Prenatal tobacco exposure modulated the association of genetic variants with diagnosed adhd and its symptom domain in children: a community based case–control study

ABSTRACT The purpose of our study was to test the hypothesis that prenatal tobacco smoking exposure (PSE) could modulate the association of genetic variants with ADHD. A community based

case-control study was conducted among Chinese children and 168 ADHD patients and 233 controls were recruited by using combination diagnosis of DSM-IV, SNAP-IV and semi-structured clinical

interview. Logistic regression analysis was performed to estimate the effect of prenatal tobacco smoking exposure and genotype frequencies on ADHD susceptibility individually by adjustment

for potential confounders. Multiplicative and additive interaction analysis were performed to evaluate the interactions between risk genes and PSE with regard to ADHD. Prenatal tobacco smoke

exposure was a significant risk factor of ADHD even after adjusted for other potential confounders. _ADRA2A_ rs553668, _DRD2_ rs1124491 and _SLC6A4_ rs6354 were identified to be associated

with ADHD. A significant multiplicative and additive gene-environment interactions were observed between the PSE and the _ADRA2A_ rs553668 in relation to ADHD and ADHD-ODD. The risk of the

genetic variants in ADHD was increased significantly if the child had prenatal tobacco exposure. The genetic risk for ADHD could be influenced by the presence of environmental risks. The

environmental and the genetic risks are not distinct to each other. More gene-environment interaction studies were needed to reveal the etiology of ADHD. SIMILAR CONTENT BEING VIEWED BY

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Article Open access 28 September 2020 INTRODUCTION Attention-deficit/hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder characterized by developmentally inappropriate

level of inattentiveness and hyperactivity. It is one of the most frequently diagnosed disorders among children and adolescents, with the pooled prevalence of 7.2%1 worldwide and 6.26%2 in

China according to the most recent meta-analysis. Previous studies reveal that individuals affected by ADHD frequently lead to various social functional impairment, such as social

communication disorder, bad peer relationships, academic performance difficulties3, develop personality disorder, substance abuse and even criminality4,5. As a complex multi-factorial

disease, exact etiology of ADHD is still elusive. The biologic basis for ADHD is thought to be largely genetic6,7. The genetic architecture of ADHD is complex and numerous single nucleotide

polymorphisms (SNPs) of genes involved in the regulation of neurotransmitter systems are identified to be associated with ADHD8,9,10.Among them, genes involved in the dopaminergic,

serotonergic and noradrenergic pathways are the most common candidate genes for ADHD studies7,11,12,13. Adding to genetic influence, environmental factors are assumed to be co-involved in

the etiology of ADHD14. Some prenatal and perinatal factors are reported to be associated with ADHD, such as younger maternal pregnant age15, caesarean, induced delivery, preterm birth, low

birth weight16, and mother smoking during pregnancy. With regard to prenatal smoking, it is constantly identified to be a risk factor for offspring hyperactivity symptom and ADHD14,17,18,19.

Evidence from epidemiological researches suggests that maternal cigarette smoking and environmental tobacco exposure during pregnancy are all in association with high risk of ADHD. Hyunjoo

Joo20 and his colleagues have described that independent and combined exposure to pre- and postnatal secondhand smoke were associated with ADHD, particularly with the hyperactivity symptom

domain. A large population-based birth cohort have provided that maternal smoking during pregnancy was associated with children hyperactivity symptom19. Although large numbers of studies

demonstrated a relationship between maternal smoking exposure during pregnancy and ADHD, most of previous studies have investigated the effect of smoking exposure only and there were few

studies on gene–environment interactions which were more likely to play the real role in the etiology of ADHD21. Importantly, the environmental risk is not distinct to the genetic

influence21, but it may be required to uncover the genetic liability14. The studies about the G ene× Environment studies of gene and smoking exposure on ADHD are limited. A gene-environment

interplay study reports that the co-exist group of prenatal smoking exposure and 10 R/10 R genotype of _DAT_ gene showed a higher mean scores of hyperactivity-impulsivity and inattentiveness

than other groups22. But the findings remain speculative since the participants are lack of a clinical diagnosis of ADHD, and the ADHD scores of the participants was derived from the

Conners’ Parent Rating Scale which will lead to the overdiagnosis of the ADHD. Developing of novel technologies have made it more common in identifying genetic risk factors of ADHD, however,

proved the genetic architecture of ADHD to be far more complex, since the genetic influences expressed only additional risks to disease susceptibility23. Therefore, gene and environment

interaction research will be an inevitable way to explore the etiologic mechanisms of ADHD. However, studies about the joint effects of gene and environmental factors on ADHD is limited14.

To fill the knowledge gap, we conducted the case-control study to elucidate the potential synergistic effect of the prenatal tobacco exposure during pregnancy and children genetic variants

on children diagnosed ADHD and its symptom domains. RESULTS CHARACTERISTICS OF PARTICIPANTS The study involved 168 ADHD patients and 233 controls. A higher proportion of males was noted with

the 5.2:1 boys/girls ratio. Compared with the controls, ADHD children were more likely to have younger mother (_p_ < 0.05). There were no significant difference between ADHD patients and

controls in terms of family history of neural system diseases, parental marital status, preterm birth (pregnancy < 37weeks), Low birth weight(birth weight <2500 g) and delivery mode,

levels of blood lead and postnatal tobacco exposure. Meanwhile, ADHD children scored higher of SNAP-IV scales (Inattention domain, hyperactive domain and ODD domain) and of CBCL total scores

than controls (Table 1). ASSOCIATIONS BETWEEN THE _ADRA2A_, _DRD2_, AND _SLC6A4_ GENETIC VARIANTS AND ADHD SUSCEPTIBILITY A total of 22 SNPs were detected. The genotype distributions of

_SLC6A4_ rs25533 and rs2020923, and _SLC6A3_ rs2975226 in the controls deviated from Hardy–Weinberg equilibrium (P < 0.05). Thus they were excluded from further analysis. Consequently, a

total of 19 SNPs remained in the subsequent analysis In individual SNP association analysis, _ADRA2A_ rs553668, _DRD2_ rs1124491 and _SLC6A4_ rs6354 were nominally significant at the

uncorrected 0.05 significance level with adjustment for age and gender. Among these SNPs, _ADRA2A_ rs553668G allele was associated with an increased risk of ADHD in the additive model, and

_SLC6A4_ rs6354 (GG + GT) was associated with an increased risk of ADHD, whereas _DRD2_ rs1124491 (AA) was associated with a decreased risk of ADHD. After FDR adjustment, _SLC6A4_ rs6354

remained statistically significant (_P_ = 0.019, Table S1). As the Table 2 illuminated, the detected SNPs were not associated with all symptom domains of ADHD. After adjusting for age and

gender, individuals carrying the _ADRA2A_ rs553668 GG or AG genotype showed an increased risk of ADHD compared with those carrying AA genotype (adjusted OR = 1.76, 95% CI = 1.12–2.77, _P_ = 

0.013), but the SNP was not associated with ADHD-ODD. Additionally, individuals who carried _DRD2_ rs1124491 AG or GG genotype displayed a risk association with inattention symptom compared

to AA genotype (adjusted OR = 2.22, 95% CI = 1.11–4.46, _P_ = 0.022). Compared with those carrying TT genotype, the risk association with ADHD-ODD was increased significantly in individuals

carrying the _SLC6A4_ rs6354 GT or GG genotype (adjusted OR = 2.18, 95% CI = 1.04–4.56, _P_ = 0.034), but no association was observed in other ADHD symptom domains. The ORs and 95% CIs

changed slightly in different adjustment model (Model 1, 2, 3, Table 2). PRENATAL TOBACCO SMOKE EXPOSURE (PSE) AS A RISK FACTOR FOR ADHD The prenatal tobacco smoke exposure (PSE) showed a

risk association with ADHD and all symptom domains, and slight change of the OR and 95% CI was observed in different adjustment model (Model1, 2, 3). The strong association was shown in ODD

symptom domain (OR = 2.80, 95% CI = 1.39–5.66) and in the hyperactivity symptom domain (OR = 2.12, 95% CI = 1.23–3.60, Table 3). INTERACTION BETWEEN PRENATAL TOBACCO SMOKING EXPOSURE AND

GENETIC VARIANTS The MDR analysis revealed that the three-factor model including rs553668, rs1124491 and prenatal tobacco smoking exposure was the best predictor for ADHD risk (Table 4).

Furthermore, we calculated the multiplicative and additive interactions between _ADRA2A_ rs553668, _DRD2_ rs1124491, _SLC6A4_ rs6354 and prenatal tobacco smoking exposure after adjustment of

age of the child, gender, pregnancy age of mother, and postnatal smoking exposure. A significant additive interaction between PSE and _ADRA2A_ rs553668 polymorphisms is detected. The AP due

to interaction was 0.48(95% CI = 0.09–0.87), and RERI was 1.88(95% CI = 0.06–4.12), Meanwhile, multiplicative interaction was displayed in this group (_P_mul = 0.003). The multiplicative

and additive interactions were found in all symptom domain of ADHD. However, no multiplicative and additive interactions were detected between _DRD2_ rs1124491 polymorphism and PSE, nor

between _SLC6A4_ rs6354 polymorphism and PSE (Table 5). Figure 1 described the ORs and the 95% CIs for the only unfavorable genotype exposure, the only prenatal tobacco smoking exposure and

the joint effect of them with regard to ADHD and all symptom domains. Most of the risk genotypes showed an increased risk association with ADHD when co-existed with PSE compared with

individuals without PSE. The risk association of ADHD in _ADRA2A_ rs553668 GG or AG carrier without PSE was moderately increased (OR = 1.65, 95% CI = 0.93–2.90, _P_ = 0.084), compared with

AA carrier without PSE. But the OR for the rs553668 GG or AG carriers with PSE, was significantly increased to 3.54(95% CI: 1.73–7.25, _P_ = 0.001 Table S2). The group of _DRD2_ rs1124491

AG/GG genotype with PSE also showed a significant association with ADHD compare with whose without PSE (OR = 6.04, 95% CI = 2.07–17.65, _P_ = 0.001, Table S3). And _SLC6A4_ rs6354 GT/GG

genotype carriers with PSE also showed an increased association in hyperactivity and ADHD-ODD symptoms (hyperactivity domain: OR = 2.92, 95% CI = 1.10–7.75, _p_ = 0.031; ODD domain: OR = 

4.42 95% CI = 1.32–14.79, _p_ = 0.016.Table S4). DISCUSSION The current case-control study demonstrated a significantly association of prenatal tobacco exposure with all ADHD subtype and

ADHD concurring with ODD(ADHD-ODD) in children, especially a strong association with hyperactivity and ADHD-ODD, and the effect size did not diminished even after adjusted by potential

environmental influencing covariates. In addition, the study found three SNPs of _ADRA2A_, _DRD2_ and _SLC6A4_ genes associated with ADHD, but the association disappeared in some ADHD

subtypes after adjusted by potential environmental covariates. Interestingly, when grouped with prenatal tobacco exposure, however, the association of the genetic variants and ADHD

reappeared although there are no multiplicative and additive interaction between _DRD2_, _SLC6A4_ genes and PSE. With regard to the findings in the genetic analysis, the results of risk

association with ADHD in the children carrying _ADRA2A_ rs553668 GG or AG genotype were in concordance with the findings of several studies, and indicated that rs553668, being known as the

DraI site in the 3′-untranslated region of _ADRA2A_ gene, A-to-G variant increased the risk of ADHD24,25. Meanwhile, the findings showed the _DRD2_ rs1124491 in the significant association

with inattention type of ADHD. _DRD2_ gene (Dopamine Receptor D2 gene), encodes the D2 subtype of the dopamine receptor. A population-based birth cohort have reported an association of

_DRD2_ rs1124491 with ADHD in males and low persistence in female ADHD patients26,27. Moreover, a common variant _SLC6A4_ rs6354 was identified to be in association with ADHD-ODD. _SLC6A4_

rs6354 located in the untranslated(exon 2) 5′-UTR region of _SLC6A4_ gene—a potential module of exonic splicing enhancer or silencer by bioinformatics prediction— and may affect the

expression level or exonic splicing of serotonin28. Evidences show that one repeat length polymorphism-STin2 located in the untranslated exon 2 of _SLC6A4_ gene may be significantly

associated with major depressive disorder and suicide29. A limited number of G × E studies have shown that genetic factors interact with maternal smoking to heighten risk for ADHD. Anita

Thapar30 and his colleagues found maternal smoking during pregnancy was shown a significant association with offspring ADHD symptom scores in twins study after adjustment by genetic factors,

although the main effect of cigarette exposure was small. One gene-environment interaction study evaluated maternal smoking and the _DAT_ gene. In the cases with co-exist of maternal

smoking during pregnancy and 10 R/10 R genotype of _DAT_ gene, the mean score of hyperactivity-impulsivity and inattentiveness derived from the Conners’ Parent Rating Scale were higher than

other groups22. The findings in the current study infer that the susceptibility of ADHD for the unfavorable ADHD genotypes carriers might increase when the children having the history of

in-utero exposure to cigarette smoking. However, the molecular mechanisms responsible for the interaction remain unknown. Nicotine, the main psychoactive component of tobacco, can

accumulated in human brain fast when they are smoking31. The neurotoxic effects of nicotine exposure on human fetal brain development have not been studied directly. However, a large number

of animal studies have found that nicotine has obvious neurotoxic effects on fetal brain development32. Animal models confident that the nicotine can readily cross the placental barrier into

fetal circulation, and the blood-brain barrier, which protects the adult brain from toxic chemicals, is not fully formed until about six months after birth33. So, nicotine can cross the

blood-brain barrier and specifically binds to nicotinic acetylcholine receptors (nAChRs) in the fetal brain34, and nAChRs have an important role in regulating synaptic plasticity and brain

development. Concerning of animal models, chronic nicotine exposure to animal brain developmentally leads to long-term changes of decreasing presynaptic nAChRs function, which cause altering

nicotine-stimulated release of those neurotransmitters and the altering continues for a long time even after nicotine exposure has been removed35,36. And neurotransmitters dysregulation of

acetylcholine, dopamine and serotonin are assumed to be the reason of ADHD21. The decreased presynaptic nAChRs function may increase the risks of dysfunctions of dopaminergic, serotonergic

and noradrenergic neurotransmission caused by genetic variants. That may explain the strong joint effects of prenatal tobacco exposure and genetic variants on all ADHD subtypes. But the

exact reasons of the increasing risks require more extensive research. Lead is a heavy metal that exists in the environment in nature, and it is a most widely used neurotoxicants. Many

studies have shown that high blood lead levels (≥10 µg/dL) are harmful to children neurodevelopment37,38. Moreover, evidences support that longer term low level exposure (<10 µg/dL)

associated with intellectual impairment39,40. Unfortunately, the effects of lead exposure on ADHD are less clear. In the current study, no statistically significant association was found

between low blood lead levels (<10 µg/dL) and diagnosed ADHD and all the symptoms. A growing body of evidence now support that low level exposure (<5 µg/dL) also associated with

cognitive impairment, suggesting that there is no threshold for developmental neurotoxicity37,41,42. Stephani Kim5 and his colleagues examined the relationship between blood lead levels and

ADHD in children, and found that the odds ratios for the risk of ADHD is 4.63 (95% CI: 1.36–15.72) in the “≥2 mg/dL” group and the odds ratios increased to 7.25 (1.66–31.67) in the “≥3 

mg/dL” group. However, some studies reported that the association between blood lead levels and ADHD did not exhibit a dose-response relationship43. To the best of our knowledge, this paper

is the first G × E interaction research about SNPs and prenatal tobacco smoke exposure in association to ADHD and all the subtypes. The major strength of this study is the standardized

procedures of ADHD assessment in children and the precise and rigorous diagnosis21, aiming to minimize the overdiagnosis or underdiagnosis. However, several limitations have to be considered

during interpretation of the findings. The major limitation of this study is that the small sample size has decreased the power to detect the effects of the SNPs and the gene-environment

interactions. One inherent methodologic problem in this case-control design is that the retrospective assessment of PSE might introduce systematic error in the calculation of the association

between PSE and ADHD because of recall bias. In order to diminish the recall bias, we recruited the new diagnosed patients from the community survey. Although the regression model

controlled for important confounding variables such as age, gender, mother and father education, and postnatal smoke exposure status, it did not take into account other possible

environmental pollutants, such as manganese, mercury, arsenic and bisphenol A and phthalates5,44,45, which can be a focus of future research. Psychological and social environment are also

the important for child psychological development, but those did not measured in our study. This is a weakness of the study. The participants in our study were enrolled from one city of

China, which limited the results interpretation in general populations. Finally, its cross-sectional nature limits explication about the causal effects of G × E interactions. Future research

may focus on a longitudinal study to confirm the causality. METHODS PARTICIPANTS This study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science

and Technology, and all methods were performed in accordance with the approved guidelines and regulations. Informed consent was obtained from all participants and their parents. Children who

were diagnosed for ADHD by pediatric psychiatrists were recruited as the cases in the Liuzhou Women and Children Healthcare Hospital, and 6–12-year-old healthy children from 2 elementary

schools in Liuzhou City were also recruited as the controls. The exclusion criteria for both groups were: 1) a history of mental retardation, autism spectrum disorders, bipolar disorder,

language disorders or learning disabilities, pervasive developmental disorder; 2) a history of brain disease, seizure disorder, or other neurological disorders; 3) intelligence quotient (IQ)

 < 70; and 4) the presence of any chronic physical disease; 5) a blood lead concentration ≥10(µg/dL). Permissions were given for the eligible patients and controls to participate in the

study, and those children whose parents could not be contacted or denied consent for participation were excluded. As a result, a total of 168 cases and 233 controls (6–12 years of age) were

recruited. ASSESSMENTS OF CHILDREN ADHD The status of both the case and control subjects were confirmed with a semi-structured clinical interview with the parents and children that was

conducted according to the DSM-IV ADHD Rating Scale by a pediatric psychiatrist. Additionally, diagnoses were made that included both in categories and severity ratings using the 26-item

Chinese version of SNAP-IV scale46. The 4-point response is scored 0–3 (Not at All = 0, Just A Little = 1, Quite a Bit = 2, and Very Much = 3) on SNAP-IV scale. Subscale scores are

calculated by summing the scores on the items in the specific symptom domains (inattention, hyperactive and ODD), in which average score 0–1 means no features, 1.1–1.5 means subclinical,

1.6–2.0 means significant, and >2.1 means very severe. We defined oppositional defiant disorder as the ODD symptom domain score ≥1.6. The Child Behavior Checklist (CBCL) scale was

conducted to evaluate child potential behavior problems and the Wechsler Intelligence Scale for Children (WISC) was used to evaluate full-scale IQ of all participants. All instruments used

in the current study are validated Chinese versions. MEASUREMENT OF BLOOD LEAD CONCENTRATIONS To measure lead concentrations in blood, 2–3 mL of whole blood was drawn from each child and

kept it in heparin-containing tubes. Blood lead level in whole blood was measured using graphite furnace atomic absorption spectrometry (Spectral AA-240FS, Varian, American). The limit of

detection for blood lead was 0.1 µg/dL. None of the blood samples showed below the limit of detection. DNA EXTRACTION AND GENOTYPING Genomic DNA was extracted from peripheral blood

leukocytes using the TIANamp® Blood DNA Kit DP318 (TIANGEN BIOTECH CO., LTD., Beijing, China). The candidate genes within the dopamine, norepinephrine and serotonin neurotransmitter pathways

were selected based on recent findings47, and included 7 genes (i.e., _SLC6A3_, _SLC6A4_, _DRD2_, _ANKK1_, _DRD4_, _ADRA2A_, _SNAP25_). The screening procedure for the candidate single

nucleotide polymorphism(SNP) was as follow: First, we searched the F-SNP database (http://compbio.cs.queensu.ca/F-SNP) to retrieve information about the predictive functions of the SNPs

within these genes and those of potential effects on splicing, transcription, translation, and post translation processes were selected. Second, we filtered the SNPs by the MAF of CHB (minor

allele frequency for Han Chinese in Beijing) in the database of 1000 genomes (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) and selected SNPs with MAFs > 5% for the next

step. Third, we tested the linkage disequilibrium (LD) among selected SNPs using SNAP Pairwise LD (http://www.broadinstitute.org/mpg/snap/ldsearchpw.php), and only one was reserved if

several SNPs were in strong LD with each other (_r_2 ≥ 0.80). Consequently, a total of 16 SNPs were retained. High-quality genotyping was performed on the Sequenom MassARRAY platform (San

Diego, USA) by BIO MIAO BIOLOGICAL TECHNNOLOGY (Beijing, China) following the standard experimental procedures from the manufacturer. In addition, samples with a call rate <80% or

Hardy-Weinberg equilibrium (HWE) < 0.05 in the control group were removed. DETERMINATION OF TOBACCO EXPOSURE Tobacco exposure status was obtained from the children’s parents by a

questionnaire. The questions were constructed in two parts: mother tobacco exposure and child secondhand smoke exposure. Firstly, we asked whether the mother smoked before pregnancy or not

(>100 cigarettes) and whether she smoked during pregnancy. If yes, we then asked how many cigarette expensed per day. Only three of the mothers reported being an ex-smoker or having

smoked during pregnancy. Besides, we inquired whether or not she was exposed to secondhand smoke during pregnancy at home or in the workplace and how long she stayed in the smoking

circumstance per day/week. Secondly, we investigated whether any family members smoked at home after the child birth and how many cigarette the members expensed per day/week. CONFOUNDERS AND

COVARIATES A face-to-face questionnaire survey was administered for their parents by a trained interviewer to collect socio-demographic variables, prenatal and perinatal characteristics.

Socio-demographic variables included age, gender, and parental marital status (categorized as married, or single, separated, divorced and widowed). Prenatal and perinatal characteristics

included birth weight, pregnancy age of mother, preterm birth and the mode of delivery (cesarean section or vaginal delivery). The family history of neural system diseases and ADHD were

asked additionally, but no participant declared of ADHD family history in their families. STATISTICAL ANALYSIS Differentials in the distribution of socio-demographic variables, prenatal and

perinatal characteristics between patients and controls were compared by chi-squared test or _t_ test, when appropriate. Hardy-Weinberg equilibrium (HWE) for genotypes was calculated in

controls by a goodness-of-fit chi-squared test. Logistic regression analysis was used to estimate the effect of PSE and genotype frequencies on ADHD susceptibility by computing odds ratios

(ORs) and 95% confidence intervals (95% CIs). Four inheritance models (codominant, dominant, additive and recessive models) were estimated for each SNP. Meanwhile, the statistical power to

detect the effects of the SNPs was calculated by Power v3.0.048,49, and for SNPs with MAF of 0.11(rs6354), 0.43(rs1124491), and 0.44(rs553668), for example, we calculated that the power for

our sample size to detect an OR of 1.50 was 43.6, 74.1 and 74.8%, respectively. The Multifactor Dimensionality Reduction (MDR) 2.0 beta 8.1 program (https://sourceforge.net/projects/mdr/)

was used to assess the gene-environment interactions between risk genes and the environmental risks. The details analytic protocol was described in the previous study50,51. Furthermore,

multiplicative and additive interactions were computed. Multiplicative interaction was evaluated by the interaction term of logistic regression model. Synergy index (SI), attributable

proportion due to interaction (AP), and relative excess risk due to interaction (RERI) and their corresponding 95% CIs were calculated by additive interaction analysis, as suggested by Tomas

Andersson52. If there is no additive interaction, RERI and AP are equal to 0, or SI are equal to 1. The multiplicative and additive interaction analyses were adjusted for age of the child,

gender, pregnancy age of mother, and postnatal smoking exposure. Since a higher proportion of males and younger ADHD children(<9 years old) was noted in our study. All analysis were

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https://doi.org/10.1007/s10654-005-7835-x (2005). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the study participants and their parents. We also

acknowledge the collaboration of the staff of the Liuzhou maternal and child care hospital. This work was supported by the Fundamental Research Funds for the Central Universities (Grand

number:lzujbky-2017–87). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of

Science and Technology, Wuhan, Hubei, P. R. China Yanni Wang, Wenjing Chen & Yukai Du * Department of Child Health Care, Hospital of Maternal and Child Health of Dalian, Dalian,

Liaoning, P. R. China Dan Hu * Department of Maternal, Child and Adolescent Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, P. R. China Yanni Wang & Hongli Xue

Authors * Yanni Wang View author publications You can also search for this author inPubMed Google Scholar * Dan Hu View author publications You can also search for this author inPubMed 

Google Scholar * Wenjing Chen View author publications You can also search for this author inPubMed Google Scholar * Hongli Xue View author publications You can also search for this author

inPubMed Google Scholar * Yukai Du View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS All authors in the manuscript who have made substantial

contributions to the work. Yanni Wang designed the study did the statistical analysis, and drafted the initial manuscript. Dan Hu helped to design the study, collected the data. Wenjing Chen

assisted with data collection, Hongli Xue did the lab work. Yukai Du designed the study, directed the statistical analysis, and reviewed the manuscript. CORRESPONDING AUTHOR Correspondence

to Yukai Du. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE: Springer Nature remains neutral with regard to

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license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Wang, Y., Hu, D., Chen, W. _et al._ Prenatal Tobacco Exposure

Modulated the Association of Genetic variants with Diagnosed ADHD and its symptom domain in children: A Community Based Case–Control Study. _Sci Rep_ 9, 4274 (2019).

https://doi.org/10.1038/s41598-019-40850-w Download citation * Received: 14 June 2018 * Accepted: 25 February 2019 * Published: 12 March 2019 * DOI:

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