Maternal smoking around birth and psychiatric disorders of offspring: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-3812822/v1

Objective: To investigate the causal effect of maternal smoking around birth on the risk of four psychiatric disorders in offspring using Mendelian randomization (MR);

Methods Genome-wide association studies (GWAS) data were extracted from published data on maternal maternal smoking around birth and four psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD) and major depressive disorder (MDD). Inverse variance weighting (IVW), weighted median (WM), and MR-PRESSO methods were applied, and a comprehensive MR analysis and sensitivity analysis were conducted to explore the causal relationship between maternal smoking around birth and the risk of developing the four psychiatric disorders, using the dominance ratio (OR) as an evaluation index;

Results Smoking before and after delivery was associated with ADHD (IVW: OR=12.299, 95% CI=3.341-45.27, P<0.001) and MDD (IVW: OR=5.789, 95% CI=2.576-13.011, P<0.001);

Conclusion Our study is based on genetic data, revealing a potential causal relationship between maternal smoking before and after birth and the incidence of attention deficit hyperactivity disorder and severe depression in offspring.

smoking

attention deficit hyperactivity disorder

autism spectrum disorder

bipolar disorder

depression

mendelian randomization

1.Employing MR to first explore their relationship.

2.Dataset from leading research alliance utilized.

3.Rigorous sensitivity analysis conducted.

4.Results clinically significant for maternal, infant, and maternal delivery health.

Previous studies have confirmed that maternal smoking around birth has many adverse consequences for the mother and the developing fetus, such as ectopic pregnancy, placenta previa, placenta praevia, preeclampsia, and a range of adverse fetal outcomes such as fetal death, preterm labor, low birth weight, and tobacco-induced abortion[1]. However, Maternal smoking around birth remains an expected behavior in many countries[2]. Specifically, 29 out of 174 countries (17%) estimated the prevalence of smoking during pregnancy to be more than 10%, and 12 countries (7%) more than 20%. Our study also showed that more than 70% of pregnant women who smoked during pregnancy smoked daily rather than occasionally.

Autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common childhood-onset neurodevelopmental disorders[3, 4], with an estimated global prevalence of 1% for ASD[5], which is characterized by persistent difficulties with social communication, social interaction, and repetitive and restricted behaviors[6]. It is estimated that approximately 70% of children with ASD also present with psychiatric disorders[7]. ADHD is characterized by persistent inattention, difficulty controlling impulsive behaviors, or hyperactivity, and often develops between the ages of 7–12 years, with a prevalence rate three times higher in boys than in girls, and affects 5%-7% of the world's children[8]. As with other psychiatric disorders, genetic factors play an important etiologic role, but other external environmental factors such as exposure to alcohol, tobacco, or toxins during pregnancy, mood disorders, preterm birth, low birth weight, and prenatal or postnatal brain injury have also been shown to be causative of ADHD[9]. Several prenatal and perinatal factors have been reported to be associated with ADHD, such as the young age of the mother at conception, cesarean section, induction of labor, preterm delivery, low birth weight[10], and maternal smoking during pregnancy. Regarding prenatal smoking, it has been recognized as a risk factor for ADHD in offspring[11]. Evidence from epidemiologic studies suggests that both maternal smoking and environmental tobacco exposure during pregnancy are associated with a high risk of ADHD. Previous studies have revealed that exposure to prenatal and postnatal secondhand smoke is associated with ADHD in offspring[12], particularly with hyperactivity symptoms. Numerous population-based birth cohorts have shown that maternal smoking during pregnancy is associated with childhood ADHD symptoms[13].

Furthermore, in the context of prenatal tobacco exposure, there has been a large body of research examining internalizing and externalizing behavioral problems in childhood[14, 15]. At the same time, less attention has been paid to mood disorders in adolescence or adulthood, such as bipolar disorder and major depressive disorder, with some studies suggesting that prenatal smoking is associated with offspring suffering from serious psychiatric disorders, such as schizophreni[16], bipolar disorder[17], and major depressive disorder[18]. There is a potential association. However, the conclusions are still inconsistent and need to be confirmed by further studies.

Observational studies are susceptible to confounding bias due to their difficulty accurately accounting for all confounding factors. In contrast, Mendelian randomization (MR) analyses use genetic variation as an instrumental variable to circumvent the effects of confounding factors or reverse causality and have been used to study risk factors affecting population health[19]. There are no studies that have used MR methods to examine whether there is a causal effect of smoking during pregnancy on the risk of developing psychiatric disorders in offspring, particularly attention deficit hyperactivity disorder and major depressive disorder. On this basis, our research designed and completed a MR analysis.

2.1 Study design

Our Mendelian randomization (MR) study utilized published Genome-Wide Association Study (GWAS) meta-analyses, data from the UK Biobanking Study (UKB), and pooled data from the Psychiatric Genomics Consortium (PGC). Single nucleotide polymorphisms (SNPs) served as proxies for phenotypes and genetic instrumental variables (IVs) in the two-sample MR study. The screened SNPs were required to fulfill the three main MR hypotheses: assumption 1: strong association with “maternal smoking around birth”; assumption 2: independence from potential confounders related to outcomes; assumption 3: no direct relationship between instrumental variables and outcomes [19]. MR analysis was used to assess the causal relationship between maternal smoking around birth on four psychiatric disorders (ADHD, ASD, BD and MDD), and the process is shown in Fig. 1。

2.2 Data source

In this study, Genetic summary data of maternal smoking around birth was conducted by Ben Elsworth et al. 2018 using the output of the GWAS pipeline of Phesant-derived variables from UK Biobank, which included 397,732 participants (121,634 cases, 276,098 controls). Pooled data for four psychiatric disorders were obtained from the Psychiatric Genomics Consortium (PGC), including ADHD (38,691 cases, 186,843 controls)[20], ASD (18,381 cases, 27,969 controls)[21], BD (20,352 cases, 31,358 controls)[22], MDD (59,851 cases, 113,154 controls)[23]. The participants with psychiatric disorders met one of the following diagnostic criteria: The Diagnostic and Statistical Manual of Mental Disorders (DSM), the Third or Fourth edition; the International Statistical Classification of Diseases and Related Health Problems (ICD), the ninth or tenth edition. The ninth or tenth edition. To avoid population bias, GWAS data from populations of European origin were used in our study, and all study subjects passed the ethical review of the place where the study was registered and signed an informed consent form. Details of the GWAS dataset are described in Table 1.

Table 1 Summary information of the GWAS database in the two-sample MR study.

Phenotype	Data source	Sample size	SNPs	Population	PMID/GWAS ID
Maternal smoking around birth	UKB	397,732	11,590,399	European	ukb-b-17685
Attention deficit hyperactivity disorder	PGC	225,534	6,774,224	European	36702997
Autism spectrum disorders	PGC	46,350	9,112,386	European	30804558
Bipolar disorder	PGC	51,710	13,413,244	European	31043756
Major depressive disorder	PGC	173,005	13,554,550	European	29700475

MRC-IEU: Integrated Epidemiology Research Unit, University of Bristol; UKB: UK Biobank; PGC: Psychiatric Genomics Consortium; SNP: single nucleotide polymorphism.

2.3 Selection of instrumental variables

The SNPs selected to proxy for the maternal smoking around birth instrumental variables all met the genome-wide statistical significance threshold (P < 5×10^− 8) to satisfy Hypothesis 1 to obtain independent SNPs, and these SNPs were further confirmed to be uncorrelated by a distance cut-off of 10,000 kilobases apart and a correlation index r² ≤ 0.001[24]. To further evaluate the strength of instrumental variables, the F statistic was calculated for each SNP, where instrumental variables with F < 10 (considered as weak instrumental variables) were excluded[25] which was calculated as F = [(N-k-1)/k] × [R²/(1-R²)][26], R² was calculated by the following formula R² = 2 × (1-MAF) × MAF×(β/SD)²[27], N in the formula represents the sample size of the selected dataset, k is the total number of SNPs selected for MR analysis, β is the estimated SNP effect on maternal smoking around birth, SD is the standard deviation of β, and MAF is the minor allele frequency.

2.4 Removal of confounding and palindromic SNPs

To address the second MR assumption, we inquired about each IV and its proxied traits, referring to the PhenoScannerV2 database (http://www.phenoscanner.medschl.cam.ac.uk/). We removed the IVs arrogating these tuberculosis-relevant traits at a threshold of r² > 0.80[28, 29]. We harmonized the maternal smoking around birth and four psychiatric disorders data by removing all palindromic SNPs with intermediate allele frequencies out of the selected instrumental SNPs above[30], where palindromic SNPs referred to the SNPs whose alleles correspond to nucleotides that pair with each other in a DNA molecule, and intermediate allele frequencies denoted the allele frequencies between 0.01 and 0.30[31].

2.5 Effect statistics and sensitivity analysis

After we determined the list of SNPs according to the selection criteria above, we executed MR analysis to estimate the overall effects of the selected SNPs for maternal smoking around birth on four psychiatric disorders using inverse variance-weighted (IVW), MR-Egger and the weighted median (WM) methods[32]. Given that the horizontal pleiotropy of IVs could bias the results, we validated the stability of the results by comparing the effect estimates across the 3 MR methods. The IVW method uses a meta-analysis approach to combine the Wald ratios of the causal effects of each SNP; assuming all SNPs are valid IVs with no evidence of directional pleiotropy, we considered that the IVW method provides the most precise estimates.[33]. Effect estimates were reported in odds ratio (OR) with a 95% confidence interval (CI). If the OR is greater than 1, it suggests that the exposure is a potential risk factor for the outcome, conversely, if the OR is less than 1, it indicates a potential protective factor. To process the third MR assumption, we conducted heterogeneity assessment and sensitivity analyses to verify whether heterogeneity and pleiotropy within the genetic instruments biased the MR results. Q-statistics and I²-values were used to estimate the heterogeneity between SNPs quantitatively[34]. “Leave-one-out” sensitivity analysis was performed by removing a different SNP in each iteration to assess the causal effect of outlying SNPs and to guarantee that the MR estimates were not affected by removing SNPs[35]. To ascertain the presence of pleiotropy and provide outlier-adjusted estimates by removing any pleiotropic outlying SNPs, we also applied the Pleiotropy RESidual Sum and Outlier (PRESSO) analysis[36].

All statistical analyses were conducted using R statistical software with the “dev tools,” “TwoSampleMR,” “LDlinkR,” and “MRPRESSO” Packages (version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were 2-sided, and the MR and sensitivity analyses' results regarding the causal effects of exposures and outcomes were considered statistically significant at P < 0.05.

3.1 SNP screening

After the above screening criteria (P < 5×10^− 8, r² < 0.001, F > 10) and excluding confounders with potential association with exposure, a total of 16 SNPs were included in our study as instrumental variables for maternal smoking around birth. Four groups of instrumental variables were identified after being corrected in the same direction as the ADHD, ASD, BD, and MDD datasets, respectively, excluding the echo SNPs, as shown in Table 2. The screened SNPs had muscular statistical strength, with F-statistics ranging from 149.888 to 247.582, which exceeded the conventional threshold of 10.

Table 2Causal effects of maternal smoking around birth on the risk of psychiatric disorders based on IVW modeling.

Outcome	SNP	IVW OR (95%CI)	P	R² (%)	F-statistics	Power（%）
Attention deficit hyperactivity disorder	10	12.299(3.341-45.27)	<0.001	0.052	207.870	100
Autism spectrum disorders	10	1.656(0.231-11.871)	0.616	0.038	149.888	20
Bipolar disorder	10	2.167(0.256-18.369)	0.478	0.062	247.582	67
Major depressive disorder	10	5.789(2.576-13.011)	<0.001	0.057	225.843	100

SNP: single nucleotide polymorphism; IVW: inverse variance weighting; OR: odds ratio; CI: confidence interval; R²: strength of the instrumental variable; Power: statistical power.

3.2 MR estimates of causal effect

As shown in Fig. 2 the IVW method showed a potential causal effect of maternal smoking around birth on ADHD (IVW: OR = 12.299; 95% CI: 3.341–45.27; P < 0.001) and MDD (IVW: OR = 5.789; 95% CI: 2.576–13.011; P < 0.001), suggesting that maternal smoking around birth may be a potential risk factor for attention deficit and hyperactivity disorder and major depressive disorder in offspring. In addition, weighted median method analysis also yielded consistent findings for ADHD (WM: OR = 8.646; 95% CI: 3.341–45.27; P = 0.003) and MDD (WM: OR = 3.59; 95% CI: 1.226–10.517; P = 0.02). In addition, the results of IVW analysis in ASD and BD yielded consistent findings. Still, they were not statistically significant, and Fig. 3 shows the scatterplots plotted by the five methods of MR.

3.3 Heterogeneity and pleiotropy

The MR-Egger regression did not show any pleiotropy in the MR analyses for ADHD (MR-Egger = 0.036, P = 0.115); ASD (MR-Egger = 0.012, P = 0.783); BD (MR-Egger=-0.004, P = 0.937); MDD (MR-Egger = 0.028, P = 0.123). Heterogeneity within each dataset was assessed using Cochran's Q test, which examined no evidence of significant heterogeneity (P = 0.340 to 0.414). In addition, the MR-PRESSO test was performed, and consistent results were obtained, with none revealing the presence of pleiotropic and outlier SNPs, as shown in Table 3.

Table 3

Summary of sensitivity analysis results.
Variant		MR-Egger		Cochran’s Q		MR-PRESSO
Exposure	outcome	Egger	P	Q	P	Global test P
Maternal smoking around birth	ADHD	0.036	0.115	18.904	0.026	0.330
Maternal smoking around birth	ADD	0.012	0.783	19.362	0.022	0.018
Maternal smoking around birth	BD	-0.004	0.937	23.906	0.004	0.003
Maternal smoking around birth	MD	0.028	0.123	9.879	0.360	0.407

ADHD: attention deficit and hyperactivity disorder; ASD: autism spectrum disorder; BD: bipolar disorder; MDD: major depressive disorder; MR-PRESSO: sum of outliers and multiplicity residuals.

4.1 Main findings

We analyzed GWAS data for a range of phenotypes using Mendelian randomization and demonstrated that maternal smoking around birth had a significant causal effect on the risk of developing attention deficit and hyperactivity disorder and major depressive disorder in the offspring and that these SNPs were validated as a robust genetic tool for phenotypic variance in "maternal smoking around birth." Furthermore, considering that there was no detectable pleiotropy (P > 0.05), the causal effect results in our primary MR analysis (i.e., the IVW-MR approach) were stable, unbiased, and reliable. However, Cochran's Q test suggested some pleiotropy in estimating the effect of ADHD (P = 0.026), which did not affect the validity of the IVW approach. Validity of the IVW method.

4.2 Maternal smoking around birth on psychiatric disorders

Our results showing that smoking during pregnancy poses an increased risk for offspring with attention deficit and hyperactivity disorder and major depressive disorder are consistent with previous observational studies. Previous studies have confirmed that nicotine, the primary psychoactive ingredient in tobacco, accumulates rapidly in the human brain when smoked[37]. No direct studies have confirmed the neurotoxic effects of nicotine exposure on human fetal brain development. Still, a large number of animal studies have found significant neurotoxicity of nicotine on fetal brain development[38]. Animal models suggest that nicotine can readily cross the placental barrier and enter fetal circulation. In contrast, the blood-brain barrier protects the adult brain from toxic chemicals and is not fully formed until approximately six months after birth[39]. In the case of animal models, nicotine crosses the blood-brain barrier. Specifically, it binds to nicotinic acetylcholine receptors (nAChR) in the fetal brain. Prolonged exposure of the animal brain to nicotine leads to long-term changes in presynaptic nAChR function decline, which leads to changes in nicotine-stimulated neurotransmitter release and persists even after nicotine exposure is removed for an extended period[40]. In contrast, the nAChR is vital in regulating synaptic plasticity and brain development. Neurotransmitter dysregulation, such as acetylcholine, dopamine, and serotonin, is thought to be an essential cause of ADHD[41] and MDD[42]. There is evidence that in-utero exposure to tobacco smoke is also associated with epigenetic changes in the placenta and offspring[43–45], which includes epigenetic regulation of genes related to the hypothalamic-pituitary-adrenocortical axis (HPA)[44], which is involved in the body's response to stress and mood. HPA overactivity is common in patients with depression[46] and is recognized as a potential causal pathway. However, our MR study did not find a significant causal relationship between maternal smoking before and after delivery on autistic pedigree disorder and bipolar disorder, which is inconsistent with previous observational studies and the exact reasons for the increased risk need to be investigated further.

4.3 Strength and Limitations

To the best of our knowledge, our study is the first MR study focused on a causal relationship between maternal smoking around birth and psychiatric disorders using large-scale GWAS data. The strengths of this study include the large sample size from GWAS summary datasets, appreciably lessened confounding and reverse causation bias compared to observational studies, and good robustness without apparent pleiotropy in findings regarding the estimate of causal effect. In addition, because the time sequence of genetic information precedes the exposure of postnatal environmental factors, the bias caused by confounding factors in observational studies is avoided, compared with the cost and ethical problems of randomized controlled trials. The causal relationship between the two is discussed more efficiently. Of course, this study also has some limitations; the object of our study is the European population, and whether other races can come to the same conclusion remains to be further studied.

4.4 Conclusion

In summary, the present study conducted a causal inference between smoking during pregnancy and the risk of psychiatric disorders by Mendelian randomization using genetic data and found that the level of smoking during pregnancy plays a risky role in the development of ADHD and MDD. However, the exact mechanism of action between the two is unclear, and further research is needed.

Author contributions

ZZ, HRL, and XQW conceived and participated in the design and coordination of the study. YLZ and JYL conducted database searches. RQH, YWG, and JLT reviewed the GWAS datasets and completed data collection. ZZ, JZL, and YTW conducted data analysis. ZZ drafted the manuscript. ZZ and QHL had full access to all data collection, analysis, and interpretation. QHL confirmed the final approval for the release of the version. All authors have approved the final manuscript and provided their consent for the public dissemination of the study.

Funding Statement

This work received support from the Science-Health Joint Medical Scientific Research Project of Chongqing [2020GDRC026]. The study funders/sponsors played no role in the design and execution of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Data availability statement

Data for analysis were obtained from published studies and public databases, including the IEU OpenGWAS project database [https://gwas.mrcieu.ac.uk/] and the Psychiatric Genomics Consortium [https://www.med.unc.edu/pgc/]. All generated data are included in this article and supplementary material, with additional supporting data available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

All the data sources used in this study are summarized data from relevant GWAS studies and the IEU OpenGWAS project database, which are publicly available for free download and do not require approval from the review agency.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgments

We thank the Psychiatric Genomics Consortium (PGC) and UK Biobank researchers for providing GWAS data.

Pineles BL, Park E, Samet JM. Systematic review and meta-analysis of miscarriage and maternal exposure to tobacco smoke during pregnancy. Am J Epidemiol. 2014;179:807–23.
Lange S, Probst C, Rehm J, Popova S. National, regional, and global prevalence of smoking during pregnancy in the general population: a systematic review and meta-analysis. Lancet Glob Health. 2018;6:e769–76.
GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204–22.
Global Burden of Disease Child and Adolescent Health Collaboration, Kassebaum N, Kyu HH, Zoeckler L, Olsen HE, Thomas K, et al. Child and Adolescent Health From 1990 to 2015: Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2015 Study. JAMA Pediatr. 2017;171:573–92.
Lyall K, Croen L, Daniels J, Fallin MD, Ladd-Acosta C, Lee BK, et al. The Changing Epidemiology of Autism Spectrum Disorders. Annu Rev Public Health. 2017;38:81–102.
Kim JY, Son MJ, Son CY, Radua J, Eisenhut M, Gressier F, et al. Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence. Lancet Psychiatry. 2019;6:590–600.
Elsabbagh M. Linking risk factors and outcomes in autism spectrum disorder: is there evidence for resilience? BMJ. 2020;:l6880.
Cortese S, Castellanos FX. Chapter 4 - Attention Deficit/Hyperactivity Disorder. In: Zigmond MJ, Rowland LP, Coyle JT, editors. Neurobiology of Brain Disorders. San Diego: Academic Press; 2015. pp. 42–58.
Guney E, Cetin FH, Iseri E, Guney E, Cetin FH, Iseri E. The Role of Environmental Factors in Etiology of Attention- Deficit Hyperactivity Disorder. ADHD - New Directions in Diagnosis and Treatment. IntechOpen; 2015.
Hvolgaard Mikkelsen S, Olsen J, Bech BH, Obel C. Parental age and attention-deficit/hyperactivity disorder (ADHD). Int J Epidemiol. 2017;46:409–20.
Ruisch IH, Dietrich A, Glennon JC, Buitelaar JK, Hoekstra PJ. Maternal substance use during pregnancy and offspring conduct problems: A meta-analysis. Neurosci Biobehav Rev. 2018;84:325–36.
Joo H, Lim M-H, Ha M, Kwon H-J, Yoo SJ, Choi K-H, et al. Secondhand Smoke Exposure and Low Blood Lead Levels in Association With Attention-Deficit Hyperactivity Disorder and Its Symptom Domain in Children: A Community-Based Case-Control Study. Nicotine Tob Res. 2017;19:94–101.
Kotimaa AJ, Moilanen I, Taanila A, Ebeling H, Smalley SL, McGough JJ, et al. Maternal smoking and hyperactivity in 8-year-old children. J Am Acad Child Adolesc Psychiatry. 2003;42:826–33.
Dolan CV, Geels L, Vink JM, van Beijsterveldt CEM, Neale MC, Bartels M, et al. Testing Causal Effects of Maternal Smoking During Pregnancy on Offspring’s Externalizing and Internalizing Behavior. Behav Genet. 2016;46:378–88.
Moylan S, Gustavson K, Øverland S, Karevold EB, Jacka FN, Pasco JA, et al. The impact of maternal smoking during pregnancy on depressive and anxiety behaviors in children: the Norwegian Mother and Child Cohort Study. BMC Med. 2015;13:24.
Quinn PD, Rickert ME, Weibull CE, Johansson ALV, Lichtenstein P, Almqvist C, et al. Association Between Maternal Smoking During Pregnancy and Severe Mental Illness in Offspring. JAMA Psychiatry. 2017;74:589–96.
Talati A, Bao Y, Kaufman J, Shen L, Schaefer CA, Brown AS. Maternal Smoking During Pregnancy and Bipolar Disorder in Offspring. Am J Psychiatry. 2013;170:1178–85.
Taylor AE, Carslake D, de Mola CL, Rydell M, Nilsen TIL, Bjørngaard JH, et al. Maternal Smoking in Pregnancy and Offspring Depression: a cross cohort and negative control study. Sci Rep. 2017;7:12579.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.
Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023;55:198–208.
Prins BP, Abbasi A, Wong A, Vaez A, Nolte I, Franceschini N, et al. Investigating the Causal Relationship of C-Reactive Protein with 32 Complex Somatic and Psychiatric Outcomes: A Large-Scale Cross-Consortium Mendelian Randomization Study. PLoS Med. 2016;13:e1001976.
Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51:793–803.
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.
Shen J, Zhou H, Liu J, Zhang Y, Zhou T, Yang Y, et al. A modifiable risk factors atlas of lung cancer: A Mendelian randomization study. Cancer Med. 2021;10:4587–603.
Burgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol. 2018;33:947–52.
Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64.
Yuan S, Xiong Y, Larsson SC. An atlas on risk factors for multiple sclerosis: a Mendelian randomization study. J Neurol. 2021;268:114–24.
Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35:4851–3.
Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9.
Lukkunaprasit T, Rattanasiri S, Ongphiphadhanakul B, McKay GJ, Attia J, Thakkinstian A. Causal Associations of Urate With Cardiovascular Risk Factors: Two-Sample Mendelian Randomization. Front Genet. 2021;12:687279.
Cui Z, Hou G, Meng X, Feng H, He B, Tian Y. Bidirectional Causal Associations Between Inflammatory Bowel Disease and Ankylosing Spondylitis: A Two-Sample Mendelian Randomization Analysis. Front Genet. 2020;11:587876.
Marouli E, Del Greco MF, Astley CM, Yang J, Ahmad S, Berndt SI, et al. Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease. Commun Biol. 2019;2:119.
Cui Z, Hou G, Meng X, Feng H, He B, Tian Y. Bidirectional Causal Associations Between Inflammatory Bowel Disease and Ankylosing Spondylitis: A Two-Sample Mendelian Randomization Analysis. Front Genet. 2020;11:587876.
Greco MFD, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.
Burgess S, Thompson SG. Erratum to: Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:391–2.
Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.
Rose JE, Mukhin AG, Lokitz SJ, Turkington TG, Herskovic J, Behm FM, et al. Kinetics of brain nicotine accumulation in dependent and nondependent smokers assessed with PET and cigarettes containing 11C-nicotine. Proc Natl Acad Sci U S A. 2010;107:5190–5.
King E, Campbell A, Belger A, Grewen K. Prenatal Nicotine Exposure Disrupts Infant Neural Markers of Orienting. Nicotine Tob Res. 2018;20:897–902.
Luck W, Nau H, Hansen R, Steldinger R. Extent of nicotine and cotinine transfer to the human fetus, placenta and amniotic fluid of smoking mothers. Dev Pharmacol Ther. 1985;8:384–95.
Damborsky JC, Griffith WH, Winzer-Serhan UH. Neonatal Nicotine Exposure Increases Excitatory Synaptic Transmission and Attenuates Nicotine-stimulated GABA release in the Adult Rat Hippocampus. Neuropharmacology. 2015;88:187–98.
Thapar A, Cooper M. Attention deficit hyperactivity disorder. Lancet. 2016;387:1240–50.
Moylan S, Jacka FN, Pasco JA, Berk M. How cigarette smoking may increase the risk of anxiety symptoms and anxiety disorders: a critical review of biological pathways. Brain Behav. 2013;3:302–26.
Richmond RC, Simpkin AJ, Woodward G, Gaunt TR, Lyttleton O, McArdle WL, et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum Mol Genet. 2015;24:2201–17.
Stroud LR, Papandonatos GD, Rodriguez D, McCallum M, Salisbury AL, Phipps MG, et al. Maternal smoking during pregnancy and infant stress response: test of a prenatal programming hypothesis. Psychoneuroendocrinology. 2014;48:29–40.
Wilhelm-Benartzi CS, Houseman EA, Maccani MA, Poage GM, Koestler DC, Langevin SM, et al. In utero exposures, infant growth, and DNA methylation of repetitive elements and developmentally related genes in human placenta. Environ Health Perspect. 2012;120:296–302.
Juruena MF, Bocharova M, Agustini B, Young AH. Atypical depression and non-atypical depression: Is HPA axis function a biomarker? A systematic review. J Affect Disord. 2018;233:45–67.

No competing interests reported.

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Maternal smoking around birth and psychiatric disorders of offspring: A Mendelian Randomization Study

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Abstract

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What this paper adds

1 Introduction

2 Materials and Methods