Andreas Reif
email: reif_a@klinik.uni-wuerzburg.de

Trang Trang Nguyen

Lena Weissflog

Christian P Jacob

Marcel Romanos

Tobias J Renner

Henriette N Buttenschon

Sarah Kittel-Schneider

Alexandra Gessner

Heike Weber

Lena Weißflog

Maria Neuner

Silke Gross-Lesch

Karin Zamzow²

Susanne Kreiker

Susanne Walitza

Jobst Meyer

Christine M Freitag

Rosa Bosch

Miquel Casas

Nuria Gómez

Marta Ribasès

Mónica Bayès

Jan K Buitelaar

Lambertus A L M Kiemeney

J J Sandra Kooij

Cees C Kan

Martine Hoogman

Stefan Johansson

Kaya K Jacobsen

Per M Knappskog

Ole B Fasmer

Phil Asherson

Andreas Warnke

Hans-Jörgen Grabe

Jessie Mahler

Alexander Teumer

Henry Völzke

Ole N Mors

Helmut Schäfer²

Josep Antoni Ramos-Quiroga

Bru Cormand

Jan Haavik

Barbara Franke

Klaus-Peter Lesch³

DIRAS2 is associated with adult ADHD, related traits, and co-morbid disorders

Keywords: adult ADHD, linkage, genome-wide association, ras pathway, association study, bipolar disorder

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Introduction

Attention deficit/hyperactivity disorder (ADHD) is a prevalent psychiatric condition in childhood associated with substantial impairment. In up to 30% of all affected cases, the disorder persists into adulthood (adult ADHD, aADHD; [START_REF] Faraone | Attentiondeficit/hyperactivity disorder in adults: an overview[END_REF], although the core symptoms become less prominent, as adaptive mechanisms interfere with the presentation of the disease and as co-morbid conditions modify or even dominate the clinical picture. The latter include substance use disorders, affective and personality disorders [START_REF] Jacob | Co-morbidity of adult attention-deficit/hyperactivity disorder with focus on personality traits and related disorders in a tertiary referral center[END_REF]. Furthermore, there is accumulating evidence for neurobiological overlap with bipolar disorder (BD), with a high percentage of adult ADHD patients also suffering from BD [START_REF] Klassen | Chokka P Adult ADHD and its comorbidities, with a focus on bipolar disorder[END_REF][START_REF] Wingo | A systematic review of rates and diagnostic validity of comorbid adult attention-deficit/hyperactivity disorder and bipolar disorder[END_REF] and even more from bipolar symptoms on the dimensional level (Halmoy et al). Conversely, approximately 10% of BD patients suffer from ADHD, suggesting a subgroup of patients where pathologies overlap and presumably risk genes are shared [START_REF] Nierenberg | Clinical and diagnostic implications of lifetime attention-deficit/hyperactivity disorder comorbidity in adults with bipolar disorder: data from the first 1000 STEP-BD participants[END_REF][START_REF] Tamam | Comorbidity of adult attention-deficit hyperactivity disorder and bipolar disorder: prevalence and clinical correlates[END_REF]. Adult ADHD patients feature a distinct personality profile, with increased scores in the personality domain Neuroticism as measured with the NEO PI-R [START_REF] Costa | Revised NEO Personality Inventory (NEO PI-R) and NEO Five Factor Inventory (NEO-FFI) Manual Psychological Assessment[END_REF], while Conscientiousness, Extraversion, and Openness are decreased [START_REF] Jacob | Co-morbidity of adult attention-deficit/hyperactivity disorder with focus on personality traits and related disorders in a tertiary referral center[END_REF]. Likewise, personality assessment using the TPQ [START_REF] Cloninger | A psychobiological model of temperament and character[END_REF] reveals that Novelty Seeking and Harm Avoidance are increased in aADHD patients [START_REF] Jacob | Co-morbidity of adult attention-deficit/hyperactivity disorder with focus on personality traits and related disorders in a tertiary referral center[END_REF]. Taken together, the clinical appearance of aADHD represents a complex clinical phenotype.

ADHD in children (cADHD) was repeatedly shown to feature a strong genetic component, and a meta-analysis of twenty studies provided a heritability index of 0.76 [START_REF] Faraone | Molecular genetics of attention-deficit/hyperactivity disorder[END_REF]. However, despite this considerable heritability, only a few risk genes with small effects have been identified. This might be due to the facts that cADHD a) is a heterogeneous clinical condition (with the risk of phenocopies), b) might feature substantial ethnic background effects, and c) is a genetically complex disorder with common variants conveying low risk [START_REF] Franke | Genome-wide association studies in ADHD[END_REF] as well as individually rare variants with variable penetrance conveying high risk (Becker and Knapp, ; Lesch et al, ; Williams et al). Adult ADHD has come into the focus of genetic research more recently, but has also been shown to have a genetic basis (IMpACT Collaboration, submitted). Molecular genetic findings are even more sparse; this might partly be due to the fact that not only risk variants causing disease as such come into play, but also genes modifying the developmental trajectory of the disorder -i.e., persistence into adulthood (e.g., SLC6A3; (Franke et al) -may be of importance.

To overcome the problematic issues coming along with candidate gene studies, genome-wide approaches were increasingly used in the last years. These include genome-wide linkage analyses, eight of which have been published so far; seven of these were included in a meta-analysis (Zhou et al, 2008). Furthermore, genome-wide association studies (GWAS) were employed to find new risk genes [START_REF] Franke | Genome-wide association studies in ADHD[END_REF]Neale et al, ;Neale et al). We have previously reported on a pooled GWAS in aADHD [START_REF] Lesch | Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies[END_REF], where two single nucleotide polymorphisms (SNPs) in the top 500 localized to the intergenic region between DIRAS2 and SYK on chromosome 9q22.2. The DIRAS2/SYK locus is also supported by two linkage scans, one of which was performed in individuals from the same catchment population as the aADHD GWAS [START_REF] Romanos | Genome-wide linkage analysis of ADHD using high-density SNP arrays: novel loci at 5q13.1 and 14q12[END_REF], while the other was conducted in the UK [START_REF] Asherson | A high-density SNP linkage scan with 142 combined subtype ADHD sib pairs identifies linkage regions on chromosomes 9 and 16[END_REF]. Further interest in this region comes from a pooled GWAS in personality disorder, where SYK was the number 11 top-locus for Cluster B personality disorder (in preparation). Thus, three independent and hypothesis-free studies provide converging evidence that this locus might contribute to the genetic risk for disorders of cognitive control featuring increased impulsivity. DIRAS2 is strongly expressed in the brain, while SYK is preferentially expressed in peripheral tissues, making DIRAS2 the prime candidate gene at this locus. Intriguingly, in a recent GWAS on cognitive performance of the aging brain, DIRAS2 was associated (p=8x10 -6 ) with performance in the Boston naming test arguing for a role of DIRAS2 in cognitive processes [START_REF] Seshadri | Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study[END_REF].

These findings prompted us to conduct a study using large discovery and replication aADHD samples to assess the genetic relevance of DIRAS2 for adult ADHD. In addition, we attempted generalization of the findings to cADHD and to related disorders of impulsivity. As the discovery samples also were assessed for personality factors, we were also able to test the influence of DIRAS2 on impulsivityrelated traits which might be considered an intermediate phenotype of ADHD.

Materials and Methods

Samples Discovery sample: adult ADHD sample

The sample was previously described in greater detail (Franke et al, ;[START_REF] Jacob | Co-morbidity of adult attention-deficit/hyperactivity disorder with focus on personality traits and related disorders in a tertiary referral center[END_REF]. Briefly, 600 patients were extensively examined using a semi-structured interview as well as by applying the SCID-I and SCID-II. Personality assessment was done using NEO PI-R and TPQ. Eligibility criteria for the study were aADHD according to the diagnostic criteria of DSM-IV, onset before the age of 7 years, life-long persistence, current diagnosis and age at recruitment between 18 and 65 years.

Exclusion criteria were a current diagnosis of drug/alcohol abuse/dependence (prior to detoxification /withdrawal), or a lifetime diagnosis of BD, schizophrenia and mental retardation. A more detailed sample description including the used rating scales can be obtained from Supplementary Methods.

The control group was recruited from the Lower Franconia area in Germany and consisted of 420 healthy volunteers. This sample of volunteers was also used as a control for the association analysis for personality disorder and BD.

Family-based childhood ADHD sample

Detailed information on the sample was reported previously [START_REF] Renner | Allelic variants of SNAP25 in a family-based sample of ADHD[END_REF]. Briefly, total sample size was 166 families, of whom 109 were trios, 42 quartets and 15 were multi-sibling families.

We recruited families with at least one child affected by ADHD, combined type according to DSM-IV as diagnosed by a structured interview with the parents (KIDDIE-SADS), parent and teacher questionnaires and direct clinical observation. The index child was aged 8 years or above. Exclusion criteria were IQ below 80, co-morbid autistic or somatic disorders, primary affective disorders including BD, Tourette's syndrome, psychotic or other severe primary psychiatric disorders.

IMpACT adult ADHD samples

The adult ADHD replication samples have been described in detail elsewhere (Franke et al, 2010).

The total sample included 1035 adult ADHD patients and 1381 controls and originated from The Netherlands (285 patients and 508 controls), Norway (497 cases and 562 controls), Spain (253 patients and 311 controls), and was made available within the framework of the IMpACT consortium.

All patients were evaluated by experienced psychiatrists and diagnosed with persistent ADHD according to DSM-IV (Diagnostic and Statistical Manual for Mental Disorders) criteria and also using standardized rating scales. Consensus eligibility criteria for this study across all study sites were a diagnosis of ADHD according to the diagnostic criteria of DSM-IV, onset before the age of 7 years by retrospective diagnosis (which was confirmed by a family member, wherever possible), life-long persistence and current diagnosis. Further information on the samples as well as used rating scales can be obtained from Supplementary Methods.

Personality disorder sample

This sample has been described in detail previously [START_REF] Jacob | Cluster B personality disorders are associated with allelic variation of monoamine oxidase A activity[END_REF]. A total of 622 inpatients and outpatients of the Department of Psychiatry and Psychotherapy, University of Würzburg with personality disorders according to DSM-IV criteria participated in the study. Patients were drawn from the general population of hospital patients; every patient with a clinical diagnosis of personality disorder treated in the department from 2000 to 2004 was approached and asked to participate in the study. Inclusion criteria were personality disorder (PD) according to DSM-IV (antisocial, histrionic, borderline, narcissistic, avoidant, dependent, and obsessive-compulsive personality disorder) and age between 18 and 60 years. Exclusion criteria were medical conditions and lifetime diagnosis of schizophrenia or other psychotic disorders. The SCID-II was used to diagnose PDs, and PDs were allocated to cluster B and C operationalized as follows: cluster B (77%) (dramatic-emotional) encompasses antisocial, borderline, histrionic, and narcissistic PDs. Cluster C (37%) (anxious-fearful) includes avoidant, dependent, and obsessive-compulsive PDs. Furthermore, co-morbid axis-I disorders were assessed using the SCID-I as well as extensive clinical interview. Thereby, a lifetime diagnosis of BD (type I and II) was established in 17 of the 622 personality disorder patients (2.7%).

The assessment of the PDs, including all psychometric testing, was performed by a single experienced psychiatrist (C.P.J.).

Bipolar disorder sample

The present study investigated a sample described previously in detail (Scholz et al, 2010). This sample consisted of 170 unrelated bipolar patients (mean age 53 ± 14 years, 68 % female), from the German Lower Franconia area for whom an ICD-10 diagnosis was established by means of an extensive, semi-structured interview analogous to the AMDP interview (AMDP, 2000) carried out by two experienced psychiatrists at the University of Würzburg. Furthermore, the OPCRIT system was used in these patients [START_REF] Mcguffin | A polydiagnostic application of operational criteria in studies of psychotic illness: development and reliability of the OPCRIT system[END_REF]. A further 166 unrelated bipolar patients (mean age 43.0 ± 11.5 years, 49 % female), were ascertained according to ICD-10 diagnostic criteria for research (DCR) by means of a semi-structured interview (SCAN ver. 2.1.;World Health Organization, 1998) at the Center for Psychiatric Research, Århus University Hospital, giving a total number of 336 patients suffering from BPD. Co-morbidity data for BD with aADHD could not be obtained as these patients were ascertained during acute disease episodes where it is not possible to reliably establish a diagnosis of aADHD. None of the subjects showed significant neurological co-morbidity, epilepsy, mental retardation, or other somatic disorders suggesting organic psychosis. Patients with substance-induced disorders were excluded.

In silico replication in the SHIP GWAS

Data from the Study of Health in Pomerania (SHIP) were used [START_REF] Grabe | Mental and physical complaints in thyroid disorders in the general population[END_REF]. The target population was comprised of adult German residents (20-79 years) in a northeastern German population of 212,157 individuals. The net sample comprised 6,267 eligible subjects, of which 1,859 consecutively recruited subjects were eligible for the present analyses. The assessment of personality traits was performed with the NEO-FFI-30 [START_REF] Korner | Personality assessment with the NEO-Five-Factor Inventory: the 30-Item-Short-Version (NEO-FFI-30)[END_REF]. The sample was genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. As most of the DIRAS2 SNPs genotyped in the discovery sample (Table 1) were not captured, genotype data were derived from standard imputation procedures using the software IMPUTE v0.5.0 based on HapMap II.

All cases and controls of the above samples were of self reported German (or Danish, respectively) ancestry. The study was approved by the respective ethics committees of the participating centers and complied with the latest version of the Declaration of Helsinki. Written informed consent was provided by all participants.

Marker selection and genotyping 14 SNPs were selected, which tagged the promoter region, the 5' and 3' UTRs as well as the transcribed exon of the DIRAS2 gene, spanning a chromosomal region of 33 kb. Marker selection was performed using the Tagger algorithm (parameter: pair-wise tagging only, r 2 threshold=0.8, minor allele frequency above 5%) as implemented in HaploView (www.broad.mit.edu/mpg/haploview) (Figure 1; the haplotype structure was comparable in each sample). SNPs were genotyped using the Sequenom MassArray® system (Sequenom, San Diego, CA) coupled to a Bruker Autoflex mass spectrometer (Bruker Daltonics, Bremen, Germany) according to the manufacturer's instructions.

PCR was performed using iPlex chemistry as recommended in the MassArray® iPlex standard operating procedure. Primer sequences can be obtained on request. rs12350590, located proximal of the DIRAS2 gene, and rs9695432 in intron 1, were excluded from further analyses due to minor allele frequency (MAF) <0.01 in the discovery sample.

Statistical analysis

For all samples, we analyzed only SNPs passing quality check which was defined by MAF>0.01, call rate>90% and p value of χ² test for Hardy-Weinberg equilibrium>0.01 in controls or parents.

Inconsistent genotypes in the family sample were revealed and removed by means of PedCheck [START_REF] O'connell | PedCheck: a program for identification of genotype incompatibilities in linkage analysis[END_REF]. Generalized linear models (GLM) implemented in the R fuction glm() were carried out to test for an association between SNP genotype as independent variable and a trait of interest as dependent variable (binary: ADHD, bipolar, cluster B and C personality disorder; quantitative: NEO-PI R (Neuroticism, Extraversion, Openess, Agreeableness, Conscientiousness) and TPQ (Novelty Seeking, Harm Avoidance, Reward Dependence)). Binomial logit link or Gaussian identity link was used for binary or quantitative traits, respectively. We also performed Fisher's exact test in order to assure the asymptotic test results of glm(). Family-based association analysis was carried out using pedigree disequilibrium test (sumPDT, [START_REF] Martin | A test for linkage and association in general pedigrees: the pedigree disequilibrium test[END_REF]) to assess whether an allele is more frequently transmitted to affected offspring by taking relationships between the offspring into account.

We used the omnibus test implemented in famhap (10000 simulations; [START_REF] Becker | Maximum-likelihood estimation of haplotype frequencies in nuclear families[END_REF] to test the global hypothesis that association with ADHD may be contributed by at least one of the estimated haplotypes. The LD blocks were defined by pairwise D'>0.65 assessed using Haploview.

After the haplotype ACGCTT from block 2 (consisting of SNPs rs1331503, rs2297354, rs1331504, rs7848810, rs1412005, rs689687) was identified as the sole haplotype consistently increasing risk across all samples, a haplotype-specific association test using Unphased [START_REF] Dudbridge | Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data[END_REF] was carried out.

For the meta analysis, we assumed a common effect of the rs1412005 T allele and the ACGCTT haplotype on the risk for ADHD, as there was no effect heterogeneity across all four considered aADHD case-control samples (p value = 0.43 and 0.29, respectively, Woolf's test for heterogeneity, obtained with the R function meta.MH()). A joint analysis assessing the common OR was done by means of GLM described above, adjusted for fixed country effect. This approach was also applied to the total data set including all patients (adult ADHD, bipolar and personality disorders) and controls in order estimate the cross disorder effects of DIRAS2 SNPs.

Unless otherwise specified, p values are two-sided p values unadjusted for multiple testing. The significance level was set to 0.05. Our study primarily aimed at detecting ADHD-associated SNP variants of DIRAS2, whereas analyses for other traits were exploratory. Therefore, we used the Bonferroni method to calculate p values adjusted for testing multiple SNPs and haplotypes for association with ADHD.

Power calculations were performed under a two-sided nominal significance level of 0.05 using QUANTO Version 1.0 (http://hydra.usc.edu/gxe). We assumed an effect size between 1.1 and 1.5 for the rs1412005 T allele and between 1.3 and 3.0 for the ACGCTT haplotype. By given disease prevalence of 5%, we computed the power to detect these variants in the replication sample consisting of 1035 aADHD patients and 1381 controls from IMpACT, under a frequency of 25% and 5% for the rs1412005 T allele and the ACGCTT haplotype, respectively. All assumptions nearly corresponded with nominal 95% confidence intervals and frequencies estimated from the discovery sample.

Results

Association of DIRAS2 with adult ADHD

Table 1 displays information about functional and physical location of each SNP in/near DIRAS2. In the discovery sample of 600 adult ADHD patients and 420 controls from Germany, four out of 12 investigated SNPs had p<0.05 in the association test with aADHD (Table 2). An empirical test at the gene level for association of at last one of 12 SNPs yielded a p=0.0127 (10,000 permutations). Upon haplotype testing, both LD blocks (Figure 1) showed evidence of association with nominally significant p values (p=0.006 for block 1 and p=0.033 for block 2).

Replication in 1035 aADHD patients and 1381 controls from IMpACT did not show any significant SNP association (all p >0.07; Table 2). Also for each individual country, we found neither an association on the single marker (p>0.1, Suppl Table 1) nor on the haploblock level (p>0.09). However, consistency of the effect size estimates was observed indicating increased aADHD risk for carriers of the A allele of rs1331503, the T allele of rs1412005 and the haplotype ACGCTT from block 2 (consisting of SNPs rs1331503, rs2297354, rs1331504, rs7848810, rs1412005, and rs689687; Table 3) across the four samples. Association testing for this specific haplotype provided a significant result in the initial sample from Germany (p=0.0006) which replicated in the additional independent combined samples from IMpACT (p=0.026). To further explore the association of DIRAS2 with aADHD, we performed five meta-analyses, for the best four SNPs of the discovery sample (rs7854469, rs16906711, rs1331503, rs1412005) and for the risk haplotype ACGCTT, in all 1635 aADHD cases and 1801 controls from Germany, The Netherlands, Spain and Norway taking population strata into account. A p=0.04 was found for the rs1412005 T allele in the association test with aADHD (p value adjusted for five tests: 0.22; OR=1.12, 95%-CI: 1.00-1.25; Figure 2). The risk haplotype ACGCTT (9.0% in cases and 6.5% in controls) was associated with an increased risk for aADHD, and this effect was statistically significant even after adjusting for multiple testing (p=0.00028, p value adjusted for five tests: 0.001, OR=1.45, 95% CI: 1.19-1.77; Figure 3). This haplotype contains the T allele of SNP rs1412005.

DIRAS2 and childhood ADHD

In order to extend our findings for DIRAS2 to cADHD, we also analyzed 166 independent nuclear families with offspring affected by cADHD. The rs1412005 T allele was not significantly overtransmitted (p=0.65), but the C allele of the neighboring SNP rs7848810 -also a promoter SNP -was (p=0.046, transmission rate: 53%; Table 4). Haplotype analysis provided evidence of significant association of the haploblock 2 (p=0.0231) with a p value for the specific haplotype ACGCTT slightly exceeding 0.05 (p=0.0692, frequency among transmitted versus non-transmitted haplotypes: 11% versus 6.5%, OR=2.01, 95% CI: 0.80-5.00). rs7848810 was also associated with co-morbid dyslexia (p=0.015).

Expanding the phenotype: association of DIRAS2 with personality disorders and bipolar disorder

To examine whether DIRAS2 is specifically linked to ADHD or whether the association generalizes to disorders sharing the feature of impulsivity and emotional instability (independent of co-morbidity with ADHD), we performed association analyses in two further independent samples: patients suffering from personality disorders and BD. We found nominal evidence of association of rs1412005, the SNP implicated in aADHD (see above), with Cluster B personality disorder (p=0.031) and BD (p=0.026). The OR of the T allele for both disorders was estimated at around 1.26 (Table 5).

The T allele also tended towards increasing risk for cluster C personality disorder (p=0.086) with the same OR estimate of 1.26.

DIRAS2 relates to personality factors linked to aADHD

The Würzburg aADHD and personality disorder samples had also been tested for personality dimensions, as assessed by two questionnaires (NEO-PI R: Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness; and TPQ: Novelty Seeking, Harm Avoidance, Reward Dependence). Given the association of DIRAS2 variants with personality disorders (see above), and considering that aADHD goes along with a distinct personality profile [START_REF] Jacob | Co-morbidity of adult attention-deficit/hyperactivity disorder with focus on personality traits and related disorders in a tertiary referral center[END_REF], we additionally investigated whether personality scores may be also be affected by DIRAS2 variants.

Table 6 represents all nominally significant association results. In the aADHD sample, associations with p<0.05 were found for Neuroticism, Extraversion, Agreeableness, Conscientiousness and Harm Avoidance (p values between 0.002 and 0.033). The three SNPs rs1331503, rs1412005 and rs689687, which were associated with these personality traits, are located in haplotype block 2 and the transcriptional control region of the gene. In the PD sample, nominal evidence of association was found for Extraversion, Agreeableness, Conscientiousness, Novelty Seeking and Harm Avoidance (p values between 0.008 and 0.040; Table 6). In comparison to the aADHD sample, different SNPs showed association in this sample and almost all of them are located in the haplotype block 1. However, rs7854469 was associated both with aADHD (see above) and the ADHD-relevant personality trait Extraversion (p=0.0075, Table 6) in PD. Also the aADHD-associated SNP rs7848810 was associated with TPQ Novelty Seeking (p=0.0396, Table 6) and NEO PI-R Conscientiousness (p=0.0123, Table 6) in the PD sample.

Due to the pitfalls of multiple testing, inevitable in personality genetics, replication attempts are paramount. We have thus analyzed 1859 subjects from a general population study (SHIP) who have been examined for personality factors; apart from rs7848810, SNPs had to be imputed from data acquired on the Affymetrix Human SNP Array 6.0 platform. In doing so, rs16906711 was found to be associated with NEO PI-R Neuroticism (p=0.046, Suppl Table 2), and rs1331504 with NEO PI-R Conscientiousness (p=0.002, Suppl Table 2).

Discussion

Based on the results of four hypothesis-free approaches converging on the chromosomal locus 9q22.2 -two linkage scans for cADHD, a pooled GWAS of adult ADHD and a pooled GWAS of personality disorder -we examined the DIRAS2 gene by genotyping 14 markers, selected to tag the promoter region, the 5' and 3' UTRs and the only coding exon of the gene, in a total of seven independent patient samples. ADHD findings replicated across adult ADHD samples, with metaanalytic treatment implicating the ACGCTT haplotype (which spans the regulatory region of DIRAS2) and one of the SNPs contributing to this haplotype (rs1412005, T allele), and generalized to ADHD in childhood. However, after correction for multiple testing, only the association of the haplotype remained significant arguing that the association of DIRAS2 with ADHD most likely is not due to one of the examined single markers but rather to a variant segregating with this risk haplotype.

Furthermore, expanding the phenotype to impulsivity disorders and impulsivity-related traits pointed towards a role for DIRAS2. While two of the associated SNPs are located in the 3' UTR and potentially involved in expression regulation (rs7854469 and rs16906711), two others (rs7848810 and rs1412005) were located upstream of the transcription start site (Figure 1). Scanning of the transcriptional control region SNPs for putative overlapping transcription factor binding motifs revealed that the sequence carrying the major allele of rs7848810 contained predicted response elements for PBX1 and for the PBX-HOXA9 dimer. PBX1 is known for its developmental regulation of neuropeptide Y expression [START_REF] Mayer | Analysis of a repressor region in the human neuropeptide Y gene that binds Oct-1 and Pbx-1 in GT1-7 neurons[END_REF], whereas PBX-HOX dimers generally play important roles in vertebrate development [START_REF] Moens | Hox cofactors in vertebrate development[END_REF]. Minor allele carriers may therefore have an impaired developmental expression of DIRAS2. Meta-analytic treatment of the aggregated ADHD data provided further evidence especially for ACGCTT haplotype, which includes the rs1412005 T allele and spans the regulatory region of DIRAS2, to be associated with aADHD. We therefore hypothesize that alterations in the expressional control of DIRAS2 underlie the ADHD association, although further studies have yet to corroborate our findings.

Evidently, association of DIRAS2 in not specific to ADHD, but also occurs for other phenotypes such as bipolar and personality disorders. A cross disorder analysis for all disorders combined (aADHD, bipolar, and personality disorder) provided an OR effect for the rs1412005 T allele of 1.13 (95%CI:

1.02-1.26, p=0.02). Given the considerable co-morbidity of these disorders with aADHD, our findings may explain an (although presumably small) part of the genetic variance underlying these clinical observations. Clues to which neurobiological (endo-)phenotype is modulated by DIRAS2 might be obtained from personality genetics, as nine DIRAS2 SNPs were associated with personality domains in aADHD or PD patients. Specifically, aADHD patients carrying the rs1412005 T allele, showing metaanalytic evidence of increasing risk towards aADHD, exhibit on average 3.6 point lower Conscientiousness and 1.4 point higher Harm Avoidance scores than non-carriers (Table 6). Thus, DIRAS2 seems to predispose to a characteristic personality profile going along with ADHD, which might best be characterized by reduced Conscientiousness and increased Harm Avoidance. However, it has to be noted that associations were quite disparate between samples so that these data can only be considered preliminary ahead of further replication.

What are the molecular mechanisms underlying these association findings? As almost nothing is known about the function of DIRAS2, we can only speculate. The expression profile of DIRAS2 is specific to the brain displaying marked expression in the prefrontal cortex, the anterior cingulate cortex and the amygdala. While the former two structures are involved in working memory, error detection and executive control -all of which are impaired in ADHD -the latter is also a key component in fear conditioning and anxiety; dampened amygdala response might well lead to increased risk taking behavior. While DIRAS2´s expression pattern endorses a potential pathophysiological role in ADHD and personality, the relevant molecular cascades are elusive. Di-Ras2, the protein encoded by DIRAS2, belongs to the small GTPase Ras family, whose members have role in cell morphogenesis and neurogenesis. Within the Ras family, Di-Ras2 seems to have unusual biochemical properties [START_REF] Kontani | Di-Ras, a distinct subgroup of ras family GTPases with unique biochemical properties[END_REF]. Unlike other Ras kinases, it fails to activate the MAP kinase, phosphoinositide 3-kinase or AKT pathways. To date, no effector mechanisms have been described, and due to its specific catalytic characteristics, it is even unclear whether Di-Ras2 is regulated by GTP binding at all. Highly selective expression in raphe serotonin neuron subpopulations (Wylie et al) points to a role in the regulation of the caudal serotonergic system; however, precise pathways again remain elusive. Despite it being a commonplace statement, in this case further studies are clearly needed to even speculate on the neurobiological and behavioral role of Di-ras2.

Several caveats have to be considered in the interpretation of our data. Given the genetic effect sizes (odds ratio) estimated in the discovery sample (1020 German individuals), our replication sample (2416 individuals from IMpACT) had sufficient power to detect the effects of the rs1412005 T allele (59-100%) and the ACGCTT haplotype (88-100%). Despite this fact, there still is a chance of falsepositive findings. This is relevant as most associations on the single marker level would not withstand correction for multiple testing, which becomes an even more prominent issue when examining multiaxial dimensional data such as personality traits. However, since we achieved the essential goal of targeted replication in several independent populations at least for the risk haplotype, and in conjunction with previous evidence from linkage studies and GWAS, we are confident to report a true association. As all nearby (± one megabase) genes are not expressed in the brain, it appears unlikely that genetic variants outside of DIRAS2, but in linkage disequilibrium with the scrutinized polymorphisms are pathophysiologically relevant. We therefore suspect that genetic variation within DIRAS2, in linkage disequilibrium with the interrogated SNPs, are the "true" underlying risk alleles, a hypothesis which will have to be tested in further studies using re-sequencing approaches followed by functional studies. Finally, as we did not obtain strong positive findings from all samples, small individual sample size, background, epistatic or founder effects or even gene-environment interaction may also play a role explaining that DIRAS2 genetic variants contribute to ADHD and related phenotypes in some, but not all populations.

In conclusion, converging evidence from several hypothesis-free and hypothesis-driven approaches suggest that DIRAS2 is a candidate gene for ADHD and related behavioral domains. Due to lack of data on the encoded protein, the underlying molecular mechanisms nevertheless are far from being understood, so that future functional studies will have to bridge the genotype-phenotype gap.

A.L.M. Verbeek, D.W. Swinkels and B. Franke. Finally, we thank all patients, families and controls for their participation in this study.

Tables and Figures

Figure 1. LD plot of DIRAS2 aligned to the gene structure and the significant SNPs from all samples.

Please not that the LD plot is not exactly matched to the SNPs, as precise scaling was not possible: rs9695432 is the last SNP of block , while rs1331503 is the first SNP of block 2. rs16906711 is associated with aADHD, Cluster C PD, Cluster B PD, and Harm Avoidance in PD; rs9695432 with aADHD and personality domains; rs1331503 with aADHD and personality domains; rs2297354 with aADHD and personality domains; rs7848810 with aADHD, Cluster B PD, BD, and personality domains; and rs1412005 with aADHD and personality domains. Table 1. General SNP information with minor allele frequency (MAF, %).

Table 2. SNP results of case-control based association analyses for aADHD. Table 3. Association analysis of the ACGCTT haplotype for the aADHD case-control samples.

Figure 2 .

 2 Figure 2. Forest plot of the rs1412005 T allele for the aADHD case-control association study.

Figure 3 .

 3 Figure 3. Forest plot of the ACGCTT haplotype for the aADHD case-control association study.

Table 4 .

 4 Results of family-based association analysis for cADHD.

Table 5 .

 5 Results of case-control association analysis for co-morbid disorders (BD and personality disorder).

Table 6 .

 6 Nominally significant results of association analysis for quantitative personality traits.

	Suppl

Table 1 .

 1 SNP results of case-control based association analyses for aADHD by population.

	Suppl

Table 2 .

 2 Personality domains in the SHIP study.

Table 1 .

 1 General SNP information with minor allele frequency (%).

							German	German	Spanish	Dutch	Norwegian	Bipolar	Personality
	SNP	SNP	Physical	Region	Major	Minor	aADHD	cADHD	aADHD	aADHD	aADHD	disorder	disorder
	NR	ID	position		allele	allele	cases	controls	parents	offspring	cases	controls	cases	controls	cases	controls	cases	cases
							n=600	n=420	n=166	n=244	n=253	n=311	n=285	n=508	n=497	n=562	n=336	n=622
	1	rs7868194 92409604 3' region	A	G	43.2	47.0	45.4	45.2	46.7	50.3	48.4	47.9	45.9	46.9	43.4	47.4
	2* rs12350590 92410160 3' region	C	G	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	3	rs7854469 92412049	3' UTR	T	A	7.5	10.2	9.8	8.1	14.9	11.6	11.4	8.9	9.0	8.9	9.9	9.1
	4	rs1542479 92412869	3' UTR	G	A	4.3	2.8	3.7	2.8	3.6	5.4	4.6	4.4	4.7	5.2	3.5	3.5
	5	rs16906711 92413098	3' UTR	C	G	2.9	1.5	2.2	1.5	2.8	3.4	2.5	2.5	2.3	2.3	2.0	3.0
	6	rs690111	92417110 Intron 1	G	T	26.1	24.1	29.3	29.9	24.7	21.8	26.3	26.6	25.8	26.5	27.3	24.3
	7	rs1017753 92417721 Intron 1	C	T	47.7	50.1	49.5	49.8	49.8	45.6	47.8	48.7	49.0	48.2	47.4	49.7
	8*	rs9695432 92417994 Intron 1	G	A	0.5	1.5	0.4	0.5	1.6	0.7	1.8	0.9	1.5	0.6	0.5	0.9
	9	rs1331503 92442337 Intron 1	T	A	37.8	32.3	32.1	33.4	35.9	35.7	34.1	32.9	33.9	33.1	35.7	36.4
	10	rs2297354 92444160 Intron 1	C	G	2.0	1.2	1.3	1.1	1.2	0.7	1.6	0.7	2.1	2.6	1.5	1.0
	11	rs1331504 92445648 Intron 1	G	C	8.7	8.4	6.9	6.8	7.8	8.3	7.1	6.4	7.4	6.6	6.7	8.1
	12	rs7848810 92447678 Promoter	C	A	28.2	31.5	30.3	28.0	33.1	29.7	28.5	28.3	22.2	24.3	28.0	28.4
	13	rs1412005 92448981 Promoter	G	T	27.9	23.0	26.1	25.7	26.3	23.9	26.4	25.2	26.4	26.0	28.2	27.6
	14	rs689687	92450021 Promoter	T	C	19.4	18.5	16.1	13.7	19.0	19.8	17.0	17.2	17.3	19.0	20.3	19.0
	*SNPs were excluded from further analyses due to low MAF (<0.01)

Table 2 .

 2 SNP results of case-control based association analyses for aADHD.

	SNP	SNP	German discovery sample	IMpACT replication sample		Pooled data
	NR	ID	P value	OR (allele)	95%-CI	P value	OR (allele)	95%-CI	P value	OR (allele)	95%-CI
	1	rs7868194	0.0880	1.18 (A)	0.98 -1.42	0.4395	1.05 (A)	0.93 -1.18	0.1174	1.08 (A)	0.98 -1.20
	3	rs7854469	0.0394	1.40 (T)	1.02 -1.93	0.0720	1.19 (A)	0.98 -1.43	0.6023	1.04 (A)	0.89 -1.23
	4	rs1542479	0.0905	1.55 (A)	0.93 -2.59	0.3378	1.15 (G)	0.87 -1.51	0.9958	1.00 (A)	0.79 -1.27
	5	rs16906711	0.0453	1.97 (G)	1.01 -3.81	0.7288	1.07 (C)	0.74 -1.55	0.4271	1.14 (G)	0.83 -1.55
	6	rs690111	0.3392	1.11 (T)	0.90 -1.37	0.8043	1.02 (T)	0.89 -1.16	0.4684	1.04 (T)	0.93 -1.17
	7	rs1017753	0.2741	1.11 (C)	0.92 -1.33	0.4749	1.04 (C)	0.93 -1.17	0.2353	1.06 (C)	0.96 -1.17
	9	rs1331503	0.0125	1.27 (A)	1.05 -1.54	0.5812	1.03 (A)	0.92 -1.17	0.0678	1.10 (A)	0.99 -1.22
	10	rs2297354	0.2101	1.62 (G)	0.76 -3.44	0.6599	1.11 (G)	0.70 -1.77	0.2880	1.24 (G)	0.84 -1.82
	11	rs1331504	0.8241	1.04 (C)	0.75 -1.44	0.5302	1.07 (C)	0.86 -1.34	0.5195	1.06 (C)	0.88 -1.28
	12	rs7848810	0.1244	1.17 (C)	0.96 -1.42	0.9898	1.00 (A)	0.88 -1.14	0.3942	1.05 (C)	0.94 -1.17
	13	rs1412005	0.0182	1.29 (T)	1.04 -1.58	0.3785	1.06 (T)	0.93 -1.21	0.0437	1.12 (T)	1.00 -1.25
	14	rs689687	0.6039	1.07 (C)	0.84 -1.36	0.3791	1.07 (T)	0.92 -1.24	0.6344	1.03 (T)	0.91 -1.17
	Nominal p-values < 0.05 are in bold print

Table 3 .

 3 Association analysis of the ACGCTT haplotype* for the studied aADHD case-control samples.

		Frequency: N (%)		Association test
	Sample	cases	controls	P value	OR	95%-CI
	Germany	89 (8.94)	30 (4.57)	0.0006	2.05	1.34 -3.14
	Netherlands	46 (9.54)	66 (7.45)	0.1815	1.31	0.88 -1.94
	Spain	28 (6.48)	25 (4.46)	0.1815	1.48	0.85 -2.58
	Norway	76 (10.08)	78 (8.35)	0.2213	1.23	0.88 -1.71
	* Haplotype consists of SNPs rs1331503, rs2297354, rs1331504, rs7848810, rs1412005 and rs689687

Table 4 .

 4 Results of family based association analysis for cADHD.

	SNP	SNP	Over-transmission	PDT
	NR	ID	Allele	Rate (%)	P value
	1	rs7868194	G	50.9	0.6507
	3	rs7854469	T	50.8	0.2059
	4	rs1542479	G	50.5	0.1779
	5	rs16906711	C	50.2	0.3711
	6	rs690111	G	50.9	0.4982
	7	rs1017753	T	50.9	0.6911
	9	rs1331503	A	52.2	0.4245
	10	rs2297354	C	50.3	0.2253
	11	rs1331504	G	50.1	0.8997
	12	rs7848810	C	52.8	0.0463
	13	rs1412005	T	51.4	0.6565
	14	rs689687	T	51.3	0.1456*
	Nominal p-values < 0.05 are in bold print
	*call rate=77%

Table 5 .

 5 Results of case-control association analysis for co-morbid bipolar disorder and personality disorders.

	SNP	SNP		Biplolar			Cluster B			Cluster C
	NR	ID	P value OR (allele)	95%-CI	P value OR (allele)	95%-CI	P value OR (allele)	95%-CI
	1	rs7868194	0.1794	1.15 (A)	0.94 -1.42	0.9272	1.01 (G)	0.84 -1.22 0.3893	1.11 (G)	0.88 -1.39
	3	rs7854469	0.8687	1.03 (T)	0.73 -1.44	0.0868	1.35 (T)	0.96 -1.91 0.5588	1.12 (A)	0.77 -1.63
	4	rs1542479	0.4349	1.26 (A)	0.70 -2.26	0.5663	1.17 (A)	0.68 -2.00 0.3782	1.34 (A)	0.70 -2.56
	5	rs16906711	0.4935	1.32 (G)	0.59 -2.94	0.0454*	1.95 (G)	1.01 -3.75 0.0220*	2.33 (G)	1.13 -4.80
	6	rs690111	0.1752	1.18 (T)	0.93 -1.50	0.7622	1.03 (T)	0.83 -1.29 0.8047	1.03 (G)	0.79 -1.36
	7	rs1017753	0.2906	1.12 (C)	0.91 -1.38	0.9950	1.00 (C)	0.83 -1.21 0.3930	1.10 (T)	0.88 -1.39
	9	rs1331503	0.1923	1.16 (A)	0.93 -1.44	0.1416	1.16 (A)	0.95 -1.42 0.1950	1.18 (A)	0.92 -1.50
	10	rs2297354	0.6345	1.24 (G)	0.51 -3.02	0.5963	1.28 (C)	0.51 -3.18 0.8929	1.07 (G)	0.38 -2.99
	11	rs1331504	0.2404	1.27 (G)	0.85 -1.89	0.6503	1.08 (G)	0.77 -1.53 0.5419	1.14 (G)	0.74 -1.76
	12	rs7848810	0.1645	1.17 (C)	0.94 -1.47	0.1065	1.18 (C)	0.96 -1.46 0.7176	1.05 (C)	0.82 -1.34
	13	rs1412005	0.0256	1.32 (T)	1.03 -1.67	0.0305	1.27 (T)	1.02 -1.59 0.0855	1.26 (T)	0.97 -1.64
	14	rs689687	0.3988	1.12 (C)	0.86 -1.47	0.7428	1.04 (C)	0.81 -1.34 0.8141	1.04 (C)	0.77 -1.40
	Nominal p-values < 0.05 are in bold print
	*pHWE=0.001

Table 6 .

 6 Nominally significant results of association analysis for quantitative personality traits. SNPs with p-value < 0.05 in at least one analysis are in bold print. Se, standard error.

	SNP	SNP	Personality		aADHD cases			PD patients
	NR	ID	trait	P value	Beta (allele)	Se	P value	Beta (allele)	Se
	9	rs1331503	Neuroticism	0.0241	3.51 (A)	1.55	0.3113	1.72 (A)	1.70
	14	rs689687	Neuroticism	0.0327	4.14 (C)	1.93	0.5747	1.15 (C)	2.05
	3	rs7854469	Extraversion	0.4172	2.07 (A)	2.55	0.0075	6.32 (T)	2.36
	9	rs1331503	Extraversion	0.0065	3.68 (T)	1.35	0.6695	0.59 (T)	1.38
	13	rs1412005	Extraversion	0.0088	3.83 (G)	1.46	0.7600	0.44 (T)	1.42
	3	rs7854469	Agreeableness	0.8963	0.26 (T)	1.96	0.0116	4.24 (A)	1.67
	10	rs2297354	Agreeableness	0.2065	4.78 (C)	3.78	0.0288	10.03 (C)	4.57
	14	rs689687	Agreeableness	0.0284	2.80 (C)	1.28	0.2645	1.28 (T)	1.15
	6	rs690111 Conscientiousness	0.9152	0.17 (T)	1.60	0.0448	2.87 (T)	1.43
	9	rs1331503 Conscientiousness	0.0173	3.24 (T)	1.36	0.1876	1.77 (T)	1.34
	12	rs7848810 Conscientiousness	0.9051	0.18 (C)	1.52	0.0123	3.49 (A)	1.39
	13	rs1412005 Conscientiousness	0.0135	3.61 (G)	1.46	0.1013	2.26 (G)	1.38
	14	rs689687 Conscientiousness	0.0043	4.84 (T)	1.69	0.1166	2.50 (T)	1.59
	3	rs7854469	Novelty Seeking	0.8075	0.16 (A)	0.65	0.0224	1.35 (T)	0.59
	12	rs7848810	Novelty Seeking	0.3585	0.35 (A)	0.38	0.0396	0.73 (C)	0.36
	1	rs7868194 Harm Avoidance	0.1634	0.62 (A)	0.45	0.0126	1.08 (G)	0.43
	7	rs1017753 Harm Avoidance	0.0897	0.74 (C)	0.43	0.0257	0.95 (T)	0.43
	9	rs1331503 Harm Avoidance	0.0023	1.30 (A)	0.42	0.7309	0.16 (A)	0.46
	13	rs1412005 Harm Avoidance	0.0018	1.43 (T)	0.46	0.7284	0.17 (T)	0.48
	14	rs689687	Harm Avoidance	0.0033	1.54 (C)	0.52	0.6540	0.25 (T)	0.55

Acknowledgements

This study was supported by the DFG (Grant RE1632/1-1, /1-3 and /5 to AR, KFO 125 to AR, CPJ, AW, SW, HS and KPL; SFB 581 to KPL, SFB TRR 58 to AR and KPL; RTG 1252, to AR and KPL; GR 1912/5-1, to HG), BMBF (IZKF Würzburg, 01KS9603, to KPL; IZKF N-27-N, to AR) and the EC (NEWMOOD LSHM- CT-2003-503474, to KPL). The Norwegian part of the study was supported by the Research Council of Norway and the Western Norway Regional Health Authority (Helse Vest). Financial support for the Spanish part of the study was received from "Instituto de Salud Carlos III-FIS, Spain" (PI040524, PI041267), "Fundació La Marató de TV3" (092330/31) and "Agència de Gestió d'Ajuts Universitaris i de Recerca-AGAUR"(2009SGR-00971). MR is a recipient of a "Ramón y Cajal" contract from "Ministerio de Ciencia e Innovación" (Spain). The Dutch part of the project was supported by the Hersenstichting Nederland (Fonds Psychische Gezondheid). SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the 'Center of Knowledge Interchange' program of the Siemens AG. We thank J. Romanos, A. Boreatti-Hümmer and M. Heine for their dedicated assistance in patient recruitment. T. Töpner, N. Steigerwald, G. Ortega, J. Auer, C. Gagel and N. Döring are credited for excellent technical assistance. We thank Mariana Nogueira and Montse Corrales for their involvement in the clinical assessment in Spain, M. Dolors Castellar and others from the "Banc de Sang i Teixits" (Hospital Vall d'Hebron, Barcelona) for their collaboration in the recruitment of control subjects, and Cristina Sánchez-Mora for her participation in the preparation of DNA samples from patients and controls." We thank Marije Boonstra and Marten Onnink for help with patient inclusion, as well as Marlies Naber and Angelien Heister for DNA handling and genotyping. The Dutch controls were derived from the Nijmegen Biomedical Study. Principal investigators of the Nijmegen Biomedical Study are L.A.L.M. Kiemeney, M. den Heijer,

Conflict of interests:

In the past 3 years, Jan K. Buitelaar has been a consultant to/member of advisory board of and/or speaker for Janssen Cilag BV, Eli Lilly, Bristol-Myer Squibb, Organon/Shering Plough, UCB, Shire, Medice and Servier. He is not an employee of any of these companies. He is not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents or royalties. Sandra Kooij has received research grants from Janssen BV en Shire, and is on the speaker's bureau of Janssen and Eli Lilly. All other authors declare no conflicts of interest.


Hein S Hokken-Koelega

Albert Raat

Eric A P Hofman

Vincent W V Steegers

Jaddoe

MSc Büşra Durmuş

MD Lamise Ay

MD, PhD Anita Cs Hokken-Koelega

MD, PhD Hein Raat

MD, PhD Albert Hofman

MD, PhD Eric A P Steegers

MD, PhD Vincent W V Jaddoe
email: v.jaddoe@erasmusmc.nl.

Maternal smoking during pregnancy and subcutaneous fat mass in early childhood. The Generation R Study

Keywords: 1; Tables: 4 body composition, cohort, maternal smoking, obesity, paediatrics, skinfold

Maternal smoking during pregnancy and subcutaneous fat mass in early childhood. The Generation R Study

INTRODUCTION

Active maternal smoking during pregnancy is a common and preventable specific adverse environmental exposure for the fetus [1][START_REF] Abel | Smoking during pregnancy: a review of effects on growth and development of offspring[END_REF][START_REF] Neovius | Forecast of future premature mortality as a result of trends in obesity and smoking: nationwide cohort simulation study[END_REF]. Maternal smoking during pregnancy is associated with fetal growth retardation and increased risks of preterm birth and lower birth weight [START_REF] Cnattingius | The epidemiology of smoking during pregnancy: smoking prevalence, maternal characteristics, and pregnancy outcomes[END_REF][START_REF] Andres | Perinatal complications associated with maternal smoking[END_REF][START_REF] Castles | Effects of smoking during pregnancy. Five meta-analyses[END_REF].

Maternal smoking during pregnancy also seems to increase the risk of obesity in the offspring [START_REF] Von-Kries | Maternal smoking during pregnancy and childhood obesity[END_REF][START_REF] Ino | Maternal smoking during pregnancy and offspring obesity: meta-analysis[END_REF]. The mechanisms underlying these associations may include developmental adaptations leading to changes in body composition and appetite behaviour [START_REF] Barker | Fetal origins of coronary heart disease[END_REF][START_REF] Bateson | Developmental plasticity and human health[END_REF]. A recent systematic review suggested that prenatal smoke exposure leads to a higher body mass index in childhood [START_REF] Oken | Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis[END_REF]. However, using body mass index as outcome does not give information about body composition [START_REF] Wells | Measuring body composition[END_REF][START_REF] Nevill | Relationship between adiposity and body size reveals limitations of BMI[END_REF]. Studies relating maternal smoking during pregnancy with direct measures of body composition in the offspring might be important since adverse body composition and especially unfavourable fat distribution are important contributors to metabolic syndrome outcomes [START_REF] Holzhauer | Sonographic assessment of abdominal fat distribution in infancy[END_REF]. Thus far, studies on the association between maternal smoking during pregnancy and measures of body composition are scarce and showed inconsistent results [START_REF] Oken | Associations of maternal prenatal smoking with child adiposity and blood pressure[END_REF][START_REF] Leary | Smoking during pregnancy and offspring fat and lean mass in childhood[END_REF][START_REF] Widerøe | Does maternal smoking during pregnancy cause childhood overweight?[END_REF]. Skinfold thickness is a valid and easy to perform measurement for subcutaneous fat mass assessment in young children in epidemiological studies [START_REF] Wells | Measuring body composition[END_REF][START_REF] Nevill | Relationship between adiposity and body size reveals limitations of BMI[END_REF].

For the present study, we hypothesized that active maternal smoking during pregnancy leads to higher peripheral, central and total subcutaneous fat mass in young childhood. We examined in a population-based prospective cohort study among 907 Dutch mothers and children followed from early fetal life onwards, the associations of exposure to maternal smoking during pregnancy with peripheral, central and total subcutaneous fat mass at the ages of 1.5, 6 and 24 months.

METHODS

Design

This study was embedded in the Generation R Study, a population-based prospective cohort study of pregnant women and their children from fetal life onwards in Rotterdam, the Netherlands [START_REF] Jaddoe | The Generation R Study Biobank: a resource for epidemiological studies in children and their parents[END_REF][START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF]. Enrolment in the study was aimed at early pregnancy (gestational age <18 weeks) but was possible until birth of the child. All children were born between April 2002 and January 2006, and form a prenatally enrolled birth-cohort that is currently followed until young adulthood. Of all eligible children in the study area, 61% were participating in the study at birth [START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF]. Additional detailed assessments of fetal and postnatal growth and development were conducted in third trimester of pregnancy in a subgroup of 1232 Dutch mothers and their children in order to exclude bias due to ethnicity [START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF]. Dutch ethnicity was defined as having two parents and four grandparents born in the Netherlands [START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF][20].

Pregnant mothers participating in the Generation R study, who met this criterion, were approached for the additional measurements [START_REF] Jaddoe | The Generation R Study Biobank: a resource for epidemiological studies in children and their parents[END_REF][START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF]. Of all approached women, 80% agreed to participate in the subgroup study. In total, 1039 children participated in at least one of the postnatal assessments at the ages of 1.5, 6 and 24 months. The study protocol was approved by the Medical Ethical Committee of the Erasmus Medical Centre, Rotterdam.

Written informed consent was obtained from all participants or their parents.

DATA COLLECTION AND MEASUREMENTS

Maternal smoking during pregnancy: Information about maternal smoking was obtained by postal questionnaires sent in first, second and third trimester of pregnancy. Response rates for these questionnaires were 91%, 80%, and 77%, respectively [START_REF] Jaddoe | The Generation R Study: design and cohort update 2010[END_REF]. Active maternal smoking at enrolment was assessed in the first questionnaire by asking the mother whether she smoked during her pregnancy. We grouped mothers into three categories: 1) never smoked; 2) smoked only until their pregnancy was acknowledged (first trimester only); and 3) continued to smoke during pregnancy. This first questionnaire was sent to all mothers, independently of gestational age at enrolment. In the second and third questionnaires, the mothers were asked whether they had smoked during the past 2 months (yes/no). Mothers who reported in the first questionnaire not to smoke or to have smoked until their pregnancy was acknowledged, but reported to have smoked in the second or third questionnaire were recategorized as ''continued smoking''. Among smokers, the number of cigarettes daily smoked was categorized into the categories: no smoking, <5 cigarettes/day and >5 cigarettes/day.

Subcutaneous fat mass measurements and anthropometrics

Subcutaneous fat mass was measured as skinfold thickness (SFT) at the ages of 1.5, 6 and 24 months on the left side of the body at four different sites (biceps, triceps, suprailiacal and subscapular) according to standard procedures by using a skinfold calliper (Slim Guide, Creative Health Products) [START_REF] Lohman | Anthropometric Standardization Reference Manual: Abridged Edition[END_REF]. Four well-trained medical assistants performed all measurements [START_REF]Reliability of anthropometric measurements in the WHO Multicentre Growth Reference Study[END_REF]. The consensus between and among observers for the medical assistants was analysed using the intraclass correlation coefficient (ICC) [START_REF] Bland | Statistical methods for assessing agreement between two methods of clinical measurement[END_REF][START_REF] Shrout | Intraclass correlations: uses in assessing rater reliability[END_REF].

Intraobserver ICC was 0.88 and interobserver ICC was 0.76. Total subcutaneous fat mass was calculated from the sum of biceps SFT + triceps SFT + suprailiacal SFT + subscapular SFT. Central subcutaneous fat mass was calculated from the sum of suprailiacal SFT + subscapular SFT. Peripheral subcutaneous fat mass was calculated from the sum of biceps SFT + triceps SFT [START_REF] Ketel | Superiority of skinfold measurements and waist over waist-to-hip ratio for determination of body fat distribution in a population-based cohort of Caucasian Dutch adults[END_REF][START_REF] Birmingham | Subscapular and triceps skinfold thicknesses, body mass index and cardiovascular risk factors in a cohort of middle-aged employed men[END_REF]. Body length was measured in supine position to the nearest millimeter using a neonatometer and at the age of 24 months, height was measured by a Harpenden stadiometer (Holtain Limited, Dyfed, U.K.) in standing position. Weight was measured in naked infants to the nearest grams at the ages of 1.5 and 6 months by an electronic infant scale (SECA) and at the age of 24 months by a mechanical personal scale (SECA).

Covariates

Birth weight, date of birth and sex were obtained from midwife and hospital registries at birth. Information about highest attained maternal educational level (low, moderate and higher), and parity (primipara, multipara) were obtained at enrolment in the study [START_REF] Walter | Variable selection: current practice in epidemiological studies[END_REF]. Educational level of the mother was defined according to the classification of Statistics Netherlands [28].

Maternal height and weight were assessed at enrolment. Height and weight were measured while the mother stood without shoes and without heavy clothing. Body mass index was calculated (kg/m 2 ). Information on breastfeeding (yes/no) was obtained by postnatal questionnaires at the ages of 2, 6, and 12 months. Mothers were asked whether they ever breastfed their child (yes/no) and at what age they quitted breastfeeding.

Population for analysis

From the total of 1106 children and their mothers who gave consent for participating in the postnatal phase, 1039 children participated in at least one of the postnatal assessments at the ages of 1.5, 6 and 24 months (Figure 1). Children without complete information on maternal smoking during pregnancy (n = 111) were excluded from the analyses. Of the remaining 928 live births with complete data on maternal smoking, information about the sum of skinfold thickness measurements in at least one of the three visits was available in 907 children. Twins (n = 24) were not excluded from the analyses, as they did not differ in the outcome measure from the singletons and no differences in results were observed after excluding them. Missing skinfold measurements were mainly due to crying behaviour.

Percentage of maternal smoking was not different between participating mothers (n = 907)

and those lost to follow-up (n = 325).

Statistical analysis

Differences in baseline characteristics between the maternal smoking categories were compared with Student's t-test for continuous variables and Chi-square tests for categorical variables. Similarly, we tested the differences in peripheral, central and total subcutaneous fat mass between the maternal smoking categories at the ages of 1.5, 6 and 24 months. The associations between period of maternal smoking during pregnancy (no, first trimester only, continued) and peripheral, central and total subcutaneous fat mass at the ages of 1.5, 6 and 24 months, were assessed using multiple linear regression models. The models were adjusted for potential confounders including child's age at visit, sex, maternal education, maternal height and weight, breastfeeding (yes/no), current height and observer of the skinfold measurement. Postnatal smoking, birth weight, parity, maternal alcohol use and maternal age were not included in the final models since they did not materially change the effect estimates. Similar regression models were used for assessing the associations of the reported number of cigarettes smoked in both first and third trimester of pregnancy and peripheral, central, and total subcutaneous fat mass. The associations between period of maternal smoking during pregnancy and anthropometrics like height, weight and body mass index at the ages of 1.5, 6 and 24 months, were also assessed using multiple linear regression models. These models were adjusted for potential confounders like age at visit, sex, maternal education, maternal height and weight, and breastfeeding (yes/no).

Tests for trends were performed by treating each categorized variable as a continuous term and by entering the variable into the fully adjusted linear regression model. To handle missing values in covariates (< 20% missing), we performed multiple imputations with a software package by generating 5 independent datasets for all analyses. Imputations were based on the relationships between all covariates included in this study using the Markov Chain Monte Carlo method. All measures of association are presented with their 95% CI.

Statistical analyses including the multiple imputations were performed using the Statistical Package of Social Sciences version 17.0 for Windows (SPSS Inc., Chicago, IL, USA).

Results

Of all mothers included in the analyses, 9.2% (n = 83) reported smoking in first trimester only and 13.8% (n = 125) reported continued smoking during pregnancy (Table 1). Mothers who continued smoking were younger and lower educated than mothers who never smoked during pregnancy. Mean birth weights of children from mothers who never smoked during pregnancy and who continued smoking were 3,533 grams (SD 545) and 3,267 grams (SD 573), respectively. In children of mothers who continued smoking, the highest rate for small size for gestational age (14.4%) and lowest rate for ever breastfeeding (82.5%) were observed. As compared to mothers who did participate, those who did not participate were younger (p < 0.01). Furthermore, they had a lower educational level and were more likely to smoke (p <0.01). Their children were also born more frequently preterm and with a lower birth weight (p < 0.05) (data not shown).

Table 2 shows that we observed no differences in the unadjusted peripheral, central and total subcutaneous fat mass measurements during childhood between the offspring of nonsmoking mothers, mothers who smoked in first trimester and mothers who continued smoking. Table 3 gives the associations between period of maternal smoking, and the number of cigarettes smoked per day among mothers who smoked in first trimester only and those who continued smoking with peripheral, central and total subcutaneous fat mass at the ages of 1.5, 6 and 24 months. Smoking in first trimester only and continued smoking were not significantly associated with subcutaneous fat mass at any age. Offspring of mothers who continued to smoke more than 5 cigarettes per day tended to have higher peripheral, central and total subcutaneous fat mass at the age of 24 months (P for trend > 0.05). However, it is likely a chance finding given it didn't seem to be an a priori association from the methods and multiple comparisons. Table 4 shows the associations between period of maternal smoking, and the number of cigarettes smoked per day among mothers who smoked in first trimester only and those who continued smoking with height, weight and body mass index at the ages of 1.5, 6 and 24 months. Children of mothers who continued smoking tended to have a lower height and weight at all ages compared to non-smoking mothers. Offspring of mothers who smoked more than 5 cigarettes per day among those who smoked in first trimester only and those who continued smoking, tended to have a lower height and weight (P for trend < 0.10).

The association with body mass index at these ages seems not to be consistent, but tends to a negative relation with anthropometrics.

DISCUSSION

Main findings

This population-based prospective cohort study showed that maternal smoking in first trimester only and continued smoking during pregnancy were not associated with peripheral, central and total subcutaneous fat mass at the ages of 1.5, 6 and 24 months.

Strengths and weaknesses

Major strengths of this study are the population-based prospective design with subjects being studied from early pregnancy onwards, and information about a large number of potential confounders available. However, some methodological issues need to be considered. Of the 1039 children of this study who participated postnatally, information about smoking during pregnancy at enrolment was missing in 11% of all mothers. This non-response would lead to biased effect estimates if the associations of maternal smoking during pregnancy with skinfold measurements in early childhood would be different between those included and not included in the analyses. However, this seems unlikely because biased estimates in cohort studies mainly arise from loss to follow-up rather than from non-response at baseline [START_REF] Nohr | Does low participation in cohort studies induce bias?[END_REF][START_REF] Jacobsen | Selection by socioeconomic factors into the Danish National Birth Cohort[END_REF].

The percentage of mothers who smoked during pregnancy may have been higher among those not included in the present analyses than among those who were included. This might have led to loss of statistical power and some under-estimation of the estimated effects. Of the postnatal participants, skinfold thickness measurements were performed in at least 80%.

Missing skinfold measurements were mainly due to crying behaviour. Information about maternal smoking during pregnancy was collected by questionnaires, without reference to any skinfold measurement. Although assessing smoking during pregnancy by questionnaire seems to be a valid method, misclassification may occur [START_REF] Klebanoff | Accuracy of self-reported cigarette smoking among pregnant women in the 1990s[END_REF]. Underreporting of maternal smoking across the various smoking categories may be present and have led to misclassification. In general, underreporting would lead to underestimation of differences between children from smoking and non-smoking mothers. Also, our study group was ethnic homogeneous and the mothers were highly educated. This may limit the generalizability of our results [START_REF] Jacobsen | Selection by socioeconomic factors into the Danish National Birth Cohort[END_REF]. Finally, we used skinfold thickness as a measure of subcutaneous fat mass.

Skinfold thickness provides a simple, easy, and quick yet highly informative assessment of regional fatness in most age groups and can be used in large-scale epidemiological studies, but has a limitation in differentiating between lean and fat mass of the whole body [START_REF] Wells | Measuring body composition[END_REF].

Comparison of main findings with other studies

Obesity in both childhood and adulthood lead to different risk factors for cardiovascular diseases, diabetes type II and overall mortality [START_REF] Osler | The influence of birth weight and body mass in early adulthood on early coronary heart disease risk among Danish men born in 1953[END_REF][START_REF] Setia | Convergence of body mass index of immigrants to the Canadian-born population: evidence from the National Population Health Survey (1994-2006)[END_REF][START_REF] Nguyen | Influence of childhood parental history of type 2 diabetes on the pre-diabetic and diabetic status in adulthood: the Bogalusa Heart Study[END_REF][START_REF] Sabanayagam | Serum gamma-glutamyl transferase level and diabetes mellitus among US adults[END_REF][START_REF] Olafsdottir | Unfavourable risk factors for type 2 diabetes mellitus are already apparent more than a decade before onset in a population-based study of older persons: from the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik)[END_REF][START_REF] De Sousa | Derivation and external validation of a simple prediction model for the diagnosis of type 2 diabetes mellitus in the Brazilian urban population[END_REF][START_REF] Corpeleijn | Obesity and impaired renal function: potential for lifestyle intervention?[END_REF][START_REF] Klenk | Body mass index and mortality: results of a cohort of 184,697 adults in Austria[END_REF][START_REF] Khashan | The effects of maternal body mass index on pregnancy outcome[END_REF][START_REF] Rzehak | Period-specific growth, overweight and modification by breastfeeding in the GINI and LISA birth cohorts up to age 6 years[END_REF][START_REF] Pearce | Sex differences in the association between infant feeding and blood cholesterol in later life: the Newcastle thousand families cohort study at age 49-51 years[END_REF][START_REF] Balk | Relationship between long-term coffee consumption and components of the metabolic syndrome: the Amsterdam Growth and Health Longitudinal Study[END_REF].

Various studies suggest that exposure to maternal smoking during fetal life leads to overweight and obesity in childhood. A systematic review by Oken suggested that prenatal maternal smoking exposure leads to a 50% increased risk of overweight in childhood at the ages of 3-33 years [START_REF] Oken | Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis[END_REF]. Also, a recent meta-analysis using 17 studies showed that maternal smoking during pregnancy was consistently associated with obesity in children with a mean age of 9 years [START_REF] Ino | Maternal smoking during pregnancy and offspring obesity: meta-analysis[END_REF]. It has been suggested that there is a dose-response association between the number of smoked cigarettes and the risk of childhood obesity [START_REF] Suzuki | The association between maternal smoking during pregnancy and childhood obesity persists to the age of 9-10 years[END_REF]. These studies used body mass index as measure for childhood obesity. Using body mass index does not provide information about fat distribution [START_REF] Wells | Measuring body composition[END_REF]. A limited number of studies focused on the associations of maternal smoking during pregnancy with direct measures of body composition instead of body mass index. A study in Southampton showed in 448 neonates that maternal smoking in late pregnancy was weakly associated with a lower fat mass percentage and greater lean mass percentage [START_REF] Harvey | Parental determinants of neonatal body composition[END_REF]. Another study in Sweden and Norway observed in 315 children aged 5 years that maternal smoking was associated with an increased risk of skinfold thickness higher than the 85 th percentile, however the associations attenuated after adjustment for confounders like total energy intake of the parents [START_REF] Widerøe | Does maternal smoking during pregnancy cause childhood overweight?[END_REF]. In contrast, a study in Boston, among 746 children at the age of 3 years, showed no association of maternal smoking during pregnancy with central adiposity, measured by the subscapular and triceps ratio, which has been found to be a valid proxy for intra-abdominal adipose tissue among children [START_REF] Oken | Associations of maternal prenatal smoking with child adiposity and blood pressure[END_REF].

A study in Bristol, the United Kingdom found in 5,689 children with mean age of 10 years that maternal smoking at any time during pregnancy is associated with higher offspring total fat mass measured with Dual X-ray Absorptiometry (DXA). Maternal smoking was also associated with higher lean mass, but to a lesser extent [START_REF] Leary | Smoking during pregnancy and offspring fat and lean mass in childhood[END_REF]. In our study, we used skinfold thickness as measure of subcutaneous fat mass at the ages of 1.5, 6 and 24 months. We did not find significant associations between maternal smoking during pregnancy and peripheral, central and total subcutaneous fat mass in early childhood. We also did not observe associations between the number of cigarettes smoked during pregnancy and peripheral, central and total subcutaneous fat mass in the offspring. Most previous studies assessed the associations between maternal smoking and adiposity at older age. The lack of association in this study might be due to the younger age groups, or use less advanced measuring techniques. This might also be the explanation for the inconsistent associations between maternal smoking during pregnancy and body mass index. In the future, genome wide association studies might identify new genetic loci related to skinfold thickness [START_REF] Psaty | Genome-wide association studies and large-scale collaborations in epidemiology[END_REF].

Studies examining the influence of prenatal smoke exposure in different periods of pregnancy might identify critical time periods, which are important from both a developmental and preventive perspective. We showed that both maternal first trimester smoking and continued smoking were not associated with peripheral, central and total subcutaneous fat mass until the age of 2 years.

Conclusion

Our findings suggest that intrauterine exposure to maternal cigarette smoke in different periods during pregnancy does not influence peripheral, central and total subcutaneous fat mass development in early childhood. Further studies are needed in children at older ages and to identify associations of maternal smoking during pregnancy with other measures of body composition like DXA and Magnetic Resonance Imaging.

Table 1 Characteristics of mothers and their children according to category of maternal smoking during pregnancy N = 907 No smoking (n = 699, 77.1%) Smoked in first trimester only (n = 83, 9.2%)

 1 Differences in maternal and child characteristics (compared with the nonsmoking category) were evaluated using Students t-test for continuous variables and chi-squared tests for categorical variables. Gestational age was logtransformed for the t-test. Birth weight in SDS was used for the t-test.

	Continued smoking
	(n =125, 13.8%)

b. c.

Small for gestational age is defined as the lowest 5% of gestational age adjusted birth weight. d. *P < 0.05 and **P < 0.01.

Table 2 Subcutaneous fat mass measurements according to maternal smoking during pregnancy category

 2

		No smoking	Smoked in first
				Continued smoking
	N = 907	(n = 699, 77.1%)	trimester only
				(n =125, 13.8%)
			(n = 83, 9.2%)
	1.5 months (n = 758)
	Age (months)	1.60 (1.12 -2.47)	1.59 (1.08 -2.49)	1.63 (1.03 -2.66)
	Triceps (mm)

Table 3 Associations of maternal smoking during pregnancy with subcutaneous fat measures in children aged 1.5, 6 and 24 months

 3

	1.5 months

Table 4 Associations of maternal smoking during pregnancy with anthropometrics in children aged 1.5, 6 and 24 months

 4

Acknowledgments:

The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, Rotterdam, the Rotterdam Homecare Foundation, Rotterdam and the Stichting Trombosedienst and Artsenlaboratorium Rijnmond (STAR), Rotterdam. We gratefully acknowledge the contribution of participating mothers, general practitioners, hospitals, midwives and pharmacies in Rotterdam.

Funding: The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Center, Rotterdam, the Erasmus University Rotterdam and the Netherlands Organization for Health Research and Development. The researchers are independent from the funders. The study sponsors had no role in study design, data analysis, interpretation of data, or writing this report. Dr Jaddoe reports receipt of funding from the Netherlands Organization for Health Research and Development (ZonMw 90700303).

Abbreviations

Values are regression coefficients (95% confidence interval) using multiple linear regression models. Values are regression coefficients (95% confidence interval) using multiple linear regression models. * P < 0.05 and ** P < 0.01.