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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 advance online publication, 20 January 2015; doi:10.1038/mp.2014.183

INTRODUCTION Attention deficit hyperactivity disorder (ADHD) is the most prevalent psychiatric condition of childhood, affecting 2–10% of school age children worldwide. Its features include extreme levels of motor activity, impulsivity and inattention. Individuals with ADHD may present with predominantly inattentive or hyperactive symptoms or, more commonly, a combination of both (ADHDcombined type). These symptoms are chronic and persist into adulthood in ~ 30–60% of cases and are associated with lowered academic functioning, increased risk for drug abuse and negative consequences for family and peer relations.1,2 Although environmental influences (such as low birth weight, delivery complications, toxin exposure and food additives) have been identified, genetic factors are recognised as the critical etiological component of ADHD. Large twin studies have consistently shown higher monozygotic than dizygotic concordance rates with heritability estimates ~ 75–90%.3 Although the genetic architecture of ADHD is not known, a multi-factorial model is consistent with the high prevalence of ADHD in the general population and the high concordance rate in monozygotic twins (68–81%) but modest risk to first-degree relatives (~20%). This article provides a state-of-theart review of the molecular genetics of ADHD. Findings from candidate gene and genome-wide association studies (GWAS) are integrated using bioinformatics and complex network analysis. Whereas the vast majority of genetic studies have treated ADHD as a unitary construct, we argue that a shift towards heterogeneity reduction, including the use of empirically derived

endophenotypes and data-driven classification techniques, must now be used to advance the field. THE MOLECULAR GENETICS OF ADHD The last two decades of molecular genetic research in complex diseases including psychiatric conditions has been fuelled by the common disease common variant (CDCV) hypothesis. The CDCV hypothesis argues that common genetic variations (allele frequency 45%) of low penetrance in the population are the major contributors to genetic susceptibility to common diseases. Although there are examples where the CDCV hypothesis has proven useful for mapping genes underlying complex diseases such as Crohn's disease and Alzheimer's disease,4,5 most of the reported associations are of minor/modest effect size and account for a small proportion of the heritability of the associated disease/ trait.6 An alternative hypothesis is the common disease rare variant (CDRV) hypothesis which predicts that multiple rare variations (⩽5% frequency) have a cumulative effect that accounts for a significant proportion of the genetic risk for common conditions7 and that much of the genetic association signals reported under the CDCV approach actually represent diluted risk signals of rare, highly penetrant causal variants.8 Earlier psychiatric genetic association studies pursued the CDCV hypothesis with a candidate gene approach (pre-specified gene of interest), using a single or limited number of genetic markers, to examine the relationship between a gene and a disease condition. Advances in microarray technologies (high throughput

1 School 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

Attention deficit hyperactivity disorder Z Hawi et al

2 Table 1.

Candidate genes showing replicated evidence of association with ADHD

Gene

Associated variant

Location

Biological function

References

SLC6A3

40 bp VNTR

3′ UTR

DRD4

48 bp VNTR

Exon

Regulator of extracellular dopamine and mediates the reuptake of dopamine from the synapse. GPCR activated by the neurotransmitter dopamine.

DRD5

5ʹ flanking

SLC6A4

148 bp dinucleotide repeats 40 bp indel

Cook et al.91a; Gizer et al.92b La Hoste et al.93a; Gizer et al.92b Daly et al.94a; Gizer et al.92b

5ʹ flanking

HTR1B

rs6296

Exon1

SNAP25

rs3746544

3ʹ UTR

SLC9A9

Inversion breakpoints

Region 3p14—q21

LPHN3

Haplotype encompassing exons rs550818

Exon 4–19

Encodes a member of the latrophilin subfamily of GPCR. May act in signal transduction and cell adhesion.

Intron

GPCR kinase. Thought to be involved in vesicle trafficking, cell adhesion and increasing the speed of cell migration. Overexpression of GIT1 is known to regulate the beta2-adrenergic receptor. Mediates several biological processes including neurotransmission and is reported to associate with neurodegenerative conditions.

GIT1 NOS1

180–210 bp CA repeat

Exon

Transduces extracellular signals in the form of dopamine into several intracellular responses, including effects on adenylate cyclase, Ca2+ levels and K+ conductance. A member of a transporter family that is Na+ and Cl dependent. Mediates the reuptake of serotonin from synapses. GPCR for serotonin. A prime target for antidepressant drugs and psychoactive substances Plasma membrane protein essential for synaptic vesicle fusion and neurotransmitter release A member of large solute carrier family 9. Acts in electroneutral exchange of hydrogen/sodium ions across membranes.

Manor et al.95a; Gizer et al.92b Hawi et al.96a; Gizer et al.92b Brophy et al.97a; Gizer et al.92b de Silva et al.98a; Lasky-Su et al.21c; Mick et al.23c Arcos-Burgos et al.99a; Ribases et al.100d Won et al.101a Reif et al.102a; 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 genome-wide differences between patients and controls in 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 variant approach has been interrogated at genome-wide level using copy number of variations (CNV) and only a limited number of single-nucleotide variant analyses have been performed across all psychiatric disorders.9,10 GENETIC ASSOCIATION STUDIES OF ADHD IN THE PRE-GWAS ERA Dysregulation in biogenic neurotransmission has traditionally been implicated in the aetiology of ADHD. The clinical effectiveness of stimulant medications (such as methylphenidate), which act on both dopamine and noradrenaline11 and the neurochemistry of animal models12–14 provide firm support for dysregulation of key neurotransmitters in ADHD. Table 1 lists candidate genes from association or linkage studies that have been implicated in ADHD and includes references to the initial report and subsequent confirmations. Selection of these genes was based on (1) confirmation of original reports of association with ADHD via either meta-analysis of candidate gene studies or independent GWAS or (2) linkage evidence substantiated with association findings in large sample sizes and/or subsequent validation using animal models. In line with biological models of ADHD, these findings have revealed several ADHD risk loci mapped to biogenic neurotransmission and/or functionally related genes or pathways, such as SLC6A3, DRD4, DRD5, SLC6A4, HTR1B, SNAP-25, LPHN3 and NOS1. In many cases preliminary evidence for the functionality of the associated gene variants also exists. For example, the 10repeat allele of the DAT1 VNTR has been linked to altered expression using both in vitro gene reporter and quantitative PCR assays and in vivo human molecular imaging.15,16 Further, numerous studies have documented that allelic variation at the Molecular Psychiatry (2015), 1 – 9

DAT1 VNTR influences neurocognitive measures in both ADHD and non-clinical samples.17,18 GWAS IN ADHD SNP-GWAS In childhood ADHD, four case-control GWAS19–22, two familybased GWAS23,24 and a quantitative trait loci GWAS25 have been 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 adults.26,27 In addition, a meta-analysis has been performed on the child studies28 however, neither the childhood or adult GWAS nor the subsequent meta-analysis have yielded genome-wide significance (P ⩽ 5 × 10 − 8). In contrast, a family-based association analysis of the childhood quantitative trait loci GWAS based on six traits derived from ADHD clinical and symptom measures identified significant associations mapped to the Cadherin13 (CDH13) and glucose–fructose oxidoreductase domain 1 (GFOD1) genes.25 In addition, trends towards association with CDH13 were reported in a case-control GWAS and a meta-analysis of genomewide linkage scans.19,29 CDH13 has been reported to act as a negative regulator of neural cell growth and to associate with reduced brain volumes in ADHD individuals.30,31 Thus when taken together, these findings support a role for the CDH13 gene in ADHD. Likewise, the association of GFOD1 with ADHD has received further support in a recently published family-based study, yet its aetiological role in ADHD remains unknown.32 Overall ADHD-GWA studies have had limited success in identifying associations at the critical significance level (P ⩽ 5 × 10 − 8), however, strong trends towards association (arbitrarily set at P ⩽ 1 × 10 − 5) have been observed. Supplementary Table 1 comprehensively catalogues each of the leading ADHDGWAS signals (P ⩽ 1 × 10 − 5) alongside its biological function (as specified by the PANTHER, Gene card, UniProtKB and OMIM databases). As these ADHD-GWAS association signals largely © 2015 Macmillan Publishers Limited

Attention deficit hyperactivity disorder Z Hawi et al

represent near-significant results, not all of them will represent real risk loci (many, for example, are likely to be type 1 errors). Nevertheless, while results for any individual genetic marker may not quite reach GWAS significance, a strong clustering of nearsignificant results in a particular biological process/pathway, may itself have significance. The membership of any given gene within a particular biological process/pathway can be determined by consulting biological classification systems (such as g:Profiler) that categorise genes into functional categories. Under a null hypothesis of no enrichment in particular biological processes/pathways, the genes that are associated with a disorder will fall randomly within the different functional categories. Functional profiling examines the alternative hypothesis, determining those categories in which the genes of interest are over-represented and providing for each category a corrected P-value that reflects the departure of the enrichment rate from the null distribution. To assess whether the genes listed in Supplementary Table 1 cluster within specific biological processes, we used g:Profiler, a freely available collection of web tools, which characterise and interpret a given list of disease-associated genes in the context of their biomedical ontologies and pathways. g:Profiler examines every functional category/term in the gene ontology(GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and BioGRID protein–protein interaction databases and outputs those functional terms with a significant enrichment. The enriched functional terms are then ranked by their corrected statistical significance value.33 Table 2 lists those biological processes that are enriched for the ADHD-associated genes defined above. Notably, the most significantly enriched functional categories for the ADHD-GWAS association signals are ‘nervous system development’ (GO:0007399), ‘neuron projection morphogenesis’ (GO: 0048858) and ‘oxogenesis’ (GO:0007409). Further, ‘cell–cell communication’, ‘glutamatergic synapse/receptor signaling’ (KEGG:04724) and ‘multicellular organismal development’ (GO:0007275) were represented at less significant levels. A similar enrichment analysis conducted on the top 85 ADHD GWAS associations (identified prior to 2011) using Ingenuity pathway software demonstrated a highly significant enrichment of the functional gene category known as ‘neurological disease’. This category involves genes that have previously been associated with a diverse range of neurological conditions. Further, using BiNGO bioinformatics, Poelmans et al.34 observed enrichment of the GO processes ‘calcium ion binding’ and ‘hexokinase activity’ both of which have important roles in neurite migration. Functional enrichment analysis can therefore amalgamate seemingly disparate gene findings and point to biological processes that may have an important role in the aetiology of ADHD. CNV-GWAS CNVs are large rare chromosomal structural abnormalities that account for about 13% of the human genome.35 These variations which are the result of recombination- or replication-based events35 have been implicated in the aetiology of several psychiatric conditions.36 The major disease mechanism involves gene dosage effects, whereby CNV rearrangement may result in complete loss (due to deletion) or overexpression (due to duplication) of genes. To date eight CNV-GWAS have been published for childhood ADHD.22,32,37–42 No excess of CNVs was observed in ADHD in a Caucasian sample from the USA investigated by Elia et al.37 However, Jarick et al.41 showed that children with ADHD have a significantly increased frequency of CNVs at the PARK2 gene, a gene that has also been implicated in schizophrenia (SZ).43 Further, two studies by Williams et al.38,40 reported a significant excess of CNVs ⩾ 100 kbp in ADHD cases and a significantly increased burden and enrichment of duplicated CNVs ⩾ 100 kb that spanned genes (1.2-fold). Notably, CNV duplications spanning a nicotine receptor gene (CHRNA7) were associated with ADHD.40 © 2015 Macmillan Publishers Limited

This finding is interesting given evidence from behavioural pharmacology of a potential role for the alpha-7 nicotinic receptor in attention.44 More recently, Elia et al.32 investigated the contribution of CNVs to the aetiology of ADHD using large discovery and replication samples of European ancestry. They reported that CNVs affecting the metabotropic glutamate receptor genes (GRM1, GRM5, GRM7 and GRM8) were enriched across all cohorts. These rare variant findings overlap with strong leads from GWAS (see Supplementary Table 1 highlighting, for example, GRM5) and our functional enrichment analysis presented above, thereby highlighting the relevance of the glutamatergic synapse/ receptor signaling pathway to ADHD and the potential overlap between common and rare variant approaches to ADHD.21 It is also worth noting that CNVs reported to associate with ADHD across a number of studies were significantly enriched in genes/ loci (such AUTS2, CNTNAP2, Neurexin 1 gene (NRXN1) and Chr15q13) which have previously been linked to autism spectrum disorder (ASD) and SZ38–40 reinforcing the notion that genetic association may cut across psychiatric diagnostic boundaries. Although the ADHD CNV findings reviewed above provide intriguing evidence for a role for these large structural variations in ADHD, several points are worth noting. First, the majority of CNVs implicated thus far in ADHD are evidently not highly penetrant (that is, not causally linked to ADHD) as they were also detected (albeit less frequently) in control samples. Second, although evidence for overlap between common variant and CNV associations can be found (for example, CNVs reported by Lionel et al.39 encompass both the DRD5 locus (Table 1) and an ADHD linkage peak (15q13)), in general there is minimal overlap and further studies are now required. Third, most of the reported CNVs show limited intersection between individual patients, meaning that any one rare variant identified in a particular individual with ADHD may have limited explanatory value for the broader ADHD population. Notwithstanding these limitations, CNV associations identified even in small sub-sets of individuals with ADHD may provide important signposts to biological pathways which if perturbed, may confer risk to the development of ADHD. Given the rarity of these variations, clearly large sample sizes and/or meta-analyses are now required to establish the role of CNVs in psychiatric conditions such as ADHD. NETWORK ANALYSES OF ADHD GENES As with other psychiatric disorders, the monogenic concept of ADHD has now been supplanted by a more plausible polygenic hypothesis where multiple risk genes (each of minor/modest 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 to be enriched within specific functional categories. This suggests that the emphasis on any individual candidate gene should be shifted to consider a broader network view of biological pathways involving ADHD-implicated genes. Recently, Cristino et al.45 performed a detailed complex network analysis based on the protein–protein interactions found in the two most complete databases, Biogrid46 and HPRD,47 which together archive over 360 000 genetic and protein interactions.45 Analysis of the constructed network revealed that genes involved in biological processes such as synaptic transmission, catecholamine metabolic processes, G-protein signaling pathways and cell migration were over-represented in ADHD. Further, many of these genes showed considerable interactions with genes identified as trending towards significance in GWAS. For example, Neurexin 1 (a cell adhesion molecule) and Inositol 1,4,5-trisphosphate receptor (both trending towards significance in ADHD-GWAS) interact with SNAP25 (candidate gene) via synaptotagmin 1 (involved in neurotransmitter release) and protein kinase cAMP-dependent catalytic alpha (PRKACA), a signaling molecule important for a Molecular Psychiatry (2015), 1 – 9

3

Attention deficit hyperactivity disorder Z Hawi et al

4 Table 2.

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

GO/KEGG ID GO:0007399

P-value

Biological process

5.06E − 04 Nervous system development

GO:0048812α 2.56E − 03 Neuron projection morphogenesis

Genes MOBP, AK8, PTCH1, BCL11A, CPLX2, MAP1B, GRM5, RHOC, TGFB2, ZNF423, DNMT3B, GRIK1, UNC5B, NDN, NR4A2, FOXP1, HOXB1, TRIO, MYT1L, MAGI2, ATXN2, CLASP2, CTNNA2, ARSB 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 morphogenesis

BCL11A, PTHLH, MAP1B, RDH10, NDN, MAGI2

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 GO:0048589

7.57E − 03 Developmental growth

GO:0048699Ψ 1.40E − 02 Generation of neurons

BCL11A, MAP1B, RHOC, TGFB2, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2 BCL11A, PTHLH, MAP1B, RDH10, NDN, FOXP1, EREG, MAGI2 PTCH1, BCL11A, MAP1B, GRM5, RHOC, TGFB2, DNMT3B, UNC5B, NDN, NR4A2, FOXP1, TRIO, MAGI2, ATXN2, CLASP2, CTNNA2

GO:0040007

1.54E − 02 Growth

GO:0030182

1.96E − 02 Neuron differentiation

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

CDH13, PTCH1, BCL11A, PTHLH, MAP1B, RDH10, TGFB2, NDN, PPM1F, FOXP1, EREG, MAGI2, ATXN2 PTCH1, BCL11A, MAP1B, RHOC, TGFB2, DNMT3B, 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 predict novel candidate genes that interact strongly with, or form important network connections between, ADHD-associated genes. For example, the network analysis of Cristino et al.45 demonstrated that the protein STX1A is an excellent new candidate as it interacts with six ADHD-associated targets including: SNAP25, the gabanergic (SLC6A1), noradrenergic (SLC6A2), dopaminergic (SLC6A3) and serotonergic (SLC6A4) biogenic transporters and SYP (a glycoprotein participating in synaptic transmission). A role for STX1A is biologically very plausible as it is a member of a gene family (including SNAP25) that is essential for docking of synaptic vesicles and the control of neurotransmitter exocytosis. INTEGRATING COMMON DISEASE COMMON VARIANT AND RARE VARIANT ACCOUNTS OF ADHD The CDCV hypothesis postulates that the aetiological effect of common variants is largely driven by the disruption of complex Molecular Psychiatry (2015), 1 – 9

regulatory mechanisms rather than perturbation of a single function by one (or a few) highly penetrant mutation(s). The validity of this hypothesis has been questioned by the finding that DNA variants, which influence gene expression show limited overlap with candidate or GWAS hits.48 Further, common SNPdisease associations observed under GWAS explain only a small proportion of the heritability of complex phenotypes. For example, GWAS identified 32 common SNP associations with Crohn's disease, yet these only explained ~ 20% of the overall disease variance.4 In ADHD, SNP heritability for common variants (examining the total contribution of SNPs to ADHD liability) was 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 limited number of significant GWAS findings for ADHD thus far, a role for common DNA variants in the aetiology of ADHD seems likely. Yet it is clear that genetic mapping using the CDCV hypothesis has captured only a small proportion of variation in © 2015 Macmillan Publishers Limited

Attention deficit hyperactivity disorder Z Hawi et al

5 many diseases including ADHD and, that it fails to allow for the contribution of other factors including rare variants. Further, notwithstanding the reproducibility of candidate gene findings and the fact that GWAS have been completed for large number of genetic conditions/traits (as of November 2014, the catalog of 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 majority of disease-associated variants have been defined. In ADHD, Stergiakouli et al.21 examined whether SNPs trending towards GWAS significance influenced the same biological pathways as associated CNVs. They observed that the pathways enriched for GWAS-SNPs significantly overlapped with those enriched for rare CNVs, indicating that common and rare variants are likely to inform processes of relevance to the aetiology of ADHD. Further, a whole-exome analysis performed on ADHD nuclear family (father, two siblings and unaffected mother) identified non-synonymous rare mutations in four brainexpressed genes that have been implicated in other neuropsychiatric conditions (ATP7B, CSTF2T, ALDH1L1 and METTL3).10 However, none of the mutations seemed to be highly penetrant and ADHD causative. More recently, excess of rare variants (synonymous and non-synonymous substitutions) were reported to be carried on the seven-repeat allele of the DRD4-VNTR, a common allele of the VNTR, which has been reliably associated with ADHD.49,50 However, as yet none of the investigated samples has sufficient power to yield firm conclusions regarding the role of rare variants in ADHD or any other genetic complex phenotype. SHARED GENETIC COMPONENTS ACROSS PSYCHIATRIC CONDITIONS Large population based twin and epidemiological studies51,52 have led to increased recognition of symptom overlap (comorbidity) among psychiatric disorders. This in turn has been reflected in changes to the Diagnostic and Statistical Manual of Mental Disorders (DSM) with the fifth edition now allowing for dual diagnosis of conditions such as ADHD and ASD. It is now also widely acknowledged that the high rate of comorbidities and the co-segregation amongst psychiatric phenotypes may suggest a shared genetic architecture. For instance, part of the shared genetic liability for ADHD and bipolar disorder may be explained by DNA variations in intron 8 of the dopamine transporter gene.53 Similarly, ADHD19, ASD54 and SZ55 share a susceptibility risk loci in the form of the NRXN1 a member of the cell adhesion pathway, which is known to function in neuronal cell adhesion (a critical property for synaptic formation and cell signaling pathways). Further evidence for shared genetic liability comes from a large GWAS that involved 27 888 controls and 33 332 ethnically matched cases with psychiatric disorders including ADHD, ASD, SZ, bipolar disorder and unipolar disorders.56 This study found cross-disorder SNP associations in L-type voltage-gated calcium channel subunits of the genes CACNA1C and CACNB2.56 Pathway and network analysis using GWAS data in all experimentally validated pathways (KEGG pathways and Genomes database) shows significant association of five pathways (including synaptic neurotransmission) that are common to both schizophrenia and bipolar disorder.57 The abundant overlap of SNP, gene and pathway associations reported using candidate genes and GWAS may explain some of the shared clinical overlap (comorbidity) among psychiatric conditions. It also suggests the presence of common vulnerability mechanisms that contribute to conditions such as ADHD. These findings may contribute to the identification of shared pathological mechanisms for shared clinical presentations. Clearly, the next wave of genetic association studies of ADHD will need to pay greater attention to cross-disorder associations for common comorbid conditions. For example, well validated quantitative rating scales for ASD traits could be used in © 2015 Macmillan Publishers Limited

combination with ADHD rating scales, to derive latent classes that represent ADHD individuals with and without ASD features. Strategies such as this could both serve to reduce heterogeneity (see below) as well as identifying unique and overlapping genetic signatures. FUNCTIONAL CHARACTERIZATION OF ADHD-ASSOCIATED GENES Despite advances in our ability to detect DNA variants that confer risk to complex genetic conditions, our knowledge of the functional effects of these variants is limited. Specifically, reproducible genetic associations between ADHD and the SLC6A3, DRD4, DRD5, SLC6A4, SNAP25, LPHN3, CDH13, GIT1 and NOS1 genes have been reported, yet neither the functional variant nor the exact pathological mechanism and pharmacological importance of these findings are known. Systematic screening of some complex disease-associated genes has revealed allelic variations that affect gene expression and modify disease risk.58 Measurement of allelic expression differences has been used as a quantitative method for analyzing cis-acting polymorphisms and epigenetic factors affecting gene expression and messenger RNA processing, and cis-acting elements have been found to explain 35–54% of inter individual differences in gene expression.59,60 Notably many regulatory polymorphisms are located in and around gene promoter regions and function by altering transcription. Further, some research has demonstrated the importance of genetic variations mapped to intronic regions, exon/intron boundaries and the 3′ untranslated regions in regulating gene expression. The protein–protein interaction network analysis of Cristino et al.45 involving primary candidate genes for ADHD, ASD, SZ and X-linked intellectual disability demonstrated the importance of gene expression control in the aetiology of psychiatric disorders.45 Specifically, genes showing evidence of association with these phenotypes were enriched for motifs important for transcription binding factors and/or micro-RNAs (mi-RNA) in their upstream control regions (containing cis-regulatory sequences) and downstream untranslated regions (3′ untranslated region), respectively.45 Critically, an extremely limited number of functional genomic studies have been performed in ADHD. Given the availability of promising genetic targets such as CDH13, future research should focus on characterizing the functional importance of these variants and the mechanisms by which they may influence the development of ADHD. SAMPLE SIZE AND DETECTION OF RISK GENES Seven years of GWAS in complex psychiatric conditions (including ADHD) has demonstrated that the statistical power to detect associations generally falls well short of acceptable levels (⩾80% power).61 Further, identifying and or replicating common risk variants in ADHD has proven particularly difficult in comparison to other psychiatric conditions such as schizophrenia. The limited success of nine ADHD-GWAS and a meta-analysis to detect significant associations may, in large part, be attributed to the small effect sizes of individual risk loci in combination with small samples and large correction for multiple comparisons. Although one cannot guarantee that GWAS significant hits will emerge with increased samples sizes in ADHD, this has been the case for schizophrenia where a large combined multi-stage schizophrenia GWAS of 36 989 cases and 113 075 controls identified 128 independent associations spanning 108 loci, 83 of which had not been previously reported.62 These findings for schizophrenia are instructive for ADHD as the sample size for the ADHD psychiatric genetic consortium (N = 4163) is smaller than that of the individual samples of other psychiatric disorders/traits including schizophrenia. Molecular Psychiatry (2015), 1 – 9

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6 As statistical power depends on the number of causal variants, their effect sizes and their frequency distribution, large samples (tens of thousands of subjects) of homogeneous ethnic background will be required to examine the role of rare variants in ADHD.63,64 However, it should be noted that the need for such large samples can be obviated (with some loss of information) by using collapsing methods that aggregate and analyse data at a high order level, such as at the gene or pathway level. CAN ALTERNATIVE PHENOTYPING STRATEGIES AID GENE DISCOVERY FOR ADHD? It is now widely acknowledged that clinical and aetiological heterogeneity poses a major obstacle to mapping the genetic architecture of psychiatric disorders.65–68 Despite this broad acknowledgement each of the case-control SNP or CNV-GWAS studies of ADHD performed thus far, has adopted a unitary view of the disorder, comparing large heterogeneous samples of individuals with ADHD to control samples. Although this approach is pragmatic as neither individual ADHD nor consortia cohorts have the sample size to parse by DSM subtype or the presence of comorbidity, for example, it undoubtedly introduces noise and places an upper limit on the gene discovery potential of these case-control studies. Although latent classes derived from quantitative symptom counts, for example, have been shown to be heritable and may offer greater gene discovery potential than DSM-defined subtypes, in general there has been little application of these symptom-based approaches within ADHD GWAS.69 The study by Lasky-Su et al.25 is a notable exception that suggests that gains in power may be afforded by adopting a quantitative trait, as opposed to case-control methodology.65,70 Quantitative symptom-based approaches have also been employed in both child and adult population-based cohorts.27,71 Groen-Blokhuis et al.71 recently tested the hypothesis that a polygenic risk score derived from GWAS meta-analysis of clinically defined childhood ADHD could predict continuous scores of inattention and hyperactivity rated using the Attention Problems scale (APS) of the Child Behavior Checklist (CBCL) in a populationbased child cohort (N = 2437). Polygenic risk scores predicted both maternal (pre-school and school age) and teacher (school age) ratings of AP, highlighting both the fact that APs exist on a continuum in the normal population and the potential value of dimensional ratings for genetic studies of ADHD behavior. Although a shift from categorical diagnoses to quantitative symptom measures is encouraging, we argue that the use of intermediate traits, or endophenotypes, may hold greater promise for gene discovery in ADHD.66,72,73 While the study of susceptibility genes is a valid and reliable method for improving biological understanding, its power is limited by several factors: the small size of likely individual gene effects; the heterogeneity of genetic effects; reduced gene penetrance and the presence of phenocopies within the sample. For these reasons, studies of susceptibility genes for psychiatric disorders have emphasized the utility of quantitative indices of disease risk or liability, termed endophenotypes.65,68 Endophenotypes are traits—cognitive or physiological—for example, that may be closer to dysfunction in discrete neural systems than in the broad phenotype. Since the endophenotype is thought to be less removed from the relevant gene action than diagnosis, the genetic architecture of the endophenotype may be simpler than that for a complex disorder such as ADHD.66,68,72 Arguably the best evidence for an endophenotype for ADHD exists for a number of neurocognitive measures, for example, response time variability74,75, response inhibition76–78 and temporal processing.79,80 Detailed reviews of the endophenotype approach in ADHD can be found elsewhere.66,72,73,81 Broadly speaking, studies have now demonstrated that these cognitive measures are heritable, associated with ADHD and often exhibit a familial risk profile with the performance of unaffected siblings Molecular Psychiatry (2015), 1 – 9

lying intermediate between that of ADHD probands and unrelated controls. Neurocognitive measures also have the added advantage of being readily scalable to the large sample sizes that are required for gene discovery. A number of physiological measures, derived either from human electroencephalography (for example, low frequency EEG oscillations)82 or functional brain imaging (for example, resting-state functional MRI (rsfMRI))83,84 may relate to these cognitive measures and show tremendous promise for indexing liability to ADHD. Despite the promise of the endophenotype approach to ADHD, relatively few replicated molecular genetic associations have emerged thus far. For example, a number of studies have reported isolated genetic associations between both dopaminergic and noradrenergic candidate genes, such as SLC6A3, DRD4 and SLC6A2 and response time variability in ADHD samples.85–87 In addition, a quantitative trait analysis using this phenotype in 238 ADHD and 147 control revealed suggestive linkage to chromosomes 12, 13 and 17, although these linkage signals showed little overlap with findings from linkage scans of ADHD.88 Nevertheless, a recent study by Cummins et al.89 highlights the utility of novel data analysis strategies in combination with an endophenotype approach. Working with a sample of 402 nonclinical adults, response time variability measures were derived across a range of cognitive tasks assessing aspects of executive function and attention. Principal components analysis was used to reduce the dimensionality of the data and yielded two distinct response time variability factors. Genetic association across 22 catecholamine genes was then performed separately for each principal component. Significant associations with SNPs of the ADRA2A gene and a response time variability factor were found that survived corrections for multiple comparisons both at the level of genotype and phenotype. Further, scores on this response time variability factor mediated the relationship between DNA variation in ADRA2A and self-reported ADHD symptoms. Thus by using a relatively large sample size and reduced data dimensionality, a relationship was established between a noradrenergic gene, response time variability and ADHD-like behaviours. This association was partially replicated in a recent population based study (Bastiaansen et al., personal communication). Other studies have used novel analytic strategies to determine whether ADHD samples can be decomposed into distinct subgroups using neurocognitive indices. These approaches represent the antithesis of the unitary model that has been used predominantly in genetic studies of ADHD. For example, Fair et al.90 obtained data from 498 children (213 typically developing, 285 ADHD) on 20 neuropsychological measures from a wide domain of cognitive functions implicated in the aetiology of ADHD. A factor analysis on this data identified seven factors, including one designated as response variability that could classify individuals (ADHD versus control) with 65% accuracy when used in a supervised classification algorithm. Community detection was then used to determine unique neuropsychological subgroups separately in typically developing children and those with ADHD. Four distinct cognitive subtypes were identified in the typically developing children, suggesting that multiple aetiological processes may underpin cognition even in normative samples. Community detection in the ADHD group yielded the equivalent four subgroups plus two additional subtypes. Implementing a supervised classification algorithm within each subgroup improved diagnostic accuracy, indicating that heterogeneity of individuals with ADHD appears to be nested in normal variation. An important but as yet untested implication of the approach of Fair et al.90 is that such heterogeneity reduction techniques may aid gene discovery in ADHD. Of course, this will necessitate the collection of well-powered collaborative samples of individuals with and without ADHD and a commitment to a set of empirically based endophenotypes. © 2015 Macmillan Publishers Limited

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CONCLUDING REMARKS The last 20 years has seen significant advances in our understanding of the genetic correlates of complex diseases, spurred on in part by technological advances in high throughput genotyping. To date, candidate gene and GWA analyses have identified genomic risk regions that are associated with ADHD; however, the number of associated variants (and their effect size) is small and when considered together they explain a small proportion of the variation in ADHD. Moreover it appears that each of these risk variants is neither necessary nor sufficient to cause ADHD. Further, the majority (90%) of ADHD-associated variants are intergenic (mapped between genes) or intronic and as yet have no defined functional importance. The causative variants remain elusive. The newly emerging picture of the genetic architecture of psychiatric phenotypes, including ADHD, supports a role for complex interactions within biological systems influenced by both common and rare genetic variants. Thus, network analyses will be essential to further clarify the potential of gene–gene and pathway interactions. Future genetic studies of ADHD—whether they be focused on common or rare DNA variants— should of course consider substantial increases in sample sizes as this has proven fruitful in detecting risk genes in other complex conditions. However, we argue that the current paradigm of analyzing psychiatric disorders such as ADHD as a unitary construct works against the gains in power that substantial increases in sample size hope to achieve. Instead, we advocate that a paradigm shift to incorporate dimensional approaches to ADHD, either using symptom measures or preferably empirically based endophenotypes, could accelerate gene discovery. Further, deconstructing global assessments of ADHD into component subtypes, each potentially with its own genetic-neurophysiological mechanism, should aid targeting of existing treatments as well as promoting the development of novel treatments directed at specific biological substrates. The ADHD research community has made great strides in validating candidate endophenotypes. In our view, the time has now come to empirically test the long held assumption that quantitative endophenotypes will offer the gains in power for gene discovery long sought in psychiatric genetics. CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGMENTS This work would not have been possible without the generous support provided by the NHMRC to ZH, TDRC and MAB (APP569636, APP1002458 and APP1065677, respectively). MAB is supported by a Future Fellowship from the Australian Research Council of Australia (FT130101488).

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