Epidemiological studies demonstrate an association between asthma and mental health disorders, although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and the causal link between asthma and mental health disorders.We conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between asthma from the UK Biobank and eight mental health disorders from the Psychiatric Genomics Consortium: attention deficit hyperactivity disorder (ADHD), anxiety disorder (ANX), autism spectrum disorder, bipolar disorder, eating disorder, major depressive disorder (MDD), post-traumatic stress disorder and schizophrenia (sample size 9537–394 283).In the single-trait genome-wide association analysis, we replicated 130 previously reported loci and discovered 31 novel independent loci that are associated with asthma. We identified that ADHD, ANX and MDD have a strong genetic correlation with asthma at the genome-wide level. Cross-trait meta-analysis identified seven loci jointly associated with asthma and ADHD, one locus with asthma and ANX, and 10 loci with asthma and MDD. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to the exocrine/endocrine, digestive, respiratory and haemic/immune systems. Mendelian randomisation analyses suggested that ADHD and MDD (including 6.7% sample overlap with asthma) might increase the risk of asthma.This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between asthma and three mental health disorders (ADHD, ANX and MDD). Such shared genetics implicate potential new biological functions that are in common among them.

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves fromR² = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Background: Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWASs in other populations are lacking.Methods: We included 100 285 subjects from the China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS analyses were performed on forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC) and FEV₁/FVC in the CKB. We then performed genome-wide cross-trait analysis between lung function and obesity traits (body mass index (BMI), BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference) to investigate the shared genetic effects in the CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in the CKB and their interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with the CKB using up to 457 756 subjects from the UK Biobank (UKB) for replication and investigation of ancestry-specific effects.Results: We identified nine genome-wide significant novel loci for FEV₁, six for FVC and three for FEV₁/FVC in the CKB. FEV₁and FVC showed significant negative genetic correlation with obesity traits in both the CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important biological pathways, including cell proliferation, embryo, skeletal and tissue development, and regulation of gene expression. Mendelian randomisation analysis suggested significant negative causal effects of BMI on FEV₁and on FVC in both the CKB and UKB. Lung function PRSs significantly modified the effect of change in BMI on change in lung function during an average follow-up of 8 years.Conclusion: This large-scale GWAS of lung function identified novel loci and shared genetic aetiology between lung function and obesity. Change in BMI might affect change in lung function differently according to a subject's polygenic background. These findings may open new avenues for the development of molecular-targeted therapies for obesity and lung function improvement.

Aims/hypothesis: The link underlying abnormal glucose metabolism, type 2 diabetes and polycystic ovary syndrome (PCOS) that is independent of BMI remains unclear in observational studies. We aimed to clarify this association using a genome-wide cross-trait approach. Methods: Summary statistics from the hitherto largest genome-wide association studies conducted for type 2 diabetes, type 2 diabetes mellitus adjusted for BMI (T2DM_adjBMI), fasting glucose, fasting insulin, 2h glucose after an oral glucose challenge (all adjusted for BMI), HbA_1c and PCOS, all in populations of European ancestry, were used. We quantified overall and local genetic correlations, identified pleiotropic loci and expression–trait associations, and made causal inferences across traits. Results: A positive overall genetic correlation between type 2 diabetes and PCOS was observed, largely influenced by BMI (r_g=0.31, p=1.63×10^–8) but also independent of BMI (T2DM_adjBMI–PCOS: r_g=0.12, p=0.03). Sixteen pleiotropic loci affecting type 2 diabetes, glycaemic traits and PCOS were identified, suggesting mechanisms of association that are independent of BMI. Two shared expression–trait associations were found for type 2 diabetes/T2DM_adjBMI and PCOS targeting tissues of the cardiovascular, exocrine/endocrine and digestive systems. A putative causal effect of fasting insulin adjusted for BMI and type 2 diabetes on PCOS was demonstrated. Conclusions/interpretation: We found a genetic link underlying type 2 diabetes, glycaemic traits and PCOS, driven by both biological pleiotropy and causal mediation, some of which is independent of BMI. Our findings highlight the importance of controlling fasting insulin levels to mitigate the risk of PCOS, as well as screening for and long-term monitoring of type 2 diabetes in all women with PCOS, irrespective of BMI. Graphical

Asthma is a chronic and heterogeneous respiratory disease with many risk factors that typically originate during early childhood. A complex interplay between environmental factors and genetic predisposition is considered to shape the lung and gut microbiome in early life. The growing literature has identified that changes in the relative abundance of microbes (microbial dysbiosis) and reduced microbial diversity, as triggers of the airway-gut axis crosstalk dysregulation, are associated with asthma development. There are several mechanisms underlying microbial dysbiosis to childhood asthma development pathways. For example, a bacterial infection in the airway of infants can lead to the activation and/or dysregulation of inflammatory pathways that contribute to bronchoconstriction and bronchial hyperresponsiveness. In addition, gut microbial dysbiosis in infancy can affect immune development and differentiation, resulting in a suboptimal balance between innate and adaptive immunity. This evolving dysregulation of secretion of pro-inflammatory mediators has been associated with persistent airway inflammation and subsequent asthma development. In this review, we examine current evidence around associations between the airway and gut microbial dysbiosis with childhood asthma development. More specifically, this review focuses on discussing the integrated roles of environmental exposures, host metabolic and immune responses, airway and gut microbial dysbiosis in driving childhood asthma development.