Attention deficit hyperactivity disorder (ADHD) is a multifactorial neurodevelopmental disorder, yet the interplay between ADHD polygenic risk scores (PRSs) and other risk factors remains relatively unexplored. The authors investigated associations, confounding, and interactions of ADHD PRS with birth-related, somatic, and psychosocial factors previously associated with ADHD.

Methods:

Participants included a random general population sample (N=21,578) and individuals diagnosed with ADHD (N=13,697) from the genotyped Danish iPSYCH2012 case cohort, born between 1981 and 2005. The authors derived ADHD PRSs and identified 24 factors previously associated with ADHD using national registers. Logistic regression was used to estimate associations of ADHD PRS with each risk factor in the general population. Cox models were used to evaluate confounding of risk factor associations with ADHD diagnosis by ADHD PRS and parental psychiatric history, and interactions between ADHD PRS and each risk factor.

Results:

ADHD PRS was associated with 12 of 24 risk factors (odds ratio range, 1.03–1.30), namely, small gestational age, infections, traumatic brain injury, and most psychosocial risk factors. Nineteen risk factors were associated with ADHD diagnosis (odds ratio range, 1.20–3.68), and adjusting for ADHD PRS and parental psychiatric history led to only minor attenuations. Only the interaction between ADHD PRS and maternal autoimmune disease survived correction for multiple testing.

Conclusions:

Higher ADHD PRS in the general population is associated with small increases in risk for certain birth-related and somatic ADHD risk factors, and broadly to psychosocial adversity. Evidence of gene-environment interaction was limited, as was confounding by ADHD PRS and family psychiatric history on ADHD risk factor associations. This suggests that the majority of the investigated ADHD risk factors act largely independently of current ADHD PRS to increase risk of ADHD.

Attention deficit hyperactivity disorder (ADHD) is a prevalent and often persistent neurodevelopmental condition affecting 5%–7% of children and 2.5%–5% of adults (1, 2). Family, twin, and genome-wide association studies (GWASs) have demonstrated the importance of genetic factors in ADHD, with heritability estimated at 70%–80% in twin data and 22% from single-nucleotide polymorphisms (SNPs) (SNP-based heritability, h²_SNP) (3, 4). Several birth-related (e.g., low birth weight), somatic (e.g., infections, seizures), and psychosocial (e.g., low family income, parental psychiatric disorders) factors have also been associated with risk of ADHD (5–8). However, the complex interplay between ADHD polygenic liability and such risk factors (defined here as any attribute, characteristic, or exposure of an individual that increases the likelihood of developing ADHD) (6) remains poorly understood. Given that many ADHD risk factors are themselves heritable, risk factor associations may be mediated in part or fully by shared genetic effects influencing both the risk of exposure and ADHD. Such effects are captured by associations between ADHD polygenic liability (both at the family and individual level) and the risk factor (i.e., gene-environment correlations) and by attenuated risk factor outcome associations when accounting for ADHD polygenic liability (i.e., genetic confounding). The impact of a risk factor may also vary by ADHD polygenic liability (i.e., gene-environment interaction). Examining these three types of gene-environment interplay is important in understanding the impact of genes and specific environments on ADHD, as well as their joint effects, and to identify causal and potentially modifiable risk factors (9, 10).

In the largest ADHD GWAS to date (3), including 20,183 individuals diagnosed with ADHD and 35,191 control subjects, 12 genome-wide significant independent loci were identified. Moreover, ADHD polygenic risk scores (PRSs), which capture the sum of an individual’s risk alleles weighted by their effect size in an independent GWAS, were associated with increased risk of being diagnosed with ADHD (3). This further supports the highly polygenic nature of ADHD, meaning that hundreds or thousands of common genetic variants, each of small effect size, contribute to its etiology. ADHD PRS is thus a potentially useful tool for investigating gene-environment interplay (9). A recent meta-analysis (11) found ADHD PRS to be associated not only with ADHD but also with measures of lower socioeconomic status. Further, phenome-wide association analyses in ∼330,000 UK Biobank participants, a general population sample, also found ADHD PRS to be associated with sociodemographic characteristics (e.g., lower education and income, younger maternal age, smoking) (12). Similar associations were reported in a population-based cohort of >7,000 mothers, which in addition found ADHD PRS to be associated with certain prenatal risk factors linked to ADHD (e.g., infections). Little evidence of association was found for other maternal illnesses (e.g., diabetes, hypertension) and birth-related factors (e.g., low birth weight, preterm delivery) (13).

Hence, there is growing evidence to suggest that polygenic liability of ADHD may increase the risk of exposure to certain ADHD risk factors, even in the absence of an ADHD diagnosis. However, research evaluating such gene-environment associations is still lacking for many ADHD risk factors, particularly in the somatic disease domain (11). Moreover, studies comparing differentially exposed siblings have found that many risk factors linked to clinical ADHD diagnosis may be better explained by unmeasured familial (genetic and/or environmental) confounding (5). This suggests that ADHD polygenic liability, measured at the level of the family or the individual, may confound previously reported associations between putative risk factors and ADHD diagnosis. Indeed, trio design studies suggest that associations of parental (genetic) risk factors with children’s ADHD symptoms are mainly attributable to the effects of genes shared by parents and children (direct genetic transmission), rather than via genetically influenced parenting environments (genetic nurture) (14, 15). While highly informative, genotyped trios are rare. Instead, measures such as parental psychiatric history and ADHD PRS are more commonly used as proxies of ADHD polygenic liability in epidemiological studies. However, only a few studies have investigated genetic confounding of ADHD risk factor associations using both ADHD PRS and family psychiatric history (16). Finally, there has been little research evaluating gene-environment interactions using ADHD PRS, with exposures largely limited to socioeconomic factors and parenting style (11). Recent work in the Danish iPSYCH2012 case cohort, the world’s largest genotyped ADHD sample, found that both ADHD PRS and several psychosocial risk factors, including parental psychiatric history, were associated with increased risk of ADHD, yet there was limited evidence of interaction between the risk factors and the PRS (16).

In this study, we used the iPSYCH2012 case cohort to evaluate gene-environment interplay in ADHD for 24 birth-related, somatic, and psychosocial factors previously associated with ADHD (including parental psychiatric history) (5–8) and measured in Danish national registers. First, we estimated gene-environment associations between ADHD PRS and each risk factor in the general population. Next, we examined the extent to which associations between the risk factors and ADHD diagnosis were confounded by ADHD PRS and parental psychiatric history. Finally, we evaluated gene-environment interaction between the risk factors and ADHD PRS on ADHD diagnosis.

Methods

Sample

The Integrative Psychiatric Research consortium (iPSYCH2012) is a case-cohort sample nested in the Danish population born between May 1, 1981, and December 31, 2005 (17). From this study base, a 2% (N=30,000) random population sample (referred to as the subcohort) and all individuals with an ADHD diagnosis (ICD-10 code F90.0) were selected. Due to the random selection, 288 individuals with ADHD were selected both as case subjects and for the subcohort. iPSYCH2012 is linked to Danish national population registers using an anonymized version of the unique personal identification number assigned to everyone registered in Denmark. ADHD diagnoses were obtained from the Danish Psychiatric Central Research Register, which includes psychiatric hospital admissions since 1969 and outpatient admission since 1995 (18). DNA extraction and genotyping in iPSYCH2012 has been described elsewhere (17). For details on imputation, principal components analysis, and quality control, see Schork et al. (19). We restricted our sample to individuals alive and living in Denmark at age 5 who were unrelated (no closer than third-degree kinship estimated using KING, version 1.9) and of European ancestry, leaving 13,697 individuals with ADHD and 21,290 population control subjects without ADHD. A flowchart describing the study population is presented in Figure S1 in the online supplement.

ADHD Polygenic Risk Score

We derived ADHD PRSs in iPSYCH2012 based on a linear combination of an externally trained PRS and an internally trained PRS. This approach (20) was recently developed and validated in iPSYCH, where the “meta-PRS” showed a mean prediction R² for ADHD similar to that of the more widely used approach of meta-analyzing the marginal effect estimates of GWAS summary statistics from different studies. For details, see Albiñana et al. (20). Briefly, we used LDPred (21) to derive an externally trained ADHD PRS, using SNP weights from the ADHD GWAS summary statistics from the Psychiatric Genomics Consortium cross-disorder GWAS, not including iPSYCH2012 (22). For the internally trained PRS, we leveraged having access to individual-level SNP data on a large number of individuals by calculating an internally trained ADHD PRS in an unrelated European-ancestry subset of the iPSYCH2012 sample. SNP weights for the internally trained PRS were obtained from a mixed-model prediction implemented in BOLT-LMM (23). The final (meta) ADHD PRS was a linear combination of the internally and externally trained ADHD PRS variables, standardized to the mean and standard deviation in the subcohort.

Birth-Related, Somatic, and Psychosocial Risk Factors

To identify birth-related, somatic, and psychosocial risk factors robustly associated with ADHD in previous research, we conducted a targeted literature review, considering phenotypic, familial, and common genetic variant studies (see the online supplement for details). We searched PubMed through May 2021 to identify meta-analyses, umbrella reviews, systematic reviews, and consensus statements of ADHD risk factors and correlates and snowballed from these. Based on this review, we kept risk factors showing phenotypic and/or familial associations in at least two independent samples. From these, we selected 24 risk factors that were available with sufficient coverage in Danish national registers. The risk factors and evidence of their association with ADHD based on previous research are presented in Table 1.

TABLE 1. Summary of the literature on the phenotypic, familial, and genetic links between ADHD and selected risk factors^a

Domain and Risk Factor	Phenotypic Associations	Familial Associations	Gene-Environment Correlation	Gene-Environment Interaction
Birth-related
Low birth weight/very preterm	OR=3.04 (2.19, 4.21)^b (43)	Sibling analyses, OR/HR range, 2.36–2.44; twin comparison shows significantly higher ADHD symptoms in cotwin with lower birth weight^c (5)	Birth weight, r_g=−0.13 (0.04), p<0.0008 (3)	n/a
Small for gestational age	OR/RR range, 1.13–1.30^c (44)	Sibling analysis, HR=2.3 (2.0, 2.8); 0.24 SD (0.14–0.34) higher mean ADHD symptom score^c (5)	n/a	n/a
Apgar score <7 at 5 minutes	OR=1.31 (1.12, 1.54)^b (45)	n/a	n/a	n/a
Somatic
Hypertension (or hypertension during pregnancy [HDP])	Hypertension in adults, prevalence rate, 1.90 (1.83, 1.97) (46) Hypertension in children, OR=3.26 (3.00, 3.55) (47)	HDP, OR=1.29 (1.22, 1.36)^b (48) HDP, HR=1.10 (1.05, 1.16); siblings differentially exposed to HPD, HR=1.09 (0.95, 1.24) (49)	NS association of ADHD PRS with hypertension in a general population sample (N=7,088)^c (11)	n/a
Infection	Hospital-treated infections, HR=2.09 (1.78, 2.46) (50) Drug treatment for infections, HR=1.56 (1.34, 1.82) (50) Drug treatment for infections in first 2 years of life, HR=1.26 (1.20, 1.33) (51)	Maternal pre-pregnancy: drug-treated infection, HR=1.14 (1.10, 1.19); hospital treated infection, HR=1.22 (1.09, 1.38) (52) During pregnancy: maternal drug-treated infection, HR=1.13 (1.09, 1.17); hospital treated infection, HR=1.22 (1.12, 1.33) (52) Post-pregnancy: drug-treated infection, HR=1.09 (1.06, 1.14); hospital treated infection, HR=1.11 (1.01, 1.22) (52) NS association with paternal infections (52) NS association of maternal infection in sibling analyses^c (5) NS association of drug treatment for infections in first 2 years of life in sibling analysis^c (5)	Childhood ear infections r_g=0.20 (0.05), p<2.0×10^–4 (53)	n/a
Any autoimmune disease	Any autoimmune disease, IRR=1.24 (1.10, 1.40) (54)	Any maternal autoimmune disease, HR=1.20 (1.03, 1.38)^b (55)	Serum CRP level, r_g=0.23 (0.06) p<2.0×10^–4 (53) Psoriasis, r_g=0.23 (0.07), p<1.0×10^–3 (53) Rheumatoid arthritis, r_g=0.16 (0.05), p<9.0×10^–4 (53) Tuberculosis susceptibility, r_g=0.36 (0.11), p<1.6×10^–3 (53) NS r_g for remaining autoimmune diseases after correction for multiple testing (53)	n/a
Asthma	OR=1.53 (1.41, 1.65)^b (56) OR=1.34 (1.24, 1.44)^b (57)	Maternal asthma, OR/HR range 1.41–1.50^c (58, 59) Paternal asthma, HR=1.13 (1.08, 1.18) (59)	Asthma, r_g=0.20 (0.05), p<1.21×10^–5 (60)	n/a
Atopic disease	Atopic dermatitis, OR=1.32 (1.20, 1.45)^b (61); OR=1.43 (1.09,1.88)^b (57) Allergic rhinitis, OR=1.52 (1.43, 1.63)^b (61); OR=1.59 (1.13, 2.23)^b (57)	Parental allergic rhinitis, OR=1.3 (1.1–1.5) (62) Sibling, any atopic diseases, RR=1.13 (1.10, 1.15) (63) Sibling, atopic dermatitis, RR=1.10 (1.04, 1.16) (63) Sibling, allergic rhinitis, RR=1.17 (1.14, 1.21) (63)	Allergies, r_g=0.11 (0.04), p=0.058 (53)	n/a
Type 1 diabetes (T1D)	T1D, OR=1.30 (1.20, 1.40) (47) T1D, IRR=1.31 (1.03, 1.63) (54) T1D, NS in males and females (64)	Maternal T1D, OR=1.53 (1.27, 1.85)^b (55) Paternal T1D, OR=1.20 (1.13, 1.28)^b (65)	T1D, r_g=–0.14 (0.06), p=0.14 (53)	n/a
Epilepsy	OR=3.46 (3.34, 3.58) (47) OR=3.47 (3.33, 3.62) (39) IRR=2.72 (2.53, 2.91) (66)	Maternal RR=1.7 (1.1, 2.7) (67) Maternal OR=1.85 (1.75, 1.96) (39) Paternal OR=1.64 (1.54, 1.74) (39) Full-sibling OR=1.56 (1.46, 1.67) (39) Maternal half-siblings OR=1.28 (1.14, 1.43) (39) Paternal half-siblings OR=1.10 (0.96, 1.25) (39) Cousins OR=1.15 (1.10, 1.20) (39) r_g estimated using quantitative genetic methods in sibling data=0.21 (0.02, 0.40) (39)	Epilepsy, r_g=0.14 (0.12), p=0.24 (38)	n/a
Traumatic brain injury (TBI)	Mild TBI, RR=2.0 (z=6.5) p<0.0005^b (68) Any TBI, HR=4.57 (4.31, 4.85) (69)	Maternal TBI, HR=1.45 (1.42, 1.48) (70) Paternal TBI, HR=1.21 (1.17, 1.24) (70) Sibling TBI, OR=1.24 (1.14, 1.36) (71)	n/a	A significant interaction between ADHD PRS and TBI (mild TBI vs. no TBI) (t=−2.1, df=1427, p=0.04). ADHD PRS positively associated with ADHD symptom score in youths without TBI (t=3.5, df=1427, p=0.005), but not in those with mild TBI (t=20.4, df=196, p=0.70) (41)
Psychosocial
Low income	Individuals with ADHD had an income ratio of 0.83 (0.82, 0.84), equivalent to 17% lower income on average compared to individuals without ADHD (72)	Low parental income, OR range 1.33–4.51^c (8) 2.3 (2.1, 2.5) percentage points (73) Sibling analysis, HR=1.37 (1.07, 1.75); significant association with family income decline and externalizing problems^c (5)	ADHD PRS negatively associated with income in general population: β=−0.172 (SE=0.04), p<0.01 (74)	NS interaction between parental income level and ADHD PRS in iPSYCH (16)
Low education	Failure to complete high school, OR=3.70 (1.96, 6.99)^b (75) No tertiary education, OR=6.47 (4.58, 9.14)^b (75) Lower academic attainment, OR=3.35 (3.00, 3.75) (76)	Low maternal education, OR=1.91 (1.20, 3.03)^b (8) Low paternal education, OR=2.1 (1.27, 3.47)^b (8) Parental education, 3.5 (3.3, 3.7) percentage points (73)	Years of education, r_g=−0.53 (0.02), p<1.44×10^–80 (3) ADHD PRS associated with lower educational attainment in seven of nine studies in systematic review^c (11)	NS interaction between parental education level and ADHD PRS in iPSYCH (16)
Unemployment	Unemployment, OR=1.97 (1.01, 3.85)^b (75) Unemployment, OR=1.39 (1.25, 1.53) (76) Individuals with ADHD had, on average, 12.19 (11.89, 12.49) more days of unemployment (72)	Parental unemployment ≥6–12 months, 2.1 (1.8, 2.3) percentage points (73)	ADHD PRS negatively associated with employment in general population: β=−0.107 (SE=0.04), p<0.01 (74) Child ADHD PRS associated with lower family SES, β=−0.17 (−0.21, −0.13), p<1.32×10^–13 (77), and higher SES adversity, β=0.10 (0.01, 0.20), p=0.028 (78)	NS interaction between parental unemployment and ADHD PRS in iPSYCH (16)
Single parenthood	n/a	OR=1.28 (1.08, 1.52)^b (8)	n/a	n/a
Age at child’s birth	Early pregnancy, OR=2.77 (0.67, 11.37)^b (75) Early pregnancy, HR=2.30 (1.94, 2.73) (79)	Lowest maternal age category, OR=1.49 (1.19, 1.87)^b (80) Lowest paternal age category, OR=1.75 (1.31, 2.36)^b (80) Nonlinear association of paternal age (p<0.005); ADHD risk highest in fathers age <20, 2nd highest in fathers age ≥45, compared to fathers ages 31–35^b (80) Younger maternal age NS or protective in sibling analyses. Higher paternal age associated with increased risk in one sibling analysis^c (5)	Age at first birth, r_g=−0.612 (0.034), p=3.69×10^–61 (3) Age at first birth, r_g=−0.68 (0.034), p=1.86×10^–89, and ADHD PRS associated with age at first birth, R²=1.10×10^–02, p<1.20×10^–303 (81)	n/a
Parental psychiatric disorder	Comorbid psychiatric and substance use disorders in individuals with ADHD, OR=2.53 (1.48, 4.32)^b (75)	Any parental psychopathology, RR=2.85 (1.77, 4.59)^b (82) Maternal history of psychiatric disorders, HR=2.16 (1.97, 2.37) (83) Paternal history of psychiatric disorders, HR=2.21 (2.01, 2.43) (83)	r_g range, 0.13–0.74 with externalizing disorders, intellectual disability, alcohol dependence, depression, autism, Tourette’s disorder, bipolar disorder, and schizophrenia (4, 84) ADHD PRS significantly associated with a general psychopathology factor in children (85, 86) ADHD PRS showed strong evidence of associations with externalizing behaviors and inconclusive evidence for addiction, autism, autistic traits, and broader mental health in systematic review^c (11) ADHD PRS associated with parental psychiatric disorders within ADHD individuals in iPSYCH, b=0.07 (0.04, 0.11), p<0.0001 (87)	NS interaction between parental unemployment and ADHD PRS in iPSYCH (16)

^aFor phenotypic and familial associations, we preferentially present pooled and, when available, adjusted risk estimates from the largest available published meta-analyses, followed by evidence from systematic reviews. Otherwise, we present estimates from large-scale population studies, prioritized based on sample size and the inclusion of clinically diagnosed ADHD. Associations are reported as risk estimates (risk ratio, odds ratio, or hazard ratio) with 95% confidence intervals in parentheses. For gene-environment correlations and interactions, we present results of the largest available genetic correlation analyses using the linkage disequilibrium score regression (LDSC) method and summarize the available ADHD PRS literature. Genetic correlations (r_g) are reported with standard errors in parentheses and p values corrected for multiple testing according to the method stated in the cited publication. PRS associations are presented based on information provided in the cited publication. HR=hazard ratio; n/a=not available (i.e., no studies found); NS=not significant; OR=odds ratio; PRS=polygenic risk score; r_g=genetic correlation; RR=risk ratio; t=t test(n–1).

^bPooled, and when reported adjusted, risk estimates from cited meta-analysis.

^cRange of estimate from cited systematic review.

TABLE 1. Summary of the literature on the phenotypic, familial, and genetic links between ADHD and selected risk factors^a

Enlarge table

For risk factor definitions, see Table S1 in the online supplement. We extracted information on sex, date of birth, migration, death, and parents’ anonymized personal identification numbers from the Danish Civil Registration System (24). We used the Danish Medical Birth Register to obtain information on birth weight, gestational age, and 5-minute Apgar score (25). Somatic diseases in parents (maternal hypertensive disorders and infections during pregnancy, and parental autoimmune disorders) and in the index child (infections, asthma, atopic disease, type 1 diabetes, epilepsy, and traumatic brain injury [TBI]) were identified in the Danish National Patient Register, which includes ICD-coded specialist inpatient care since 1977 and outpatient care since 1995 (26). Because asthma and atopic diseases are not routinely treated in specialist care, we also used drug prescriptions from the Danish National Prescription Registry to identify asthma and atopic disease (see Table S1 in the online supplement for included drugs and definitions). The Danish National Prescription Registry includes information on all prescriptions redeemed at Danish pharmacies since 1995 (27). Psychosocial factors (parental income, education, employment status, single-parent household, and age at birth of index child) were derived using data from Denmark’s socioeconomic registers (28). Finally, parental history of any psychiatric disorder was identified using the Danish Psychiatric Central Research Register. Parental psychiatric history was both included as a risk factor and evaluated as a proxy of ADHD genetic liability when testing for genetic confounding. We used history of any psychiatric disorder rather than parental ADHD specifically because ADHD shows familial co-aggregation with a range of psychiatric disorders (29, 30), and because the coverage of ADHD in the parent generation is limited, given that ADHD was rarely diagnosed in Denmark before the 1990s and remains underdiagnosed in adults (31, 32).

The iPSYCH2012 study was approved by the Danish Scientific Ethics Committee, the Danish Health Data Authority, the Danish Data Protection Agency, and the Danish Newborn Screening Biobank Steering Committee. The Danish Scientific Ethics Committee, in accordance with Danish legislation, has, for this study, waived the need for informed consent in biomedical research based on existing biobanks (17, 33).

Statistical Analysis

We first confirmed that our proxies of ADHD polygenic liability (PRS and parental psychiatric history) were associated with ADHD diagnosis in the full cohort by running two separate weighted Cox models, adjusted for sex, birth year, and the first four principal components. We estimated incidence rate ratios (IRRs) with 95% confidence intervals for ADHD across ADHD PRS deciles (compared to the lowest decile) and for parental psychiatric history (compared to none). We also ran a standard logistic regression, estimating the risk of ADHD associated with a one standard deviation increase in the ADHD PRS, and the proportion of variance explained (Nagelkerke R²) by the ADHD PRS, by comparing the regression with the ADHD PRS to a reduced model with covariates only.

Gene-environment associations.

To obtain population-representative estimates of the associations between ADHD PRS and each risk factor, we ran logistic regressions in the iPSYCH2012 subcohort, that is, the randomly selected general population sample including 288 individuals with diagnosed ADHD. Associations are presented as odd ratios with 95% confidence intervals per standard deviation of ADHD PRS, adjusted for sex, birth year, and ancestry using the first four principal components. Exposures that could vary over time were defined at the time of ADHD diagnosis or end of follow-up, whichever came first.

Genetic confounding.

To estimate whether associations between our putative risk factors and ADHD may be confounded by polygenic liability for ADHD, we then used weighted Cox models to estimate the associations of the other 24 risk factors with ADHD diagnosis. We modeled age as the underlying timescale, and further adjusted for sex, birth year, and cohort effects by including gender-specific birth cohorts (in 5-year bands) as strata (i.e., allowing for separate nonparametric underlying hazard) and the first four principal components (model 1). To evaluate confounding, we further adjusted the associations for ADHD PRS (model 2), parental psychiatric history (model 3), and both (model 4). Analyses were run in the full case-control sample, that is, all individuals with ADHD (N=13,697) and individuals without ADHD from the subcohort (N=21,290). We used Kalbfleisch and Lawless weights to account for the oversampling of cases (34). Individuals were followed from age 5 until their first ADHD diagnosis, death, emigration, or end of follow-up (December 31, 2012), whichever came first. In all Cox models, somatic risk factors were modeled as time-varying exposures, and the remaining risk factors as time-fixed covariates defined at birth, or before the index child’s 5th birthday (see Table S1 in the online supplement).

Gene-environment interaction.

We investigated (multiplicative) interactions between each risk factor and ADHD PRS (treated as a mean-centered continuous variable [mean=0, SD=1]) on the risk of ADHD diagnosis by modeling a differential linear effect of ADHD PRS across levels of each risk factor, using weighted Cox models adjusted for sex, birth year, and the first four principal components.

Sensitivity analyses.

To evaluate whether associations between the ADHD PRS and each risk factor in the general population were driven by the ADHD individuals included in the subcohort (N=288), we reran the analyses excluding them. To evaluate the impact of genotype exclusions, we first ran a logistic regression to investigate potential differences in the distribution of ADHD diagnosis between individuals included in the main analyses and those excluded based on relatedness and ancestral principal component outliers (2,758 individuals with ADHD and 4,351 population control subjects without ADHD). We then repeated the main analyses outlined above, without genotype exclusions.

To safeguard against multiple testing, we present false discovery rate (FDR) corrected p values derived using the Benjamini–Hochberg method, accounting for the number of tests in each set of analyses separately. Data management and analyses were performed using Stata, version 16 (www.stata.com).

Results

The full case-cohort population consisted of 34,987 individuals (14,108 females and 20,879 males), including 13,697 diagnosed with ADHD (3,606 females and 10,091 males). The mean age at first ADHD diagnosis was 15.6 years among females and 13.0 years among males. Higher ADHD PRS was associated with ADHD diagnosis; a one standard deviation increase in the ADHD PRS was associated with a 55% increased risk of ADHD (odds ratio=1.55, 95% CI=1.51, 1.59; Nagelkerke R²=5.1%), and individuals in the highest ADHD PRS decile had a more than fourfold increased risk of ADHD compared to those in the lowest decile (IRR=4.42, 95% CI=3.96, 4.93). These estimates are similar to ADHD PRS associations previously reported in iPSYCH (3, 16). The risk of ADHD among individuals in the highest ADHD PRS decile was similar to the risk associated with having two parents with a history of psychiatric disorders, compared to none (IRR=3.29, 95% CI=2.46, 4.41) (see Figure S2 in the online supplement).

Gene-Environment Associations

In the general population sample, ADHD PRS was statistically significantly associated with 12 of the 24 ADHD risk factors after FDR correction (Figure 1; see also Table S2 in the online supplement). Among birth-related risk factors, higher ADHD PRS was associated with being small for gestational age (odds ratio=1.08, 95% CI=1.03, 1.13), but not with birth weight or 5-minute Apgar score. Among somatic factors, ADHD PRS was associated with maternal autoimmune disorder (odds ratio=1.14, 95% CI=1.05, 1.24), having had one infection (odds ratio=1.07, 95% CI=1.05, 1.10) and five or more infections (odds ratio=1.14, 95% CI=1.05, 1.24), and mild TBI (odds ratio=1.11, 95% CI=1.05, 1.17), but showed limited evidence of association with maternal hypertension and infection during pregnancy, paternal autoimmune disorder, asthma, atopic disease, type 1 diabetes, and epilepsy. ADHD PRS was associated with most of the family psychosocial risk factors (FDR p values <0.001), apart from maternal and paternal employment at birth, which did not survive correction for multiple testing. For example, higher ADHD PRS was linked to income in the lowest quintile (maternal odds ratio=1.19, 95% CI=1.14, 1.24; paternal odds ratio=1.19, 95% CI=1.14, 1.24), low education (maternal odds ratio=1.16, 95% CI=1.12, 1.20; paternal odds ratio=1.17, 95% CI=1.13, 1.21), living in a single-parent household during the first 5 years of life (odds ratio=1.26, 95% CI=1.16, 1.36), being under age 20 at birth of index child (maternal odds ratio=1.29, 95% CI=1.17, 1.42; paternal odds ratio=1.25, 95% CI=1.04, 1.50), and parental psychiatric history (in one parent, odds ratio=1.13, 95% CI=1.07, 1.20; in both parents, odds ratio=1.30, 95% CI=1.05, 1.61).

FIGURE 1. Gene-environment associations of ADHD polygenic risk score with ADHD risk factors in the iPSYCH2012 subcohort (N=21,578)^a
^aOdds ratios and 95% confidence intervals (error bars) reflect the increase in risk of exposure per one standard deviation increase in the ADHD polygenic risk score. Odds ratios for binary exposures (0/1) are shown without reference. Odds ratios for exposures with more than two levels are shown with reference. For exposure definitions, see Table S1 in the online supplement.

Genetic Confounding

Nineteen of the 24 evaluated risk factors were associated with an increased rate of ADHD after FDR correction (p<0.0001) (Table 2). Within each domain (birth-related, somatic, and psychosocial) the strongest associations were observed for low birth weight (<2.5 kg) (model 1, IRR=1.85, 95% CI=1.65, 2.08), epilepsy (model 1, IRR=2.38, 95% CI=2.06, 2.75), and low parental education (model 1, maternal IRR=3.47, 95% CI=3.24, 3.72; paternal IRR=3.68, 95% CI=3.42, 3.96). Maternal hypertensive disorders during pregnancy, maternal and paternal autoimmune disease, and atopic disease and type 1 diabetes in the index child were not statistically significantly associated with ADHD diagnosis. Including ADHD PRS (model 2) or parental psychiatric history (model 3) alone as a covariate in the models resulted in very minor changes of the observed associations. Adjusting for both ADHD PRS and parental psychiatric history (model 4) led to only minor attenuations of the IRRs (i.e., nonoverlapping confidence intervals for the minimally adjusted model 1 and the fully adjusted model 4) for maternal (model 4, IRR=2.96, 95% CI=2.75, 3.17) and paternal (model 4, IRR=3.15, 95% CI=2.92, 3.40) education at elementary school level or less, compared to an academic degree.

TABLE 2. Genetic confounding of ADHD risk factor associations with ADHD diagnosis in 13,697 individuals with ADHD and 21,290 population control individuals without ADHD from the iPSYCH case cohort^a

		Model 1 (Minimally Adjusted)			Model 2 (ADHD PRS)			Model 3 (Parental Psychiatric History)			Model 4 (Fully Adjusted)
Domain, Risk Factor, and Level	Exposed Cases (N)	IRR	95% CI	p_adj	IRR	95% CI	p_adj	IRR	95% CI	p_adj	IRR	95% CI	p_adj
Birth-related
Birth weight
<2,500 g	772	1.85	1.65, 2.08		1.91	1.69, 2.15		1.74	1.55, 1.96		1.79	1.58, 2.03
2,500–3,999 g	10,429	Ref			Ref			Ref			Ref
≥4,000 g	2,423	0.86	0.81, 0.92	<0.0001	0.87	0.82, 0.92	<0.0001	0.88	0.83, 0.93	<0.0001	0.88	0.82, 0.94	<0.0001
Small for gestational age
No	11,899	Ref			Ref			Ref			Ref
Yes	1,725	1.50	1.39, 1.61	<0.0001	1.46	1.35, 1.58	<0.0001	1.43	1.33, 1.55	<0.0001	1.39	1.29, 1.51	<0.0001
Apgar score at 5 minutes
10	12,376	Ref			Ref			Ref			Ref
<10	1,167	1.20	1.10, 1.30	<0.0001	1.20	1.09, 1.31	<0.0001	1.19	1.09, 1.30	<0.0001	1.19	1.09, 1.30	<0.002
Somatic
Maternal hypertensive disorder during pregnancy
No	13,504	Ref			Ref			Ref			Ref
Yes	193	1.10	0.91, 1.33	0.49	1.12	0.92, 1.37	0.87	1.10	0.90, 1.34	0.77	1.11	0.90, 1.36	0.89
Maternal infection during pregnancy
No	13,030	Ref			Ref			Ref			Ref
Yes	667	1.64	1.45, 1.85	<0.0001	1.59	1.40, 1.81	<0.0001	1.56	1.38, 1.77	<0.0001	1.53	1.34, 1.74	<0.0001
Maternal autoimmune disorder by child’s 5th birthday
No	13,276	Ref			Ref			Ref			Ref
Yes	421	1.08	0.94, 1.24	0.49	1.03	0.89, 1.19	0.82	1.02	0.89, 1.18	0.77	0.98	0.85, 1.14	0.89
Paternal autoimmune disorder by child’s 5th birthday
No	13,406	Ref			Ref			Ref			Ref
Yes	291	1.21	1.03, 1.43	0.11	1.23	1.04, 1.47	0.09	1.18	0.99, 1.40	0.30	1.18	0.99, 1.42	0.35
1 infection
No	7,528	Ref			Ref			Ref			Ref
Yes	6,169	1.56	1.49, 1.63	<0.0001	1.52	1.45, 1.60	<0.0001	1.52	1.45, 1.59	<0.0001	1.49	1.42, 1.57	<0.0001
≥5 infections
No	13,097	Ref			Ref			Ref			Ref
Yes	600	2.20	1.93, 2.52	<0.0001	2.15	1.87, 2.47	<0.0001	2.00	1.74, 2.30	<0.0001	1.94	1.68, 2.25	<0.0001
Asthma
No	11,302	Ref			Ref			Ref			Ref
Yes	2,395	1.46	1.37, 1.55	<0.0001	1.42	1.33, 1.52	<0.0001	1.42	1.33, 1.52	<0.0001	1.39	1.30, 1.49	<0.0001
Atopic disease
No	12,798	Ref			Ref			Ref			Ref
Yes	899	0.97	0.89, 1.06	0.49	0.97	0.88, 1.06	0.82	0.98	0.89, 1.07	0.77	0.98	0.89, 1.08	0.89
Type 1 diabetes
No	13,658	Ref			Ref			Ref			Ref
Yes	39	0.87	0.58, 1.30	0.49	0.95	0.63, 1.44	0.82	0.89	0.59, 1.33	0.77	0.97	0.64, 1.48	0.89
Epilepsy
No	13,195	Ref			Ref			Ref			Ref
Yes	502	2.38	2.06, 2.75	<0.0001	2.37	2.03, 2.77	<0.0001	2.29	1.97, 2.66	<0.0001	2.30	1.97, 2.70	<0.0001
Traumatic brain injury
No	12,134	Ref			Ref			Ref			Ref
Mild	1,272	1.94	1.78, 2.11		1.85	1.69, 2.03		1.88	1.72, 2.06		1.79	1.63, 1.97
Severe	291	1.94	1.63, 2.32	<0.0001	1.88	1.56, 2.25	<0.0001	1.84	1.53, 2.21	<0.0001	1.78	1.47, 2.15	<0.0001
Psychosocial
Maternal income at birth
1st quintile	3,952	2.59	2.40, 2.80		2.41	2.22, 2.60		2.42	2.24, 2.62		2.25	2.08, 2.44
2nd quintile	3,348	2.19	2.02, 2.37		2.04	1.88, 2.21		2.11	1.95, 2.29		1.98	1.82, 2.15
3rd quintile	2,742	1.81	1.67, 1.95		1.75	1.61, 1.90		1.78	1.64, 1.92		1.72	1.59, 1.87
4th quintile	2,115	1.39	1.28, 1.51		1.36	1.25, 1.48		1.38	1.27, 1.49		1.35	1.24, 1.47
5th quintile	1,540	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001
Paternal income at birth
1st quintile	4,255	2.90	2.69, 3.13		2.73	2.52, 2.95		2.65	2.45, 2.87		2.50	2.30, 2.71
2nd quintile	3,103	2.13	1.97, 2.31		2.02	1.86, 2.19		2.07	1.91, 2.24		1.97	1.81, 2.14
3rd quintile	2,526	1.70	1.57, 1.84		1.66	1.53, 1.80		1.69	1.56, 1.83		1.65	1.51, 1.79
4th quintile	2,184	1.48	1.36, 1.61		1.47	1.35, 1.60		1.46	1.35, 1.59		1.45	1.34, 1.58
5th quintile	1,504	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001
Maternal education at birth
Elementary	6,136	3.47	3.24, 3.72		3.15	2.94, 3.38		3.24	3.02, 3.47		2.96	2.75, 3.17
High school	5,366	1.71	1.60, 1.82		1.65	1.55, 1.76		1.69	1.59, 1.81		1.64	1.53, 1.75
Academic degree	1,989	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001
Paternal education at birth
Elementary	5,274	3.68	3.42, 3.96		3.34	3.09, 3.60		3.47	3.22, 3.74		3.15	2.92, 3.40
High school	6,209	1.91	1.78, 2.04		1.82	1.70, 1.95		1.87	1.75, 2.01		1.79	1.67, 1.92
Academic degree	1,543	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001	Ref		<0.0001
Maternal employment at birth
Yes	13,550	Ref			Ref			Ref			Ref
No	139	2.42	1.82, 3.23	<0.0001	2.30	1.70, 3.10	<0.0001	1.59	1.16, 2.18	<0.03	1.55	1.12, 2.14	0.05
Paternal employment at birth
Yes	13,352	Ref			Ref			Ref			Ref
No	170	2.75	2.08, 3.64	<0.0001	2.85	2.14, 3.79	<0.0001	2.07	1.53, 2.79	<0.0001	2.19	1.62, 2.95	<0.0001
Living in a single-parent household first 5 years of life
No	8,364	Ref			Ref			Ref			Ref
1 year	1,275	2.12	1.94, 2.31		2.04	1.86, 2.23		2.00	1.82, 2.19		1.93	1.76, 2.12
2 years	1,147	2.76	2.50, 3.05		2.54	2.29, 2.82		2.56	2.31, 2.84		2.35	2.11, 2.62
3 years	1,012	2.69	2.43, 2.99		2.45	2.20, 2.74		2.45	2.20, 2.73		2.25	2.01, 2.51
4 years	919	3.01	2.68, 3.37		2.76	2.45, 3.12		2.75	2.45, 3.09		2.55	2.26, 2.88
5 years	980	3.01	2.69, 3.37	<0.0001	2.73	2.43, 3.08	<0.0001	2.71	2.41, 3.04	<0.0001	2.47	2.19, 2.79	<0.0001
Maternal age at child’s birth (years)
<20	640	2.65	2.30, 3.05		2.40	2.07, 2.79		2.44	2.10, 2.82		2.23	1.92, 2.60
20–24	3,628	1.69	1.59, 1.80		1.59	1.49, 1.70		1.64	1.54, 1.74		1.54	1.44, 1.65
25–29	5,212	Ref			Ref			Ref			Ref
30–34	3,075	0.77	0.73, 0.82		0.78	0.73, 0.82		0.77	0.73, 0.82		0.78	0.74, 0.83
≥35	1,142	0.75	0.69, 0.81	<0.0001	0.76	0.70, 0.83	<0.0001	0.73	0.67, 0.79	<0.0001	0.74	0.68, 0.81	<0.0001
Paternal age at child’s birth (years)
<20	170	2.26	1.73, 2.95		2.19	1.66, 2.90		2.13	1.62, 2.79		2.07	1.54, 2.78
20–24	2,148	1.78	1.65, 1.93		1.66	1.53, 1.80		1.72	1.58, 1.86		1.61	1.48, 1.75
25–29	4,545	Ref			Ref			Ref			Ref
30–34	3,940	0.78	0.73, 0.82		0.79	0.74, 0.84		0.78	0.73, 0.83		0.79	0.74, 0.84
35–39	1,928	0.76	0.71, 0.82		0.77	0.72, 0.83		0.76	0.71, 0.82		0.77	0.72, 0.83
≥40	874	0.83	0.75, 0.91	<0.0001	0.85	0.77, 0.94	<0.0001	0.79	0.72, 0.88	<0.0001	0.82	0.74, 0.91	<0.0001
Parental history of psychiatric disorder by child’s 5th birthday
No	11,853	Ref			Ref			—			Ref
One parent	1,676	2.34	2.15, 2.55		2.22	2.03, 2.43		—			2.22	2.03, 2.43
Both parents	168	3.29	2.46, 4.41	<0.0001	3.03	2.24, 4.11	<0.0001	—			3.03	2.24, 4.11	<0.0001

^aIncidence rate ratios (IRRs) and 95% confidence intervals are presented, adjusted for sex, birth year, and first four principal components (model 1). To evaluate whether associations were confounded by polygenic liability for ADHD, we further adjusted the models for ADHD polygenic risk score (PRS) (model 2), parental psychiatric history (model 3), and both (model 4). p_adj represents false discovery rate (FDR) corrected p values derived using the Benjamini-Hochberg method. Significant associations (p<0.05) after FDR correction are in boldface. NA=not applicable; Ref=reference category.

TABLE 2. Genetic confounding of ADHD risk factor associations with ADHD diagnosis in 13,697 individuals with ADHD and 21,290 population control individuals without ADHD from the iPSYCH case cohort^a

Enlarge table

Gene-Environment Interaction

Results from the Cox models including interaction terms between the continuous ADHD PRS and each risk factor on the risk of ADHD are presented in Table S3 in the online supplement. We found only tentative support for interactions between ADHD PRS and four of the 24 investigated risk factors, namely, maternal autoimmune disease, TBI, paternal unemployment, and lower paternal age at birth of index child (Figure 2), suggesting that higher ADHD PRS may have a somewhat stronger effect on the risk of being diagnosed with ADHD in individuals exposed to either of these factors. However, only the interaction with maternal autoimmune disease survived FDR correction (p<0.01) (see Table S3 in the online supplement), showing a lower effect of ADHD PRS on risk of ADHD diagnosis in children of mothers with an autoimmune disorder (IRR=1.19, 95% CI=1.05, 1.35) compared to mothers without an autoimmune disorder (IRR=1.55, 95% CI=1.51, 1.59).

FIGURE 2. Gene-environment interactions showing the differential linear effect of ADHD polygenic risk score across levels of risk factors on ADHD diagnosis^a
^aIncidence rate ratios and 95% confidence intervals (shaded areas) reflect the differential linear effect per one standard deviation increase in ADHD polygenic risk score (PRS) across levels of each risk factor, adjusted for sex, birth year, and the first four principal components. Results are shown only for risk factors showing tentative evidence of interaction with ADHD PRS (i.e., p<0.05 before FDR correction) in interaction analyses. Full results are presented in Table S3 in the online supplement. For exposure definitions, see Table S1 in the online supplement.

Sensitivity Analyses

Excluding the 288 individuals with ADHD who were randomly selected into the iPSYCH2012 subcohort had minimal effect on estimated odds ratios (see Table S4 in the online supplement), suggesting that the observed ADHD PRS associations in the general population were not primarily driven by individuals diagnosed with ADHD. Sensitivity analyses investigating the impact of exclusions due to relatedness and non-European ancestry showed no evidence of differences in the distribution of ADHD diagnosis between included (N=34,987) and excluded (N=7,109) individuals (odds ratio=0.99, 95% CI=0.93, 1.04). Repeating the analyses without exclusions (a total N of 42,096, including 16,455 individuals with ADHD) gave results nearly identical to those of the main analyses (see Tables S5–S7 in the online supplement).

Discussion

In this study, we used ADHD PRS to investigate gene-environment interplay for a broad range of ADHD-associated risk factors. We found that ADHD PRS was associated with exposure to 12 of the 24 evaluated risk factors in the general population, including certain birth-related and somatic risk factors, and broadly with psychosocial risk factors, albeit with small effect sizes. In the full case cohort, 19 of the 24 evaluated risk factors were associated with an increased risk of being diagnosed with ADHD, yet ADHD PRS and parental psychiatric history had minimal confounding effects on these associations. Finally, we found limited support for gene-environment interactions between ADHD PRS and the 24 risk factors.

Our results replicate previous findings in iPSYCH2012 (16) of ADHD PRS being associated with parental unemployment, lower education, lower income, and psychiatric disorders. We add to this by showing that higher ADHD PRS is also associated with growing up in a single-parent household and being born to young parents. Similar ADHD genetic correlations with lower educational attainment and younger age at child’s birth have previously been reported in the UK Biobank (Table 1). Further, ADHD PRS was in the present study associated with being small for gestational age, TBI, and infections in childhood. Together with previous research (11, 16), our results suggest that gene-environment correlations are pervasive in ADHD, particularly for psychosocial adversity. Such findings challenge hypothesized causal pathways for many ADHD risk factors and highlight that these ADHD risk factor associations may be better explained by partly shared genetic mechanisms. Our findings also indicate that ADHD PRS, and the GWAS that informs its derivation, captures not only ADHD risk alleles but likely also genetic variants influencing heritable ADHD risk correlates (e.g., birth weight, socioeconomic indicators) (35).

Beyond this, we investigated confounding by ADHD genetic liability, using both parental psychiatric history and ADHD PRS as proxies. We present a targeted review of the literature linking ADHD to the putative ADHD risk factors included in this study at the phenotypic, familial, and common genetic variant level. We replicated a majority (19/24) of these associations in a large case-cohort sample including 13,697 individuals with ADHD. Despite evidence that ADHD PRS was associated with exposure to half of the risk factors in the general population, adjusting for ADHD PRS, parental psychiatric history, or both led to only minor attenuations of the phenotypic association between these risk factors and ADHD diagnosis. This suggests that ADHD PRS and parental psychiatric history act largely independently of the other investigated risk factors to influence the development of ADHD. It further highlights that adjusting for ADHD PRS and parental psychiatric history is unlikely to fully capture genetic or familial confounding in epidemiological studies. For the former, this is not surprising, as current ADHD PRS captures only a fraction of genetic effects involved in ADHD and explains at most ∼5% of variance in ADHD diagnosis and ∼0.7%–3.3% of variance in ADHD traits, according to a recent meta-analysis (3, 11). For the latter, we were not able to investigate confounding by parental ADHD specifically. However, this is unlikely to fully explain the weak evidence for confounding by parental psychiatric history, as ADHD shows strong familial and genetic links to many other psychiatric disorders (4, 7). Together with previous reports, our findings suggest that researchers aiming to identify causal risk factors for ADHD should consider using other genomic and/or quasi-experimental family-based methods to address genetic and familial confounding (5, 36, 37).

We found only tentative support for gene-environment interactions between ADHD PRS and TBI, parental employment and age at child’s birth, and maternal autoimmune disorder, with only the latter surviving correction for multiple testing. Utilizing PRS to detect gene-environment interaction is challenging for several reasons, including the low power of current ADHD PRSs and the fact that disorder-predictive PRSs do not necessarily capture genetic variants linked to a differential susceptibility to risk exposures (9). Nevertheless, if greater analytic power can be achieved through larger studies combining genetic and environmental information, investigating gene-environment interplay (e.g., correlations, confounding, and interactions) using robust methodological approaches may help elucidate heterogeneous pathways to ADHD. We highlight three examples based on our findings here: First, epilepsy was associated with a 2.3-fold increased risk of ADHD, but with no evidence for associations or interactions between epilepsy and ADHD PRS. GWAS analyses thus far also do not support a genetic correlation between epilepsy and ADHD at the level of common variants (38), whereas one large sibling study reported a moderate genetic correlation (∼0.20) between ADHD and epilepsy (see Table 1) (39). This suggests that epilepsy may be more likely to act as an independent pathway to ADHD (e.g., through neuronal insults), or that the disorders associate due to common environmental and/or rare genetic risk factors. Second, mild TBI has been linked to ADHD, yet it remains unclear whether the association is causal, as untreated ADHD is itself associated with an increased risk of accidents and injuries (40). In support of this, we found higher ADHD PRS to be associated with higher risk of TBI. We also found tentative evidence that the effect of ADHD PRS on ADHD risk may be more pronounced in those with no or severe TBI. One previous study has reported ADHD PRS to be associated with higher ADHD traits in youths without TBI, but not in those with mild TBI (41). Together, these data may suggest that individuals with mild TBI may require a lower genetic burden to be diagnosed with ADHD, although further studies are needed to test this hypothesis. Finally, our results add to a growing body of evidence linking ADHD to immune-related diseases (Table 1). ADHD PRS was associated with maternal autoimmune disease and early-life infections. Moreover, maternal autoimmune disease showed evidence of interaction with ADHD PRS, potentially suggesting that autoimmune/inflammatory diseases affect ADHD risk both through a shared genetic vulnerability and through neuroinflammatory pathways (42).

Our findings must be interpreted in light of several limitations. First, although this study represents the largest and most comprehensive investigation of gene-environment interplay using ADHD PRSs to date, the study may still have been underpowered to detect interactions of modest effect sizes, and we were limited to studying risk factors recorded with sufficient coverage in the Danish national registers. Second, our sample included clinically treated individuals with ADHD according to an ICD-10 diagnostic code of F90.0, and our findings may not extend to the broader ADHD population. Third, to avoid associations arising as a result of population stratification, our analyses were restricted to individuals of European ancestry, and our results may not generalize to populations of other ancestry and to individuals in the iPSYCH cohort of non-Danish backgrounds, for whom risk factor distributions may vary. Nevertheless, the impact of these exclusions is likely modest, as analyses with and without genotype exclusions showed virtually identical results.

Conclusions

ADHD polygenic liability is linked to modest risk increases of both ADHD diagnosis and exposure to ADHD risk factors in the general population. Our findings underscore the importance of accounting for genetic confounding in order to identify causal risk factors, and highlight that ADHD PRS and parental psychiatric history are unlikely to fully capture such confounding. Finally, we found tentative support for gene-environment interactions between ADHD PRS and certain risk factors. Yet, observed ADHD PRS effects were small, meaning that our results are still far from translation into clinical and prevention practices. As the size and number of genotyped samples with information on both ADHD and associated risk factors increase, this may change, and future research using methods including genome-wide-by-environment interaction studies or plasticity PRS to further understand the mechanisms underlying observed gene-environment associations and interactions in ADHD will be important.

iPSYCH–Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen and Aarhus, Denmark (all authors); National Center for Register-Based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark (Brikell, Wimberley, Albiñana, Vilhjálmsson, Agerbo, Mortensen, Petersen, Dalsgaard); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (Brikell); Center for Integrated Register-Based Research–CIRRAU, Aarhus University, Aarhus, Denmark (Wimberley, Agerbo, Mortensen, Dalsgaard); Bioinformatics Research Center, Aarhus University, Aarhus, Denmark (Vilhjálmsson); Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark (Børglum, Demontis); Center for Genomics and Personalized Medicine, Central Region Denmark and Aarhus University, Aarhus, Denmark (Børglum, Demontis); Neurogenomics Division, Translational Genomics Research Institute, Phoenix (Schork); Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark (Schork, LaBianca, Werge, Nordentoft); Department of Clinical Medicine, University of Copenhagen, Copenhagen (Werge); Center for GeoGenetics, GLOBE Institute, University of Copenhagen, Copenhagen (Werge); Department for Congenital Disorders, Statens Serum Institut, Copenhagen (Hougaard); Copenhagen Research Center for Mental Health, Mental Health Services–CORE in the Capital Region of Denmark (Nordentoft); Psychosis Research Unit, Aarhus University Hospital–Psychiatry, Denmark (Mors).

Send correspondence to Dr. Brikell ([email protected]).

Presented in part at the World Congress of Psychiatry Genetics 2021 Virtual Congress, October 11–15, 2021.

Drs. Petersen and Dalsgaard contributed equally to this work.

The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724, and R248-2017-2003), the European Union’s FP7 Programme (grant 602805, “Aggressotype”), the European Union’s Horizon 2020 Programme (grant 667302, “CoCA”), NIMH (grant 1U01MH109514-01 to Dr. Børglum), and the universities and university hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Center for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to Dr. Børglum). Dr. Dalsgaard’s research is further supported by Helsefonden (grant 19-8-0260) and the European Union’s Horizon 2020 Programme (grant 847879). Dr. LaBianca acknowledges support from the Research Fund of the Mental Health Services–Capital Region of Denmark R4A92.

Dr. Vilhjálmsson has served on Allelica’s scientific advisory board. Dr. Børglum has received research grants from the Lundbeck Foundation. Dr. Demontis has received speaking fees from Takeda. The other authors report no financial relationships with commercial interests.

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Volume 180
Issue 1

January 01, 2023
Pages 73-88

Metrics

Keywords

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History

Received 7 November 2021

Revised 18 February 2022

Accepted 2 May 2022

Published online 7 September 2022

Published in print 1 January 2023

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Interplay of ADHD Polygenic Liability With Birth-Related, Somatic, and Psychosocial Factors in ADHD: A Nationwide Study

Abstract

Objective: