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The genetic mechanisms involved in attention deficit hyperactivity disorder (ADHD) are being studied with considerable success by several centres worldwide. These studies confirm prior hypotheses about the role of genetic variation within genes involved in the regulation of dopamine, norepinephrine and serotonin neurotransmission in susceptibility to ADHD. Despite the importance of these findings, uncertainties remain due to the very small effects sizes that are observed. We discuss possible reasons for why the true strength of the associations may have been underestimated in research to date, considering the effects of linkage disequilibrium, allelic heterogeneity, population differences and gene by environment interactions. With the identification of genes associated with ADHD, the goal of ADHD genetics is now shifting from gene discovery towards gene functionality – the study of intermediate phenotypes ('endophenotypes'). We discuss methodological issues relating to quantitative genetic data from twin and family studies on candidate endophenotypes and how such data can inform attempts to link molecular genetic data to cognitive, affective and motivational processes in ADHD. The International Multi-centre ADHD Gene (IMAGE) project exemplifies current collaborative research efforts on the genetics of ADHD. This European multi-site project is well placed to take advantage of the resources that are emerging following the sequencing of the human genome and the development of international resources for whole genome association analysis. As a result of IMAGE and other molecular genetic investigations of ADHD, we envisage a rapid increase in the number of identified genetic variants and the promise of identifying novel gene systems that we are not currently investigating, opening further doors in the study of gene functionality. Background avenues of investigation. International research has estab- Research into the etiology of attention deficit hyperactiv- lished that there is a strong genetically inherited contribu- ity disorder (ADHD) exemplifies the way that inter-disci- tion to ADHD and the genetic mechanisms involved are plinary research fosters collaboration and opens up new being sorted with considerable success by several centres Page 1 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 worldwide. Recent review and meta-analyses of available Linkage disequilibrium and direct versus indirect data demonstrate an emerging set of findings that confirm association Across small intervals of the genome (10,000 – 100,000 prior hypotheses about the role of genetic variation within genes involved in the regulation of catecholamine neuro- base pairs), a phenomenon called linkage disequilibrium transmitters in susceptibility to ADHD [1,2]. Despite the (LD) is observed. LD is the non-random assortment of importance of these findings, uncertainties remain due to alleles at two distinct loci, meaning that the genotype at a the very small effect sizes that are observed, with average second locus can be marginally predicted by the genotype odds ratios in the range of 1.1 to 1.5. Under simple addi- at the first locus. This non-random assortment gives rise to tive multi-gene models it is feasible that there exist numer- marginal information about a second locus from the gen- ous small genetic effects and we can estimate the otype of a first locus. So, the genetic markers reported to contribution of the current loci to phenotypic variance be associated with ADHD may not be the causal variants (Table 1). Assuming an additive model, the variants iden- (functionally significant variants or FSVs), but rather tified so far explain around 3.3% of the variance, which is nearby genetic markers that are tagging true causal vari- only 4% of the heritable component (assuming heritabil- ants through LD. The strength of association between tag- ity for ADHD of 80%). ging markers (usually single nucleotide polymorphisms or SNPs) and the causal variant is directly proportional to However, it is possible that the observed effects do not r (the squared correlation between two markers), a com- reflect the true strength of the associations and we have mon measure of LD . Further information about LD merely detected one or more pointers, behind which lie and its uses and measures are available [4-6]. larger genetic effects. Further work is required to establish the true size of the genetic effects and to use genetic infor- Direct association is the analysis of the functional allele, mation to refine the clinical and neurocognitive pheno- whereas indirect association is the analysis of a secondary types associated with the genetic markers. Underestimates allele garnering marginal signal by means of LD with the of effect size can arise for several reasons and a number of functional allele. For example, for the genes listed in Table difficulties exist in identifying associated genes and deriv- 1 there is evidence that genetic variants associated with ing accurate estimates of effect size using genetic associa- ADHD in the dopamine D4 receptor gene (DRD4) and tion studies. Some of the most likely causes are listed in the serotonin transporter promotor region (HTTLPR) may Table 2 and discussed in more detail below. So until we alter the expression or function of the genes (reviewed in have performed further investigations we cannot be confi- Asherson et al., 2004 ). In contrast, the genetic variants dent that the genes identified so far do not make a more within or close to the dopamine D5 receptor (DRD5), substantial contribution. In the following sections we will synaptosomal associated protein (SNAP-25), dopamine consider the effects of linkage disequilibrium, allelic het- beta-hydroxylase (DBH) and serotonin IB receptor erogeneity, population differences and gene by environ- (5HT1B) genes are not thought to alter gene function ment interactions. themselves. Rather, the variants that have been genotyped are in LD with, and therefore tag, nearby functional genetic changes that do alter protein structure or expres- sion. Analysis of the dopamine transporter gene (DAT1) is Table 1: Average odds ratios and 95% confidence (CI) from the pooled analysis of genetic variants found to be associated with ADHD in more than one study (Faraone et al., 2005) . Quantitative trait effects are estimated for these key findings using the variance components 2 relative risk calculator http://pngu.mgh.harvard.edu/~purcell/gpc/vc2rr.html. This program calculates the threshold, assuming a standard normal trait distribution, such that the QTL variance for the discrete category based upon this threshold would be the same as the QTL variance for the continuous measure. Assuming an additive genetic model, the proportion of phenotypic variance explained by the associated genes is around 3.2%. The number of families needed to replicate these findings with a nominal alpha of 0.05 and 80% is listed, in addition to the power from a sample of 200 families for the same significance level. Gene OR 95% CI Allele frequency QTL Number of families to replicate Power in sample of 200 cases with 80% power and 200 controls DRD4 1.16 1.03 1.31 0.12 0.001 3196 0.115 DRD5 1.24 1.12 1.65 0.35 0.004 728 0.341 DAT1 1.13 1.03 1.24 0.73 0.001 2748 0.125 DBH 1.33 1.11 1.59 0.5 0.007 391 0.561 SNAP-25 (T1065G) 1.19 1.03 1.38 0.5 0.003 1043 0.253 SERT (HTTLPR) 1.31 1.09 1.59 0.6 0.006 466 0.490 HTR1B 1.44 1.14 1.83 0.71 0.010 315 0.652 Page 2 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 Table 2: Alternative explanations for small genetic effects in association studies of ADHD. This table lists potential explanations for small effect sizes in ADHD that range between 1.1 and 2.0. Studies to include or exclude each of these possibilities have yet to be completed, so the true size of the genetic effects remains unknown at this time. Multiple genes of small effect Main effect sizes of individual genes are small. Genetic influences consist mainly of common alleles, each making a small additive contribution to genetic effects. Allelic heterogeneity Average effect sizes of individual causal variants are small. The average effect size could be contributed by common variants, each conferring a small genetic effect and/or one or more rare variants conferring larger genetic effects. Tagging markers (indirect association) Strength of the observed association is proportional to the correlation between the genotyped marker(s) and the causal variant(s). This arises since not all the markers investigated are necessarily causal variants themselves, but may be tagging nearby functional genetic variants. The strength of the association will decrease with decreasing correlation between the tagging marker and functional variant. Tagging phenotype Strength of association is proportional to the correlation between the measured phenotype and underlying genetic liability. This arises since we do not know the best way to measure underlying genetic liability for a disorder. Phenotypic measurements are proxy variables that serve to tag the assumed underlying distribution of genetic risk. The strength of the association will decrease with decreasing correlation between the phenotypic measures and genetic liability Interactions between adjacent loci Variants within a gene may interact with each to alter gene function. This can arise since genetic variants may have functional consequences that depend on variation at a second variable site. An example that has been proposed is an interaction between the intro 8 and 3'UTR variants in the dopamine transporter gene (described in text). Higher-order interactions Main effects of individual genes may make little or no contribution to phenotypic variance. Genetic effects may be mediated by interaction with environment risks (gene by environment interactions) or other genetic loci (gene by gene interactions, referred to as epistasis). ongoing and the functional status of the associated association can only be estimated by direct association; marker is not yet clear . this can only occur once we have a comprehensive under- standing of how all variation affects each gene product. The extent to which direct or indirect association is the case for the variants hypothesized to be associated with a As polymorphism detection efforts increase and a higher disorder such as ADHD, is difficult to assess. Biological density of genetic variants across genes become available, significance cannot be declared as a result of a genetic the chances of typing causal variants directly, or markers association study but only through functional assays . strongly correlated with such variants, is increasing (see However, such assays are far more expensive to run and Figure 1 for historical overview). Groundbreaking more difficult to interpret than genetic association. For advances are being made in the rapid identification of this reason genetic association studies aim to cull the list SNPs throughout the genome [13,14] and efficient geno- of candidate variants to a likely few for biological investi- typing platforms that enable simultaneous genotyping of gation. Further information about likely candidates can hundreds of thousands of SNPs are already available . be derived from haplotype analysis (the analysis of multi- ple closely linked markers). By typing multiple markers in Haplotype association in ADHD A haplotype refers to a specific sequence along a single a region we may be able to distill our list of suspects to a few variants on a specific haplotype. chromosome. For example, if we consider SNP markers, each with two possible alleles (A/a, B/b, C/c, D/d, E/e, F/ Association with common genetic variants or haplotypes f), there are 2 or 64 possible combinations along a single in ADHD has so far been interpreted to mean that a com- chromosome. However, for markers close together and in mon causal variant exists that confers a small genetic risk LD with each other (i.e. correlated with each other), there for ADHD. Yet it remains feasible that rare alleles, which is usually limited haplotype diversity. This means that confer larger genetic risks in a subset of individuals, may only certain combinations commonly occur. For example, exist if they are correlated with the common genetic mark- if we assume equal allele frequencies of 0.5, the chances ers or haplotypes that have been identified so far. With the of the any single haplotype occurring (e.g. ABCDEF) exception of the dopamine D4 receptor gene [9-12], the would be 1/64. However, due to limited haplotype diver- genes thought to be associated with ADHD have not been sity we might find that 20% of chromosomes contain the fully investigated. For this reason we cannot say whether ABCDEF haplotype and another 40% of chromosomes the genetic variants associated with ADHD are likely to be the AbCDef haplotype. Such markers are said to tag each causal variants themselves or might tag common causal within a population, since their relationship is non-ran- variants in the region, or even rare variants with a larger dom and they are associated with each other to form effect in a subset of individuals. The true strength of the blocks or groups of highly correlated genetic changes. Page 3 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 1976 Classical genetic markers e.g. HLA Small multiplex families 1975 Southern blotting 1985 PCR 1990 Simple sequence repeats Large multiplex families First sib-pair studies (n=200) 1994 3,000 SSR map 1999 SNP consortium Large sib-pair studies and meta- analysis 2000 Human sequence SNP arrays Large scale association studies HAPMAP Whole genome arrays Whole genome association H Figure 1 istorical perspective on gene mapping in common disorders Historical perspective on gene mapping in common disorders. Initial studies, before DNA markers became available, relied on classical genetic markers such as blood or HLA types and therefore provided very limited information on a few regions of the human genome. The early genetic markers that used restriction enzymes to cut DNA at specific DNA sequences could identify sites that differed by one or more DNA bases. These restriction fragment length polymorphisms (RFLPs) were analyzed using a technique called Southern blotting that could identify one or a few markers at a time and was a relatively slow process. Link- age analysis came of age with the identification of another class of genetic variants, the simple sequence repeats (SSRs) that commonly consist of between two to four base pairs that are repeated in variable number tandem sequences (e.g. (AC)n) and are found approximately one every 50 thousand base pairs (Kb) across the genome. Around 3,000 such SSRs were identified for the first major human genome map in the mid 1990's, whereas only 400 of such markers are required for a first pass linkage scan. More recently the SNP consortium was established to identify single nucleotide polymorphisms (SNPs) that occur far more frequently, approximately one every 500 base pairs and are therefore useful for high-density association mapping. These are key to current studies since association, unlike linkage, can only be detected by markers that are correlated with functional variants in the population and are informative over very small distances. The HapMap project was set up to genotype SNPs across the genome in representative populations and establish the structure of linkage disequilibrium. High-density arrays that can be used to genotype between 350,000 – 500,000 SNPs in a single assay are now available and provide between 65–75% coverage for all SNPs with a minor allele frequency greater than 0.05. Further development of 1,000,000 plus arrays will be able to detect all common variation across the genome. As an example of a haplotype association in ADHD, we eral previous studies have documented the association of can consider recent findings from the analysis of the ADHD with a repeat length polymorphism in the 3'- dopamine transporter gene (DAT1) and ADHD . Sev- untranslated region (3'-UTR) of DAT1, although averaged Page 4 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 across studies the odds ratio is small and there is evidence functional variants; either there is a small effect across the of heterogeneity [2,16]. We recently genotyped an addi- entire sample (consistent with multiple additive genetic tional repeat length polymorphism within intron 8 of the effects) or a large effect in a subset of samples (consistent same gene and found that in two independent popula- with genetic heterogeneity). tions from the United Kingdom and Taiwan, risk for ADHD was associated with a specific combination of alle- Allelic heterogeneity and gene-wide tests of association les at the two loci. Only chromosomes with a 10-repeat Allelic heterogeneity refers to a situation where multiple allele in the 3-untranslated region and a 3-repeat allele in causal variants of differing frequency and effect size exist intron 8 were associated with risk for ADHD (see Figure 2 within a gene. Additionally, different populations may for explanation of these terms and Figure 3 for illustration exacerbate this problem of allelic heterogeneity, as it is of the haplotype specific association). Since none of the common for variation across the genome to be present or other common allelic combinations (10/2 and 9/3) con- absent, depending on the population studied. Further- ferred risk for ADHD, we concluded that either the 10/3 more, LD, which is derived from population history, haplotype tags a functional variant that occurs on this changes between populations . The extent to which haplotype background (i.e. neither of the markers studied this is the case is still unclear, but a number of studies so far are functional), or that there is a direct interaction have observed different haplotype structures across differ- between two functional variants. We subsequently ent populations [19-21]. reported a similar pattern of findings using the stage I sample of 680 families from the International Multi-cen- For these reasons, gene-wide testing that analyzes all tre ADHD Gene project . sequence variation across a gene may provide a better frame for assessing evidence for association with a partic- The DAT1 haplotype association is illustrated in Figure 3. ular trait or disorder. Conceptually, there may be more Although the most common explanation for these find- than one variant within a gene conferring risk to a disor- ings is the existence of a common variant of DAT1 confer- der such as ADHD. A gene-wide test of association takes ring a small genetic risk to ADHD, it is also possible that this possibility into account by summing up all the evi- a rare allele exists on the background of the 10/3 haplo- dence for association with markers occurring across an type. For example, a rare risk allele (A+) conferring a large entire gene. At the same time gene-wide tests of associa- risk for ADHD might occur on chromosomes with the 10/ tion adjust significance values for multiple variants span- 3 haplotype. The 10/3/A+ haplotype would therefore be a ning a gene. Gene-wide testing therefore aims to allay some relatively rare subset of all chromosomes with the 10/3 of the difficulties of allelic heterogeneity by allowing for haplotype and would be associated with a large risk for multiple association signals to contribute to a single piece ADHD, whereas chromosomes with the alternative 10/3/ of evidence. Gene-wide tests of significance also provide a A- haplotype would confer no risk for ADHD. For these useful framework for dealing with the vast number of tests reasons we cannot be certain of the size of the genetic inherent in genome-wide association . Fundamental effect until the entire gene has been extensively investi- to a gene-based approach is a comprehensive analysis of gated by re-sequencing and identification of all potential each gene, which requires re-sequencing in multiple indi- Illustration Figure 2 of a typical protein-coding gene Illustration of a typical protein-coding gene. The promoter sequence regulates the process of messenger RNA (mRNA) pro- duction. mRNA is the template from which proteins are translated by matching of amino acids to the mRNA sequence. The gene is divided into exons (yellow), which are the coding regions for the amino acids in the protein. The untranslated regions (red) are found at either end of the mRNA and have various regulatory functions affecting mRNA expression and protein translation; because these regions appear in the mature mRNA molecule, they are also classified as exon sequences. Introns (blue) are found in the primary transcript and are spliced out to form the mature mRNA molecule. Sequences flanking each exon direct the splicing process. Additional elements regulating mRNA production can be found both within introns as well as outside of the gene. Genetic variation in any of the functional regions may alter either protein structure or expression. Page 5 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 dopamine D4 receptor gene (DRD4) in the IMAGE sam- UK sample ple illustrate the problem. In a sample of 680 families we Taiwanese sample just hit nominal significance for the DAT1 association, whereas for DRD4 we required a total dataset of over 0.6 1,100 families . For both associations the observed 0.4 odds ratios were very close to those reported in the meta- 0.2 analysis by Faraone et al.  (Table 1). Second, few studies have taken a comprehensive approach 2 - 9 3 - 9 2 - 10 3 - 10 -0.2 to the analysis of individual genes by scanning genetic var- iation across entire gene regions. An example of a compre- -0.4 hensive gene-based approach is reported in a recent study -0.6 of the noradrenergic transporter gene (NET1) . This initial study aimed to screen the entire region spanning -0.8 NET1. This was achieved by selecting database SNPs with minor allele frequencies greater than 5% that occurred within known functional regions; upstream promotor Log of the odds ratios for b mo Figure 3 etween rphism ADHD and the in s in DAT1 ha tron plotype specific associations 8 and 3'-UTR repeat poly- region, 5' and 3' untranslated regions, coding regions Log of the odds ratios for haplotype specific associations between ADHD and the intron 8 and 3'-UTR repeat poly- (exons) and intron sequences flanking each exon. The var- morphisms in DAT1. Only chromosomes that contained the ious sequences that make up the DNA sequence for typical specific combination of the 3-repeat allele at the intron 8 protein coding genes are illustrated in Figure 2. Since marker and the 10-repeat at the 3'-UTR marker were over- NET1 has not been fully sequenced in multiple individu- transmitted from heterozygote parents to their affected off- als, we do not know the location of all potential func- spring with ADHD (adapted from Brookes et al., 2005) . tional variants within the gene, which might for example include regulator elements in non-coding regions of the gene. Additional tagging SNPs were therefore selected, which were predicted to tag polymorphic variants (through LD) that are currently unknown and therefore viduals to identify all potential causal variants. Although not available for direct association analysis. In total we at this time re-sequencing of all potential genes associated identified 26 SNPS and screened these for association with ADHD is prohibitively expensive, it is envisaged that with ADHD in case and control samples. Three SNPs were technical developments will make such an approach fea- identified that showed nominal significance. Two of SNPs sible within the next decade. that had previously been reported to show no association with ADHD [24-26] were also negative in this study. Screening candidate genes for association The candidate gene approach has been successful in iden- The small effect sizes that we observed for the three nom- tifying several genetic variants that are associated with inally associated SNPs led us to conclude that, after adjust- ADHD. This has been largely a matter of good fortune, in ment for the 26 SNPs tested, there was no evidence for the sense that the genes investigated initially were selected association between ADHD and NET1. In a subsequent since they code for protein targets of many treatments study, Bobb et al.  reported on genetic variants that used in general psychiatry, including stimulants used to had previously been reported to show nominal associa- treat ADHD. Such studies are, however, far from complete tion with ADHD and found significant association with and in some cases candidate genes have been prematurely two of the three SNPs that we had identified (rs998424 described as not associated with ADHD. This has occurred and rs3785157), although with the opposite SNP alleles. for two main reasons. The two markers associated with ADHD in both studies are strongly correlated with each other, having an r-square First, sample sizes used to date are insufficient to reliably statistic of 0.93 in the UK sample and can therefore be detect small effect sizes, similar to those identified so far. considered to tag a single genetic association. However, Table 1 lists the sample sizes required to replicate the most despite evidence for association with the same two SNPs significant findings reported to date, assuming 80% in two studies, we cannot be confident in these findings power of detection and a nominal alpha value of 0.05. We due the different directional effects of the SNP alleles. Fur- have also listed the amount of power at the same alpha ther studies will therefore be required to clarify whether level to detect these genetic effects with a sample size of the SNP cluster tagged by these two markers is associated 200, which is similar to that used in many published stud- with ADHD or whether these are merely chance observa- ies to date. Our most recent analyses of DAT1 and the tions. Page 6 of 13 (page number not for citation purposes) log (odds ratio) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 We have used a similar approach in the first set of analyses An emerging literature on the effects of gene-environment using the IMAGE stage I dataset of 680 families. Our aim interactions on behavioural disorders and an outline of in this study was to complete a systematic scan of genes the methodological issues has recently been reviewed that are functionally related to the main candidate gene . Using a longitudinal population sample from Dun- systems identified to date. Our criteria for selection of can- edin in New Zealand, Caspi, Moffitt and colleagues didate genes included analysis of genes with a priori evi- reported three key findings. First they hypothesized that a dence of association with ADHD and genes involved in functional polymorphism in the promoter region of the the regulation of the neurotransmitter pathways impli- gene encoding the neurotransmitter metabolizing enzyme cated by the previous associations. We identified a total of monoamine oxidase A (MAOA), would moderate the 52 genes that fell into the categories of brain expressed effect of child maltreatment in the cycle of violence. Their catecholamine (dopamine, noradrenaline, serotonin) results showed that maltreated children with genotypes transporters, receptors, metabolism and catabolism genes. that conferred low levels of MAOA expression were more Additional categories included synaptic vesicle genes asso- likely to develop conduct disorder, antisocial personality ciated with synaptosomal associated protein gene (SNAP- disorder and adult violent criminal behaviour than chil- 25) and clock genes involved in regulation of circadian dren possessing high activity variants of MAOA . In rhythms. In total we identified 1,536 SNPs with reported the second study they hypothesized that a functional var- minor allele frequencies greater than 5% that fell within iant in the promoter region of the serotonin transporter known functional sequences or tagged common haplo- gene (HTTLPR) would moderate the influence of stressful types spanning each gene. Since some genes had a very life events on depression. They found that individuals high proportion of non-validated SNPs, we included 230 with 1 or 2 copies of the HTTLPR short allele exhibited SNPs with non-validation status, of which only 13% more depressive symptoms, diagnosable depression, and turned out to be polymorphic. Of the 1,306 SNPs that suicidality following stressful life events than individuals were reported to be validated, only 68% were polymor- homozygous for the long allele . This finding has phic in our sample, including 91% of 556 SNPs with gen- been replicated in several further studies and is now one otypes available from Caucasian samples in the of the most consistent findings in psychiatric genetics [31- International HapMap database ; HapMap is an inter- 33]. In the third study they reported that a functional var- national resource for the selection of SNPs across the iant of the catechol-O-methyltransferase gene (COMT whole genome. Our final dataset included 928 polymor- Val158Met) would moderate the risk of cannabis use by phic SNPs spanning 3,121 thousand bases pairs (kb) with adolescents on the later development of psychosis in an average SNP density of 1 every 3.36 kb. Despite the adult life . large sample size, we could only draw a few firm conclu- sions . We found nominal significance with one or These three findings highlight the importance of consider- more genetic markers in eighteen genes, including the two ing the effects of environmental exposure in the search for most replicated findings in the literature: DRD4 and genetic risk factors. Moffitt, Caspi and Rutter noted several DAT1. Gene-wide tests adjusted for the number of mark- important methodological points in their review . ers studied in each gene identified associations with First, they noted that several of their initial reports were TPH2, ARRB2, SYP, DAT1, ADFRB2, HES1, MAOA and subsequently replicated, indicating the robust nature of PNMT. Further studies will be needed to confirm or refute some G × E interactions on human behaviour. Second, the observed associations. that in each case the environmental risk involved had shown an association with the disorder in previous epide- Gene by environment interactions miological studies. In other words, they were known envi- To date most genetic studies in ADHD have focused on ronmental pathogens. Third, in several of the reports it the detection of genetic variants that have a main effect on was noted that there was no main effect of gene alone. the risk for behavioural disorders. However, it has been This has important implications since the search for recognized for a long time that gene-environment interac- genetic associations with behavioural disorders would tions are likely to play an important role on risk for behav- have been unsuccessful in these examples if interaction iour and in some cases will be present in the absence of with the environmental pathogen had not been taken into main effects. What is not widely understood is that the account. These findings have promoted a new wave of heritable component estimated from family, twin and interest in gene-environment research, although identify- adoption studies indexes both the main effects of genes ing such interactions remains a major challenge. Unlike plus the effects of gene-environment interaction. For this the DNA variation, where we know that we will soon be reason environmental research remains critical to our able to scan the entire human genome for associated understanding of psychiatric disorders, even for those that genetic variants, environmental research will depend on are highly heritable such as ADHD. careful selection of appropriate and measurable environ- mental risks. Information ascertainment on environmen- Page 7 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 tal risk factors will present particular methodological logical measures of brain function in addition to the anal- challenge, as prospective cohort studies are likely to con- ysis of behavioural phenotypes. tain too few ADHD cases for meaningful analysis, while retrospective recall data on risk exposure in case and con- 'Tagging phenotypes' trol design is likely to be confounded by recall bias. It is The term 'tagging phenotype' refers to the fact that the therefore particularly timely to examine the genetic contri- measures of ADHD used in genetic studies, such as DSM- bution of ADHD quantitative traits amongst subjects from IV diagnosis or symptom checklists, are unlikely to map prospective cohorts, as well as gathering retrospective data directly onto the underlying distribution of genetic liabil- of risk exposure in ADHD cases. Convergence of evidence ity. The strength of the correlation between the measures from both prospective and retrospective data can then used and genetic liability will therefore contribute to provide cross validation of findings from both strategies. reduced estimates of genetic effect sizes. For this reason it Nevertheless, this will be necessarily a time consuming has been argued that 'endophenotypes' (see below) that and costly process and requires reasonable prior hypothe- are thought to be more direct measures of brain processes ses to be generated. than behavioural phenotypes might correlate more strongly with genetic risk factors for ADHD. Yet we cannot Once a specific gene-environment interaction has been take this assumption for granted; evidence is as yet lack- identified, the next set of questions is to clarify the precise ing. At this stage we advocate the use of cognitive endo- mechanisms involved. This is not always immediately phenotypes to delineate the causal pathways that mediate obvious, since apparent interactions with an environmen- genetic effects on behaviour, as discussed below, rather tal variable may have several causes, including the possi- than their use as primary gene mapping tools. bility that scaling effects in the outcome measure can give rise to statistical interactions when the true mechanism is Cognitive endophenotypes a simple additive effect . The term 'endophenotype' was adopted for use in psychi- atric research by Gottesman and colleagues [36,37], who As an example in ADHD research we consider the recent proposed the following criteria: (1) The endophenotype is report of an interaction between the mothers use of alco- associated with illness in the population; (2) The endo- hol during pregnancy and genetic variants of the phenotype is heritable; (3) The endophenotype is prima- dopamine transporter gene (DAT1) on the risk for devel- rily state-independent (manifests in an individual opment of childhood ADHD . In this study only those whether an illness is active or not); (4) Within families, individuals carrying the DAT1 risk alleles whose mothers endophenotype and illness co-segregate, and (5) The used alcohol during the pregnancy showed an increased endophenotype found in affected family members is risk for ADHD. Yet there are several plausible explana- found in non-affected family members at a higher rate tions for this observation. First, there may be a direct toxic than in the general population. effect of alcohol on the developing fetus. Further work to establish this causal link needs to focus on more detailed With the primary emphasis on delineating causal path- analysis that considers the timing and amount of alcohol ways, the study of cognitive endophenotypes represents a used by mothers during the pregnancy. However, other second stage in molecular genetic research on ADHD: causal relationships need to be considered since maternal once genetic associations are identified in the IMAGE and drinking may be correlated with parental behaviours that other ADHD samples, the functionality of the risk genes – could act as more proximal risk factors, such as levels of the mechanisms by which the genes increase the risk for critical comments, quality of parenting and maternal psy- the disorder – becomes a key research focus. Such investi- chopathology including ADHD. Interactions with varia- gation of the cognitive and motivational processes in bles that reflect parental behaviour may also index genetic ADHD within a genetic design is at its early stages (for loading consistent with the increased co-transmission of reviews of initial findings, (see [38-41]). For future interacting genes (gene-gene interaction also referred to as progress, a careful selection of cognitive-experimental epistasis). For example, in this study we also found pre- measures based on theory-driven phenotypic and quanti- liminary evidence for gene-environment correlation tative genetic investigations is recommended. between the DAT1 risk alleles and prenatal use of alcohol. Although we controlled for this in our analysis, it high- Here, we discuss methodological issues relating to quanti- lights the complexity of interpreting gene-environment tative genetic data from twin and family studies on candi- effects where genes cause change in parent as well as off- date endophenotypes and how such data can inform spring behaviour. Well-designed epidemiological studies attempts to link molecular genetic data to cognitive, affec- are one approach, but direct testing of environmental tive and motivational processes in ADHD. Processes that hypotheses may require the use of animal behavioural have been proposed to be affected in ADHD include state and genetic models and a focus on more direct neurobio- regulation, response inhibition, working memory, aspects Page 8 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 of attention, temporal processing, 'delay aversion' and A key methodological limitation in several of the previous reward processing [42,43]; a consensus is yet to emerge on twin studies relates to small sample sizes and therefore detailed aspects of the theoretical arguments. limited statistical power. Studies with somewhat larger sample sizes suggest a moderate degree of heritability for Estimating heritability and familiality several measures of reaction time, working memory and Gottesman and colleagues [36,37] included heritability as general executive function performance [45-50]. We the second criterion in their list endophenotype criteria. reported twin model fitting data at the Eunethydis 2005 As a clear distinction has not always been made between meeting from 400 twin pairs on a go/no-go task, a reac- the terms 'heritability' and 'familiality', we first describe tion time task ('fast task'), digit span backwards and a how such estimates are obtained. 'delay aversion' task. Several key measures of reaction time, inhibition and working memory performance indi- A twin design is required to estimate heritability . The cated a moderate degree of genetic influence . These logic behind quantitative genetic analyses of twin data has tasks are also being applied to a large sub-set of the three parts. First, monozygotic (MZ) twins share all their IMAGE sample. inherited parental chromosomes and are therefore genet- ically identical, whereas dizygotic (DZ) twins, like ordi- Yet we also demonstrated how the true extent of genetic nary full siblings, share on average only half of their influences may have been underestimated, due to possi- parental chromosomes and therefore 50% of inherited ble effects on analyses from measure unreliability  genetic variation. For shared environmental influences (see also Luciano et al., ). This is because test-retest MZ and DZ twins are expected to correlate to the same reliability sets an upper limit for the heritability estimates. extent. As such, when the similarity of MZ twins is greater Even a high test-retest reliability of .8 indicates that 20% than the similarity of DZ twins, this indicates a genetic of the variance cannot be accounted for; in twin model fit- contribution to the behaviour being measured. In model ting the 20% would be 'added' to the variance component fitting, this yields a 'narrow sense' heritability estimate that reflects a combination of child-specific environmen- (additive genetic variance). Second, if only genes were tal influences and measurement error, hence deflating the influencing their behaviour, MZ twins' behaviour should heritability estimate (and possible shared environmental be at least twice as similar as DZ twins'. If, however, DZ estimate). Given that many cognitive-experimental meas- twin pairs are less than twice as similar as MZ twin pairs, ures have at best moderate-to-good reliability (see, for this indicates that environments the children share in example, Kuntsi et al., , caution is required when common have enhanced their similarity. In model fitting, interpreting heritability estimates, especially across stud- this yields an estimate for shared environmental variance. ies, if test-retest data are not available. A combination of Third, if MZ twins, despite sharing all their genes, are not test-retest reliability data and twin model fitting data can perfectly identical in their behaviour, this indicates that provide information on variables that will have maxi- experiences unique to each twin have reduced the twins' mum reliability and therefore maximum sensitivity to behavioural similarity or the possibility of measurement genetic individual differences . error. In model fitting, this yields an estimate for child- specific environmental variance, which also includes Multivariate quantitative genetic analyses measurement error. Beyond the initial requirement of heritability or familial- ity, an endophenotype for ADHD also needs to show The key difference between twin and sibling designs is shared genetic or familial influences with those on ADHD. that, whereas twin design produces the three estimates – This can be investigated using multivariate quantitative heritability, shared environmental influences and child- genetic analyses. specific environmental influences – sibling designs can- not distinguish between genetic influences (heritability) In multivariate twin analysis, MZ and DZ correlations are and shared environmental influences: 'familiality' reflects compared across traits: that is, one twin's scores on the first the combination of genetic and shared environmental trait are correlated with the co-twin's scores on the second effects. trait. If the cross-trait twin correlations are greater for MZ than for DZ twins, this implies shared genetic influences Heritability and familiality of candidate endophenotypes on the two traits. A genetic correlation (r ) indicates the in ADHD extent to which genetic influences on one trait overlap The cognitive-experimental variables that indicate signifi- with those on another trait (regardless of their individual cant heritability or familiality are targets for molecular heritabilities). Based on the genetic correlation and the genetic investigations. The existing twin and family data individual heritability of each trait, the extent to which on measures of attention and executive functions were shared genetic influence generates the phenotypic correla- recently reviewed by Doyle et al . tion between the traits can be estimated. Page 9 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 In a sibling design, significant within-individual cross- project with a final projected dataset of 1,400 families. trait correlations are indicative of common etiological Clinical data and cryopreserved lymphocytes or estab- influences on the traits; significant cross-sibling cross-trait lished cell lines are deposited at Rutgers University, to correlations imply that these common etiological influ- ensure the dataset will be available to international inves- ences are familial. Where mean scores on a trait, such as a tigators now and in the future. The overall aim of the cognitive ability, are available from probands, their (unse- IMAGE project is to take a systematic approach to screen- lected) siblings and controls, the extent of shared familial ing the genome for novel genes and gene systems using a effects on the proband selection variable (such as ADHD) combination of categorical and quantitative trait and the second trait (such as cognitive ability) can also be approaches to linkage and association. This dual quantified using group familial correlation. This bivariate approach is well supported by recent findings that suggest index, which varies between 0 and 1, indicates the extent the existence of genes of moderate effect size (e.g. odds to which the scores of the siblings on the second trait ratios > 3) co-acting with multiple genes of small effect regress away from the population mean and towards the (e.g. odds ratio < 3). A quantitative trait locus genome proband mean. Alternatively, if adopting a binary linkage scan is planned for summer 2006, which we pro- approach, the extent to which 'unaffected' siblings of pose to follow with genome association studies using the probands show the second trait (such as poor perform- latest generation of high-density SNP arrays. ance on a cognitive task) can be studied as an indicator of an endophenotype. The sample consists of European Caucasian subjects recruited from twelve specialist clinics in eight countries: Initial data suggest shared genetic or familial effects on Belgium, Germany, Holland, Ireland, Israel, Spain, Swit- ADHD and response variability and aspects of executive zerland and United Kingdom. The initial stage I sample function performance. The finding on shared genetic/ consists of 680 DSM-IV combined type probands with familial effects on ADHD and response variability was 808 siblings, of which 102 also had combined type first suggested by our small-scale twin study  and has ADHD, making a total of 782 affected individuals. Since subsequently been replicated in an independent family we evaluated all available siblings, which included 102 study  as well as, most recently, in a sub-set of the combined type probands from 808 siblings recruited into value (risk to siblings/pop- IMAGE sample (unpublished data). Further possible the study, we estimated the λ shared familial effects with ADHD have been indicated for ulation prevalence) for combined subtype to be around performance on tasks measuring inhibition and set shift- 6%, using the population prevalence estimates from a ing [55,54]. recent survey in the United Kingdom ; a similar esti- mate to that reported in dizygotic twins by Todd et al. Multivariate quantitative genetic analyses can also be used . Although the DSM diagnostic criteria were not to investigate the extent of shared genetic influences designed to be genetically homogeneous categories, the across multiple cognitive-experimental variables. Such analysis from Todd and colleagues suggests that DSM-IV analyses can be informative, both theoretically regarding combined type ADHD may be a genetically homogeneous possible causal pathways, and in guiding molecular subgroup, since this subtype falls within a single empiri- genetic analyses. If meaningful composite scores can be cally derived latent class that shows high levels of subtype created, these are likely to have increased reliability com- concordance in monozygotic and dizygotic twin pairs. pared to the original scores. Such composite scores would also help reduce the need for multiple comparisons Although the IMAGE sample is a relatively large clinical within a molecular genetic investigation. sample, we have seen that association signals have been very small. For this reason we should expect that at a The IMAGE project genome-wide level, positive association signals will still These are exciting times in the world of human molecular not stand out above the background distribution of asso- genetics. The sequencing of the human genome in 2000 ciation findings. Larger scale collaborative projects will has been rapidly followed by the development of interna- therefore be required if we are to find many of the genes, tional resources for whole genome association analysis. particularly those that fall within novel genes and gene Micro-array technology is already available for the simul- systems, that influence risk for ADHD. We (and others) taneous analysis of 500,000 SNP markers across the therefore aim to establish international collaborations to human genome and these resources will be further devel- generate the very large datasets, in the order of two to five oped in years to come. thousand samples, for whole genome analysis. The International Multi-centre Gene (IMAGE) project is Concluding remarks and future directions well placed to take advantage of these emerging resources. As a result of these new studies, we envisage a rapid The IMAGE sample consists of a European multi-site increase in the number of identified genetic variants asso- Page 10 of 13 (page number not for citation purposes) Behavioral and Brain Functions 2006, 2:27 http://www.behavioralandbrainfunctions.com/content/2/1/27 ciated with ADHD and the promise of identifying novel chiatric conditions and developmental course, including gene systems. The size of the genetic effects ADHD identi- the persistence/desistance of psychiatric symptoms and fied to date are small, although as we have discussed in co-morbidity between psychiatric disorders and traits. As some cases these may be tagging larger effects. Translation a result, we can look forward to new insights and potential of these effect sizes into 'real world significance' is difficult advances in our treatment of ADHD throughout the to determine in advance of completing the task of gene lifespan. identification. Clearly, if one or more genetic variants are identified with a large influence on risk for ADHD, these Competing interests might play a key role in diagnostic prediction and forming The author(s) declare that they have no competing inter- targets for drug development. ests. However, it is entirely feasible that larger genetic effects Authors' contributions will not be identified and the risks from any single genetic This article is based, in part, on presentations by each of th variant will be small and have little predictive value on the authors at the 16 Eunethydis meeting, Valencia, 6–9 their own. In this scenario diagnostic predictions would October 2005. JK, BN and PA drafted the manuscript, with likely be expressed as a probability depending on the further contributions from WC and SF. All authors read additive effects of multiple genetic variants. It is, however, and approved the final manuscript. expected that the additive effects of genes will compro- mise the function of only one or a few neurobiological Acknowledgements The IMAGE project is a multi-site, international effort supported by NIH pathways, and the identification of even small genetic grant R01MH62873 to S. Faraone. Project Principal Investigators are Philip effects will help to identify the key pathways involved, Asherson, Tobias Banaschewski, Jan Buitelaar, Richard P. Ebstein, Stephen thereby identifying novel systems for drug discovery and V. Faraone, Michael Gill, Ana Miranda, Fernando Mulas, Robert D. Oades, investigation of critical interactions with environmental Herbert Roeyers, Aribert Rothenberger, Joseph Sergeant, Edmund Sonuga- risks. Clearly the current data implicate genetic variation Barke, Eric Taylor and Hans-Christoph Steinhausen. The IMAGE-London of dopamine pathways in the etiology of ADHD, although cognitive endophenotype research is funded by UK Medical Research this finding was predicted in advance of the genetic stud- Council grant G0300189 to J. Kuntsi. The twin project, on which we ies and does not therefore come as a great conceptual reported data at the Eunethydis meeting, is funded by Wellcome Trust advance in the field. The key additional question is grant GR070345MF to J. Kuntsi and P. Asherson. B. Neale is supported by National Eye Institute Grant EY-12562. whether genetic approaches that do not rely on a priori hypotheses will uncover novel genes and neurodevelop- References mental processes that had not been previously consid- 1. Faraone SV, Perlis RH, Doyle AE, Smoller JW, Goralnick JJ, Holmgren ered. Furthermore, by linking genetic findings to direct MA, Sklar P: Molecular genetics of attention-deficit/hyperac- neurobiological markers of brain function using cognitive tivity disorder. Biol Psychiatry 2005, 57(11):1313-1323. 2. Asherson P: Attention-Deficit Hyperactivity Disorder in the neuroscience and direct experimentation on model sys- post-genomic era. Eur Child Adolesc Psychiatry 2004, 13(Suppl tems, the effects of genetic variation associated with 1):I50-70. 3. 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Asherson P, Kuntsi J, Taylor E: Unravelling the complexity of attention-deficit hyperactivity disorder: a behavioural genomic approach. Br J Psychiatry 2005, 187:103-105. Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours — you keep the copyright BioMedcentral Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp Page 13 of 13 (page number not for citation purposes)
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