N = 74) diagnosed according to DSM-5 clinical consensus and standardized psychiatric instruments. Detailed temporal profiles of exposure to 10 metals over the prenatal and early childhood periods were measured using tooth biomarkers. We used recurrence quantification analysis (RQA) to characterize properties of cyclical metabolic patterns of these metals. Regularity (determinism) and complexity (entropy) of elemental cycles was consistently reduced in ADHD for cobalt, lead, and vanadium . Further, we found elemental pathways and dynamical features specific to ADHD vs ASD, and unique characteristics associated with ADHD/ASD combined presentation. Dysregulation of cyclical processes in elemental metabolism during prenatal and early postnatal development not only encompasses pathways shared by ADHD and ASD, but also comprise features specific to either condition.Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions of overlapping etiologies and phenotypes. For ASD, we recently reported altered elemental metabolic patterns in the form of short and irregular zinc and copper cycles. Here, we extend the application of these biomarkers of prenatal and early postnatal elemental metabolism to distinguish between individuals diagnosed with ADHD and/or ASD and neurotypical controls. We recruited twins discordant for ADHD, ASD and other neurodevelopmental diagnoses from national twin studies in Sweden ( Heritability estimates average 75% with a polygenic liability comprising both common and rare variants, suggesting that environmental exposures are also at play5. However, the role of specific environmental exposures and their metabolism in the etiology of ADHD and co-occurring neurodevelopmental conditions, such as autism spectrum disorder (ASD)6, remains unclear7. Few studies have investigated the association between elemental exposures and ADHD . In addition, those studies have exclusively relied on cross-sectional measures of exposure, primarily blood metal concentrations. They found differences between ADHD and non-ADHD controls for lead, mercury, manganese, iron, zinc, and copper18. Unfortunately, this research does not include information on the timing of exposures relative to their effects on neurodevelopment, or permit contrasts between different neurodevelopmental disorders. For example, lead is the most widely investigated toxic exposure linked to ADHD, but study findings derived from a single time-point measurement do not resolve temporal variations in exposure during critical developmental windows3. Using novel tooth matrix biomarkers, we recently demonstrated that essential and toxic elements are differentially regulated in ASD19. This was achieved by focusing analyses on time variant cycles in elemental metabolism, which would be missed in analyses relying solely on single time-point concentrations. The combination of multiple cohorts in that study, each drawn from different sites with varying enrollment protocols, nonetheless did not allow a fine scale differentiation of ASD and ADHD phenotypes, as were characterized in the RATSS cohort.Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition affecting an estimated 5.3% of children and 2.5% of adults worldwide6. We utilize recurrence quantification analysis (RQA)22, a non-linear method of characterizing signal dynamics, particularly the frequency, duration and complexity of cyclical processes in longitudinal time-series measures of elemental metabolism. In a well-characterized population-derived sample of twins in Sweden24, which includes monozygotic twins discordant for ADHD, we apply RQA to fine scale temporal profiles of metal uptake during the prenatal and early postnatal period made possible by recently developed tooth matrix biomarkers. The twin design allows us to account for underlying genetic factors, especially in monozygotic twins.Here, we test the hypothesis that cycles of essential and toxic elements are dysregulated during prenatal and early postnatal development in ADHD and that these profiles distinguish individuals with ADHD from individuals with ASD as well as ADHD/ASD combined presentation23. Participant recruitment and tooth collection within RATSS have been previously described24. Briefly, we collected and analyzed teeth from 74 participants: 30 complete twin pairs, 1 triplet group, and 11 individuals from twin pairs whose sibling did not donate a tooth. For our primary analysis, we measured metals in 13 cases of ADHD, 8 ASD, 12 ADHD/ASD combined, and 41 neurotypical controls (see Table Study samples were pulled from the Roots of Autism and ADHD Twin Study in Sweden (RATSS)25), Diagnostic Interview for ADHD in Adults (DIVA 2.026), Autism Diagnostic Observation Schedule 2nd Edition (ADOS-227), and Autism Diagnostic Interview- Revised (ADI-R28). IQ testing was performed with the following measures depending on participant’s age and verbal capacities: Wechsler Intelligence Scale for Children-IV (WISC-IV29), Wechsler Adult Intelligence Scale-IV (WAIS-IV30), and the Leiter International Performance Scale-Revised30).Participants were diagnosed using a consensus process with several experienced clinicians, and according to DSM-5 criteria, endorsed by information from the following standardized instruments: Kiddie Schedule for Affective Disorders and Schizophrenia  laser ablation unit equipped with a 193 nm ArF excimer laser was connected to an Agilent Technologies 8800 triple-quad ICP-MS . Ablation was carried out under a helium atmosphere which is mixed with argon via Y-piece before introduction to the ICP-MS. Instrument sensitivity , oxide formation  and fractionation  were monitored daily using NIST SRM 612 (trace elements in glass). Data were collected as metal to calcium ratios  to control for any variations in the mineral content within a tooth and between samples.Our approach to laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) tooth metals analysis and assigning developmental times has been detailed elsewhere33, we used non-linear methods—recurrence quantification analysis (RQA) and cross-recurrence quantification analysis (CRQA)—to characterize dynamic, cyclical properties of environmental exposures and their metabolism. Briefly, time-series data sampled from teeth were used to construct recurrence plots of single elements or cross-recurrence plots of two elements. We investigated the temporal features generated by recurrence plots, focusing on determinism, mean diagonal length, entropy, and recurrence time. These features measure diagonal line structures, or cyclical events, in recurrence plots. Determinism measures the ratio of diagonal lines  to vertical and/or horizontal lines  effectively defining the periodicity of an elemental time-series. Mean diagonal length measures the mean duration of diagonal lines or cycles, and recurrence time measures the interval between these cycles. Entropy captures the complexity of cyclic activity by measuring the variability in cycle lengths. These measures were similarly generated for CRQA, but captured the temporal relationship between two elemental signals. These methods are summarized in Fig. 34 in Matlab v2016b (Mathworks).As previously described in Curtin et al.Prior to applying inferential statistical testing, all variables were evaluated to confirm assumptions of normality in value distributions. Values of ±2 SDs were excluded from some variables to meet assumptions of normality. For analyses of recurrence features , linear mixed models were used to test main dichotomous effects of the presence/absence of an ADHD-diagnosis on these features. Twin pairs were modeled as random variables, while also controlling for sex, gestational days, birth weight, and comorbid ASD status. Additional covariates, such as zygosity and IQ, were initially included in model construction but were ultimately excluded as these yielded no improvement in model fit , had no significant effects, or caused no changes in the significance of other covariates included in modeling. False discovery rate (FDR) adjustments, stratified by metal pathway (or cross recurrence), were applied to raw p-values relating to ADHD effects; unless otherwise noted, all reported p-values reflect FDR adjustment. Adjusted p-values less than or equal to 0.05 are reported as significant.factoextra package.We additionally implemented dimensionality-reduction techniques to investigate the utility of these methods in characterizing neurotypical, ADHD, ASD, and ADHD/ASD comorbid phenotypes. We first applied principal component analysis (PCA), an unsupervised dimensionality-reduction technique, to investigate relationships among features derived from RQA, and evaluate the efficacy of derived components. Measures derived from RQA of single elements and cross-recurrence (CRQA) analyses, including Determinism, Entropy, Mean Diagonal Length, and Recurrence Time, were centered and scaled for PCA. Following inspection of a scree plot, 15 components were retained for subsequent analyses, as these explained >80% of total variance and all estimated eigenvalues were greater than 1. Linear models were used to evaluate if derived components differed between subjects diagnosed as neurotypical, ASD, ADHD, or comorbid with ASD and ADHD. PCA was implemented in R v 3.5.2 with the mass package.We next complemented the unsupervised analysis described above with a linear discriminant analysis (LDA), a supervised dimensionality-reduction technique, to evaluate the separation of ADHD, ASD, comorbid ASD/ADHD and neurotypical behavioral phenotypes on the basis of elemental metabolic features extracted via RQA. This analysis simultaneously evaluated all 60 features derived from eight elemental pathways and seven cross-recurrences to separate behavioral diagnoses along a lower dimensional space. Subjects with missing values for any of these features were excluded from this analysis. For each discriminant axis calculated, the correlation between raw RQA features and discriminant scores was calculated to provide a standardized measure of variable loading/importance as it related to a given axis. Analyses were implemented in R v 3.5.2 with the determinsim; these effects are summarized per-metal in Supplemental Table P = 0.017), lead , and vanadium ; and, there was a tendency for copper to be reduced . These results indicate that in ADHD cases there is instability in the cyclic metabolic activity of these elements, relative to what occurs in typically developing twins. We found a significant effect of birth weight in zinc , but no other significant effects relating to determinism across metals in our covariate adjustments .We first analyzed the cyclical processes of individual elemental profiles in ADHD cases and compared those to neurotypical controls. We found that ADHD cases consistently showed a decrease in overall rhythmicity of metal levels over time, which is measured by P = 0.016) and vanadium , compared to neurotypical controls , lead , and vanadium  in ADHD cases , but none of the other covariates included in these models had significant effects across elements. Additionally, we found no significant differences in the intervals between cyclical processes  between cases and controls.We also saw significant reduction in the duration and complexity of some elemental cycles. In ADHD cases, the duration of elemental cycles, measured by mean diagonal length, was reduced in lead  , and tended to be reduced in zinc-tin cycles  and zinc-calcium cycles    was the only element-pair to show differences in recurrence time, reflecting the interval durations between cyclic periods  and linear discriminant analysis (LDA) to leverage both unsupervised and supervised dimensionality-reduction techniques to achieve this goal.We have previously reported how elemental regulation in ASD differs from typical developmentP = 0.04) and comorbid cases , as shown in Fig. th principal component, which was significant elevated in ADHD participants , but was depressed in subjects comorbid for ASD/ADHD . In principal components 4 and 8, we found effects associated with ASD diagnosis, with significantly reduced scores on principal component 4 , and elevated scores on component 8 for ASD  and comorbid cases . Dysregulation along principal component 6 was specific to comorbid ASD/ADHD cases, with significant reductions relative to neurotypical controls . Supplementary Fig. Our initial analysis with PCA identified 15 components with eigenvalues exceeding 1, accounting for 82.79% of total variance across RQA/CRQA features. Among this subset of components, we compared component scores among participants, and identified 5 axes relating to neurobehavioral phenotypes, as shown in Fig. We complemented this approach with a linear discriminant analysis (LDA) to visualize separation of these classes across a low-dimensional feature space. This projection, shown in Fig. Our results show that cyclical processes involved in the metabolism of essential elements and toxic metals during fetal and early postnatal development differ significantly in ADHD and neurotypical development. We used tooth matrix biomarkers to measure detailed pre- and postnatal temporal profiles of lead, cobalt, zinc, vanadium, and other elements, and tested the hypothesis that alterations of metal metabolism cycles are associated with ADHD. Overall, we found that the stability, duration, and complexity of cyclical processes were reduced in ADHD cases compared to controls. These findings jointly suggest that the metabolism of essential and toxic elements is affected in ADHD. Critically, we were able to identify distinct cyclical properties specific to ADHD alone, as well as shared elemental pathways common to both ADHD and ASD.7, specifically, rather than neurodevelopmental conditions like ASD in general. Interestingly, we were not only able to identify distinct elemental signatures for those with either ADHD or ASD, but also in combined behavioral presentations. This finding at the molecular level is in broad agreement with clinical features of these conditions where significant overlap is observed in symptomatology38.In ADHD, we saw decreased cycle stability (determinism), duration , and complexity (entropy) in cobalt, copper, lead, zinc, and vanadium exposure profiles. Previous studies have shown that increased levels of lead are correlated with the severity of ADHD symptoms41. A handful of studies describe the possible roles for zinc in ADHD. Zinc is an essential element involved in dopamine pathways that are assumed to be involved in ADHD etiology. Zinc deficiency has been hypothesized to cause dysfunction of the dopamine transporter42, impaired dopaminergic transmission41 and modulation of melatonin, a regulator of dopamine function and potential pathway of amphetamine treatment of ADHD43 or ADHD-related sleep problems44. Several enzymes believed to have an essential role in the neurophysiology of ADHD are dependent on copper45. A dysregulation of zinc or copper may also increase susceptibility to oxidative damage of tissues or oxidative stress of the brain by damaging antioxidant defenses, a possible pathophysiology of ADHD47. Excess copper may promote the oxidation of dopamine and its metabolite salsolinal, leading to the degeneration of dopaminergic neurons46. Lead may contribute to dopaminergic dysfunction48 and disrupt the blood-brain barrier49.Our findings are also bolstered by human and animal studies where exposure to environmental chemicals altered pathways relevant to the symptoms observed in ADHDOur study is limited by a small sample size, although it was sufficient to distinguish significant differences in cyclical metal properties between ADHD, ASD, ADHD/ASD combined, and neurotypical development. As a consequence of this, it was not possible to implement robust cross-validation procedures, and these results should therefore not be interpreted as a generalizable algorithm for predictive phenotyping or diagnostic assessment. Nonetheless, the utility of both supervised and unsupervised methods in separating neurodevelopmental phenotypes, and the general overlap in features that discriminate ASD & ADHD, suggest that elemental dynamics, particularly relating to zinc periodicity, may provide a powerful signature for future studies to leverage for predictive phenotyping. The differentiation of behavioral phenotypes including ADHD, ASD and their combined presentation from elemental metabolic features further emphasizes the significance of these processes in neurodevelopment, and the potential future applications of these methods in a broader sample. The use of twins in our study allowed us to control for underlying genetic factors and to increase our power to detect differences between diagnostic groups. However, further work is needed in non-twin participants to confirm and extend our results. Organic environmental chemicals may also play a role in ADHD, a topic not addressed in our study.Studies of environmental exposures and ADHD have primarily focused on the magnitude of exposures as determined by concentrations in blood, urine or other biological matrices. By analyzing dynamical processes in elemental exposure profiles, we have uncovered differences in elemental metabolism that would not be apparent in measures of exposure intensity alone. Furthermore, our findings have implications for early detection of ADHD, ASD, and ADHD/ASD combined cases because the signatures we have observed are present prenatally and in early postnatal development. Pathways we have identified, once validated, may also offer targets for prevention of toxic exposures and early intervention in the form of modification of elemental metabolism towards a healthy molecular phenotype.Supplemental Material


Studies indicate that children with Attention Deficit Hyperactivity Disorder (ADHD) have deficits in social and emotional functions. It can be hypothesized that these children have some deficits in early stages of facial emotion discrimination. Based on this hypothesis, the present study investigated neural correlates of early visual processing during emotional face recognition in this group compared with typically developing children using the Event-Related Potentials (ERPs).Nineteen boys between the ages of 7 and 11 years diagnosed with ADHD (Combined type) based on DSM-IV-TR classification were compared with 19 typically developing children matched on age and gender. The participants performed an emotional face recognition task while their brain activities were recorded using the event-related potentials procedure.A significant reduction in the Early Posterior Negativity (EPN) for happy and angry faces has been revealed in ADHD children compared to normal ones (P<0.05).The present study supports the notion that individuals with ADHD have some impairments in early stage of emotion processing which can leading to their misinterpretation of emotion in faces. Facial expression recognition seems to be different in children with attention-deficit/hyperactivity disorder compared to normal children.Early posterior negativity can be utilized as an objective index to measure facial expression in children with versus without attention-deficit/hyperactivity disorder.Children with Attention-Deficit/Hyperactivity Disorder (ADHD) have some impairment in emotional relationship, possibly due to deficits in emotional processing. The present study investigated neural correlates of emotional face processing in this group compared with typically developing children using the Event-Related Potentials (ERP).A total number of 19 children diagnosed with ADHD were compared with 19 typically developing children matched with their age, gender and IQ. The participants performed an emotional face recognition while their brain activities were recorded using an ERP procedure. The pictures comprised four expressions . We defined four buttons for each facial expressions on a joy stick. All participants were invited to the laboratory of ERP recording. During the ERP session, the participants were seated in a comfortable chair in a dimly lit room. All children were instructed to press on the button for each expression when they recognized the target stimuli.Current results showed reduced brain function for happy and angry recognition in ADHD children compared to normal ones. We could interpret that ADHD children have some impairments in early facial emotion processing and allocation resources of attention to emotion which can lead to their misinterpretation of emotion faces and inappropriate reaction to them in their relationship which is of special importance in understanding their cognitive-processing problems. In addition, this finding might be useful in future planning of ADHD treatments.1.Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder characterized by inattentiveness and hyperactivity/impulsivity. This disorder affects 5%–12% of children and adolescents . These cRecognition of emotional expressions, as the main component of nonverbal communication, is important to regulate adaptive behaviors in social interaction . StudiesImpaired recognition of emotions can be caused by problems in orienting to emotional stimuli. An eye tracking study ERP is a research tool with high temporal resolution to investigate cognitive functions like stages of emotional processing . This prP100 component is bilaterally localized in occipital areas and fusiform gyrus. It is primarily involved in visual attention and initial sensory encoding . Batty aThe EPN is an indicator of processing emotional valence and arousal of visual images and faces . EPN refSome studies have found larger EPNs for fear and anger compared to happiness . Wieser According to We aimed to evaluate the early stages of facial emotion processing in children with ADHD, and the EPN is a useful ERP component for this purpose. Literature reporting the neural basis of emotional dysfunction in children with ADHD is scarce. To our knowledge and according to a review article by 2.2.1.Nineteen boys aged between 7 and 11 (Mean±SD age: 9.21±1.13 y) years and diagnosed with ADHD were compared with 19 (Mean±SD age: 9.73±1.04 y) typically developing boys, matched on age, gender, and years of education. Typically developing children were recruited from elementary schools in Tehran, Iran. Children with ADHD (combined type) were selected from drug-naive patients referred to a child and adolescent psychiatric clinic. Diagnosis was made by a child and adolescent psychiatrist, based on DSM-IV-TR  criteria.Conners’ Parent Rating Scale-Revised (short form) was administered on both groups to confirm the diagnosis of ADHD and determine severity of symptoms. The subjects with T-scores of CPRS-subscales above 65 were excluded from the normal group. Members of both groups were right-handed and had normal visual acuity. In addition, Intelligence Quotient (IQ) of all participants were evaluated according to the Wechsler Intelligence Scale for Children-Revised (WISC-R) IQ test  .2.2.We selected 6 faces  in jpg format, from Cohn-Kanade AU-Coded Facial Expression Database. Each face had 4 facial expressions; happy, angry, sad, and neutral. Luminance and contrast of all images were equalized across stimuli using the Photoshop software. The photos were in black and white colors and positioned within rectangular frames (261×365 pixel array). The pictures were viewed for 2000 ms, followed by a white fixation point on the light gray background (1024×768 pixels) for 1400 ms with ±100 ms time randomization.The stimuli were presented on the middle of the screen. The task was designed using the Eevoke software. The task included 1 practice and 5 experimental blocks. Each block comprised 48 trials; 6 faces with 4 expressions which were repeated 2 times that began with presenting a starting picture for 200 ms. Hence, there were 60 random repeats of each expression . We provAll participants were invited to the laboratory of ERP recording before taking any medications. A consent form was obtained from the subjects’ parents. During the ERP session, participants were seated on a comfortable chair in a dimly lit room, at 60 cm distance from a 17-inch LG computer screen. The participants were asked to always look at the center of the screen without any eye movements, if possible, and to only blink during the intervals. The subjects were monitored by a camera from another room during task performance to ensure their attendance to the stimuli. All children were instructed to press on the appropriate button once recognizing each facial expression.Each block begins with the presentation of a starting picture for 200 ms, followed by the trial which begins with the presentation of a target stimulus (2000 ms) of the 4 stimuli of facial expressions , followed by a fixation cross (1400±100 ms). Participants should press a key on joystick during each trial (stimuli presentation until the end of fixation). Each block comprised 48 trials.2.3.Electroencephalography (EEG) was recorded from 64 Ag-AgCl electrodes mounted in an electrode cap  using the extended 10–20 system. ASA 4.7.1 software was used for data acquisition. Electrode impedances were maintained below 10 kΩ. The sampling rate was 512 Hz. EEG was recorded online with a 50-Hz notch filter. EEG data were analyzed offline using MATLAB R2013a software. Raw data were filtered with a band-pass filter of 0.1 to 80 Hz and referenced to the mastoids average. The eye movement artifacts were canceled using the Independent Component Analysis (ICA). In addition, the remaining artifacts with deflection amplitudes of ±100 μV were eliminated (primarily through automatic artifact reduction). Artifact-free EEG recordings were then segmented into epochs ranging from 200 ms prestimulus to 800 ms poststimulus. Each channel baseline epoch was corrected by the prestimulus voltage subtraction.We investigated early emotion processing based on the EPN component. This component is more negative for emotional stimuli compared to neutral ones . Conside2.4.Repeated measures Analysis of Variance (ANOVA) was used to analyze group differences, comprising the following core factors: hemisphere (left and right), and facial expression  as the within-subjects variable, and the groups (patients and controls), as the between-subjects variable. Greenhouse-Geisser Correction was applied to the degrees of freedom. P<0.05 was considered as statistically significant. The Paired sample t test was used to analyze significant effects including emotional versus neutral faces. We followed-up the significant effects by a separate repeated measures ANOVA for each group . Indepen3.3.1.Characteristics of the ADHD and typically developing groups are summarized in 3.2.2.1, 75.72=6.25, P=0.003) using the repeated measures ANOVA. The interaction of facial expression and group was also significant in terms of EPN amplitude . There were no significant main effects or interaction with regard to other factors including hemisphere and facial expression, among the groups (P>0.05). Post-hoc analysis revealed significant main effects for sad (P=0.03), happy (P=0.02), and angry (P=0.001) faces, compared to the neutral ones . Paired sample t test revealed a significant increase in the EPN amplitude for angry , and happy , but not sad faces , compared to neutral ones as well as angry faces compared to happy expressions . There was no significant main effect of facial expression in the ADHD group ; , angry , happy , compared to neutral faces; and for angry expressions, compared to happy faces   . Figure 4.The current study aimed to evaluate the early stages of facial expression processing using the EPN component in patients with ADHD, compared to typically developing children, while viewing happy, angry, sad and neutral face images. We expected to observe emotional processing impairment in children with ADHD. The results revealed that angry and happy faces, significantly elicited the increased EPN amplitude in occipito-temporal electrodes in healthy children. However, the ADHD group showed no significant facial expression effect, based on the EPN. Thus, the obtained data supported our hypothesis that children with ADHD were different from normal children in early stages of facial expression processing.The current results revealed a significant main effect of facial expression. Our results revealed a modulation of the EPN component with angry and happy expressions, but not sad ones in normal children. Additionally, we observed the increased EPN measures for angry expressions, compared with happy faces in typically developing children. This finding is consistent with other studies reporting angry faces produced a larger EPN than happy faces . Larger The facial expression had no significant effect on the ADHD group, based on the EPN component. To our knowledge, only ERP findings of According to Considering our findings and previous studies, reduced EPN amplitude for emotional stimuli reveals an impairment in the early stages of emotion processing in children with ADHD. This deficit can lead to impairment in recognizing emotional faces. Based on this finding, providing attentional training to emotional stimuli may improve their impairment in recognizing emotions and lead to better emotional relationship in individuals with ADHD.All parents filled an informed consent which was written in accordance with the Declaration of Helsinki. Children verbally agreed to participate in the experiment.


What is the risk of attention-deficit/hyperactivity disorder in children prenatally exposed to valproate and other antiepileptic drugs?In a population-based cohort study of 913 302 children in Denmark, prenatal exposure to valproate was significantly associated with a 48% increased risk of attention-deficit/hyperactivity disorder compared with children with no valproate exposure. No association was identified for other antiepileptic drugs.The findings of this study corroborate that counseling is appropriate for the use of valproate in pregnancy and in women of childbearing potential. Valproate is an antiepileptic drug (AED) used in the treatment of epilepsy and many other neurological and psychiatric disorders. Its use in pregnancy is associated with increased risks of congenital malformations and adverse neurodevelopment in the offspring and may be associated with an increased risk of attention-deficit/hyperactivity disorder (ADHD).To determine whether prenatal exposure to valproate and other AEDs is associated with an increased risk of ADHD in the offspring.This was a population-based cohort study of all live-born singleton children in Denmark from January 1, 1997, to December 31, 2011 (N = 913 302). Information on prenatal exposure to AEDs, including valproate, was obtained from the Danish National Prescription Registry and all children with ADHD were identified . The cohort was followed up from birth until the day of the ADHD diagnosis, death, emigration, or December 31, 2015, whichever came first. Data were analyzed in September 2018.Maternal use of valproate and other AEDs in pregnancy.Cox regression estimates of the hazard ratio of ADHD. Estimates were adjusted for potential confounders.The cohort included 913 302 children . A total of 580 were identified as having been exposed to valproate during pregnancy; of them, 49 (8.4%) had ADHD. Among the 912 722 children who were unexposed to valproate, 29 396 (3.2%) had ADHD. Children with prenatal valproate exposure had a 48% increased risk of ADHD  compared with children with no valproate exposure. The absolute 15-year risk of ADHD was 4.6%  in children unexposed to valproate and 11.0%  in children who were exposed to valproate in pregnancy. No associations were found between other AEDs and ADHD.Maternal use of valproate, but not other AEDs, during pregnancy was associated with an increased risk of ADHD in the offspring. These findings have important implications for the counseling of women of childbearing potential using valproate. This population-based cohort study of children in Denmark evaluates whether maternal use of valproate or other antiepileptic drugs during pregnancy is associated with the risk of attention-deficit/hyperactivity disorder (ADHD) in offspring. By Danish law, analyses of anonymous data do not require ethical review board approval. However, the study was approved by the Danish Data Protection Agency. Because the data were deidentified, informed consent was not required. The study follows the Strengthening the Reporting of Observational Studies in Epidemiology , death, emigration, or December 31, 2015, whichever came first. Data were analyzed in September 2018.We conducted a population-based cohort study and included in the study population all singleton children born alive in Denmark between January 1, 1997, and December 31, 2011. The cohort was followed up from birth until the day of the ADHD diagnosis  since January 1, 1996. In Denmark, all antiepileptic prescriptions filled by patients are prescribed by hospital physicians or general practitioners. We defined the exposure window to the period from 30 days before the estimated day of conception to the day of birth, and included children with an estimated time of conception after February 1, 1996, ie, children born after January 1, 1997, and no later than December 31, 2011. Exposure to AEDs was defined as any redeemed prescriptions with the Anatomical Therapeutic Chemical code N03A (AEDs), including N03AG01  or N05BA09 (clobazam), within the exposure window.The Danish National Prescription RegistryMonotherapy was defined as pregnancies in which the mothers had redeemed prescriptions for only 1 type of AED, and polytherapy was defined as pregnancies in which the mothers had redeemed prescriptions for more than 1 type of AED within the exposure window. For both monotherapy and polytherapy, the mothers may also have redeemed prescriptions for other types of medications than AEDs. Besides valproate, we also estimated the risk of ADHD among the offspring to mothers with other frequently used AEDs during pregnancy , because these AEDs were the most commonly used AEDs in Denmark during the study period.The mean daily dose of AED was estimated from the total amount of AED redeemed from 30 days before pregnancy to birth, divided by the number of days in the same period.15 was used to identify children who were diagnosed for the first time with ADHD, based on ICD-10-DCR codes F90 and F98.8. We also used the Danish National Prescription Registry14 to identify offspring who used medication that is used for ADHD treatment . In Denmark, the vast majority (>70%) of children with ADHD are diagnosed in an outpatient clinic at a public hospital, free of charge.16 Most of the remaining children diagnosed with ADHD outside hospital departments, however, would be identified from filled prescriptions for ADHD medications.The Danish Psychiatric Central Research Register17 We used the Danish National Patient Register18 to identify children diagnosed with congenital malformations 15 and mothers diagnosed with epilepsy before the birth of the child . We identified mothers diagnosed with psychiatric disorders before the birth of their child (ICD-8 codes 290-315 and ICD-10-DCR codes F0.00-F99.9) from the Danish Psychiatric Central Research Register.Information on parity was obtained from the Danish Medical Birth Registry.We used Cox regression to estimate the hazard ratio (HR), including 95% CI, for ADHD for children with prenatal valproate exposure, with the age of the child as the underlying time scale and separate baseline diagnostic rates (stratum) for each birth year group to adjust for the decreasing use of valproate in pregnancy and the increasing prevalence of ADHD. The proportional hazards assumption was evaluated for all variables by comparing estimated log[−log] curves over the different categories of variables investigated. Control for the lack of independence of children within the same family was obtained using a robust (Huber-White) variance estimator. The HRs were adjusted for risk factors for ADHD, including maternal age at conception , maternal psychiatric history (yes or no), maternal epilepsy (yes or no), maternal diabetes (yes or no), sex of the child, and parity . We used competing risk regression to estimate the absolute risk (cumulative incidence) of ADHD in the first 15 years of life after prenatal valproate exposure in pregnancy.To assess possible confounding by indication, we compared the risk of ADHD in offspring of women who used valproate during pregnancy with that of women who discontinued their use of valproate at least 30 days before the estimated date of conception . This analysis addresses whether the illness that triggered valproate use rather than valproate exposure was associated with ADHD in the child. In sensitivity analyses, we also estimated the risk of ADHD after stratifying the risk according to dose of valproate, trimester of exposure (first vs later), monotherapy vs polytherapy, and maternal use of other AED, after adjusting for maternal smoking (yes or no) and after excluding children with congenital malformations. Further, we performed sensitivity analyses by excluding children with epilepsy and children whose mother had a diagnosis of ADHD and by increasing the exposure period before the estimated time of conception from 30 days to 90 days.Finally, we estimated the HR of ADHD with follow up at 3 years of age. Data analyses were performed using Stata version 12 statistical software (StataCorp LP).We identified 913 302 children born in Denmark from January 1, 1997, to December 31, 2011, who contributed more than 10.2 million person-years of observation . In this cohort, 580 children had been exposed to valproate during pregnancy (monotherapy and polytherapy combined) and 912 722 had not. A total of 29 445 persons were identified with ADHD . Of the 912 722 children who were unexposed to valproate, 29 396 (3.2%) had ADHD. Of the 580 children who had been exposed to valproate, 49 (8.4%) had ADHD. The mean age at ADHD diagnosis in this study was 8.8 years .Overall, the children who were prenatally exposed to valproate had a 48% increased risk of ADHD  compared with the unexposed children . The absWhen restricting the cohort to the 7620 children born to women with epilepsy, valproate use during pregnancy (n = 516) was associated with a 39% higher risk of ADHD in children  compared with the risk in children born to mothers with epilepsy who did not use valproate during pregnancy (n = 7104) . The poiIn the offspring of women who used valproate in monotherapy during pregnancy (n = 431), the risk of ADHD was increased by 52% compared with the risk in the offspring of women who did not use AEDs during pregnancy  . CompareWhen compared with prenatal exposure to lamotrigine (using this as the reference), the risk of ADHD associated with prenatal valproate exposure was more than 2-fold increased , carbamazepine increased the risk by 79% , and clonazepam increased the risk by 96% . Compared with the offspring exposed to lamotrigine in pregnancy, the risk of ADHD associated with prenatal exposure to oxcarbazepine was not significantly increased   . Compared with the offspring of children who were unexposed to valproate, the risk of ADHD was 52% higher for exposure in the first trimester   in the offspring of 431 women who used valproate as monotherapy and 1.43  in the offspring of 149 women who used valproate as polytherapy when compared with the offspring of 899 941 women who did not use an AED during pregnancy  had a higher risk of ADHD . However, the risk of ADHD did not significantly differ for the offspring of 227 women who were estimated to use valproate in low-dose monotherapy (≤750 mg) compared with the offspring of 899 941 unexposed women   compared with the unexposed children  compared with the unexposed children  compared with the unexposed children  compared with the unexposed children , and the association with offspring ADHD was modest. Thus, even in a cohort of this size, the additional number of children who would develop ADHD due to prenatal valproate exposure is low.The association between prenatal exposure to valproate and an increased risk of ADHD was quite robust and persisted after adjusting for maternal psychiatric disorders, maternal epilepsy, maternal diabetes, maternal age, sex, year of birth, and parity, and the association remained after excluding women who smoked during pregnancy.15 may not capture all mothers with psychiatric disorders (including ADHD) or misuse of alcohol, especially when intake is moderate. In addition, the Danish National Patient Register18 may not capture all women with epilepsy. Thus, we cannot exclude that the association between maternal valproate use in pregnancy and ADHD in the offspring may be, at least in part, due to unmeasured confounding.The Danish Psychiatric Central Research Register4 Possible explanations for this discrepancy include differences in the cohorts analyzed as well as smaller sample sizes, higher attrition rates, and shorter follow-ups in the studies included in the meta-analysis than in the present study.4The results run counter to a recent meta-analysis of 4 studies including a total of 750 individuals, which did not find an association between prenatal valproate exposure and offspring ADHD symptoms.25Although not significant, analyses of other types of AEDs suggest that these AEDs may also carry a risk of ADHD in the offspring. For example, we found a possible risk increase associated with carbamazepine and clonazepam, which has not been previously described.26The risk of ADHD was related to valproate exposure mainly in the first trimester, but the number of cases exposed only in later trimesters was low; our findings therefore do not exclude the possibility that valproate exposure across all stages of pregnancy may be associated with an increased risk of ADHD in the offspring. When examining doses of valproate, prenatal exposure to estimated higher doses tended to be associated with higher risks of ADHD than exposure to estimated lower doses of valproate, although the difference between high and low doses was not significant. Previous studies have indicated that the teratogenic and neurodevelopmental effects of valproate may be higher for high daily doses of valproate during pregnancy.27 we found that valproate exposure increased the risk of congenital malformations  in the Danish Psychiatric Central Research Register and found that the risk of misclassification of patients is relatively low.30 Analyses of ADHD after prenatal valproate exposure based on hospital diagnoses and analyses based on filling of prescriptions for ADHD medication showed similar results .Maternal use of valproate during pregnancy was associated with a small but significantly increased risk of ADHD in the offspring, even after adjusting for maternal psychiatric disease, maternal epilepsy, and other potential confounding covariates. These findings have important implications for the counseling of women of childbearing potential who are undergoing treatment with valproate, and they support warnings issued by authorities.


Bartonella spp. are globally distributed bacteria that cause endocarditis in humans and domestic animals. Recent work has suggested bats as zoonotic reservoirs of some human Bartonella infections; however, the ecological and spatiotemporal patterns of infection in bats remain largely unknown. Here we studied the genetic diversity, prevalence of infection across seasons and years, individual risk factors, and possible transmission routes of Bartonella in populations of common vampire bats (Desmodus rotundus) in Peru and Belize, for which high infection prevalence has previously been reported. Phylogenetic analysis of the gltA gene for a subset of PCR-positive blood samples revealed sequences that were related to Bartonella described from vampire bats from Mexico, other Neotropical bat species, and streblid bat flies. Sequences associated with vampire bats clustered significantly by country but commonly spanned Central and South America, implying limited spatial structure. Stable and nonzero Bartonella prevalence between years supported endemic transmission in all sites. The odds of Bartonella infection for individual bats was unrelated to the intensity of bat flies ectoparasitism, but nearly all infected bats were infested, which precluded conclusive assessment of support for vector-borne transmission. While metagenomic sequencing found no strong evidence of Bartonella DNA in pooled bat saliva and fecal samples, we detected PCR positivity in individual saliva and feces, suggesting the potential for bacterial transmission through both direct contact  and environmental  exposures. Further investigating the relative contributions of direct contact, environmental, and vector-borne transmission for bat Bartonella is an important next step to predict infection dynamics within bats and the risks of human and livestock exposures. Bartonella are globally distributed bacteria that can cause endocarditis in humans and domestic animals. Bats have been implicated as a likely reservoir host for these bacteria, but little is known about how prevalence varies over time, routes of transmission, and the genetic diversity of Bartonella in bats. We present results from a two-year, spatially replicated study of common vampire bats, which have previously shown high infection prevalence of Bartonella and could pose risks of cross-species transmission due to their diet of mammal blood. We found that vampire bat Bartonella is genetically diverse, geographically widespread and endemic, and that individual-level infection risk is highest for large, male, non-reproductive bats. Phylogenetic analysis supported vector-borne transmission, and we found support for potential transmission through direct contact and fecal exposures through PCR. Confirming whether arthropod vectors are the main route of transmission for bat Bartonella is needed for understanding infection dynamics in bats and for predicting risks of cross-species transmission to humans and livestock. Bartonella spp. are of particular interest, as these Gram-negative bacteria cause bacteremia and endocarditis in both humans and livestock [Candidatus B. mayotimonensis [Bats (Order: Chiroptera) serve as reservoir hosts for viruses of concern for human and animal health ,2 includivestock ,11 and eivestock –17. Moremonensis –20, a camonensis .Bartonella, understanding transmission within bats is critical for understanding how Bartonella persists in bat populations and for assessing spillover risks [Bartonella has been identified in bat flies and ticks [Bartonella in dog and cat saliva [Bartonella host specificity in Neotropical bat communities could not only reflect transmission through close contacts between species in multi-species roosts, but could also stem from transmission through generalist vectors [Bartonella might also be transmitted through exposure to feces between bats and to humans that enter roosts or to domestic animals exposed to bat feces [Given the zoonotic potential of bat-associated er risks ,23. Ectoer risks ,19,24, ier risks ,26 and bnd ticks –29. Whilnd ticks , the hignd ticks ,32 couldnd ticks –33. Trant saliva ,35 as wet saliva . Phyloge vectors ,24,37. Bat feces ,38.Bartonella in bats are also uncertain. In rodents, Bartonella prevalence varies through time [Bartonella genotypes, with notable differences in genotypes from Latin American and those from Africa, Europe, and Asia [Bartonella transmission and the role that dispersal plays in the spatial dynamics of this infection [In addition to the potential risks of cross-species transmission from bats to livestock and humans, the infection dynamics of ugh time ,40, but ugh time . Individand Asia . Howevernfection .Desmodus rotundus) have high prevalence of Bartonella throughout their large geographic range in Latin America [Bartonella transmission to humans and livestock either from bites during blood feeding or through vector sharing facilitated by close proximity [Bartonella, including its geographic structure across the vampire bat range as well as individual and temporal correlates of infection status. To explore possible transmission routes of this bacterial pathogen, we also test for associations between bat fly infestation and Bartonella infection status, which would support vector-borne transmission, and by screening bat saliva and fecal samples for evidence of Bartonella DNA, which would support transmission through bites or grooming and environmental exposure to bacteria shed in feces, respectively.Common vampire bats  at –80°C until laboratory analyses. For Peru sites sampled in 2016, we also recorded the number of bat flies per vampire bat [Samples were collected as described in Becker et al.  in 2015 pire bat .Field procedures were approved by the University of Georgia Animal Care and Use Committee (A2014 04-016-Y3-A5) and the University of Glasgow School of Medical Veterinary and Life Sciences Research Ethics Committee (Ref08a/15); all procedures were conducted in accordance with accepted guidelines for humane wildlife research as outlined by the American Society of Mammalogists . Bat capBartonella by Becker et al. [gltA) [Bartonella-positive samples, we randomly selected 5–10 positive samples per site for Sanger sequencing (n = 51). PCR products were purified with DNA Clean & Concentrator Kits (Zymo Research) and sequenced in both directions at the Georgia Genomics Facility. Resulting chromatograms were checked for quality and trimmed using Geneious (Biomatters) [n = 35; the quality of 16 chromatograms was too low). Sequences were considered part of the same genotype if they had >96% identity in gltA, an established cut-off for Bartonella species identification [p value generated via a Monte Carlo procedure with 1000 simulations [Bartonella genotypes were associated with region .We analyzed samples that were previously screened for the presence of r et al.  using ne. [gltA) . Among tmatters) . Post-trfication . Sequencfication . We usedulations  to assesBartonella across Latin America and therefore included our new sequences plus all previously reported gltA sequences from Desmodus rotundus. Dataset 2 was designed to capture the relatedness of the new sequences to all previously described Bartonella spp. regardless of isolation source, which comprised sequences generated in this study plus sequences obtained by conducting a BLAST search of each new sequence against GenBank, selecting the top 10 hits, and removing duplicates. For both datasets, consensus sequences were aligned using MAFFT. Phylogenetic analyses were carried out in MrBayes using the GTR+gamma model suggested by jModeltest2 [Desmodus-associated sequences), we fit a codon partitioned substitution model by linking rates in codon positions 1 and 2 separately from codon position 3. For dataset 2, we used a simpler non-partitioned model because the more complex codon-partitioned model failed to converge. Dataset 2 included one sequence from Brucella abortus (Genbank Locus: MIJI01000003.1) as an outgroup [Bartonella by country  using Bayesian Tip Association Significance Testing (BaTS) [Two datasets were created for phylogenetic analyses. Dataset 1 was designed to assess the spatial structure of vampire bat–associated deltest2 . For datoutgroup . Both daoutgroup . With dag (BaTS) . We hereg (BaTS) .Desmodus rotundus to test whether temporal variation (season and year) and individual risk factors  explain differences in Bartonella infection, using generalized mixed effects models (GLMMs) with binomial errors and a logit link fit with the lme4 package in R [n = 6). To assess seasonality in infection, we fit a separate GLMM with season  as a predictor to data from two sites in Peru (AM1 and CA1) sampled across seasons (n = 63). We also fit a generalized additive model (GAM) with restricted maximum likelihood, binomial response, and a cyclic cubic regression spline for Julian date using the mgcv package [We analyzed 193 samples from age in R ,60. We f package . We randBartonella infection, we fit a single GLMM with bat age, forearm size, sex, and reproductive status; we also included interactions between sex and reproduction, sex and age, sex and forearm size, and reproduction and forearm size. We included categorical livestock biomass as a predictor in the GLMM to control for a previously observed negative association with Bartonella infection (121/173 positive bats) [n = 189), included bat ID nested within site as a random effect, and calculated marginal R2 (R2m) to assess model fit [n = 40 bats sampled in Peru in 2016), we fit two separate GLMs with bat fly intensity and presence as predictors to test whether ectoparasites explained Bartonella infection status. We fit a separate GLM with quasi-Poisson errors to test for sex and age differences in bat fly intensity.To identify individual risk factors for ve bats) . We fit odel fit . FinallyBartonella through biting, grooming, blood sharing, or fecal–oral exposure, we used metagenomic data from a parallel study to screen vampire bat saliva and fecal samples for Bartonella DNA. Three saliva and three fecal pools were shotgun sequenced, each containing nucleic acid extractions from swabs collected from ten vampire bats from one to two colonies. Pooled samples contained individuals from the same colonies of bats tested for Bartonella in blood through PCR, though not necessarily the same individuals.To examine possible transmission of As described previously , total nBartonella genotypes detected in this study using nucleotide BLAST [Bartonella sequences from this study, retaining only the best alignment  for a single query–subject pair. To investigate the presence of Bartonella species other than genotypes detected in blood samples from vampire bats, cleaned reads were de novo assembled into contigs using the assembly only function of SPAdes [Bartonella using protein alignment in Diamond [gltA gene is not highly transcribed, we also tested sequences for matches to Bartonella DNA-directed RNA polymerase subunit B (rpoB). We selected two rpoB sequences (Genbank accessions KY629892 and KY629911) from a study of vampire bat Bartonella [gltA gene to our blood sequences, and we used Bowtie2 to map quality filtered reads and contigs to those sequences [Reads were demultiplexed according to barcode and quality filtered using TrimGalore ,64 with de BLAST  against f SPAdes . Individ Diamond , and clortonella  for whicequences .gltA in DNA from individual saliva and fecal swab samples that made up metagenomic pools . As with our blood samples, we randomly selected a subset of positive amplicons for Sanger sequencing.Lastly, because nucleic acid pools were DNase treated for metagenomic sequencing, potentially reducing detection sensitivity, we used the same nested PCR protocol as used for blood-derived DNA  to test Bartonella prevalence across the 193 vampire bats included in this study was 67%. Our phylogenetic analysis of 35 vampire bat Bartonella sequences showed 78.8–100% pairwise identity in gltA and revealed at least 11 paraphyletic genotypes , although most vampire bat Bartonella genotypes were still widely distributed . Genotypes 1 and 2 were detected across all regions, and genotypes 7–10 were detected within both Belize and Peru, highlighting the broad distribution of vampire bat Bartonella genotypes  from Mexico , again confirming the wide geographic distribution of these genotypes. Other sequences(9/35) fell within the same clade (>96% pairwise identity) as Bartonella from bat flies (Strebla diaemi) in Panama (JX416251), from Parnell's mustached bat (Pteronotus parnellii) in Mexico , from phytophagous bats in Peru  and Guatemala , or from Mexican vampire bats as noted above. Eight sequences were novel (<96% identity to GenBank sequences) but were most similar to Bartonella from phytophagous bats in Costa Rica  and from Mexican vampire bats .We also assessed the phylogenetic position of our vampire bat enotypes . Half ofB. bovis from livestock in Israel and Malaysia , to B. chomelii from cattle in Spain , to B. capreoli from elk in the United States , and to B. schoenbuchensis from roe deer in Germany ; indeed, posterior support for a bat–ruminant clade was low  and from carnivores . However, these livestock, rodent, and carnivore sequences formed separate phylogenetic clades from bat- and bat fly–derived Bartonella sequences  nor within individual sites . The seasonality GLMM for the western Peruvian Amazon (n = 63) showed no difference in odds of infection between spring, summer, and fall  but showed changes in infection from negative to positive  and from positive to negative  and were dropped from the final GLMM (R2m = 0.28). The odds of Bartonella infection were highest for vampire bats with larger forearms  and for males , were marginally higher for non-reproductive individuals , and did not differ between subadult and adult bats ; our sample did not contain juveniles. Individual bat fly intensities were highly variable  and showed overdispersion (ϕ = 5.08 in an intercept-only quasi-Poisson GLM). The bat fly GLMs showed that neither ectoparasite intensity  nor ectoparasite presence  were associated with Bartonella infection status. We note that the majority of infected bats in this sample were infested with at least one bat fly (31/36), limiting conclusive assessment of the ectoparasite–infection relationship. Our multivariable quasi-Poisson GLM showed that ectoparasite load did not vary by bat sex  or bat age .After controlling for site-level livestock biomass, vampire bat sex and forearm size were the strongest predictors of infection ; no inteBartonella gltA sequences detected in the blood or to previously published Bartonella rpoB sequences. The saliva pool from Amazonas had no matching Bartonella-like reads or contigs  matched to Bartonella, suggesting the presence of poorly characterized Bartonella species present or that these may be matches to other bacteria.There were no matches in any of the screened saliva and fecal metagenomic pools to the  contigs , while ogltA in 21.4% of saliva samples (6/28) and 30% of fecal samples (9/30). For swab samples that were also assessed by PCR in blood , both corresponding positive saliva samples were positive in blood; most positive fecal samples were also PCR positive in blood, although one fecal-positive sample was PCR negative in blood (n = 4) and fecal (n = 5) samples, phylogenetic analyses suggested that all sequences shared a minimum of 97% identity to one or more of our 35 blood-derived Bartonella sequences  or other bat species that share Bartonella genotypes with vampire bats may be conceivable and could be resolved by further field surveys combined with population genetic analyses of alternative bat host species, arthropod vector species, and Bartonella genotypes.The rtonella . These pigratory , dispersBartonella, emphasizing the general need for more longitudinal studies to understand how pathogens persist in bat populations [Bartonella was detected at relatively high prevalence across both study years within each sampling site, and neither year nor its interaction with site were predictive within our analyses. Similarly, no temporal patterns in Bartonella were observed for a limited sample of Myotis mystacinus, Pipistrellus spp., Myotis daubentonii, and Nyctalus noctula in the United Kingdom [Bartonella infections in rodents, which show high prevalence in summer and fall due to seasonality in birth and ectoparasite intensity [Bartonella prevalence in bats has been proposed to stem from persistent infection [n = 162) and subadult (n = 28) bats, limiting more robust tests of age-dependent infection. Alternatively, Bartonella infections could be chronic and vary in infection intensity over time or could become latent , particularly as infection does not appear to confer long-term immunity [Few studies have examined temporal patterns of bat ulations ,6. Here, Kingdom . Such fintensity ,40. The ntensity ). While nfection , this senfection . Howeverimmunity . Such exBartonella infection status, with odds of infection being higher in larger, male, and non-reproductive bats. While subadult status itself was not an important predictor of Bartonella infection, these findings could suggest higher risk in young male bats that are relatively large for their age. Our previous work has shown stronger innate immune defense  in reproductive  vampire bats, also suggesting greater susceptibility of non-reproductive hosts [Bartonella infection [Bat forearm size, sex, and reproductive status were important predictors of ve hosts . Similarnfection , and younfection  and dispnfection . Larger nfection .Bartonella in other hosts [Bartonella infection but did not differ in their bat fly intensities compared to females. Weak correspondence between bat fly intensity at the time of sampling and Bartonella infection thus may cast doubt on bat flies as a primary transmission route. Time lags could provide one reason for this discrepancy, given that new Bartonella infections may take days or weeks to develop and become detectable and over which time ectoparasite load may have changed due to the mobile nature of bat flies [Bartonella presence in bats with and without bat flies had little statistical power (31/36 Bartonella-positive bats were infested with at least one bat fly). Given that ectoparasitism predicted Bartonella infection more generally across a Mexian bat community [Bartonella genotypes fell within the same clade as Bartonella from streblid bat flies [Bartonella genotypes, although one vampire bat did show complete sequence homology with the Bartonella from its paired bat flies [Bartonella in bat flies and hosts has been interpreted as evidence of vector-borne transmission in other bat species [Bartonella genotypes between vampire bats and their various ectoparasites  would shed additional light on possible routes of vector-borne transmission.Although vector-borne transmission is generally assumed for er hosts ,19,25,77er hosts ,78, infeat flies ,36,79. Oommunity , larger at flies . A recenat flies . As gene species ,54, furtBartonella DNA matching to gltA or rpoB in either saliva or fecal pools. This absence could be explained in that the short sequences (345–425 bp) used as targets, and the large size of bacterial genomes together make the likelihood of detecting a specific gene low. However, the Bartonella-like reads and contigs recovered from saliva and feces were short fragments (51–258 bp) and showed low homology to known Bartonella from GenBank , this dBartonella in saliva and feces suggests that direct contact and environmental exposure could serve as complemenry transmission routes to arthropod vectors. The presence of Bartonella in saliva samples contrasts with previous work showing an absence of Bartonella in vampire bat saliva [Bartonella in fecal samples could also suggest environmental transmission between bats [Bartonella could further pose potential risks to humans or livestock, either through bites during feeding or by environmental exposure of humans that enter roosts or to domestic animals exposed to bat feces [Bartonella in Mexican ruminants did not identify being bitten by vampire bats as a risk factor for infection [Bartonella might reduce their potential to infect humans or livestock, given the high host specificity of most bat flies [Bartonella transmission within vampire bat colonies and to other bat species. While our analyses of ectoparasitism only considered bat flies, we have observed heavy tick burdens of vampire bats in other field sites . Bartonella has been detected in ticks infesting other bats [Bartonella ancashensis and B. bacilliformis in vampire bat fecal samples, and these species cause notable infectious disease in humans likely through phlebotomine sand flies in Andean regions of Peru [Bartonella in vampire bats, their various ectoparasites, and sympatric prey are therefore needed to examine the contributions of different transmission routes for bacterial spread within vampire bats and to recipient prey and to confirm whether saliva and feces represent viable transmission routes. Given the high rates of bat bites and proximity to wildlife, humans, and domestic animals that define vampire bat ecology, such efforts to verify the possibility and frequency of oral and environmental exposures would elucidate Bartonella transmission dynamics in this common host species and the risks of cross-species transmission.Similar prevalence of t saliva ,81, proveen bats . Both saat feces ,38,48,82at flies ,32. Howeat flies , facilither bats , and theher bats . Metagen of Peru ,85. ContS1 Table(CSV)Click here for additional data file.S2 TableSequences with greater than 96% similarity were assumed to be part of the same genotype for downstream analyses.(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 TableBartonella-like reads were identified by Diamond protein blast, and read matches are a list of those that were identified as Bartonella based on nucleotide blast. Bartonella-like contigs were identified by Diamond protein blast, and contig matches are a list of those that were identified as Bartonella based on nucleotide blast. Length is contig length in base pairs, and K-mer coverage is the depth of coverage for the largest k-value used in the SPAdes assembly.Department describes where samples in a pool were taken. (CSV)Click here for additional data file.S5 TableBartonella genotype.Sequences with greater than 96% similarity were assumed to be part of the same (CSV)Click here for additional data file.S1 FigBartonella seqeunces from our study are listed with genotype, bat ID numbers, and accession numbers. Sequences from GenBank are colored by host taxa and provided with accession numbers, host species, and sampling location; red tips display recent gltA sequences from Brazilian bats and rodents.(TIFF)Click here for additional data file.S2 Fign = 63) and GAM . Data points are jittered and colored by infection status .Modeled prevalence (black line) and 95% confidence intervals (grey) from the GLMM Click here for additional data file.S3 FigInfected bats are shown in red, uninfected bats are shown in black.(TIFF)Click here for additional data file.S4 FigBartonella PCR results for saliva (A) and fecal samples (B) for which blood was also assessed for evidence of infection.Counts of positive and negative (TIFF)Click here for additional data file.


To investigate the relationship between symptom of attention-deficit/hyperactivity disorder (ADHD) and allergic rhinitis (AR) in AR children of different genders and ages.Four hundred and sixty-five allergic rhinitis children aged 6–12 years old were recruited in this study. Skin-prick test, Pediatric Rhinoconjunctivitis Quality of Life Questionnaire (PRQLQ), Total Nasal Symptoms Score (TNSS) and the Swanson, Nolan, and Pelham version IV scale (SNAP-IV) were recorded. Patients were divided into AR with ADHD and AR without ADHD, according to the SNAP-IV scale results.Children with the inattention/hyperactivity scale (IHS) > 1.25 accounted for 26.4% of all children with AR. The TNSS with IHS > 1.25 group were significantly higher than the IHS ≤ 1.25 group. Univariate analysis showed that age, gender, duration of AR symptoms, skin index, and PRQLQ subscales were associated with symptoms of hyperactivity and attention deficit (IHS > 1.25). After normalizing the age and gender factors, duration of AR symptoms and skin index correlated with IHS > 1.25. After stratifying age and gender, the correlation between IHS > 1.25 and skin index and PRQLQ subscales was mainly found in male children, and the association between the duration of AR symptoms and IHS > 1.25 was reflected in each group.ADHD in children with AR is associated with severity, duration, and skin index of AR, and this association is more pronounced in male children.The online version of this article (10.1186/s13052-019-0703-1) contains supplementary material, which is available to authorized users. Attention-deficit/hyperactivity disorder (ADHD) is one of the most frequently diagnosed disorder found in both children and adults. ADHD is manifested by inattention, hyperactivity, cognitive deficit, and/or impulsivity. It affects approximately 3–5% of youth and 2.5–5% of adults . The incA cross-sectional study was performed to investigate the relationship between symptom of ADHD and AR in AR children of different genders and ages at the Second Affiliated Hospital of Wenzhou Medical University , China. All the parents and/or participants signed informed consent forms before the assessment and were evaluated by a pediatrician/otolaryngologist and neurologist. The study was approved by the Second Affiliated Hospital of Wenzhou Medical University  Ethics Committee.The patients were recruited in the ear, nose, and throat/pediatrician outpatient clinic over a 2-year period from September 2016 to December 2018. Children with AR were enrolled based on the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines , 12. An The Total Nasal Symptoms Score (TNSS), used to assessing nasal symptoms, is the sum of the scores of the four nasal symptoms  on a scale of 0 to 3 .Skin-prick tests were performed by using 18 common Chinese inhaled and food allergens as well as negative and positive controls. The allergens tested were those of dermatophagoides pteronyssinus, dermatophagoides farinae, cockroach, the molds alternaria alternate and aspergillus fumigatus, Baker’s yeast, wheat, sieversiana pollen, sesame, croaker, crab, silk, shrimp, cat, dog, egg, milk, soybean, and peanut. A mean wheal diameter of 3 mm that of the negative control was considered positive. Atopy was considered to be present when one or more allergens yielded a positive skin-prick test result. To determine the reaction intensity of SPT, we can calculate the skin index (SI), namely the ratio of food sensation-induced wheal to histamine control response, which can reveal differences in individual skin responsiveness  where + The Pediatric Rhinoconjunctivitis Quality of Life Questionnaire  consistsThe Swanson, Nolan, and Pelham version IV scale (SNAP-IV) is a 26-item scale used to evaluate ADHD symptoms and its severity . It inclThe inattention/hyperactivity scale (IHS) asks parents to rate their children for 18 DSM-IV category A symptoms of ADHD  were calculated by logistic regression. The relative OR values of all independent variables and their 95% confidence intervals control age and gender factors. At the same time, the age and gender were stratified and the above analysis was repeated.Data was collected and statistically analyzed using SPSS version 22. Comparison of data was done using Student’s t-test for parametric data and chi-square χ2 test for non-parametric data. Two tailed n = 67) were excluded. Therefore, 465 participants  were analyzed in the current study , allergen type (P = 0.276), and other allergic diseases (P = 0.413) had no correlation with attention deficit and hyperactivity symptoms. However, the skin index and symptom presence time of the child are risk factors for attention deficit and hyperactivity symptoms after normalizing gender and age factors  . The relationship between skin index and IHS > 1.25 was not statistically significant , influx of eosinophils into the into the inflammatory sites of allergic tissue (IL-5) . InflammSleep problems are common in children with allergic rhinitis. Previous study revealed that the onset of upper airway inflammation due to allergic triggers in subjects under three years of age may be related to the subsequent development of SDB after 8–10 years  . Minor This study has some limitations as different parental education level may affect their judgments when filling out the questionnaire, which may cause bias data. Besides, our study is a cross-sectional survey that did not cover the whole range of ADHD patients to establish a causal relationship. Further follow-up of this cohort is warranted to elucidate causal associations.This study demonstrated that attention deficits in children with AR are associated with symptoms severity, duration and skin index, especially in male children. The mechanism of the relationship between AR and ADHD symptoms has not been clarified and needs further studies. Interventions incorporating strategies that focus on allergic disease management and collaborative care for children with ADHD deserve further investigation.Additional file 1:Table S1. The IHS question-items, derived from DSM-IV category A symptoms of ADHD (DOCX 13 kb)


Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results.Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop.An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at Bioinformatics online. A knowledge graph (KG) uses an appropriate ontology to express domain knowledge as a graph of relationships (edges) between entities (nodes), with related nodes connected by edges. KG databases such as Neo4j allow KGs to be queried using a query language such as Cypher that is designed to find matching relationship paths or sub-graphs within the KG. Queries intended to find longer paths or larger sub-graphs often result in an explosion in the number of matching paths. To facilitate rapid exploration of a KG for hypothesis generation or exploration of identified relationships, an interface is required to enable a user to quickly explore the results of complex queries without the need to write custom commands or computer code. Although tools exist for visually querying and exploring the results of a KG such as the Neo4j browser, these tools are often insufficient for queries that return numerous paths that are best represented as sub-graphs.Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) and focus on capabilities enabled by the ROBOKOP user interface (UI). ROBOKOP was motivated by our work on the Biomedical Data Translator program (‘Translator’), which is funded by the National Center for Advancing Translational Sciences (http://robokopkg.renci.org).Herein, we describe Sciences , b. ROBOwhat genes are associated with Ebola?’ or explore more complex paths such as ‘find a clinical outcome pathway that provides a mechanistic explanation for the effectiveness of imatinib in the treatment of asthma’.KGs of biomedical concepts and associate software to visualize and explore them have had several recent implementations such as BioGraph  and matching text in titles and abstracts to concepts from a predetermined set of biomedical ontologies. A confidence score for each answer is calculated based on the resistance distance  referenced within abstracts. OmniCorp is built by processing all PubMed abstracts with the SciGraph Named Entity Recognition API (distance  between http://robokop.renci.org/apidocs).The ROBOKOP web-based UI enables users to specify queries, store and recall query results and graphically explore the ranked list of answers. The storage format for results allows users to filter answers by the unique instances assigned to each node in the query specification. This approach enables users to quickly explore thousands of potential sub-graphs, even when the ranking of answers suggests that a given sub-graph may not be relevant to a specific query. For instance, certain users may be less interested in identifying answers with a substantial amount of existing publication support than in identifying answers that may have little publication support, but might suggest new insights or hypotheses for subsequent testing. The metadata for each node and edge, including provenance information, can be viewed, as well as relevant supporting publications. A series of publicly accessible APIs are used by the UI to communicate with server-side software (The ROBOKOP backend and UI can be used to explore the ROBOKOP KG and identify answers to biomedical queries or generate hypotheses for future biomedical research. Questions and ranked answers are stored and can be explored in a customized ROBOKOP UI. The ROBOKOP query specification and query format enable efficient storage of graph-structured answers.ROBOKOP is under active development, with performance and feature enhancements deployed regularly. Planned development includes additional methods for local KG exploration, techniques for more rapid query iteration and integration with graph-based machine-learning methods.http://robokopkg.renci.org; the UI is available at http://robokop.renci.org/ and an API is available at http://robokop.renci.org/apidocs.A public instance of the KG is available at btz604_Supplementary_DataClick here for additional data file.


KAT2B with a posterior probability of 0.467 in the dorsolateral prefrontal cortex and TMEM161B with a posterior probability of 0.838 in the amygdala. Furthermore, pathway enrichment identifies dopaminergic and norepinephrine pathways, which are highly relevant for ADHD. Overall, our findings highlight the power of TWAS to identify and prioritize putatively causal genes.Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental psychiatric disorder. Genome-wide association studies (GWAS) have identified several loci associated with ADHD. However, understanding the biological relevance of these genetic loci has proven to be difficult. Here, we conduct an ADHD transcriptome-wide association study (TWAS) consisting of 19,099 cases and 34,194 controls and identify 9 transcriptome-wide significant hits, of which 6 genes were not implicated in the original GWAS. We demonstrate that two of the previous GWAS hits can be largely explained by expression regulation. Probabilistic causal fine-mapping of TWAS signals prioritizes  et al. perform transcriptomic imputation using these data and 12 brain-relevant tissues from GTEx and CMC to identify 9 genes associated with ADHD by TWAS, 3 of which had not yet been reported for ADHD.A recent GWAS reported 12 genetic loci for attention deficit/hyperactivity disorder (ADHD). Here, Liao  The disorder has been shown to be highly heritable and increases risk of substance abuse, suicide, and risk-taking behavior2. Brain-imaging studies have identified various different regions, such as the cerebellum and frontal cortex, to be implicated in ADHD4. Twin studies have estimated the narrow-sense heritability of ADHD to be ~70%, suggesting a strong genetic component is driving the phenotypic variance5. Recently, a large-scale genome-wide association study (GWAS) identified 12 loci that were significantly associated with ADHD6. Despite the significant success of GWAS in delineating elements that contribute to the genetic architecture of psychiatric disorders, the loci identified are frequently difficult to characterize biologically. Often, these studies associate loci with the nearest gene, which inevitably leads to a bias for longer genes, and may not necessarily accurately depict the locus’s real effect. In contrast, transcriptomic studies have allowed for more interpretable biologically relevant results due to their use of disease-relevant cell-types and tissue, as well as the availability of databases detailing the tissue-specific expression7. It is also important to denote that transcriptomic studies conducted for brain disorders tend to have small sample size, by comparison to the studies of conditions where disease relevant tissue is more easily obtainable than brain tissue.Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder globally affecting 2.5% of adults and 5% of children7. This method derives the relationship between genotypes and gene expression to create reference panels consisting of predictive models applicable to larger independent datasets8. Ultimately, TI provides the opportunity to increase the ability to detect putative genes with small effect sizes that are associated with a disease.Recently, transcriptomic imputation (TI) was developed and is a powerful method to integrate genotype and expression data from large consortia, such as the Genotype-Tissue Expression (GTEx) through a machine-learning approachTo identify genetically regulated genes associated with ADHD, we leverage the largest ADHD cohort currently available to conduct a transcriptome-wide association study (TWAS); the cohort consists of 19,099 ADHD cases and 34,191 controls from Europe. Brain-tissue derived TI panels were used, including the 11 brain-relevant tissue panels from GTEx 53 v7 and the CommonMind Consortium (CMC). Here, we show that nine genes reach within tissue panel Bonferroni-corrected significance. We additionally identify three loci and genes that were not previously implicated with ADHD. Through conditional analyses, we demonstrate that several of the genome-wide significant signals from the ADHD GWAS are driven by genetically regulated expression. Gene set analyses of the Bonferroni-corrected TWAS genes have identified relevant pathways, among which dopaminergic neuron differentiation and norepinephrine neurotransmitter release cycle. Additionally, by querying the top eQTLs identified by TWAS in phenome databases, we identify several phenotypes previously associated with ADHD, such as educational attainment, body mass index (BMI), and maternal smoking around birth. Finally, genetic correlation of the pheWAS traits demonstrate that several Bonferroni-corrected significant correlations with risk-related behaviors, such as increased number of sexual partners and ever-smoking. In conclusion, TWAS is a powerful method that increases statistical power to identify small-effect size in genes associated with complex diseases such as ADHD.7   explaining 0.848 of the variance   Fig. . It was nce Fig. . Conditi 1) Fig. .Fig. 2ReELOVL1, CCDC24, and ARTN depending on the panel demonstrates expression-driven signals in a previously implicated ADHD loci  explaining 0.774 of the variance   Fig. . The CTCCCDC24, ARTN, AP006621.1, CTC-498M16.4, and MED8 remained significant. The long non-coding RNA LINC00951 and ST3GAL3 did not reach transcriptome-wide significance in the individual panels, but the combined omnibus score increased power to detect a signal  , norepinephrine neurotransmitter release cycle , and triglyceride lipase activity  when analyzing all genes together. Interestingly, several relevant cellular regions such as the axon and dendritic shaft were also enriched  GWAS for each of the phenotypes8. We identified nine genes-associated with ADHD risk and different tissue types, localizing to five different regions in the genome. Interestingly, conditional and joint analyses demonstrated that the TWAS expression signals were driving the significance for several previously implicated ADHD loci when conditioned on the top TWAS gene. The multi-gene conditioning of ELOV1, CCDC24, and ARTN led to explained 77.4% of the GWAS signal. This suggests that there is little residual association signal from the genetic variant in the GWAS locus after accounting for these predicted expression signals. Similarly, CTC-498M16.4 conditioning also explained a large variance of the GWAS locus. Future studies could interrogate whether expression differences are consistent with these findings. Furthermore, for smaller genes, such as AP006621.5 gene, they would normally go unnoticed due to the many larger protein-coding genes nearby. However, our TWAS results demonstrated that the expression of AP006621.5 fully explained the suggestive ADHD GWAS signal, highlighting the power of TWAS to prioritize genes of interest. Moreover, across all brain tissue types, the significant hits were consistently seen in the following biologically relevant tissue for ADHD: cerebellum, dorsolateral prefrontal cortex, frontal cortex, basal ganglia, and anterior cingulate cortex. These regions are consistent with previously implicated deficit points in the frontal-subcortical catecholamine and dopamine networks for ADHD10. Furthermore, certain genes were Bonferroni-corrected significant only in certain brain tissue types. For instance, CCDC24 was significant only in the cerebellum, ELOVL1, TIE1, and MED8 were specific to the DLPFC. Since expression regulation may be common across tissue types, it was interesting to not see consistency across panels. For instance, MED8 had a P-value of 2.72E−07 in the DLPFC but a P-value of 0.157 in the frontal cortex. Although it may be due to tissue-specificity, it is important to note that it may also be due to panel-specific effects and the quality of the RNA data and panel size from GTEx and CMC. Another example, ARTN, may not be brain tissue-specific, since it was significant in the omnibus test and in more than one brain tissue, but instead may be dysregulated at large. However, many TWAS hits tend to be correlated due to co-expression. Causal gene prioritization programs, such as using FOCUS probabilistically help fine-map towards credible genes11.ADHD is a common disorder that affects millions of people worldwide. While recent GWAS has been successful and identifying risk loci associated with ADHD, the functional significance of these associations continue to remain elusive due to the inability to fine-map to tissue-specific and tissue-relevant genes. Here, we conducted an ADHD TWAS using the summary statistics of over 50,000 individuals from the most recent ADHD GWAS. This approach creates genotype-expression reference panels using public consortia through machine-learning approaches, allowing for imputation and association testing of independent large-scale dataKAT2B in the credible-set with a posterior probability of 0.467 in the dorsolateral prefrontal cortex. The literature shows that ADHD has been associated with weaker function of the prefrontal cortex compared to healthy individuals12. KAT2B is a lysine histone acetyltranferase highly expressed in the brain13. Previous evidence has suggested that lysine acetylation is importance for brain function and proper development13. At another locus, TMEM161B, a transmembrane protein, had the highest posterior inclusion probability of 0.838 in the amygdala. Brain imaging studies have shown that the amygdala has decreased volume in ADHD patients14. Genetic variants in the gene have also been previously associated with major depressive disorder (MDD), which is a disorder that is often co-morbid with ADHD15. Furthermore, genetic correlation of ADHD and MDD has been shown to have a significant positive genetic correlation16. Another gene at this locus, CTC-498M16.4, was included in the credible-set for multiple relevant brain tissue types, such as the hypothalamus, hippocampus, and amygdala as well. Although, the posterior inclusion probability was lower for this gene, CTC-498M16.4, also known as lnc-TMEM161B-3:2, a lncRNA was amongst the top prioritized hits for the TWAS, omnibus test, and fine-mapping.Fine-mapping of TWAS hits included ELOVL1, TIE1, and MED8 were co-expressed, with ELOVL1 and MED8 having a stronger co-expression. These hits also were specific to the DLPFC. Similarly, CCDC24 and ARTN clustered together separate from the former three genes and are implicated in cerebellar tissue, despite all six hits resulting from the same locus. It is likely that these two clusters represent two separate hits and the genes within the cluster are simply co-expressed. This suggests that the same locus can have multiple unique TWAS hits across different tissues, and gene clustering could reaffirm if there is a tissue-specific dysregulation8. Interestingly, pathway and GO enrichment reinforced several pathways that have previously been reported as biologically relevant. Both dopaminergic and noradrenergic contributions have been implicated in the pathogenesis of ADHD10. For the top eQTLs associated with each transcriptome-wide significant gene, many re-occurring phenotypes relevant to ADHD were present, such as ever-smoker and number of sexual partners. A genetic correlation between those available traits from public GWAS data and the most recent ADHD GWAS found inverse correlation for education attainment, consistent with studies on educational outcome with ADHD. Additionally, a genetic correlation for risky behaviors such as ever-smoker and maternal smoking around birth were positively correlated. Maternal smoking has often been suggested to be a risk factor for ADHD17. However, the positive genetic correlation could suggest pleiotropy for genetic loci associated with both phenotypes. This would be consistent with pheWAS results showing that some eQTLs were highly associated with both ADHD and smoking. Two recent studies have inquired about ADHD eQTLs and both found overlapping results. In Fahira et al. (2019), the researchers used Sherlock, which is a colocalization method and investigated eQTLs in GTEx, but do not consider the CMC18. Next, the researchers used a summary mendelian randomization was done to identify putatively causal genes, however, this method does not consider tissue specificity. In contrast, FOCUS accounts for this11. Similarly, Gamazon et al. (2019) has done a multi-tissue analyses in several neuropsychiatric traits, such as ADHD, bipolar disorder, schizophrenia using PrediXcan20. It focuses primarily on large comparisons between these traits but does not go in as in depth into ADHD. Although, similarly identify several hits overlapping with the results in this paper: ARTN, MED8, and TIE1.Clustering the TWAS hits into a gene network based on co-expression identified that TMEM161B and KAT2B associated with ADHD. To conclude, TWAS is a powerful statistical method to identify small and large-effect genes associated with ADHD and helps with understanding the molecular underpinning of the disease.We conclude this study with several caveats and potential follow-up studies. First, TWAS associations could potentially be due to confounding because the gene expression levels that were imputed are derived from weighted linear combinations of SNPs. These SNPs could be included in non-regulatory mechanisms driving the association and risk, ultimately inflating certain statistics. Although the permutation tests and probabilistic fine-mapping used in this study try to protect against these spurious chance events, there is still a possibility of this occurring. Second, a follow-up study will require a large replication cohort, which may be difficult to ascertain, since this current largest GWAS dataset was used in this study. Future studies could investigate the possibility of using gene-risk scores in additional cohorts to validate any findings from this study. Finally, a given gene may have other regulatory features that do not go through eQTLs and still have downstream effect on the trait. Here, we successfully managed to identify several putatively causal genes such as 21. Details pertaining to participant ascertainment and quality control were previously reported by Demontis et al.6 The data used in this paper includes only the European population from the ADHD GWAS .Summary statistics were obtained through the ADHD Workgroup of the Psychiatric Genomics Consortium (PGC-ADHD)7. Here, we used 10 brain tissue panels from GTEx 53 v7 and the CommonMind Consortium (CMC)22. A strict Bonferroni-corrected study-wise threshold was used: P = 4.97E−07  . FUSION was used to conduct the transcriptome-wide association testing. The 1000 Genomes v3 LD panel was used for the TWAS. FUSION utilizes several penalized linear models, such as GBLUP, LASSO, Elastic Net7. Additionally, a Bayesian sparse linear mixed model (BSLMM) is used. FUSION computes an out-sample R2 to determine the best model by performing a fivefold cross-validating of every model. After, a multiple degree-of-freedom omnibus test was done to test for effect in multiple reference panels. This test will account for pairwise correlation between functional features. The threshold for the omnibus test was P = 4.64E−06  (number of genes tested for omnibus).TI was done using eQTL reference panels derived from tissue-specific gene expression coupled with genotypic data using panels from FUSION7. The defined regions include only the transcribed region of the genes. Each ADHD GWAS SNP association was conditioned on the joint gene model one SNP at a time. To assess inflation of imputed association statistics under the null of no GWAS association, a permutation test  was conducted to shuffle the QTL weights and empirically determine an association statistic. Permutation was done for each of the significant loci using FUSION. The loci that pass the permutation test demonstrate levels of heterogeneity captured by expression and are less likely to be co-localization due to chance. It should be noted that the permutated statistic is very conservative and truly causal genes could fail to reject the null due to the QTLs having complex and high linkage disequilibrium.To determine how much GWAS signal remains after the expression association from TWAS is removed, joint and conditional testing was done for genome-wide Bonferroni-corrected TWAS signals using FUSION11. FOCUS identifies genes for each TWAS signal to be included in a 90%-credible set while controlling for pleiotropic SNP effects. The same TWAS reference panels for FUSION were used as in the analysis described above.To address the issue of co-regulation in TWAS, we used the program FOCUS  to directly model predicted expression correlations and to give a posterior probability for causality in relevant tissue typeshttps://genenetwork.nl) RNA-sequencing database 23. This was used because GeneNetwork it identifies co-regulated genes within each pathway, which can help differentiate whether co-regulation is due to proximity to the same eQTL in TWAS or converges independent from different TWAS hits. Briefly, a principal component analysis (PCA) is done on the 31,499 RNA-seq and the eigenvector coefficients for reliable principal components (PC). Co-regulation scores are calculated between genes, which is the correlation between the eigenvector coefficients for each pair. Next, for each reliable PC, it is determined how much it explains each biological pathway, which is defined as the group of genes annotated with the term in databases such as GO Function. A t-test was done between eigencoefficients of the genes annotated to a term to any other term in the database. Finally, compared between samples enrichment is calculated by using a Mann–Whitney U-test between the Z-score of the gene set compared to the Z-score of the genes not included in the network. Genes meeting a Bonferroni significance threshold of P = 9.94E−07  was used. Agnostic analyses of pathways in databases such as Reactome and GO were done to identify pathways relevant to ADHD.Due to the stringent Bonferroni-corrected significance, we relaxed the threshold for pathway analyses since Bonferroni-correction assumes independence and genes tend to be correlated due to co-expression. A relaxed nominal Bonferroni-corrected threshold of 0.10 (uncorrected 9.94E−07) was used because co-regulation in TWAS signals violate the independence assumption required for Bonferroni-correction, making it too strict, especially for gene-set enrichment. For gene-set enrichments, more genes will allow for better recapitulation and prioritization of appropriate pathways. Gene clustering was done using the GeneNetwork v2.0 (https://atlas.ctglab.nl).To identify phenotypes associated with the top eQTL for each TWAS gene, a phenome-wide association study (pheWAS) was done for each SNP. The top three phenotypes (excluding ADHD) were reported. PheWAS was done using public data provided by GWASAtlas (https://atlas.ctglab.nl), which uses LDSC to determine genetic correlation24. The most recent GWAS data (as of 2019) for each trait was used for the correlation6. The significance threshold was corrected for the number of tested traits with a Bonferroni correction.To determine the genetic relationship between ADHD and the phenotypes identified from pheWAS, genetic correlation of the traits was done for available GWAS data. This was done using GWASAtlas (Further information on research design is available in the Peer Review FileSupplementary InformationSupplementary Dataset 1Description of Additional Supplementary FilesReporting Summary