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Attention Deficit-Hyperactivity Disorder (ADHD) is a complex psychiatric and neurodevelopmental disorder that develops during childhood and spans into adulthood. ADHD’s aetiology is complex, and evidence about its cause and risk factors is limited. We leveraged genetic data from genome-wide association studies (GWAS) and performed latent causal variable analyses using a hypothesis-free approach to infer causal associations between 1387 complex traits and ADHD. We identified 37 inferred potential causal associations with ADHD risk. Our results reveal that genetic variants associated with iron deficiency anemia (ICD10), obesity, type 2 diabetes, synovitis and tenosynovitis (ICD10), polyarthritis (ICD10), neck or shoulder pain, and substance use in adults display partial genetic causality on ADHD risk in children. Genetic variants associated with ADHD have a partial genetic causality increasing the risk for chronic obstructive pulmonary disease and carpal tunnel syndrome. Protective factors for ADHD risk included genetic variants associated with the likelihood of participating in socially supportive and interactive activities. Our results show that genetic liability to multiple complex traits influences a higher risk for ADHD, highlighting the potential role of cardiometabolic phenotypes and physical pain in ADHD’s aetiology. These findings have the potential to inform future clinical studies and development of interventions.

Suicide is a major public health problem in Mexico and around the world. Genetic predisposition for major depressive disorder (MDD) has been associated with increased risk for suicidal behaviors (SB) in populations of European ancestry (EA). Here, we examine whether MDD polygenic risk scores (MDD PRS), derived from a genome-wide association study involving EA individuals, predict SB, including ideation, planning, and attempt, among Mexican youth using a longitudinal design. At baseline, participants (N = 1,128, 12-17 years, 55% women) were interviewed and genotyped as part of a general population survey on adolescent mental health. Eight years later, they were recontacted for a follow up visit (N = 437, 20-25 years, 63% women). At both assessments, individuals reported on their engagement in SB within the past year. MDD PRS were significantly positively associated with SB, particularly suicide ideation and planning during adolescence, accounting for ~4-5% of the variance in these outcomes. In contrast, associations between MDD PRS and SB during young adulthood did not reach statistical significance. Our results suggest that increased genetic liability for depression increased risk for SB, particularly during adolescence, expanding our knowledge of the genetic underpinnings of SB.

Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e−8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.11 95% CI [1.04, 1.19], p

In the present study, we sought to identify causal relationships between obesity and other complex traits and conditions using a data-driven hypothesis-free approach that uses genetic data to infer causal associations. We leveraged available summary-based genetic data from genome-wide association studies on 1498 phenotypes and applied the latent causal variable method (LCV) between obesity and all traits. We identified 110 traits causally associated with obesity. Of those, 109 were causal outcomes of obesity, while only leg pain in calves was a causal determinant of obesity. Causal outcomes of obesity included 26 phenotypes associated with cardiovascular diseases, 22 anthropometric measurements, nine with the musculoskeletal system, nine with behavioural or lifestyle factors including loneliness or isolation, six with respiratory diseases, five with body bioelectric impedances, four with psychiatric phenotypes, four related to the nervous system, four with disabilities or long-standing illness, three with the gastrointestinal system, three with use of analgesics, two with metabolic diseases, one with inflammatory response and one with the neurodevelopmental disorder ADHD, among others. In particular, some causal outcomes of obesity included hypertension, stroke, ever having a period of extreme irritability, low forced vital capacity and forced expiratory volume, diseases of the musculoskeletal system, diabetes, carpal tunnel syndrome, loneliness or isolation, high leukocyte count, and ADHD. Our results indicate that obesity causally affects a wide range of traits and comorbid diseases, thus providing an overview of the metabolic, physiological, and neuropsychiatric impact of obesity on human health.

Samples can be prone to ascertainment and attrition biases.The Australian Genetics of Depression Study is a large publicly recruited cohort (n=20,689) established to increase the understanding of depression and antidepressant treatment response. As part of the recruitment, participants donated a saliva sample and were given the option to consent to linkage of prescription records for research purposes. This study investigates differences between participants who donated a saliva sample or agreed to linkage of their records compared to those who did not. We observed that older, male participants with a higher education were more likely to donate a saliva sample. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder and social anxiety disorder were associated with lower odds of donating a saliva sample whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was also associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia whereas there was no significant genetic effect for neuroticism. Overall, our results show moderate differences among these subsamples. Most current epidemiological studies do not adjust, nor search, for attrition biases at the genetic level. The possibility to do so is a strength of samples such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.

Background Migraine is a complex neurological disorder that is considered the most common disabling brain disorder affecting 14 % of people worldwide. The present study sought to infer potential causal relationships between self-reported migraine and other complex traits, using genetic data and a hypothesis-free approach. Methods We leveraged available summary statistics from genome-wide association studies (GWAS) of 1,504 phenotypes and self-reported migraine and inferred pair-wise causal relationships using the latent causal variable (LCV) method. Results We identify 18 potential causal relationships between self-reported migraine and other complex traits. Hypertension and blood clot formations were causally associated with an increased migraine risk, possibly through vasoconstriction and platelet clumping. We observed that sources of abdominal pain and discomfort might influence a higher risk for migraine. Moreover, occupational and environmental factors such as working with paints, thinner or glues, and being exposed to diesel exhaust were causally associated with higher migraine risk. Psychiatric-related phenotypes, including stressful life events, increased migraine risk. In contrast, ever feeling unenthusiastic / disinterested for a whole week, a phenotype related to the psychological well-being of individuals, was a potential outcome of migraine. Conclusions Overall, our results suggest a potential vascular component to migraine, highlighting the role of vasoconstriction and platelet clumping. Stressful life events and occupational variables potentially influence a higher migraine risk. Additionally, a migraine could impact the psychological well-being of individuals. Our findings provide novel testable hypotheses for future studies that may inform the design of new interventions to prevent or reduce migraine risk and recurrence.

The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37–2.54]), recent suicide attempt (OR = 1.88 [1.14–3.09]), higher use of tobacco (OR = 1.05 [1.02–1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06–1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68–0.83]), escitalopram (OR = 0.75 [0.67–0.85]) and venlafaxine (OR = 0.78 [0.68–0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30–0.67]), escitalopram (OR = 0.45 [0.27–0.74]) and citalopram (OR = 0.32 [0.15–0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.

BackgroundSleep apnoea is characterised by periods of halted breathing during sleep. Despite its association with severe health conditions, the aetiology of sleep apnoea remains understudied, and previous genetic analyses have not identified many robustly associated genetic risk variants.MethodsWe performed a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts (NTotal=523,366), followed by a multi-trait analysis of GWAS (MTAG) to boost power, leveraging the high genetic correlation between sleep apnoea and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional & joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal=1,477,352; Ncases=175,522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.ResultsOur MTAG analysis uncovered 49 significant independent loci associated with sleep apnoea risk. Twenty-nine variants were replicated in the 23andMe cohort. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.ConclusionsOur study uncovered multiple genetic loci associated with sleep apnoea risk, thus increasing our understanding of the aetiology of this condition and its relationship with other complex traits.

Study ObjectiveSleep is essential for both physical and mental health, and there is a growing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel inferred causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data.MethodsWe used summary-level statistics from genome-wide association studies and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 other phenotypes.ResultsWe identify 84 inferred causal relationships. Among other findings, connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping, and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues may increase insomnia risk, possibly through an increased risk of respiratory disease or socio-economic related factors.ConclusionOverall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.

RationalePneumonia is a respiratory condition with complex aetiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation.MethodsWe analysed pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9,980 cases and 86,519 controls). We perform genome-wide association study (GWAS) meta-analysis, gene-based test, colocalisation, genetic correlation, latent causal variable and polygenic prediction in an independent Australian sample (N=5,595) to draw insights into the genetic aetiology of pneumonia risk.ResultsWe identify two independent loci on chromosome 15 (lead SNPs rs2009746 and rs76474922) to be associated with pneumonia(pIL127, PBX3, APOBRand smoking related genesCHRNA3/5, associated with pneumonia. Evidence ofHYKKandPBX3involvement in pneumonia risk was supported by eQTL colocalisation analysis. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. Latent causal variable analysis suggests a strong genetic causal relationship cardiovascular health phenotypes and pneumonia risk. Polygenic risk scores (PRS) for pneumonia significantly predicted self-reported pneumonia history in an independent Australian sample, albeit with a small effect size (OR=1.11 95%CI=[1.04-1.19], pConclusionsAltogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.

SUMMARY Background The bidirectional relationship between depression and chronic pain is well recognized but their clinical management remains challenging. Here we characterize the shared aetiology and risk factors for their comorbidity using large population cohorts to advance understanding of pharmacological treatment outcomes. Methods Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health (N=13,839). Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Findings Chronic pain was associated with an increased risk of depression (OR=1.86 [1.37–2.54]), recent suicide attempt (OR=1.88[1.14–3.09]), higher use of alcohol, tobacco and painkiller misuse. Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR=0.75[0.68–0.83]), escitalopram (OR=0.75[0.67–0.85]) and venlafaxine (OR=0.78[0.68–0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR=0.45[0.30–0.67]), escitalopram (OR=0.45[0.27–0.74]) and citalopram (OR=0.32[0.15–0.67]) specifically for chronic pain reported lower benefits compared to other participants taking these same medications but not for chronic pain. Interpretation The findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further assessment is warranted in targeted interventional trials. This approach will advance precision psychiatry and assist in clinical management by choosing the most suitable treatment for patients, informed by specific symptoms and comorbidities. Funding source Data collection for the Australian Genetics of Depression Study was possible thanks to funding from the Australian National Health and Medical Research Council (NHMRC) to NGM (GNT1086683); data collection for the Prospective Imaging Study of Ageing: Genes, Brain and Behaviour was possible thanks to an NHMRC Dementia Research Team Grant administered by QIMR Berghofer Medical Research Institute (GNT1095227). The research funders did not participate in the study design, data analyses, results interpretation or writing of the manuscript.

Study ObjectiveSleep is essential for both physical and mental health. There is an increasing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data.MethodsWe used genetic data and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 different phenotypes.ResultsWe identify 84 significant causal relationships. Among other findings, poor health of musculoskeletal and connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues increases insomnia, potentially through an increased risk of respiratory disease.ConclusionOverall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.