahey C et al. Excess of rare To date, candidate gene and GWA analyses have identi ed novel loss-of-function variants in synaptic genes in schizophrenia and autism genomic risk regions that are associated with ADHD; however, the spectrum disorders. 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Hyperlocomotion and indifference to cocaine and amphetamine in mice lacking the dopamine newly emerging picture of the genetic architecture of psychiatric 379 – phenotypes, including ADHD, supports a role for complex transporter. Nature 1996; :606 612. fl 13 Gainetdinov RR, Wetsel WC, Jones SR, Levin ED, Jaber M, Caron MG. Role of interactions within biological systems in uenced by both common serotonin in the paradoxical calming effect of psychostimulants on hyperactivity. and rare genetic variants. Thus, network analyses will be essential Science 1999; 283:397–401. to further clarify the potential of gene–gene and pathway 14 Russell VA. The nucleus accumbens motor-limbic interface of the spontaneously interactions. Future genetic studies of ADHD—whether they be hypertensive rat as studied in vitro by the superfusion slice technique. Neurosci focused on common or rare DNA variants— should of course Biobehav Rev 2000; 24:133–136. consider substantial increases in sample sizes as this has proven 15 Fuke S, Suo S, Takahashi N, Koike H, Sasagawa N, Ishiura S. The VNTR poly- fruitful in detecting risk genes in other complex conditions. morphism of the human dopamine transporter (DAT1) gene affects gene However, we argue that the current paradigm of analyzing expression. Pharmacogenomics J 2001; 1: 152–156. psychiatric disorders such as ADHD as a unitary construct works 16 Spencer TJ, Biederman J, Faraone S V, Madras BK, Bonab A, Dougherty DD et al. fi against the gains in power that substantial increases in sample Functional genomics of attention-de cit/hyperactivity disorder (ADHD) risk size hope to achieve. Instead, we advocate that a paradigm shift to alleles on dopamine transporter binding in ADHD and healthy control subjects. Biol Psychiatry 2013; 74:84–89. incorporate dimensional approaches to ADHD, either using 17 Markant J, Cicchetti D, Hetzel S, Thomas KM. Relating dopaminergic and choli- symptom measures or preferably empirically based endopheno- nergic polymorphisms to spatial attention in infancy. Dev Psychol 2014; 50: types, could accelerate gene discovery. Further, deconstructing 360–369. global assessments of ADHD into component subtypes, each 18 Boonstra AM, Kooij JJS, Buitelaar JK, Oosterlaan J, Sergeant JA, Heister JGAM potentially with its own genetic-neurophysiological mechanism, et al. An exploratory study of the relationship between four candidate genes and should aid targeting of existing treatments as well as promoting neurocognitive performance in adult ADHD. Am J Med Genet B Neuropsychiatr the development of novel treatments directed at specific Genet 2008; 147:397–402. biological substrates. The ADHD research community has made 19 Neale BM, Medland S, Ripke S, Anney RJL, Asherson P, Buitelaar J et al. Case- great strides in validating candidate endophenotypes. In our view, control genome-wide association study of attention-deficit/hyperactivity dis- 49 – the time has now come to empirically test the long held order. J Am Acad Child Adolesc Psychiatry 2010; : 906 920. 20 Hinney A, Scherag A, Jarick I, Albayrak Ö, Pütter C, Pechlivanis S et al. Genome- assumption that quantitative endophenotypes will offer the gains wide association study in German patients with attention deficit/hyperactivity in power for gene discovery long sought in psychiatric genetics. disorder. Am J Med Genet B Neuropsychiatr Genet 2011; 156B:888–897. 21 Stergiakouli E, Hamshere M, Holmans P, Langley K, Zaharieva I, Hawi Z et al. CONFLICT OF INTEREST Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am J Psychiatry 2012; 169:186–194. The authors declare no conflict of interest. 22 Yang L, Neale BM, Liu L, Lee SH, Wray NR, Ji N et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am J ACKNOWLEDGMENTS Med Genet B Neuropsychiatr Genet 2013; 162B:419–430. This work would not have been possible without the generous support provided by 23 Mick E, Todorov A, Smalley S, Hu X, Loo S, Todd RD et al. Family-based genome- the NHMRC to ZH, TDRC and MAB (APP569636, APP1002458 and APP1065677, wide association scan of attention-deficit/hyperactivity disorder. J Am Acad Child respectively). MAB is supported by a Future Fellowship from the Australian Research Adolesc Psychiatry 2010; 49: 898–905.e3. Council of Australia (FT130101488). 24 Neale BM, Lasky-Su J, Anney R, Franke B, Zhou K, Maller JB et al. Genome-wide association scan of attention deficit hyperactivity disorder. 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