Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function

Role of miRNAs in the pathogenesis of psoriasis and psoriatic arthritis: a genome-wide Mendelian randomization study

BACKGROUND

MicroRNAs (miRNAs) are critical in the onset and treatment of skin diseases, but the miRNAs causally associated with psoriasis (PSO) and psoriatic arthritis (PsA) remain unclear. This study aims to identify miRNAs with causal associations with PSO and PsA.

METHODS

Five Mendelian randomization (MR) methods were employed, using miRNA expression quantitative trait loci (mirQTL) data as exposure variables and PSO and PsA as outcome variables. This approach was used to uncover the causal links of miRNAs with both PSO and PsA, with robust sensitivity analyses ensuring the stability of our findings. Finally, miRNet and enrichment analyses were used to predict target genes of the causal miRNAs and their potential biological roles.

RESULTS

Our robust findings indicated that miR-27b-3p, miR-204-5p, and miR-6891-3p were notably associated with an enhanced risk of PSO. Additionally, miR-6891-3p was greatly associated with an enhanced risk of PsA. Conversely, miR-29c-3p, miR-181a-3p, miR-181a-5p, miR-181b-5p, and miR-199a-3p were substantially associated with a reduced risk of both PSO and PsA. Enrichment analyses revealed that the target genes of these causal miRNAs were markedly enriched in biological pathways such as apoptosis, Wnt, and PI3K-AKT signaling.

CONCLUSION

This study identifies eight miRNAs causally associated with PSO and five miRNAs associated with PsA, with no observed heterogeneity or pleiotropy. These findings offer potential biomarkers for the diagnosis and treatment of PSO and PsA. Key Points • We conducted the first genome-wide MR study to explore the causal relationships between miRNAs and PSO and PsA. • The study found stable and reliable causal effects of 8 miRNAs on PSO and 5 miRNAs on PsA. • These miRNAs provide important insights into elucidating the pathophysiological mechanisms of PSO and PsA and developing new therapeutic approaches.

Evaluation of silicosis combined with type 2 diabetes mellitus based on the quantitative CT measured parameters

Background

Type 2 diabetes mellitus (T2DM) and silicosis both have significant impacts on cardiopulmonary health. However, there is a lack of research investigating whether the presence of T2DM causes additional damage to the heart and lungs of patients with silicosis. This study aims to assess the alterations in pulmonary and cardiac structures, as well as lung function, in patients with silicosis who also have T2DM, and to explore the factors influencing lung function.

Methods

We included 30 silicosis patients with T2DM and 30 silicosis patients without diabetes. The two groups were matched by age, silicosis stage, smoking history, and dust exposure duration. Demographic details, occupational history, hematological results, computed tomography (CT)-measured cardiac and lung parameters, and pulmonary function test (PFT) results were collected. We compared these parameters between the two groups, evaluated the correlation between lung function and CT parameters in the diabetic group, and analyzed factors affecting lung function in this group.

Results

The silicosis combined with T2DM group showed significantly higher values for body mass index (BMI), the longest diameters from left to right of the left ventricle (LVLR), blood glucose, and triglycerides compared to the silicosis but without T2DM group (all P<0.05). The silicosis combined with T2DM group showed significantly lower total lung mass, and the ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) compared to the silicosis but without T2DM group (all P<0.05). In the silicosis with T2DM group, FEV1/FVC ratio showed significant correlations with total lung mass, the longest diameters from left to right of the left atrium, LVLR, and the longest diameters from left to right of the right atrium (r values were 0.51, 0.47, 0.40, and 0.44, respectively; all P<0.05). Multivariate analysis revealed that BMI and the LVLR were independent determinants of FEV1/FVC ratio in the silicosis with T2DM group (t values were -3.367 and 2.471, respectively; all P<0.05).

Conclusions

Diabetes mellitus induces structural changes in the lungs and heart of patients with silicosis and exacerbates the impairment of lung function. BMI and LVLR are key determinants of the FEV1/FVC ratio highlighting the need for enhanced comprehensive management strategies.

Genomic network analysis characterizes genetic architecture and identifies trait-specific biology

Pervasive genetic overlap across human complex traits necessitates developing multivariate methods that can parse pleiotropic and trait-specific genetic signals. Here, we introduce Genomic Network Analysis (GNA), an analytic framework that applies the principles of network modelling to estimates of genetic overlap derived from genome-wide association study (GWAS) summary statistics. The result is a genomic network that describes the conditionally independent genetic associations between traits that remain when controlling for shared signal with the broader network of traits. Graph theory metrics provide added insight by formally quantifying the most important traits in the genomic network. GNA can discover additional trait-specific pathways by incorporating gene expression or genetic variants into the network to estimate their conditional associations with each trait. Extensive simulations establish GNA is well-powered for most GWAS. Application to a diverse set of traits demonstrate that GNA yields critical insight into the genetic architecture that demarcate genetically overlapping traits at varying levels of biological granularity.

Associations of dietary inflammation index and composite dietary antioxidant index with preserved ratio impaired spirometry in US adults and the mediating roles of triglyceride-glucose index: NHANES 2007-2012

BACKGROUND

Previous studies have shown that inflammatory and antioxidant dietary patterns can modify the risk of COPD, yet few studies have examined the association of these diets with its early signs (PRISm), and the potential role of metabolic disorders remains to be elucidated.

METHODS

Data from 9529 individuals who participated in the 2007-2012 National Health and Nutrition Examination Survey (NHANES) were analyzed. The Dietary Inflammation Index (DII) and the Dietary Antioxidant Composite Index (CDAI) were assessed using 24-h dietary recall, multiple metabolic indicators were calculated according to biochemical markers, and lung function parameters defined PRISm cases. Individual and joint effects of DII and CDAI were evaluated by generalized linear models and binary logistic regression models, and mediation effects of metabolic indicators were further explored by causal mediation analysis.

RESULTS

Increased DII was associated with decreased lung function (FEV1: β = -18.82, FVC: β = -29.2; OR = 1.04) and increased metabolic indicators (β = 0.316, 0.036, 0.916, 0.033, and 0.145 on MAP, UA, TC, TyG, and MS, respectively). Contrary to this, CDAI were positively and negatively associated with lung function (FEV1: β = 3.42; FVC: β = 4.91; PRISm: OR = 0.99) and metabolic indicators (β < 0), respectively. Joint effects of DII and CDAI indicated the minimal hazard effects of DII on TyG (β = -0.11), FEV1 (β = 72.62), FVC (β = 122.27), and PRISm (OR = 0.79) in subjects with high CDAI when compared with those with low CDAI (low DII + high CDAI vs. high DII + low CDAI). Furthermore, TyG mediated 13.74 %, 8.29 %, and 21.70 % of DII- and 37.30 %, 20.90 %, and 12.32 % of CDAI-FEV1, -FVC, and -PRISm associations, respectively.

CONCLUSIONS

These findings indicated that CDAI can attenuate the adverse effects of DII on metabolic disorders and lung function decline, which provides new insight for diet modification in preventing early lung dysfunction.

Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension

BACKGROUND

Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels.

METHODS

In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP.

RESULTS

Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP.

CONCLUSIONS

The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.

Genetic influences on circulating retinol and its relationship to human health

Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.

Multiomic prioritisation of risk genes for anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder associated with marked morbidity. Whilst AN genetic studies could identify novel treatment targets, integration of functional genomics data, including transcriptomics and proteomics, would assist to disentangle correlated signals and reveal causally associated genes.

METHODS

We used models of genetically imputed expression and splicing from 14 tissues, leveraging mRNA, protein, and mRNA alternative splicing weights to identify genes, proteins, and transcripts, respectively, associated with AN risk. This was accomplished through transcriptome, proteome, and spliceosome-wide association studies, followed by conditional analysis and finemapping to prioritise candidate causal genes.

RESULTS

We uncovered 134 genes for which genetically predicted mRNA expression was associated with AN after multiple-testing correction, as well as four proteins and 16 alternatively spliced transcripts. Conditional analysis of these significantly associated genes on other proximal association signals resulted in 97 genes independently associated with AN. Moreover, probabilistic finemapping further refined these associations and prioritised putative causal genes. The gene WDR6, for which increased genetically predicted mRNA expression was correlated with AN, was strongly supported by both conditional analyses and finemapping. Pathway analysis of genes revealed by finemapping identified the pathway regulation of immune system process (overlapping genes = MST1, TREX1, PRKAR2A, PROS1) as statistically overrepresented.

CONCLUSIONS

We leveraged multiomic datasets to genetically prioritise novel risk genes for AN. Multiple-lines of evidence support that WDR6 is associated with AN, whilst other prioritised genes were enriched within immune related pathways, further supporting the role of the immune system in AN.

Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches

We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.

Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance

Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (rg = 0.368, SE = 0.076, P = 1.61 × 10-6), cannabis use disorder (rg = 0.309, SE = 0.033, P = 1.97 × 10-20) and nicotine (rg = 0.117, SE = 0.043, P = 7.0 × 10-3), as well as drinks per week (rg = 0.087, SE = 0.021, P = 6.36 × 10-5), cigarettes per day (rg = 0.11, SE = 0.024, P = 4.93 × 10-6) and life-time cannabis use (rg = 0.234, SE = 0.029, P = 3.74 × 10-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.

Genetics-informed precision treatment formulation in schizophrenia and bipolar disorder

Genetically informed drug development and repurposing is an attractive prospect for improving patient outcomes in psychiatry; however, the effectiveness of these endeavors is confounded by heterogeneity. We propose an approach that links interventions implicated by disorder-associated genetic risk, at the population level, to a framework that can target these compounds to individuals. Specifically, results from genome-wide association studies are integrated with expression data to prioritize individual "directional anchor" genes for which the predicted risk-increasing direction of expression could be counteracted by an existing drug. While these compounds represent plausible therapeutic candidates, they are not likely to be equally efficacious for all individuals. To account for this heterogeneity, we constructed polygenic scores restricted to variants annotated to the network of genes that interact with each directional anchor gene. These metrics, which we call a pharmagenic enrichment score (PES), identify individuals with a higher burden of genetic risk, localized in biological processes related to the candidate drug target, to inform precision drug repurposing. We used this approach to investigate schizophrenia and bipolar disorder and reveal several compounds targeting specific directional anchor genes that could be plausibly repurposed. These genetic risk scores, mapped to the networks associated with target genes, revealed biological insights that cannot be observed in undifferentiated genome-wide polygenic risk score (PRS). For example, an enrichment of these partitioned scores in schizophrenia cases with otherwise low PRS. In summary, genetic risk could be used more specifically to direct drug repurposing candidates that target particular genes implicated in psychiatric and other complex disorders.

The genetic architecture of pneumonia susceptibility implicates mucin biology and a relationship with psychiatric illness

Pneumonia remains one of the leading causes of death worldwide. In this study, we use genome-wide meta-analysis of lifetime pneumonia diagnosis (N = 391,044) to identify four association signals outside of the previously implicated major histocompatibility complex region. Integrative analyses and finemapping of these signals support clinically tractable targets, including the mucin MUC5AC and tumour necrosis factor receptor superfamily member TNFRSF1A. Moreover, we demonstrate widespread evidence of genetic overlap with pneumonia susceptibility across the human phenome, including particularly significant correlations with psychiatric phenotypes that remain significant after testing differing phenotype definitions for pneumonia or genetically conditioning on smoking behaviour. Finally, we show how polygenic risk could be utilised for precision treatment formulation or drug repurposing through pneumonia risk scores constructed using variants mapped to pathways with known drug targets. In summary, we provide insights into the genetic architecture of pneumonia susceptibility and genetics informed targets for drug development or repositioning.

Variation in cardiovascular disease risk factors among older adults in the Hunter Community Study cohort; a comparison of diet quality versus polygenic risk score

Background

The interplay between cardiovascular disease (CVD) genetic risk indexed by a polygenic risk score (PRS) and diet quality still requires further investigation amongst older adults or those with established or treated CVD. The present study aimed to evaluate the relative contribution of diet quality, measured using the Australian Recommended Food Score (ARFS) and PRS, with respect to explaining variation in plasma lipids CVD outcomes in the Hunter Cohort.

Methods

The study comprised a secondary analysis of cross‐sectional data from the Hunter Cohort study. Single‐nucleotide polymorphisms from previously derived polygenic scores (PGSs) for three lipid classes were obtained: low‐density lipoprotein, high‐density lipoprotein and triglycerides, as well as PRS for coronary artery disease (CAD) from the PGS catalogue. Regression modelling and odds ratios were used to determine associations between PRS, ARFS and CVD risk.

Results

In total, 1703 participants were included: mean ± SD age 66 ± 7.4 years, 51% female, mean ± SD total ARFS 28.1 ± 8 (out of 74). Total diet quality and vegetable subscale were not significantly associated with measured lipids. By contrast, PGS for each lipid demonstrated a markedly strong, statistically significant correlation with its respective measured lipid. There was a significant association between CAD PRS and 5/6 CVD phenotypes (all except atrial fibrillation), with the largest effect size shown with coronary bypass. Adding dietary intake as a covariate did not change this relationship.

Conclusions

Lipid PGS explained more variance in measured lipids than diet quality. However, the poor diet quality observed in the current cohort may have limited the ability to observe any beneficial effects. Future research should investigate whether the diet quality of older adults can be improved and also the effect of these improvements on changes in polygenic risk.

The Australian Recommended Food Score (ARFS) had little association with lipid and cardiovascular disease (CVD) endpoints.

Lipid polygenic score (PGS) explained more variance in measured lipids than diet quality.

The lipid PGS was associated with all three lipid parameters and some CVD endpoints, especially high cholesterol.

Coronary artery disease polygenic risk score was associated with CVD endpoints angina, coronary bypass, heart attack, high cholesterol and hypertension and some lipid parameters (high‐density lipoprotein cholesterol).

Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.

Advancing the use of genome-wide association studies for drug repurposing

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.