Previous genome-wide linkage studies applied the affected sib-pair design; one investigated extended pedigrees of a genetic isolate. Here, results of a genome-wide high-density linkage scan of attention-deficit/hyperactivity disorder (ADHD) using an array-based genotyping of ∼50 K single nucleotide polymorphism (SNPs) markers are presented. We investigated eight extended pedigrees of German origin that were non-related, not part of a genetic isolate and ascertained on the basis of clinical referral. Two parametric analyses maximizing LOD scores (MOD) and a non-parametric analysis for both a broad and a narrow phenotype approach were conducted. Novel linkage loci across all families were detected at 2q35, 5q13.1, 6q22-23 and 14q12, within individual families at 18q11.2-12.3. Further linkage regions at 7q21.11, 9q22 and 16q24.1 in all families, and at 1q25.1, 1q25.3, 9q31.1-33.1, 9q33, 12p13.33, 15q11.2-13.3 and 16p12.3-12.2 in individual families replicate previous findings. High-resolution linkage mapping points to several novel candidate genes characterized by dense expression in the brain and potential impact on disorder-relevant synaptic transmission. Our study provides further evidence for common gene effects throughout different populations despite the complex multifactorial etiology of ADHD.
Attention-deficit/hyperactivity disorder (ADHD; MIM no. 143465) is a categorical classification of a complex behavioral phenotype comprising inattention, motor hyperactivity and impulsivity. The etiologically heterogeneous syndrome represents the most common child and adolescent behavioral disorder with similar prevalence rates throughout different cultural settings and independent of the applied classification system.1 ADHD is increasingly being recognized as highly persistent into adulthood associated with considerable risk for psychiatric comorbidity and failure in psychosocial adaptation.2, 3, 4 Although estimates from twin studies have consistently shown a heritability of 70–80%,5 the underlying molecular genetic mechanisms remain to be elucidated. Association studies reported inconsistent results on variants in dopaminergic, serotoninergic and synaptosomal genes.6, 7, 8, 9, 10, 11 Genome-wide linkage scans revealed several susceptibility loci: four studies employed an affected sib-pair design,7, 8, 12, 13 one investigated extended pedigrees of a genetic isolate.14 Linkage regions overlapping between the respective studies were identified across the genome; however, most findings did not reach the statistical significance threshold. Here, we report results from a linkage analysis on eight extended families densely segregating for ADHD using a ∼50 K single nucleotide polymorphism (SNP) array-based genotyping assay. Linkage analyses in extended families for a complex trait are generally based on two theoretical assumptions: (1) multiple small gene effects will contribute to the phenotype within the general population and thus should be detectable in all or several families. (2) Within each pedigree, diminished genetic heterogeneity can be assumed15; thus, strong family-specific gene effects are also likely to be found, which may or may not be extrapolated to general populations with ADHD. Family members were classified into a category of four, that is, definite ADHD; subclinical ADHD symptoms; no ADHD; unknown. For analysis of the narrow phenotype, subclinically affected individuals were classified as not affected, whereas for analysis of the broad phenotype they were classified as affected. We applied both parametric analysis methods maximizing logarithm of the odds ratio (LOD) scores for chromosomal sections (∼500 SNPs; MODglobal) or for single marker position (MODsingle) and a non-parametric approach (NPL).
Ascertainment and assessment
We ascertained families of German origin through index children referred to three outpatient clinics in Wuerzburg, Homburg or Trier. The structure of the eight families (Pedigree 1–8; P1–P8) is shown in Figure 1 and summarized in Table 1. For the index child, strict inclusion and exclusion criteria were applied. Included index children were aged 6 or above, meeting criteria for the ADHD combined type according to DSM-IV. Index children that had a birth weight >2000 g did not show any neurological or severe somatic disorder, drug abuse or autistic disorder and did not receive medication with central nervous effect (with the exception of methylphenidate). IQ was >80. P1, P2, P3 and P4 were recruited in Wuerzburg; P5, P6, P7 and P8 in Homburg and Trier. When parents reported individuals in the extended family with presumable or definite affection with ADHD, pedigrees were drawn to determine family size and structure. Reported ADHD symptoms in more than two generations resulted in intensified recruitment of further family members either by invitation to our ADHD family outpatient clinic or by home visits. All participants signed informed written consent. The study was approved by the Ethics Committees of the Julius-Maximilians-University of Wuerzburg and the Saarland Doctors' Association (SaarlÄndische Ärztekammer), respectively.
Bilineality was not an exclusion criterion for recruitment, since it was presumably present in most recruited families. While bilineal families were excluded from ascertainment in the study by Arcos-Burgos et al.,14 a similar approach was not considered useful, since intra-familiar heterogeneity cannot be completely ruled out in complex traits. We recruited extended families that were non-related bearing in mind the possibility that different major gene effects might exist within the respective families.
Children and adolescents were clinically characterized by a semi-structured interview (Kiddie-Sads-PL German version16 or Kinder-DIPS17), child behavior checklist (CBCL)18 and by an ADHD diagnostic checklist applying DSM-IV criteria categorically and dimensionally (ADHD-DC19). Teacher ratings were obtained (TRF18 or FBB-HKS20). Psychosocial impairment was measured by self-rating for work/school situation, social and family life (Sheehan disability scale; SDS);21 affective and psychosocial status was further determined by a German depression inventory (Depressionsinventar für Kinder und Jugendliche).22 Intelligence was measured by non-verbal CFT1/2023, 24 in children or by MWT25 in adults. Adult participants were also rated by the SDS and the ADHD-DC, based on a semistructured interview, thus enabling transgenerational comparability of current ADHD affection. Current and retrospective self-ratings were obtained. Symptoms were assessed by the Wender Utah Rating Scale (WURS-K),26 retrospectively. In families recruited in Homburg and Trier, additionally the Wender Reimherr Interview27 was used. A large number of the adult family members affected with ADHD were thoroughly examined by diagnostic interview for comorbidity on axis I and II (SKID-I, SKID-II).28, 29
All family members were assessed by at least two clinicians experienced in diagnosis of childhood and adult ADHD (CF, CJ, HP, TR, JR, MR, CS, JS, CV, SW). In cases when assessment was incomplete, further information on the individual's history was obtained through close relatives (in general spouse or parents). After recruitment, all participating individuals were assigned to one of four groups: (1) certain diagnosis of ADHD: fulfilling DSM-IV criteria of ADHD (six or more in at least one scale) according to ADHD-DC including rating of pervasiveness, WURS-K above cutoff (score above 30) in adults, psychosocial impairment present according to SDS (moderate, markedly or extreme impairment in more than one measure), typical childhood history; (2) subclinical affection with ADHD: four or five DSM-IV criteria for ADHD symptoms in a scale according to the ADHD-DC, WURS-K below threshold, no or mild psychosocial impairment according to SDS, inconsistent information about childhood history; (3) no affection with ADHD: 0–3 DSM-IV criteria according to ADHD-DC, no typical childhood history, no psychosocial impairment; (4) unknown. Assignation to the respective category (1–4) was discussed and met by consensus of an expert committee of four in Wuerzburg (CJ, MR, JR, SW) or committee of three in Homburg (CF, HP, CS) considering all obtained information about the respective participant. In single cases, the committee met diagnosis ‘definite ADHD’ despite a WURS-K score below cutoff, when anamnestic information, biographic data and school reports clearly contradicted the self-ratings. Likewise, inaccurate parents' reports on their children were adjusted when clinical impression and teacher reports gave clear indication for ADHD-symptoms of the child. Therefore, the rating in the ADHD-DC combined information from most applied instruments. Information on comorbid disorders diagnosed by interviews was relevant to discriminate phenocopies from ADHD-symptoms. Family members were classified as ‘unknown’, when definite assignation was not possible, reasons for this being lack of information due to non-compliance or unclear phenotype. Individuals having ADHD-symptoms without typical childhood history were considered as ‘unknown’ as well as individuals with unclear etiology of symptoms (for example, chronic substance abuse, low birth weight <2000 g, autistic symptoms).
Genomic DNA was extracted from blood samples according to standard protocols. DNA samples were diluted to a concentration of 50 ng μl−1 suspended in TE-buffer. For genotyping of ∼50 000 SNPs, the GeneChip Human Mapping 50 K Array Hind240 (dBSNP build 125) was used according to the recommendations of Affymetrix. Initially 250 ng of genomic DNA was digested by HindIII. Ligation was performed by adaptors recognizing overhanging nucleotides (HindIII adaptor, Affymetrix Inc., Santa Clara, CA, USA) and T4 DNA ligase. Subsequently, adaptor-marked fragments were amplified by a generic primer. Purity of PCR was ensured using the QUIAquick PCR purification kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol. Purified amplicons were fragmented by DNase I and labeled by GeneChip Labeling Reagent (Affymetrix). Subsequently, 50 K arrays were used for hybridization. After washing in a Fluidics Station 450, the microarrays were stained in a three-step process with streptavidin phycoerythrin, biotinylated anti-streptavidin and again streptavidin phycoerythrin. Scanning was performed by a GeneChip Scanner 3000. Genotyping data were validated using GeneChipGCOS software.
The total genotype data of 57 244 SNPs (1151 X-linked) and the corresponding map information from the GeneChip Human Mapping 50 K Array Hind240 (dBSNP build 125) were read into the program ALOHOMORA (v0.29, http://gmc.mdc-berlin.de/alohomora),30 which was used to estimate allele frequencies and to check data for gender errors, Mendelian errors, deviation from Hardy–Weinberg equilibrium (HWE) in founders. Additionally, the data were checked for unlikely individual genotypes, which contradict information about gene flow provided by all other relatives (package Merlin v1.0-alpha, http://www.sph.umich.edu/csg/abecasis/Merlin/index.html).31 After the first control step, we excluded 30 478 SNPs with unknown position (385 SNPs), minor allele frequency <0.1 (24 541 SNPs), call rate <0.8 (5679 SNPs), HWE χ2 >12 (5275 SNPs) and with Mendelian errors (202 SNPs) or unlikely genotypes occurring in more than four families (eight SNPs). To avoid linkage peaks, which could arise from the artifact of linkage disequilibrium between the markers, we clustered the SNPs into haplotype blocks (defined by pairwise r2>0.1) and selected from each block only one marker with the highest heterozygosity. A total of 10 061 autosomal SNPs (median (25–75th percentile), that is, distance 0.15 (0.05–0.37) cM, multipoint information content over all families 0.92 (0.91–0.93)) passed this second control step and were included in linkage analyses. The information content suggested by Kruglyak and colleagues32 is a measurement for certainty of inheritance patterns provided by data at a given point on the genome. The described selection procedure not only guarantees reliable data quality, but it also still preserves, due to the dense marker map, nearly perfect information content. Additionally, the reduced marker set can be more easily handled in terms of computational demands.
The ADHD status was defined narrowly or broadly according to the criteria mentioned above. We performed both parametric and non-parametric linkage (NPL) analyses in each family and in all families together using the program GENEHUNTER-MODSCORE (http://www.staff.uni-marburg.de/~strauchk/software.html). The implemented procedure maximizes parametric LOD (MOD) scores with respect to the disease-allele frequency and three penetrances.33 We used the ‘modcalc global’ option (MODglobal) to assess MOD scores under the assumption that linkage signals within a certain chromosomal section might follow a uniform inheritance pattern, because they may arise from one and the same underlying gene variant. On the other hand, for a complex trait such as ADHD, it is possible that several causal gene variants with different inheritance patterns exist in close proximity to each other. To capture this case, we used the ‘modcalc single’ option (MODsingle) maximizing for each genetic position assumed for the putative disease locus.
Linkage peaks with MOD score >3.3 (according to the standard threshold proposed by Lander and Kruglyak34 for global significant LOD score results) or NPL score >5 (according to pointwise P⩽2.9 × 10−7) were reported. A MOD score >3 is required for linkage peaks, which overlap with findings of other studies. A minimal critical linkage region (MCR) around a MOD or NPL peak was defined (closest surrounding SNPs that showed <1 MOD or NPL value below the maximum peak) to narrow a region that displays the highest probability to harbor true linkage.
Assessment of the distribution for MOD score, due to its specific nature, requires complex simulation process, which has substantial computational demands in the situation of larger pedigrees. In addition, with respect to strong dependence between the considered combinations of phenotypes, families and methods, Bonferroni adjustment for multiple testing may imply conservativeness and loss of power. Therefore, we provide here only nominal results.
Results are reported with reference to the guidelines for genome-wide linkage analyses of genetically complex traits suggested by Lander and Kruglyak.34 Linkage across all families was detected at 2q35, 5q13.1, 6q22-23, 7q21.11, 9q22, 14q12, 16q24.1 (Table 2). MOD scores of parametric analyses under the narrow and broad phenotype assumption are shown in Figures 2 and 3. Linkage in individual families was identified at 1q25.1, 1q25.3, 9q31.1-33.1, 9q33, 12p13.33, 15q11.2-13.3, 16p12.3-12.2 and 18q11.2-12.3 (Table 3).
Novel chromosomal loci across all families were predominately detected by parametric analysis of the narrow phenotype at 2q35, 5q13.1, 6q22.32-6q23.2 and 14q12 (Figures 2 and 3; Table 2). Overlap with loci reported previously was found at 7q21.11, 9q22.1-9q22.2 and 16q24.1. In particular, we have identified a novel locus on chromosome 5q13.1 showing linkage across all families with strong support from two individual families (P1 and P3). Contrasting with our findings, linkage on chromosome 5 was reported at 5p13. Fine mapping suggested an association with a predisposing haplotype of the dopamine transporter gene (SLC6A3) located at 1.45–1.5 Mb.12 In our sample, fine mapping of 5p13 revealed no segregation of marker haplotypes with ADHD thus implicating that these loci do not contribute to a large extent to the disorder in the investigated families. However, effects might still exist in the regions that are too small to be detected in our study due to lack of power.
The locus identified on chromosome 6q22.32-23.2 is close to but not overlapping with a region showing linkage to comorbid oppositional defiant disorder (ODD).7, 35 Because of the stochastic variance in linkage analysis, however, we cannot completely rule out that both linkage loci can be referred to the same interval.36 No overlap was found between previously reported regions and the novel loci at 2q35 and 14q12. In contrast, linkage at 7q21.11 is a replication of a previous finding at 7q.12 Apart from the linkage signal across all families on chromosome 9q22.1-22.2, two additional loci on chromosome 9q in two distinct families (P1: 9q31.1-33.1; P5: 9q33.2-33.3) were identified, the latter showing the highest NPL score (9.51; broad phenotype) detected in this study. These regions overlap partially with known ADHD-related loci12, 37, 38 and with a locus that showed linkage to comorbid conduct disorder.35 It is noteworthy that the linkage peak of the recent linkage publication by Asherson and coworkers39 is bordering the MCR (88.77–90.20 Mb) of our finding on chromosome 9q22. Furthermore, this region corresponds with linkage peaks from two previous scans in affected sib-pairs.12, 13 With four independent linkage findings pointing to the same interval, the existence of a relevant gene variation within this region may be assumed. Finally, we detected linkage to 16q24.1 (MCR 82.85–85.12 Mb) in the vicinity of a linkage peak also identified by Asherson and colleagues39 at chromosome 16q (LOD>3.0). Although not referred to in detail in the original publications, the finding is compatible with previously detected linkage peaks in two distinct samples with LOD scores <2.0.13, 38, 40 Like the interval at chromosome 9q22, the region at 16q24.1 is highly probable to harbor a susceptibility gene for ADHD.
Additional loci were revealed in individual families at 1q25.1, 1q25.3, 12p13.33, 15q11.2-13.3, 16p12.3-12.2 and 18q11.2-12.3 by parametric and non-parametric analyses (Table 3). The locus at 16p12.3-12.2 (P1) is in close proximity to previously reported regions.9, 13, 37 Furthermore, the high linkage peak at 12p13.33 (P2), which had a relatively high MOD score across all families, as well (MODsingle 2.92, broad phenotype), is close to a known susceptibility locus.37 The two linkage regions at 1q25.1 (P2) and 1q25.3 (P8) were also observed by Fisher and co-workers.37 In family P2, two further loci were detected, one novel locus at 18p11.2-12.3, the other locus at 15q13.1-13.3 replicated twice at that time.38, 41
The identification of several novel linkage regions as well as replication of previously reported loci provides further evidence for the highly heterogeneous genetic etiology of ADHD. The novel locus at 5q13.1 was detected in an analysis across all families and also scored high in two individual families suggesting that the identified region contains a common gene variant. Approximately 350 kb upstream of the linkage peak the PIK3R1 gene is located, coding for a regulatory subunit of the phosphatidylinositol 3-kinase protein involved in post-receptor signaling. PIK3R1 is strongly expressed in the prefrontal cortex, a brain region implicated in ADHD symptoms and stimulant treatment response.42 Although genes within replicated linkage regions seem especially promising for fine mapping and subsequent positional cloning efforts, candidate genes may also be found in novel loci, such as the syntaxin-binding protein 6 gene (STXBP6) located within the MCR of the locus at 14q12. STXBP6 is involved in neurotransmitter release, and interacts with synaptosome-associated protein receptors including SNAP 25.10 The genes encoding the γ-aminobutyric-acid (GABA) receptor subunit β-3 (GABRB3) and the amyloid β-A4 precursor protein-binding member 2 (APBA2) are located in the center of the MCR at 15q12-13.1 (P2). GABRB3 is encoding a subunit of the GABA/benzodiazepine receptor complex mediating the effects of the major inhibitory neurotransmitter, and the APBA2 product is involved in synaptic vesicle docking/fusion by interacting with STXBP1.43
Linkage findings across all eight families resulted from the contributions of 3–7 families (family score >0). Particular for the linkage region at 5q13.1 (MODglobal 4.16, all families, broad phenotype), families P1 and P3 also showed relatively high scores (MODglobal >2.7, broad phenotype) under a similar genetic model. The other linkage study that investigated extended pedigrees segregating for ADHD14 only detected a single linkage region at 4q13.2 that corresponded in two distinct families. Even though a large set of families originating from the same genetic isolate (n=16) was investigated, no further linkage peaks across families were found. In our study, all loci identified in individual families were replications of previous linkage scans in ADHD with the exception of the locus at 18p11.2-12.3. Therefore, candidate genes at these loci not only may be specific risk factors for ADHD in the respective family, but are also likely to represent common predisposing factors. The novel locus on chromosome 18, however, requires replication. Notably, our study replicated results from genome-wide scans in affected sib-pairs further emphasizing the epidemiological relevance of our findings. Overlap with loci identified in the genetic isolate was only found for regions obtained by analysis of comorbid ODD or CD.14 Since comorbid behavioral problems are frequent in ADHD, comorbidity with CD has been suspected to indicate a genetically more pronounced variant of ADHD.35, 44 Pleiotropy and variable genetic dispositions between different population groups may account for the discrepant results of the two extended pedigree-based scans.14, 35
With the application of a genome-wide high-density scan, our results appear to provide a more accurate estimation of localization of candidate gene loci than the classical micro-satellite approach. Therefore, variance in the location of linkage peaks might, in part, be due to these alternative approaches. Differences between studies may also result from low statistical power to detect true linkage due to small sample size.36 Although the total number of the investigated individuals is comparatively small in our study, the specific family structure and the dense segregation of ADHD substantially increased statistical power, and might have facilitated replication of previously reported linkage. The pattern of bilineality observed in some of our families possibly decreased power to detect true linkage, but on the other hand reflected an inherent tendency toward assortative mating commonly observed for ADHD. Thus, including bilineal families might support the notion that observed linkage—especially when replicated—will be of relevance for the prevalence of ADHD in the general population. The loss of power due to genetic heterogeneity caused by bilineality can be partially counteracted by taking all results of appropriate statistical methods into consideration. This approach compensates for the disadvantage of both the parametric analyses assuming a disease-model maximizing the LOD score and the non-parametric analyses without any model specification, for example, possibly overestimating versus possibly underestimating linkage in the respective analyses. Overlapping results obtained by both parametric (MODglobal and MODsingle) and non-parametric (NPL) statistical methods are, therefore, supportive of robust findings. The linkage scores identified in individual families by parametric analysis were comparable to NPL scores. Across all families, however, the total NPL scores were calculated without considering different degrees of informativeness between the families, and, therefore, did not match the respective MOD scores. Although, in contrast to nearly all previous genome-wide linkage scans, we discuss only those findings that met the criteria for genome-wide significance suggested by Lander and Kruglyak,34 it ought to be kept in mind that all findings are nominal and not adjusted for multiple testing. However, since our intention to obtain comparable results from various statistical approaches accounts for the dilemma of multiple testing, Bonferroni correction would be too conservative.
In conclusion, we detected novel and replicated several previously reported linkage loci for ADHD. The considerable overlap with earlier genome-wide scans indicates that even though the genetic etiology of ADHD is complex, there is increasing evidence for common gene effects throughout different populations. We propose new susceptibility genes with possible functional relevance in light of existing brain metabolic models of ADHD. This genome-wide linkage scan for ADHD employed high marker density to optimize resolution and novel strategies of data analysis were applied to meet the statistical demands imposed by sample structure and marker density. In contrast to a previously conducted scan in extended pedigrees,14 the families are unrelated, ascertained on the basis of tertiary referral and not part of a genetic isolate. The investigation of multigenerational pedigrees is one of the most promising approaches to identify genetic variants associated with disorders of multifactorial etiology, as the phenotype within families is rather homogeneous, and erroneous investigation of phenocopies is, therefore, highly unlikely. While several whole genome association (WGA) studies currently underway will likely clarify whether common variants explain any of the reported linkage peaks, some of our findings may represent the effect of rare alleles that are not detected easily by a WGA approach. Although specific limitations of the study design need to be considered, our results encourage ongoing efforts in the investigation of multigenerational families with dense segregation of genetically complex psychiatric disorders. Future investigations will have to include fine mapping, positional cloning efforts, WGA studies and meta-analytic approaches to clarify the relevance of the present findings.
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We thank all families for their participation and support. We also greatly appreciate the support from several co-workers, who contributed to organization of the study, data management and technical assistance: Andrea Boreatti-Hümmer, Annette Nowak, Gabriela Ortega, Ulrike Schülter, Nicole Steigerwald, Theresia Töpner. We thank Professor Konstantin Strauch for supporting us in using the program GENEHUNTER-MODSCORE. This study was supported by the Deutsche Forschungsgemeinschaft (DFG: KFO 125, SFB 581, ME 1923/5-1, ME 1923/5-3, GRK 1389/1) and the Bundesministerium für Bildung und Forschung (BMBF: 01GV0605).
Conflict of Interest
The authors declare no competing financial interests.
Author contributions: The study was supervised and directed by KPL, JM, CF and AW. MR, HP, CS and CJ ascertained and clinically characterized the families. JR, TR, MH and SW contributed to clinical characterization of family members. DWC, RB and DAS carried out the genetic analysis. Fine mapping was carried out by JS, CV and JM. Data analyses and genotyping were performed by KPL, MR, TR, AR and co-workers. Statistical analysis was performed by TN, AD and HS; CWK contributed to the power analysis. MR, CF, CJ and KPL wrote and revised the manuscript.
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