Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/springer-journals/attention-deficit-hyperactivity-disorder-genetic-association-study-in-Wi7jjmle9S
- Publisher
- Springer Journals
- Copyright
- 2016 Gomez-Sanchez et al.
- eISSN
- 1744-9081
- DOI
- 10.1186/s12993-015-0084-6
- Publisher site
- See Article on Publisher Site
Abstract
Background: Attention deficit hyperactivity disorder (ADHD) has a strong genetic component. The study is aimed to test the association of 34 polymorphisms with ADHD symptomatology considering the role of clinical subtypes and sex in a Spanish population. Methods: A cohort of ADHD 290 patients and 340 controls aged 6–18 years were included in a case–control study, stratified by sex and ADHD subtype. Multivariate logistic regression was used to detect the combined effects of multi‑ ple variants. Results: After correcting for multiple testing, we found several significant associations between the polymorphisms and ADHD (p value corrected ≤0.05): (1) SLC6A4 and LPHN3 were associated in the total population; (2) SLC6A2, SLC6A3, SLC6A4 and LPHN3 were associated in the combined subtype; and (3) LPHN3 was associated in the male sample. Multivariable logistic regression was used to estimate the influence of these variables for the total sample, combined and inattentive subtype, female and male sample, revealing that these factors contributed to 8.5, 14.6, 2.6, 16.5 and 8.5 % of the variance respectively. Conclusions: We report evidence of the genetic contribution of common variants to the ADHD phenotype in four genes, with the LPHN3 gene playing a particularly important role. Future studies should investigate the contribution of genetic variants to the risk of ADHD considering their role in specific sex or subtype, as doing so may produce more predictable and robust models. Keywords: Attention deficit hyperactivity disorder, ADHD, Association study, Case–control, LPHN3 an important role in the etiology of ADHD, and the mean Background estimated heritability in childhood is 76 % [4], suggesting Attention deficit hyperactivity disorder (ADHD) is one that ADHD is one of the psychiatric disorders with the of the most common neurodevelopmental disorders in most substantial genetic component. young people, affecting 5.3 % of school-age children [ 1]. Also, approximately 65 % of children with ADHD con Many association studies have investigated genetic susceptibility to ADHD. However, efforts to replicate tinue to show symptoms in adulthood [2]. these results have often been poor, yielding inconsistent ADHD is a complex and heterogeneous disorder and results as demonstrated in meta-analysis of candidate its etiology remains unidentified to date [ 3]. Family, twin and adoption studies have shown that different genes play gene studies [5], but also from linkage studies [6] and genome-wide association studies (GWAS) [7]. ADHD is a complex genetic disorder, in which environmental factors are involved and play a key role [7]. *Correspondence: cayuso@fjd.es Department of Genetics, IIS‑ Fundación Jiménez Díaz University Hospital The aim of this study was to test whether previously (IIS‑ FJD, UAM), Avda. Reyes Católicos 2, 28040 Madrid, Spain reported common genetic variants (34 polymorphisms Full list of author information is available at the end of the article © 2016 Gomez‑ Sanchez et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 2 of 10 in 18 genes) influence ADHD susceptibility in Spanish All cases included underwent clinical assessment patients. using the strengths and difficulties questionnaire (SDQ) Based on the etiology of ADHD, we chose candidate for detecting psychological morbidity [10]. Severity genes that encode functionally relevant proteins involved of ADHD symptoms was based on the ADHD rating in noradrenergic (SLC6A2, ADRA2A), dopaminergic scale-IV (ADHD RS-IV) [11], whereas overall psychoso- (SLC6A3, DRD2, DRD4, COMT, DDC), and serotoner- cial functioning was assessed by means of the children’s gic (SLC6A4, HTR2A, HTR2C) neurotransmission. In global assessment scale (CGAS) and the clinical global addition, we evaluated other candidate genes frequently impression scale (CGI) [12]. Information on obstetric reported as being related with ADHD such as STS, complications, developmental features, medical and psy- FADS2 and SNAP25. Finally, significantly reported genes chiatric history, family history, and treatment histories from GWAS studies such as CDH13, GFOD1, SLC6A9 were obtained through maternal interview. and GRM7, and genes revealed in linkage as playing a role Exclusion criteria included other psychotic disorders in ADHD susceptibility such as LPHN3 were included in (bipolar disorder or schizophrenia among others), perva- the study (Table 1). sive developmental disorders, intelligence quotient (IQ) <70, and neurological damage. Methods Patients and controls DNA extraction and genotyping A total of 320 Spanish ADHD patients of Caucasian Genomic DNA samples were obtained either from ancestry and 344 healthy children and adolescents of peripheral blood lymphocytes using an automatic DNA the same nationality and ancestry were initially included extractor (BioRobot EZ1, Qiagen, Hilden, Germany) or in this case–control study. After a quality control pro- from saliva using the Oragene DNA self-collection kit cedure, 290 patients and 340 controls were included in (DNA Genotek, Kanata, Ontario, Canada), according to the final analysis. ADHD patients were recruited and the manufacturer’s recommendations. DNA concentra- evaluated at Fundación Jiménez Díaz University Hos- tion and sample quality were assessed spectrophoto- pital, whereas the control sample was recruited at both metrically (NanoDrop ND-1000 Spectrophotometer, the aforementioned hospital and primary and second- Wilmington DE, USA). ary schools. Exclusion criteria for the control sample Candidate polymorphisms were selected based on included ADHD diagnosis or suspicion of symptomatol- their relevance as indicated in the literature on ADHD ogy, and chronic illness. The sample (cases and controls) (Table 1). comprised subjects between the ages of 6 and 18 years. All single nucleotide polymorphisms (SNPs) were Even though we did not test for the structure in our typed using TaqMan Assays-on-Demand or pre-designed cohort, a genome wide study of 800 subjects distrib- SNP genotyping assays following the manufacturer’s uted throughout Spain discarded the presence of genetic instructions (Applied Biosystems, Foster City, CA, USA). stratification [8 ]. PCR and allelic discrimination assays were run using the The study protocol was approved by the Research LightCycler 480 System (Roche Diagnostics, Mannheim, Ethics Committee of the IIS-Fundación Jiménez Díaz Germany). The results were evaluated using LightCycler University Hospital. The study was conducted accord - 480 software, version 1.5 (Roche Diagnostics, Mannheim, ing to the tenets of 2008 declaration of Helsinki. Before Germany). enrollment, parents or legal guardians signed a written For each variable number tandem repeats (VNTR) pol- informed consent form after the study objectives and ymorphism, subjects were categorized into three geno- procedures had been explained. types according to the risk allele previously described [5] as follows: SLC6A3 3´UTR VNTR (10/10, 10/-, -/-), Clinical assessment SLC6A3 intron8 VNTR (6/6, 6/-, -/-), DRD4 promoter Subjects were included in the study only after a diagno- duplication VNTR (L/L, L/S, S/S), DRD4 exon3 VNTR sis of ADHD was made by specialist clinicians according (7/7, 7/-, -/-), SLC6A4 promoter VNTR (L/L, L/S, S/S), to the diagnostic and statistical manual of mental disor- SLC6A4 intron2 VNTR (10/10, 10/-, -/-). Detection of ders, fourth edition, text revision (DSM-IV TR) [9]. Each VNTR polymorphisms was performed using fragment diagnosis was checked by two clinical researchers. Where analysis. PCR products were visualized on an ABI Prism consensus could not be reached, cases were reviewed by 3130xl DNA sequencer (Applied Biosystems Foster City, an additional clinical researcher. The children were clas - CA). The results were evaluated using the GeneMapper sified into the following ADHD subtypes: predominantly software, version 4.0 (Applied Biosystems, Foster City, inattentive subtype, predominantly hyperactive/impul- CA). Primer sequences and conditions are available upon sive subtype and combined subtype. request. Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 3 of 10 Table 1 Description of the 34 polymorphisms analysed within 18 genes for ADHD Gene Description Variant Reference SLC6A2 Norepinephrine transporter rs28386840 [19] r5569 [5] ADRA2A Adrenergic receptor alpha 2A rs1800544 [5] rs553668 [5] SLC6A3 Dopamine transporter rs2550948 [22] rs2652511 [22] rs11564750 [22] 3′UTR VNTR [5] Intron8 VNTR [5] DRD2 Dopamine receptor D2 rs1800497 [21] DRD4 Dopamine receptor D4 rs3758653 [20] Exon3 VNTR [21] Promoter duplication [21] COMT Catechol‑O‑methyltransferase rs4680 [5] rs4818 [50] DDC Dopa decarboxylase rs6592961 [51] SLC6A4 Serotonin transporter Promoter VNTR [5] Intron2 VNTR [5] HTR2A Serotonin‑2A receptor rs7322347 [51] HTR2C Serotonin‑2C receptor rs6318 [52] SLC9A9 Glycine transporter rs9810857 [53] GRM7 Glutamate receptor, metabotropic 7 rs3792452 [54] SNAP25 Synaptosomal‑associated protein 25kDA rs3746544 [5] CDH13 Cadherin 13 rs6565113 [20] GFOD1 Glucose‑fructose oxidoreductase domain containing 1 rs552655 [20] STS Steroid sulfatase rs12861247 [55] rs17268988 [55] FADS2 Fatty acid desaturase 2 rs498793 [31] LPHN3 Latrophilin 3 rs1397548 [17] rs2305339 [17] rs6551655 [17] rs1868790 [24] rs6813183 [24] rs6858066 [24] a b c d e f Position in the gene: upstream gene variant, promoter variant, exon variant, intron variant, 3′UTR variant, downstream gene variant Statistical analysis of the genetic variant on outcome was adjusted by sex For the case–control association study, Hardy–Weinberg and age (covariates). To reduce genetic heterogeneity and equilibrium for all genetic variants was assessed only in to test if there were different genetic factors for the dis - the control population because deviance from HWE in tinct ADHD subtypes, ADHD patients were subdivided cases sample might be an indication of association with into two main diagnostic groups, combined ADHD and the disorder; variants not in HWE (p value < 0.01) were inattentive ADHD. The hyperactive-impulsive ADHD excluded from the analysis. subtype was not considered due to its small sample size. A quality-control procedure was applied to the geno- To examine differences between males and females, sex- type data. The threshold applied in genotype call rates stratified analyses were performed. per sample and per polymorphism was 80 %. Logistic regression analysis was performed to analyze Logistic regression was used to examine the association the five inheritance models (codominant, dominant, of the genotype frequencies with the disorder. The effect recessive, overdominant and log-additive) [13] using Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 4 of 10 SNPstats software [14] and expressed as odds ratio (OR), Table 2 Demographic and clinical characteristics of ADHD patients and controls 95 % confidence interval (CI) and nominal significant dif - ferences (p value ≤ 0.05). If various inheritance models ADHD patients Controls had significant results, we chose the one with the lowest Age Akaike information criteria (AIC value). Mean (SD) 10.43 (2.9) 11.05 (3) Genotypes frequencies of variants located on chromo- Range 6–18 6–18 some X (HTR2C and STS genes) were analyzed only in Gender females. Male (%) 230 (79.3) 224 (66) The Benjamini and Hochberg false discovery rate Female (%) 60 (20.7) 116 (34) method was performed for multiple testing corrections ADHD diagnosis [15]. A p value threshold of 0.05 after correction was used Combined type (%) 175 (60.3) to determine significance. Risk-prediction models to Inattentive type (%) 102 (35.2) investigate the combined impact of multiple genetic vari- Hyperactive type (%) 13 (4.5) ants were applied. For this purpose, polymorphisms with Previous treatment p values ≤ 0.25 were incorporated in a forward stepwise Psychotherapeutic (%) 53 (18.27) multivariate logistic regression analysis and expressed as Pharmacological (%) 22 (7.6) the OR, 95 % CI and p value. Both (%) 52 (17.9) The variability explained for each variable as meas - No previous treatment (%) 147 (50.6) ure of the effect size of the polymorphisms (defined by ADHD–RS pseudo-r ) and the measure of model predictability Mean (SD) 27 (12) (defined by AUC value) were calculated. CGI score A post hoc analysis of statistical power was performed Mean (SD) 3.4 (0.5) with the CaTS Power Calculator software [16] assum- CGAS score ing an OR of 1.5, disorder prevalence of 5 %, significance Mean (SD) 69 (10) level of 0.05, and mean minor allele frequency (MAF) Comorbility with (%) observed of 0.30. The statistical power calculated for the Learning disabilities 63 (21.7) final sample included in this study (290 cases and 340 Oppositional defiant disorder 22 (7.6) controls) was 89, 64, and 23 % considering an additive, Conduct disorder 16 (5.5) dominant and recessive model, respectively. Tic disorder 7 (2.4) Results A total of 320 patients and 344 controls were ini- tially investigated. Thirty-four subjects were excluded polymorphisms: SLC6A4 promoter VNTR and LPHN3 because they showed genotype call rates <80 %. There - rs2305339 (Table 3; Additional file 1: Table S1). fore, 290 patients and 340 controls were included in In the combined ADHD subtype, association was sta- the final analysis. Per-marker genotype call rates were tistically significant for SLC6A2 rs28386840, SLC6A3 higher than 96 % for all variants. The genotype distribu - rs11565750, SLC6A4 promoter VNTR and LPHN3 tions of all polymorphisms were consistent with HWE rs2305339. None of the individual comparisons was sta- (p value > 0.01) in the control sample. The average tistically significant after correcting for multiple compar - age was 10.43 years (SD 2.95) for ADHD patients and isons in the inattentive subtype (Table 3 and Additional 11.05 years (SD 3.00) for the controls. 80 and 66 % of file 1: Table S1). patients and controls were male, respectively. Clinical In the logistic regression analysis for single mark- classification of the patients was the following: inatten - ers, adjusted by age, none of the individual comparisons tive subtype (n = 102), hyperactive/impulsive subtype was statistically significant after correcting for multiple (n = 13) and combined subtype (n = 175). Demographic comparisons in the female sample. In the male sample, and clinical characteristics of the sample are reported in only LPHN3 rs2305339 remained statistically significant Table 2. (Table 3 and Additional file 1: Table S1). Logistic regression results for single markers Multivariate logistic regression results When the whole sample was considered (unstratified Figures 1 and 2 show multivariate logistic regression anal- sample), logistic regression analysis for single markers, yses for the total ADHD sample, subtype and sex stratifi - adjusted by sex and age, showed statistically significant cation. The variables included in the model were ordered results after correcting for multiple comparisons in two according to the amount of variance explained (r ). Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 5 of 10 Table 3 Significant results after multiple comparison correction of logistic regression analysis for single markers Gene Variant Model Genotype Controls N (%) Cases N (%) OR (95 % CI) p value p value corrected All population L/L–L/S 241 (71.1) 239 (83.3) 1 SLC6A4 Promoter VNTR Recessive S/S 98 (28.9) 48 (16.7) 0.52 (0.35–0.77) 0.0009 0.0153 A/A–G/G 177 (52.1) 185 (63.8) 1 LPHN3 rs2305339 Overdominant A/G 163 (47.9) 105 (36.2) 0.57 (0.41–0.79) 0.0007 0.0153 Combined subtype A/A 174 (51.2) 65 (37.1) 1 SLC6A2 rs28386840 Dominant A/T–T/T 166 (48.8) 110 (62.9) 1.76 (1.19–2.59) 0.0041 0.0318 G/G 284 (83.4) 161 (92.5) 1 G/C 51 (15.1) 13 (7.5) 0.40 (0.21–0.77) 0.0026 0.0269 SLC6A3 rs11564750 Log‑additive C/C 5 (1.5) 0 (0) L/L–L/S 241 (71.1) 151 (87.3) 1 SLC6A4 Promoter VNTR Recessive S/S 98 (28.9) 22 (12.7) 0.37 (0.22–0.62) 0.0001 0.0031 A/A–G/G 177 (52.1) 114 (65.1) 1 LPHN3 rs2305339 Overdominant A/G 163 (47.9) 61 (34.9) 0.51 (0.34–0.76) 0.0008 0.0124 Male A/A 83 (36.9) 128 (55.2) 1 LPHN3 rs2305339 Codominant A/G 128 (56.9) 81 (34.9) 0.41 (0.28 –0.60) 0.0000 0.0001 OR odds ratio, CI confidence interval p values corrected based on Benjamini and Hochberg method In the total sample, eight polymorphisms located was 0.77. In the case of males, the amount of the variance in seven genes were included in the regression equa- explained was 8.5 % and the AUC was 0.69 (Table 4). tion: SLC6A4 promoter VNTR, LPHN3 (rs2305339, rs6551665), DRD4 exon3 VNTR, SNAP25 rs3746544, Discussion SLC6A3 rs11564750, SLC6A2 rs28386840 and FADS2 This study aimed to both determine whether differential rs498793. The amount of the variance explained for the genetic variants may participate in distinct ADHD sub- model was 8.5 % and the AUC was 0.69 (Table 4). types and also examine the sex-specific effects of this In the case of the combined subtype, seven polymor- impact. Multivariate regression analyses of the effects of phisms located in seven genes were included in the single genes were evaluated, but as ADHD is a complex model: SLC6A4 promoter VNTR, SLC6A2 rs28386840, polygenic disorder, the combined effect of multiple genes SLC6A3 rs11564750, LPHN3 rs2305339, DDC rs6592961, on the phenotype was also considered. GRM7 rs3792453 and FADS2 rs498793. The amount As seen in the logistic regression analysis for single of the variance explained was 14.6 % and the AUC was markers, this study provides evidence of a strong associa- 0.75. In the case of inattentive subtype, two polymor- tion between the SLC6A4 gene and ADHD in the entire phisms were included in the model, LPHN3 r6551665 population; and between SLC6A2, SLC6A3 and SLC6A4 and SNAP25 rs3746544. The amount of the variance and ADHD in the combined subtype. Special attention explained for the model was 2.6 % and the AUC was 0.60 should be given to the LPHN3 gene, since it was associ- (Table 4). ated with the presence of ADHD in the entire population, Five polymorphisms located in five genes (SLC6A3 the combined subtype and the male sample. rs11564750, SNAP25 rs3746544, LPHN3 rs6551665, In order to clarify the genetic basis of ADHD, the effects DRD4 exon3 VNTR and SLC6A2 rs28386840) and seven of multiple risk factors were examined. In the entire sam- polymorphisms located in five genes (LPHN3 (rs2305339, ple, seven genes were included in the regression equation rs6551665), SLC6A2 (rs28386840, rs5569), GRM7 (SLC6A4, LPHN3, DRD4, SNAP25, SLC6A3, SLC6A2 and rs3792452, SLC6A4 promoter VNTR and DRD4 exon3 FADS2). The involvement of these genes in ADHD has VNTR) were included in the model for females and been extensively studied [5, 17–23], some in Spanish pop- males, respectively. The amount of the variance explained ulations [24, 25]. The contribution of each gene was mod - for the sample including females was 16.5 % and the AUC est, as expected for a complex genetic disorder (ranging Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 6 of 10 Fig. 1 ROC curves analyses of the regression model, stratified by a ADHD subtype and b sex, and compared to total the population. AUC, area under de curve Fig. 2 Results for variables that were included in the multivariate from 0.4 to 1.6 %). The model explained around 9 % of the regression equation in: a the total population, and stratified analyses by b combined subtype, c inattentive subtype, d females and e variance; 7 % of this variance was due to genetic factors. A males. The p values, OR 95 % CI, and pseudo r for the individual vari‑ previous study, including 22 variants, found that 16 % of ables are shown the variance was due to genetic factors [26]. In the regression equation, two genes were included in the inattentive subtype (LPHN3 and SNAP25) and seven analyzed share genetic risk factors (LPHN3), yet SNAP25 genes (SLC6A4, SLC6A2, SLC6A3, LPHN3, DDC, GRM7 was associated with the inattentive subtype, whereas and FADS2) in the combined subtype. A remarkable SLC6A4, SLC6A2, SLC6A3, DDC, GRM7 and FADS2 importance of the SLC6A4 gene was observed, account- were implicated in the combined subtype. The presence ing for 2.9 % of the variance, above the usual threshold of common as well as specific genetic variants for each of 2 % [27]. This study showed that the clinical subtypes Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 7 of 10 Table 4 Overview of multivariate logistic regression anal- the set of variants analyzed has a higher genetic contri- ysis bution for ADHD in girls than in boys. Females are less frequently affected because a more extreme genetic load Trait Pseudo AUC (CI 95 %) Genetic r (%) variants is required for the liability threshold to be surpassed [37, 42]. Additionally, it has been reported that females All sample 8.5 0.69 (0.65–0.73) 8 referred to a clinic are more prone to exhibit other dis- Combined subtype 14.6 0.75 (0.71–0.79) 7 ruptive behaviors [43], although this seems to be a conse- Inattentive subtype 2.6 0.60 (0.54–0.67) 2 quence of referral bias [44]. Female 16.5 0.77 (0.66–0.84) 5 Our results add to extensive literature information Male 8.5 0.69 (0.64–0.74) 7 about polymorphic variants in genes whose implication AUC area under de curve, CI confidence interval in ADHD is widely known through the pathophysiology as SLC6A2, SLC6A3, SLC6A4 and LPHN3. In some cases the polymorphism associated has a known functional implication, like rs28386840, a functional promoter vari- subtype is supported by previous studies [28, 29]. How- ant of SLC6A2 gene. But there are also other polymor- ever, some of these reported associated variants differ phisms associated with any biological meaning that could between studies [24, 30–36]. be in linkage disequilibrium with other unknown func- The model showed a higher genetic loading for the tional variants directly involved in genetic susceptibility variables analyzed in combined subtype (14.6 %) than in to ADHD. These findings need to be further explored the inattentive subtype (2.6 %), finding consistent with to improve the understanding of their implication with the previously reported higher genetic loading in ADHD ADHD. comorbid symptoms [37, 38]. It is important to note the The conflicting genetic results show the difficulty of importance of sex and age in the combined subtype (r replicating across genetic association studies. Often there 5.9 %) but not in the inattentive sample. is important variation in the sample reported, particu- The model for the combined subtype seems to be more larly regarding the age, sex ratios and ethnic. Also, an predictive than the inattentive subtype (AUC 0.75 and accurate phenotype definition is crucial to obtain suc - AUC 0.60, respectively) and better than the model used cessful results in these studies. In our study a rigorously for the whole sample (AUC 0.69). This supports the idea diagnostic criteria was applied. that analyzing more homogeneous phenotypes facilitates In addition, the specific effect of a gene could be dif - the identification of genetic factors. ferent depending on the sets of genetic variants analyzed ADHD is known to have sex-based differences in sever - or the model of inheritance evaluated. In contrast with ity and clinical course [39]. Herein, differences in genetic other studies, we evaluated genetic information under susceptibility between males and females were observed. different models of inheritance without an a priori con - In females, SLC6A3, SNAP25, LPHN3, DRD4 and SLC6A2 sideration of possible genetic effects. This makes it eas - genes showed high r values (range from 2 to 3.8 %). In ier to detect genetic effects, since different genotypes males, LPHN3, SLC6A2, GRM7, SLC6A4, DRD4 and of the same gene could be associated with different LPHN3 were included in the regression equation. The phenotypes. LPHN3 gene accounted for 3.4 % of ADHD variability. The most important limitation of the study was the This analysis showed that genes such as DRD4, SLC6A2 modest sample size. The statistical power decreased and LPHN3 were associated in both sexes, with a stronger when the sample was subdivided according to ADHD effect of SLC6A3 and SNAP25 in females (r 3.8 and 3.3 % subtype or sex stratification; thus, it is difficult to deter - respectively) and a lesser effect of GRM7 and SLC6A4 in mine whether negative findings were due to low sta - males (r 0.8 %). The association between SLC6A4 and tistical power or to the absence of a true biological male sample is supported by previous studies [40], but not association. On the contrary, we only consider associa- between SLC6A3 and female sample [41]. tion that remain significant after multiple testing cor - To the best of our knowledge, sex-based differences rection in the regression logistic of single markers so we in the genetic risk for ADHD have not been previously avoid false positive (type I error) rates, giving us confi - reported in the SNAP25 and GRM7 genes. These results dent in the veracity of the results. suggest the need to explore biological evidence of sexu- ally dimorphic effects in such genes. Conclusions The percentage of variance explained in females was We report evidence of the genetic contribution of com- higher (16.5 %) than in males (8.5 %). The regression mon variants to the ADHD phenotype in four genes, with model for girls (AUC 0.77) seems to be more predic- the LPHN3 gene playing a particularly strong role. tive than for boys (AUC 0.69). These results suggest that Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 8 of 10 2, 28040 Madrid, Spain. Clinical Pharmacology Department, Instituto de The most predictable model described in this study Investigación Sanitaria Princesa (IP), Hospital Universitario de la Princesa, C/ was for females (r 16.5 %, AUC 0.77). As seen in this Diego de Leon 62, 28006 Madrid, Spain. Department of Psychiatry, IIS‑Fun‑ study, analysis of the contribution of multiple genes dación Jiménez Díaz University Hospital (IIS‑FJD, UAM), Avda. Reyes Católicos 2, 28040 Madrid, Spain. Clinical Research, BUC (Biosciences UAM + CSIC) Pro‑ provides particularly useful insight for the effort to gram, International Campus of Excellence, Universidad Autónoma de Madrid, discover the genetic basis of polygenic disorders and Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain. multigene analysis had substantial advantages over the Acknowledgements single-gene approach. However, the percentage of the We would like to thank all patients, controls, parents, and guardians for their variance in ADHD diagnosis explained remains low; participation in this study. We are grateful to Oliver Shaw for his editorial assis‑ hence, most of the genetic component in phenotypic tance. This study was supported by the following research grants: Fundacion Alicia Koplowitz (4019‑004), Biobank of Fundacion Jimenez Diaz Hospital variance remains unexplained when considering com- (RD09/0076/00101, Instituto de Salud Carlos III) and the Centre for Biomedical mon variants. Additional studies including copy num- Network Research on Rare Diseases ‑ CIBERER (06/07/0036). The work of CG‑S is ber variation [45, 46], exome sequencing studies [47] supported by a Fundacion Conchita Rabago Grant. as well as gene–gene and gene-environment interac- Competing interests tions [48, 49] could clarify the genetic contribution to The authors declare that they have no competing interests. The authors have ADHD. no proprietary or commercial interest in any of the materials discussed in this article. Future studies should investigate the contribution of genetic variants to the risk of ADHD considering their Received: 18 June 2015 Accepted: 2 December 2015 role in specific sex or subtype in order to produce more predictable and robust models, enabling the develop- ment of an accurate diagnosis and hopefully improved treatment. References 1. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. The world‑ Additional file wide prevalence of ADHD: a systematic review and metaregression analy‑ sis. Am J Psychiatry. 2007;164(6):942–8. doi:10.1176/appi.ajp.164.6.942. Additional file 1: Table S1. Significant results of logistic regression for 2. Faraone SV, Biederman J, Mick E. The age‑ dependent decline of atten‑ single markers considering a nominal p value <0.05. tion deficit hyperactivity disorder: a meta‑analysis of follow‑up studies. Psychol Med. 2006;36(2):159–65. doi:10.1017/S003329170500471X. 3. Genro JP, Kieling C, Rohde LA, Hutz MH. Attention‑ deficit/hyperactiv‑ ity disorder and the dopaminergic hypotheses. Expert Rev Neurother. Abbreviations 2010;10(4):587–601. doi:10.1586/ern.10.17. ADHD: attention deficit hyperactivity disorder; DSM ‑IV TR: diagnostic and sta‑ 4. Faraone SV, Perlis RH, Doyle AE, Smoller JW, Goralnick JJ, Holmgren MA, tistical manual of mental disorders, fourth edition, text revision; SDQ: strengths et al. Molecular genetics of attention‑ deficit/hyperactivity disorder. Biol and difficulties questionnaire; ADHD RS‑IV: ADHD rating scale ‑IV; CGAS: Psychiatry. 2005;57(11):1313–23. doi:10.1016/j.biopsych.2004.11.024. children’s global assessment scale; CGI: clinical global impression scale; SNPs: 5. Gizer IR, Ficks C, Waldman ID. Candidate gene studies of ADHD: a single nucleotide polymorphisms; VNTRs: variable number tandem repeats meta‑analytic review. Hum Genet. 2009;126(1):51–90. doi:10.1007/ polymorphisms; HWE: Hardy–Weinberg equilibrium; CI: confidence interval; s00439‑009‑0694‑x. AIC: Akaike information criteria; AUC: area under the curve; MAF: minor allele 6. Zhou K, Dempfle A, Arcos‑Burgos M, Bakker SC, Banaschewski T, Bieder ‑ frequency. man J, et al. Meta‑analysis of genome ‑ wide linkage scans of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. Authors’ contributions 2008;147B(8):1392–8. doi:10.1002/ajmg.b.30878. CIGS contributed to the design of the study, data collection process, data 7. Neale BM, Medland SE, Ripke S, Asherson P, Franke B, Lesch KP, et al. Meta‑ analysis, and wrote the first drafts of the manuscript. RRA contributed to the analysis of genome‑ wide association studies of attention‑ deficit/hyper ‑ design of the study and revision of the manuscript. VSI was involved in the activity disorder. J Am Acad Child Adolesc Psychiatry. 2010;49(9):884–97. recruitment of subjects, data collection, and revisions of the manuscript. doi:10.1016/j.jaac.2010.06.008. MR was involved in recruiting subjects, data collection, and revisions of the 8. Gayán J, Gallan JJ, González‑Pérez A, Sáez ME, Mártinez‑Larrad MT, manuscript. PTR was involved in the recruiting subjects, data collection, and Zabena C, et al. Genetic structure of the Spanish population. BMC revisions of the manuscript. IMF contributed to the statistical analysis. FAS Genom. 2010;11:326. doi:10.1186/1471‑2164‑11‑326. contributed to the revision of the manuscript. JJC contributed to the design 9. American Psychiatric Association. Diagnostic and statistical manual of men‑ of the study, recruitment of subjects, data collection, and revisions of the tal disorders. 4th ed. Washington: American Psychiatric Association; 2010. manuscript. RDR contributed to the design of the study, data analysis, and 10. Goodman R. The Strengths and difficulties questionnaire: a research note. revision of the manuscript. CA was involved in the design of the study, data J Child Psychol Psychiatry. 1997;38(5):581–6. analysis, intellectual content, and revisions of the manuscript. All authors read 11. Du Paul G. Parent and teacher ratings of ADHD symptoms: psycho‑ and approved the final manuscript. metric properties in a community‑based sample. J Clin Child Psychol. 1991;20(3):245–53. Author details 12. Shaffer D, Gould MS, Brasic J, Ambrosini P, Fisher P, Bird H, et al. A Department of Genetics, IIS‑Fundación Jiménez Díaz University Hospital (IIS‑ children’s global assessment scale (CGAS). Arch Gen Psychiatry. FJD, UAM), Avda. Reyes Católicos 2, 28040 Madrid, Spain. Centre for Biomedi‑ 1983;40(11):1228–31. cal Research on Rare Diseases (CIBERER), C/Monforte de Lemos 3‑5, Pabellón 13. Iniesta R, Guinó E, Moreno V. Statistical analysis of genetic polymorphisms 11, 28029 Madrid, Spain. Department of Pediatrics, IIS‑Fundación Jiménez in epidemiological studies. Gac Sanit. 2005;19(4):331–41. Díaz University Hospital (IIS‑FJD, UAM), Avda. Reyes Católicos 2, 28040 Madrid, 14. Sole X, Guino E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for Spain. Department of Pediatrics, La Paz University Hospital, Paseo de la Cas‑ the analysis of association studies. Bioinformatics. 2006;22(15):1928–9. tellana 261, 28046 Madrid, Spain. Department of Epidemiology, IIS‑Fun‑ doi:10.1093/bioinformatics/btl268. dación Jiménez Díaz University Hospital (IIS‑FJD, UAM), Avda. Reyes Católicos Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 9 of 10 15. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practi‑ polymorphism (5‑HTTLPR) in attention deficit hyperactivity disorder. Am cal and powerful approach to multiple testing. J R Stat Soc Ser B. J Med Genet. 2001;105(1):91–5. 1995;57:289–300. 34. Landaas ET, Johansson S, Jacobsen KK, Ribases M, Bosch R, Sanchez‑Mora 16. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient C, et al. An international multicenter association study of the serotonin than replication‑based analysis for two ‑stage genome ‑ wide association transporter gene in persistent ADHD. Genes Brain Behav. 2010;9(5):449– studies. Nat Genet. 2006;38(2):209–13. doi:10.1038/ng1706. 58. doi:10.1111/j.1601‑183X.2010.00567.x. 17. Arcos‑Burgos M, Muenke M. Toward a better understanding of ADHD: 35. Guan L, Wang B, Chen Y, Yang L, Li J, Qian Q, et al. A high‑ density single‑ LPHN3 gene variants and the susceptibility to develop ADHD. Atten Defic nucleotide polymorphism screen of 23 candidate genes in attention Hyperact Disord. 2010;2(3):139–47. doi:10.1007/s12402‑010‑0030‑2. deficit hyperactivity disorder: suggesting multiple susceptibility genes 18. Hwang IW, Lim MH, Kwon HJ, Jin HJ. Association of LPHN3 rs6551665 A/G among Chinese Han population. Mol Psychiatry. 2009;14(5):546–54. polymorphism with attention deficit and hyperactivity disorder in Korean doi:10.1038/sj.mp.4002139. children. Gene. 2015;566(1):68–73. doi:10.1016/j.gene.2015.04.033. 36. Smith TF. Meta‑analysis of the heterogeneity in association of DRD4 19. Kim CH, Hahn MK, Joung Y, Anderson SL, Steele AH, Mazei‑Robinson MS, 7‑repeat allele and AD/HD: stronger association with AD/HD combined et al. A polymorphism in the norepinephrine transporter gene alters type. Am J Med Genet B Neuropsychiatr Genet. 2010;153B(6):1189–99. promoter activity and is associated with attention‑ deficit hyperactivity doi:10.1002/ajmg.b.31090. disorder. Proc Natl Acad Sci U S A. 2006;103(50):19164–9. doi:10.1073/ 37. Hamshere ML, Langley K, Martin J, Agha SS, Stergiakouli E, Anney RJ, pnas.0510836103. et al. High loading of polygenic risk for ADHD in children with comorbid 20. Lasky‑Su J, Neale BM, Franke B, Anney RJ, Zhou K, Maller JB, et al. aggression. Am J Psychiatry. 2013;170(8):909–16. doi:10.1176/appi. Genome‑ wide association scan of quantitative traits for attention ajp.2013.12081129. deficit hyperactivity disorder identifies novel associations and confirms 38. Thapar A, Harrington R, McGuffin P. Examining the comorbidity of ADHD ‑ candidate gene associations. Am J Med Genet B Neuropsychiatr Genet. related behaviours and conduct problems using a twin study design. Br J 2008;147B(8):1345–54. doi:10.1002/ajmg.b.30867. Psychiatry. 2001;179:224–9. 21. Wu J, Xiao H, Sun H, Zou L, Zhu LQ. Role of dopamine receptors in 39. Biederman J, Mick E, Faraone SV, Braaten E, Doyle A, Spencer T, et al. ADHD: a systematic meta‑analysis. Mol Neurobiol. 2012;45(3):605–20. Influence of gender on attention deficit hyperactivity disorder in children doi:10.1007/s12035‑012‑8278‑5. referred to a psychiatric clinic. Am J Psychiatry. 2002;159(1):36–42. 22. Genro JP, Polanczyk GV, Zeni C, Oliveira AS, Roman T, Rohde LA, et al. A 40. Biederman J, Kim JW, Doyle AE, Mick E, Fagerness J, Smoller JW, et al. common haplotype at the dopamine transporter gene 5′ region is asso‑ Sexually dimorphic effects of four genes (COMT, SLC6A2, MAOA, SLC6A4) ciated with attention‑ deficit/hyperactivity disorder. Am J Med Genet B in genetic associations of ADHD: a preliminary study. Am J Med Genet B Neuropsychiatr Genet. 2008;147B(8):1568–75. doi:10.1002/ajmg.b.30863. Neuropsychiatr Genet. 2008;147B(8):1511–8. doi:10.1002/ajmg.b.30874. 23. Nyman ES, Ogdie MN, Loukola A, Varilo T, Taanila A, Hurtig T, et al. 41. Das BA, Sarkar K, Ghosh P, Das M, Bhaduri N, Ray A, et al. Significance ADHD candidate gene study in a population‑based birth cohort: of dopaminergic gene variants in the male biasness of ADHD. J Atten association with DBH and DRD2. J Am Acad Child Adolesc Psychiatry. Disord. 2013;. doi:10.1177/1087054713494004. 2007;46(12):1614–21. doi:10.1097/chi.0b013e3181579682. 42. Pauls DL. Genetic factors in the expression of attention‑ deficit hyper ‑ 24. Ribases M, Ramos‑ Quiroga JA, Sanchez‑Mora C, Bosch R, Richarte V, Palo ‑ activity disorder. J Child Adolesc Psychopharmacol. 1991;1(5):353–60. mar G, et al. Contribution of LPHN3 to the genetic susceptibility to ADHD doi:10.1089/cap.1991.1.353. in adulthood: a replication study. Genes Brain Behav. 2011;10(2):149–57. 43. Gaub M, Carlson CL. Gender differences in ADHD: a meta‑analysis and doi:10.1111/j.1601‑183X.2010.00649.x. critical review. J Am Acad Child Adolesc Psychiatry. 1997;36(8):1036–45. 25. Ribases M, Ramos‑ Quiroga JA, Hervas A, Sanchez‑Mora C, Bosch R, Bielsa doi:10.1097/00004583‑199708000‑00011. A, et al. Candidate system analysis in ADHD: evaluation of nine genes 44. Ramtekkar UP, Reiersen AM, Todorov AA, Todd RD. Sex and age dif‑ involved in dopaminergic neurotransmission identifies association with ferences in attention‑ deficit/hyperactivity disorder symptoms and DRD1. World J Biol Psychiatry. 2012;13(4):281–92. doi:10.3109/15622975.2 diagnoses: implications for DSM‑ V and ICD‑11. J Am Acad Child Adolesc 011.584905. Psychiatry. 2010;49(3):217–28. doi:10.1016/j.jaac.2009.11.011. 26. Comings DE, Gade‑Andavolu R, Gonzalez N, Wu S, Muhleman D, Blake H, 45. Williams NM, Zaharieva I, Martin A, Langley K, Mantripragada K, Fossdal et al. Multivariate analysis of associations of 42 genes in ADHD. ODD and R, et al. Rare chromosomal deletions and duplications in attention‑ conduct disorder. Clin Genet. 2000;58(1):31–40. deficit hyperactivity disorder: a genome ‑ wide analysis. Lancet. 27. Comings DE. Why different rules are required for polygenic inheritance: 2010;376(9750):1401–8. doi:10.1016/S0140‑6736(10)61109‑9. lessons from studies of the DRD2 gene. Alcohol. 1998;16(1):61–70. 46. Ramos‑ Quiroga JA, Sanchez‑Mora C, Casas M, Garcia‑Martinez I, Bosch R, doi:10.1016/S0741‑8329(97)00178‑ X. Nogueira M, et al. Genome‑ wide copy number variation analysis in adult 28. Larsson H, Lichtenstein P, Larsson JO. Genetic contributions to the attention‑ deficit and hyperactivity disorder. J Psychiatr Res. 2014;49:60–7. development of ADHD subtypes from childhood to adolescence. J doi:10.1016/j.jpsychires.2013.10.022. Am Acad Child Adolesc Psychiatry. 2006;45(8):973–81. doi:10.1097/01. 47. Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, chi.0000222787.57100.d8. Rickaby J, et al. Rare copy number variation discovery and cross‑ disorder 29. Nadder TS, Silberg JL, Rutter M, Maes HH, Eaves LJ. Comparison of multi‑ comparisons identify risk genes for ADHD. Sci Transl Med. 2011;3(95):95. ple measures of ADHD symptomatology: a multivariate genetic analysis. J doi:10.1126/scitranslmed.3002464. Child Psychol Psychiatry. 2001;42(4):475–86. 48. Ficks CA, Waldman ID. Gene‑ environment interactions in attention‑ 30. Mill J, Xu X, Ronald A, Curran S, Price T, Knight J, et al. Quantitative trait deficit/hyperactivity disorder. Curr Psychiatry Rep. 2009;11(5):387–92. locus analysis of candidate gene alleles associated with attention deficit 49. Qian Q, Wang Y, Li J, Yang L, Wang B, Zhou R, et al. Evaluation of potential hyperactivity disorder (ADHD) in five genes: DRD4, DAT1, DRD5, SNAP ‑25, gene‑ gene interactions for attention deficit hyperactivity disorder in and 5HT1B. Am J Med Genet B Neuropsychiatr Genet. 2005;133B(1):68– the Han Chinese population. Am J Med Genet B Neuropsychiatr Genet. 73. doi:10.1002/ajmg.b.30107. 2007;144B(2):200–6. doi:10.1002/ajmg.b.30422. 31. Brookes K, Xu X, Chen W, Zhou K, Neale B, Lowe N, et al. The analysis of 51 50. Michaelovsky E, Gothelf D, Korostishevsky M, Frisch A, Burg M, Carmel genes in DSM‑IV combined type attention deficit hyperactivity disorder: M, et al. Association between a common haplotype in the COMT association signals in DRD4, DAT1 and 16 other genes. Mol Psychiatry. gene region and psychiatric disorders in individuals with 22q11.2DS. 2006;11(10):934–53. doi:10.1038/sj.mp.40018694001869. Int J Neuropsychopharmacol. 2008;11(3):351–63. doi:10.1017/ 32. Bidwell LC, Willcutt EG, McQueen MB, DeFries JC, Olson RK, Smith SD, S1461145707008085. et al. A family based association study of DRD4, DAT1, and 5HTT and 51. Ribases M, Ramos‑ Quiroga JA, Hervas A, Bosch R, Bielsa A, Gastaminza continuous traits of attention‑ deficit hyperactivity disorder. Behav Genet. X, et al. Exploration of 19 serotoninergic candidate genes in adults 2011;41(1):165–74. doi:10.1007/s10519‑010‑9437‑ y. and children with attention‑ deficit/hyperactivity disorder identifies 33. Manor I, Eisenberg J, Tyano S, Sever Y, Cohen H, Ebstein RP, et al. Family‑ association for 5HT2A, DDC and MAOB. Mol Psychiatry. 2009;14(1):71–85. based association study of the serotonin transporter promoter region doi:10.1038/sj.mp.4002100. Gomez‑Sanchez et al. Behav Brain Funct (2016) 12:2 Page 10 of 10 52. Bobb AJ, Addington AM, Sidransky E, Gornick MC, Lerch JP, Greenstein 54. Park S, Jung SW, Kim BN, Cho SC, Shin MS, Kim JW, et al. Association DK, et al. Support for association between ADHD and two candidate between the GRM7 rs3792452 polymorphism and attention deficit genes: NET1 and DRD1. Am J Med Genet B Neuropsychiatr Genet. hyperacitiveity disorder in a Korean sample. Behav Brain Funct. 2013;9:1. 2005;134B(1):67–72. doi:10.1002/ajmg.b.30142. doi:10.1186/1744‑9081‑9‑1. 53. Mick E, Todorov A, Smalley S, Hu X, Loo S, Todd RD, et al. Family‑based 55. Brookes KJ, Hawi Z, Kirley A, Barry E, Gill M, Kent L. Association of the ster‑ genome‑ wide association scan of attention‑ deficit/hyperactivity disorder. oid sulfatase (STS) gene with attention deficit hyperactivity disorder. Am J Am Acad Child Adolesc Psychiatry. 2010;49(9):898–905. doi:10.1016/j. J Med Genet B Neuropsychiatr Genet. 2008;147B(8):1531–5. doi:10.1002/ jaac.2010.02.014. ajmg.b.30873. Submit your next manuscript to BioMed Central and we will help you at every step: • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit
Journal
Behavioral and Brain Functions – Springer Journals
Published: Dec 1, 2015
Keywords: neurosciences; neurology; behavioral therapy; psychiatry