Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis

Deciphering the shared genetic architecture between bipolar disorder and body mass index

BACKGROUND

The comorbidity between bipolar disorder (BD) and high body mass index (BMI) is well-documented, but their shared genetic architecture remains unclear. Our study aimed to explore this genetic correlation and potential causality.

METHODS

Utilizing large-scale genome-wide association study (GWAS) summary statistics, we quantified global and local genetic correlations between BD and BMI using linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics. Stratified LDSC characterized genetic overlap across functional categories. Cross-trait meta-analyses identified shared risk single nucleotide polymorphisms (SNPs), followed by colocalization analysis using Coloc. Bi-directional Mendelian randomization (MR) assessed causality, while tissue-level SNP heritability enrichment for BD and BMI was evaluated using LDSC-specific expressed genes and Multi-marker Analysis of Genomic Annotation.

RESULTS

We found a genetic correlation between BD and BMI, especially in localized genomic regions. Cross-trait meta-analysis identified 46 significant SNPs shared between BD and BMI, including three novel shared risk SNPs. Colocalization analysis verified two novel SNPs with shared causal variants linked to ITIH1 and TM6SF2 genes. MR analysis demonstrated a causal effect of BD on BMI, but not the reverse. Gene expression data revealed genetic correlation enrichment in five specific brain regions.

CONCLUSION

This study comprehensively analyzes the genetic correlation between BD and BMI, uncovering shared genetic architecture and identifying novel risk loci. These findings provide new insights into the interplay between BD and BMI, informing the development of diagnostic tools and therapeutic strategies.

Analysis of Rare Coding Variants in 470,000 UK Biobank Participants Reveals Genetic Associations With Childhood Asthma Predisposition

Previous studies of genetic contributions to risk of childhood asthma have implicated common variants with small effect sizes. Some studies using exome sequence data have reported associations with rare coding variants having larger effects on risk, notably an analysis of 145,000 subjects which found association with loss of function (LOF) variants in FLG, the gene coding for filaggrin. Here, we report the results of an analysis of rare nonsynonymous and LOF variants, carried out on the full UK Biobank cohort of 470,000 exome-sequenced participants. The phenotype of childhood asthma was defined as reporting asthma with onset before 18. Regression analysis was applied to gene-wise tests for association of LOF and nonsynonymous variants. Forty-five tests using different pathogenicity predictors were applied to the first cohort of 200,000 participants. Subsequently, the 100 genes showing strongest evidence for association were analysed in the second cohort of 270,000 participants, using only the best-performing predictor for each gene. For FLG, separate analyses were carried out for participants with atopic dermatitis. Three genes achieved statistical significance after correction for testing these 100 genes: FLG, IL33 and PRKCQ. The effects on asthma risk and frequencies of variants in different functional categories were characterised for these genes. Damaging coding variants were associated with increased risk of asthma in FLG and IL33 but reduced risk in PRKCQ. FLG LOF variants were also associated with the risk of atopic dermatitis, and their effect on asthma risk was higher in people who reported a diagnosis of atopic dermatitis. Rare coding variants in a small number of genes have important effects on asthma risk. Further study of individual variant effects might elucidate mechanisms of pathogenesis. This research has been conducted using the UK Biobank Resource.

Susceptibility genes for allergic conjunctivitis revealed by cross-tissue transcriptome-wide association study

Allergic conjunctivitis (AC) is a common immune-mediated ocular disorder, characterized by clinical manifestations such as ocular itching, conjunctival hyperemia, lacrimation, and mucoid discharge, which significantly impair patients' visual function and quality of life. Despite extensive research efforts devoted to uncovering the genetic predisposition to AC, the underlying pathogenic genes and molecular mechanisms remain incompletely understood, necessitating further research to elucidate its genetic basis. This study utilized AC data from the FinnGen R12 and incorporated expression quantitative trait loci data in the Genotype-Tissue Expression v8 database to perform a cross-tissue transcriptome-wide association study (TWAS). Analytical methods included functional summary-based imputation (FUSION), unified test for molecular signatures (UTMOST), and gene analysis combined with multi-marker genome annotation (MAGMA). To further validate the results, Mendelian randomization (MR) analysis and colocalization analysis were performed. Through TWAS and MAGMA analyses, 13 susceptibility genes associated with AC were identified. Following MR and colocalization analyses, three candidate genes-GAL3ST2, PDCD1 and TLR6-were ultimately selected and validated by FUMA tool, which may influence progression of AC by regulating pathways related to Toll-like receptor signaling. In conclusion, three susceptibility genes linked to the risk of AC were identified, providing new insights into the genetic mechanisms and potential pathogenic pathways underlying AC.

Genetic analysis reveals the shared genetic architecture between breast cancer and atrial fibrillation

Background

Epidemiological studies have observed an association between atrial fibrillation (AF) and breast cancer (BC). However, the underlying mechanisms linking these two conditions remain unclear. This study aims to systematically explore the genetic association between AF and BC.

Methods

We utilized the largest available genome-wide association study (GWAS) datasets for European individuals, including summary data for AF (N = 1,030,836) and BC (N = 247,173). Multiple approaches were employed to systematically investigate the genetic relationship between AF and BC from the perspectives of pleiotropy and causality.

Results

Global genetic analysis using LDSC and HDL revealed a genetic correlation between AF and BC (rg = 0.0435, P = 0.039). Mixer predicted genetic overlap between non-MHC regions of the two conditions (n = 125, rg = 0.05). Local genetic analyses using LAVA and GWAS-PW identified 22 regions with potential genetic sharing. Cross-trait meta-analysis by CPASSOC identified one novel pleiotropic SNP and 14 pleiotropic SNPs, which were subsequently annotated. Eight of these SNPs passed Bayesian colocalization tests, including one novel pleiotropic SNP. Further fine-mapping analysis identified a set of causal SNPs for each significant SNP. TWAS analyses using JTI and FOCUS models jointly identified 10 pleiotropic genes. Phenome-wide association study (PheWAS) of novel pleiotropic SNPs identified two eQTLs (PELO, ITGA1). Gene-based PheWAS results showed strong associations with BMI, height, and educational attainment. PCGA methods combining GTEx V8 tissue data and single-cell RNA data identified 16 co-enriched tissue types (including cardiovascular, reproductive, and digestive systems) and 5 cell types (including macrophages and smooth muscle cells). Finally, univariable and multivariable bidirectional Mendelian randomization analyses excluded a causal relationship between AF and BC.

Conclusion

This study systematically investigated the shared genetic overlap between AF and BC. Several pleiotropic SNPs and genes were identified, and co-enriched tissue and cell types were revealed. The findings highlight common mechanisms from a genetic perspective rather than a causal relationship. This study provides new insights into the AF-BC association and suggests potential experimental targets and directions for future research. Additionally, the results underscore the importance of monitoring the potential risk of one disease in patients diagnosed with the other.

Association and shared biological bases between birth weight and cortical structure

Associations between birth weight and cortical structural phenotypes have been detected; however, the understanding is incomprehensive, and the potential biological bases are not well defined. Leveraging data from genome-wide association studies, we investigated the associations and the shared transcriptomic, proteomic and cellular bases of birth weight and 13 cortical structural phenotypes. Mendelian randomization analyses were performed to examine associations between birth weight and cortical structure. Downstream transcriptome-wide association study (TWAS), proteome-wide association study (PWAS) and summary-based Mendelian randomization (SMR) analyses were utilized to identify the shared cis-regulated gene expressions and proteins. Finally, cell-type expression-specific integration for complex traits (CELLECT) analyses were conducted to explore the enriched cell types. The Mendelian randomization analyses found positive associations between birth weight and global cortical folding index, intrinsic curvature index, local gyrification index, surface area and volume. Downstream transcriptomic-level TWAS and SMR identified three gene expressions both linked to birth weight and at least one cortical structural phenotype (CNNM2, RABGAP1 and CENPW). Parallel PWAS and SMR analyses at the proteomic level identified four proteins linked to both phenotypes (CNNM2, RAB7L1, RAB5B and PPA2), of which CNNM2 was replicated. CELLECT analyses revealed brain cell types enriched in birth weight, including pericytes, inhibitory GABAergic neurons and cerebrovascular cells. These findings support the importance of early life growth to cortical structure, and suggest underlying transcriptomic, proteomic and cellular bases. These results provide intriguing targets for further research into the mechanisms of cortical development.

Genomic correlation, shared loci, and causal association between obesity, periodontitis and tooth loss

Observational studies have reported an association of obesity with periodontitis and tooth loss, yet findings remain inconsistent. We aim to investigate the genetic link underlying obesity-related traits (BMI [body mass index], WHR [waist-to-hip ratio], WHRadjBMI and childhood BMI), periodontitis and tooth loss. Leveraging summary statistics from large-scale genome-wide association studies, we comprehensively investigated the pair-wise genetic correlation using linkage disequilibrium score regression (LDSC) and SUPERGNOVA, identified shared loci using cross-phenotype association analysis (CPASSOC), and estimated causal association using Mendelian randomization. We identified a significant genetic correlation of obesity with tooth loss, but not with periodontitis. Partitioning the genome into LD-independent regions revealed 10 significantly shared local signals across six regions. Genome-wide cross-trait analysis uncovered 14 shared loci, four of which were colocalized: rs2064044 (PP4 = 0.94), rs6000329 (PP4 = 0.86), rs7134628 (PP4 = 0.86), and rs1286769 (PP4 = 0.90). Notably, rs1286769, identified via CPASSOC and validated through colocalization analysis, is located near RARβ, a gene associated with both BMI and denture use. Mendelian randomization revealed a nominally-significant causal association of obesity with periodontitis (P = 0.045) but a robust causal association with tooth loss represented by number of teeth (BMI: beta = [Formula: see text]0.20, 95%CI = [Formula: see text]0.26 to [Formula: see text]0.14, P = 5.27 × 10-11; WHR: beta = [Formula: see text]0.16, 95%CI = [Formula: see text]0.24 to [Formula: see text]0.08, P = 3.71 × 10-5). Results of CAUSE were consistent with main findings. From a genetic perspective, our work highlights an intrinsic link between obesity, periodontitis and tooth loss, which may add new lines of evidence and provide insights for clinical and public oral health applications.

Precision medicine for asthma treatment: Unlocking the potential of the epigenome and microbiome

Asthma is a leading worldwide biomedical concern. Patients can experience life-threatening worsening episodes (exacerbations) usually controlled by anti-inflammatory and bronchodilator drugs. However, substantial heterogeneity in treatment response exists, and a subset of patients with unresolved asthma carry the major burden of this disease. The study of the epigenome and microbiome might bridge the gap between human genetics and environmental exposure to partially explain the heterogeneity in drug response. This review aims to provide a critical examination of the existing literature on the microbiome and epigenetic studies examining associations with asthma treatments and drug response, highlight convergent pathways, address current challenges, and offer future perspectives. Current epigenetic and microbiome studies have shown the bilateral relationship between asthma pharmacologic interventions and the human epigenome and microbiome. These studies, focusing on corticosteroids and to a lesser extent on bronchodilators, azithromycin, immunotherapy, and mepolizumab, have improved the understanding of the molecular basis of treatment response and identified promising biomarkers for drug response prediction. Immune and inflammatory pathways (eg, IL-2, TNF-α, NF-κB, and C/EBPs) underlie microbiome-epigenetic associations with asthma treatment, representing potential therapeutic pathways to be targeted. A comprehensive evaluation of these omics biomarkers could significantly contribute to precision medicine and new therapeutic target discovery.

Mendelian randomization and genetic pleiotropy analysis for the connection between inflammatory bowel disease and Alzheimer's disease

BACKGROUND

The gut-microbiome-brain axis (GMBA) implies the connection between inflammatory bowel disease (IBD) and Alzheimer's disease (AD). We aimed to comprehensively explore the relation between IBD (and its subtypes) and AD, early-onset AD (EOAD) and late-onset AD (LOAD) from a genetic pleiotropy perspective.

METHODS

Relying on summary statistics (N = 472,868 for AD, 185,204 for EOAD, 191,061 for LOAD, 59,957 for IBD, 45,975 for CD, and 40,266 for UC), we first performed Mendelian Randomization to examine the causal association between IBD and AD by leveraging vertical pleiotropy. Then, we estimated global and local genetic correlations, followed by cross-trait association analysis to identify SNPs and genes with horizontal pleiotropy. Particularly, we utilized multi-trait colocalization analysis to assess the role of microbes in the common genetic etiology underlying the two types of diseases. Finally, we conducted functional enrichment analysis for pleiotropic genes.

RESULTS

We discovered suggestively causal relations between IBD (and its subtypes) and EOAD (ORIBD = 1.06 [1.01-1.11], ORCD = 1.05 [1.01-1.10], ORUC = 1.08 [1.01-1.15]) as well as between UC and LOAD (OR = 1.04 [1.01-1.08]), and discovered 44 local regions showing suggestively significant genetic correlations between IBD (and its subtypes) and AD (and EODA and LOAD). We further detected substantial genetic overlap, as characterized by 182  AD-associated, 3 EOAD-associated and 51 LOAD-associated pleiotropic SNPs as well as 291 pleiotropic genes. Pleiotropic genes more likely enriched in the GMBA-relevant tissues such as brain, intestine and esophagus. Moreover, we identified three microorganisms related to these disease pairs, including the Catenibacterium, Clostridia, and Prevotella species.

CONCLUSION

The suggestively causal associations and shared genetic basis between IBD and its subtypes with AD, EOAD and LOAD may commonly drive their co-occurrence, and gut microbes might partly explain the shared genetic etiology. Further studies are warranted to elaborate the possibly biological mechanisms underlying the two types of diseases.

Observational, causal relationship and shared genetic basis between cholelithiasis and gastroesophageal reflux disease: evidence from a cohort study and comprehensive genetic analysis

OBJECTIVE

Cholelithiasis and gastroesophageal reflux disease (GERD) contribute to significant health concerns. We aimed to investigate the potential observational, causal, and genetic relationships between cholelithiasis and GERD.

DESIGN

The observational correlations were assessed based on the prospective cohort study from UK Biobank. Then, by leveraging the genome-wide summary statistics of cholelithiasis (N = 334,277) and GERD (N = 332,601), the bidirectional causal associations were evaluated using Mendelian randomization (MR) analysis. Subsequently, a series of genetic analyses was used to assess the genetic correlation, shared loci, and genes between cholelithiasis and GERD.

RESULTS

The prospective cohort analyses revealed a significantly increased risk of GERD in individuals with cholelithiasis (hazard ratio [HR] = 1.99; 95% confidence interval [CI], 1.89-2.10) and a higher risk of cholelithiasis among patients with GERD (HR = 2.30; 95% CI, 2.18-2.44). The MR study indicated the causal effect of genetic liability to cholelithiasis on the incidence of GERD (odds ratio [OR] = 1.08; 95% CI, 1.05-1.11) and the causal effect of genetic predicted GERD on cholelithiasis (OR = 1.15; 95% CI, 1.02-1.31). In addition, cholelithiasis and GERD exhibited a strong genetic association. Cross-trait meta-analyses identified 5 novel independent loci shared between cholelithiasis and GERD. Three shared genes, including SUN2, CBY1, and JOSD1, were further identified as novel risk genes.

CONCLUSION

The elucidation of the shared genetic basis underlying the phenotypic relationship of these 2 complex phenotypes offers new insights into the intrinsic linkage between cholelithiasis and GERD, providing a novel research direction for future therapeutic strategy and risk prediction.

Disentangling the genetic overlap between ischemic stroke and obesity

OBJECTIVE

Obesity has been recognized as a risk factor for cerebrovascular diseases, with observational studies suggesting a heightened incidence of stroke. However, the genetic epidemiology field has yet to reach a consensus on the causal relationship and genetic overlap between ischemic stroke (IS) and obesity.

METHODS

We utilized linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant associations to assess the genetic correlation between body mass index (BMI) and IS. Bidirectional Mendelian randomization was employed to infer causality. We identified shared risk single nucleotide polymorphisms (SNPs) through cross-trait meta-analyses and estimated heritability using summary statistics. Summary-data-based Mendelian randomization (SMR) was applied to explore potential functional genes.

RESULTS

Our analysis revealed a significant positive genetic correlation between BMI and IS, supporting a causal link from BMI to IS. Cross-trait analysis yielded 9 and 16 shared risk SNPs for IS and small vessel stroke (SVS), respectively. We observed a notable enrichment of SNP heritability for IS and BMI in brain tissues, suggesting tissue-specific influences. The genes shared between the traits were predominantly involved in brain development, synaptic electrical activity, and immunoregulation. Notably, our SMR analysis identified the risk genes CHAF1A, CEP192, ULK4, CYP2D6, AS3MT, and WARS2 across the majority of the 14 enriched tissues shared by both traits.

CONCLUSION

Our study uncovered a significant genetic correlation and identified shared risk SNPs between BMI and IS. The identification of CHAF1A, CEP192, ULK4, CYP2D6, AS3MT, and WARS2 as potential functional genes common to both obesity and IS enriched our understanding of their genetic interplay, potentially advanced our grasp of their pathogenesis and therapeutic targets.

Integrated nasopharyngeal airway metagenome and asthma genetic risk endotyping of severe bronchiolitis in infancy and risk of childhood asthma

BACKGROUND

Infants with bronchiolitis are at increased risk of developing asthma. Growing evidence suggests bronchiolitis is a heterogeneous condition. However, little is known about its biologically distinct subgroups based on the integrated metagenome and asthma genetic risk signature and their longitudinal relationships with asthma development.

METHODS

In a multicentre prospective cohort study of infants with severe bronchiolitis (i.e. bronchiolitis requiring hospitalisation), we profiled nasopharyngeal airway metagenome and virus at hospitalisation, and calculated the polygenic risk score of asthma. Using similarity network fusion clustering approach, we identified integrated metagenome-asthma genetic risk endotypes. In addition, we examined their longitudinal association with the risk of developing asthma by the age of 6 years.

RESULTS

Out of 450 infants with bronchiolitis (median age 3 months), we identified five distinct endotypes, characterised by their nasopharyngeal metagenome, virus and asthma genetic risk profiles. Compared with endotype A infants (who clinically resembled "classic" bronchiolitis), endotype E infants (characterised by a high abundance of Haemophilus influenzae, high proportion of rhinovirus (RV)-A and RV-C infections and high asthma genetic risk) had a significantly higher risk of developing asthma (16.7% versus 35.9%; adjusted OR 2.24, 95% CI 1.02-4.97; p=0.046). The pathway analysis showed that endotype E had enriched microbial pathways (e.g. glycolysis, l-lysine, arginine metabolism) and host pathways (e.g. interferons, interleukin-6/Janus kinase/signal transducers and activators of transcription-3, fatty acids, major histocompatibility complex and immunoglobin-related) (false discovery rate (FDR)<0.05). Additionally, endotype E had a significantly higher proportion of neutrophils (FDR<0.05).

CONCLUSION

In this multicentre prospective cohort study of infant bronchiolitis, the clustering analysis of integrated-omics data identified biologically distinct endotypes with differential risks of developing asthma.

Epigenetic signatures of asthma: a comprehensive study of DNA methylation and clinical markers

BACKGROUND

Asthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma.

METHODS

The Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood from 319 participants from 94 families. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 individuals. Principal component analysis on the clinical asthma markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates.

RESULTS

221 unique CpGs reached genome-wide significance at timepoint 1 after Bonferroni correction. PC7, which correlated with loadings of eosinophil counts and immunoglobulin levels, accounted for the majority of associations (204). Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points yielded a correlation of 0.81.

CONCLUSION

We identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to a robust DNA methylation profile as a new, stable biomarker for asthma.

Dissecting shared genetic architecture between depression and body mass index

BACKGROUND

A growing body of evidence supports the comorbidity between depression (DEP) and obesity, yet the genetic mechanisms underlying this association remain unclear. Our study explored the shared genetic architecture and causal associations of DEP with BMI.

METHODS

We investigated the multigene overlap and genetic correlation between DEP (N > 1.3 million) and BMI (N = 806,834) based on genome-wide association studies (GWAS) and using the bivariate causal mixture model and linkage disequilibrium score regression (LDSC). The causal association was explored by bi-directional Mendelian randomization (MR). Common risk loci were identified through cross-trait meta-analyses. Stratified LDSC and multi-marker gene annotation analyses were applied to investigate single-nucleotide polymorphisms enrichment across tissue types, cell types, and functional categories. Finally, we explored shared functional genes by Summary Data-Based Mendelian Randomization (SMR) and further detected differential expression genes (DEG) in brain tissues of individuals with depression and obesity.

RESULTS

We found a positive genetic correlation between DEP and BMI (rg = 0.19, P = 4.07 × 10-26), which was more evident in local genomic regions. Cross-trait meta-analyses identified 16 shared genetic loci, 5 of which were newly identified, and they had influence on both diseases in the same direction. MR analysis showed a bidirectional causal association between DEP and BMI, with comparable effect sizes estimated in both directions. Combined with gene expression information, we found that genetic correlations between DEP and BMI were enriched in 6 brain regions, predominantly in the nucleus accumbens and anterior cingulate cortex. Moreover, 6 specific cell types and 23 functional genes were found to have an impact on both DEP and BMI across the brain regions. Of which, NEGR1 was identified as the most significant functional gene and associated with DEP and BMI at the genome-wide significance level (P < 5 × 10-8). Compared with healthy controls, the expression levels of NEGR1 gene were significant lower in brain tissues of individuals with depression and obesity.

CONCLUSIONS

Our study reveals shared genetic basis underpinnings between DEP and BMI, including genetic correlations and common genes. These insights offer novel opportunities and avenues for future research into their comorbidities.

A multi-level investigation of the genetic relationship between gastroesophageal reflux disease and lung cancer

Background

Observational studies have revealed a potential association between gastroesophageal reflux disease (GERD) and lung cancer (LC), but the genetic role in their comorbidity have not been fully elucidated. This study aimed to comprehensively dissect the genetic link underlying GERD and LC.

Methods

Using large-scale genome-wide association study (GWAS) data, we investigated shared genetic architecture between GERD and LC. Our analyses encompassed genetic correlation, cross-trait meta-analysis, transcriptome-wide association studies (TWASs), and the evaluation of the causality though a bidirectional Mendelian randomization (MR) analysis with sufficient sensitivities.

Results

We identified a significant genome-wide genetic correlation between GERD and overall LC (rg =0.33, P=1.58×10-14), as well as across other subtype-specific LC (rg ranging from 0.19 to 0.39). After separating the whole genome into approximately 2,353 independent regions, 5 specific regions demonstrated significant local genetic correlation, with most significant region located at 9q33.3. Cross-trait meta-analysis revealed 22 pleiotropic loci between GERD and LC, including 3 novel loci (rs537160, rs10156445, and rs17391694). TWASs discovered a total of 49 genes shared in multiple tissues, such as lung tissues, esophagus muscularis, esophagus mucosa, and esophagus gastroesophageal junction. MR analysis suggested a significantly causal relationship between GERD and overall LC [odds ratio (OR) =1.34, 95% confidence interval (CI): 1.19-1.51], as well as other subtype-specific LC (OR ranging from 1.25 to 1.76). No evidence supports a significant causal effect of LC on GERD.

Conclusions

Our findings suggest intrinsic genetic correlation underlying GERD and LC, which provides valuable insights for screening and management of LC in individuals with GERD.

Genetic correlation between smoking behavior and gastroesophageal reflux disease: insights from integrative multi-omics data

BACKGROUND

Observational studies have preliminarily revealed an association between smoking and gastroesophageal reflux disease (GERD). However, little is known about the causal relationship and shared genetic architecture between the two. This study aims to explore their common genetic correlations by leveraging genome-wide association studies (GWAS) of smoking behavior-specifically, smoking initiation (SI), never smoking (NS), ever smoking (ES), cigarettes smoked per day (CPD), age of smoking initiation(ASI) and GERD.

METHODS

Firstly, we conducted global cross-trait genetic correlation analysis and heritability estimation from summary statistics (HESS) to explore the genetic correlation between smoking behavior and GERD. Then, a joint cross-trait meta-analysis was performed to identify shared "pleiotropic SNPs" between smoking behavior and GERD, followed by co-localization analysis. Additionally, multi-marker analyses using annotation (MAGMA) were employed to explore the degree of enrichment of single nucleotide polymorphism (SNP) heritability in specific tissues, and summary data-based Mendelian randomization (SMR) was further utilized to investigate potential functional genes. Finally, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the smoking behavior and GERD.

RESULTS

Consistent genetic correlations were observed through global and local genetic correlation analyses, wherein SI, ES, and CPD showed significantly positive genetic correlations with GERD, while NS and ASI showed significantly negative correlations. HESS analysis also identified multiple significantly associated loci between them. Furthermore, three novel "pleiotropic SNPs" (rs4382592, rs200968, rs1510719) were identified through cross-trait meta-analysis and co-localization analysis to exist between SI, NS, ES, ASI, and GERD, mapping the genes MED27, HIST1H2BO, MAML3 as new pleiotropic genes between SI, NS, ES, ASI, and GERD. Moreover, both smoking behavior and GERD were found to be co-enriched in multiple brain tissues, with GMPPB, RNF123, and RBM6 identified as potential functional genes co-enriched in Cerebellar Hemisphere, Cerebellum, Cortex/Nucleus accumbens in SI and GERD, and SUOX identified in Caudate nucleus, Cerebellum, Cortex in NS and GERD. Lastly, consistent causal relationships were found through MR analysis, indicating that SI, ES, and CPD increase the risk of GERD, while NS and higher ASI decrease the risk.

CONCLUSION

We identified genetic loci associated with smoking behavior and GERD, as well as brain tissue sites of shared enrichment, prioritizing three new pleiotropic genes and four new functional genes. Finally, the causal relationship between smoking behavior and GERD was demonstrated, providing insights for early prevention strategies for GERD.

Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework

BACKGROUND

Pneumonia and lung cancer are both major respiratory diseases, and observational studies have explored the association between their susceptibility. However, due to the presence of potential confounders and reverse causality, the comprehensive causal relationships between pneumonia and lung cancer require further exploration.

METHODS

Genome-wide association study (GWAS) summary-level data were obtained from the hitherto latest FinnGen database, COVID-19 Host Genetics Initiative resource, and International Lung Cancer Consortium. We implemented a bidirectional Mendelian randomization (MR) framework to evaluate the causal relationships between several specific types of pneumonia and lung cancer. The causal estimates were mainly calculated by inverse-variance weighted (IVW) approach. Additionally, sensitivity analyses were also conducted to validate the robustness of the causalty.

RESULTS

In the MR analyses, overall pneumonia demonstrated a suggestive but modest association with overall lung cancer risk (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 - 1.44, P = 0.037). The correlations between specific pneumonia types and overall lung cancer were not as significant, including bacterial pneumonia (OR: 1.07, 95% CI: 0.91 - 1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95 - 1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92 - 1.52, P = 0.181), and COVID-19 (OR: 1.01, 95% CI: 0.78 - 1.30, P = 0.952). Reversely, with lung cancer as the exposure, we observed that overall lung cancer had statistically crucial associations with bacterial pneumonia (OR: 1.08, 95% CI: 1.03 - 1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01 - 1.19, P = 0.037). Sensitivity analysis also confirmed the robustness of these findings.

CONCLUSION

This study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes. Further prospective study is warranted to verify these findings.

Associations between cord blood acetaminophen biomarkers and childhood asthma with and without allergic comorbidities

BACKGROUND

Previous studies have linked prenatal acetaminophen use to increased asthma risk in children. However, none have explored this association while differentiating between asthma cases with and without other allergic conditions or by employing objective biomarkers to assess acetaminophen exposure.

OBJECTIVE

To evaluate whether the detection of acetaminophen biomarkers in cord blood is associated with the subgroups of asthma both with and without allergic comorbidities in children.

METHODS

Acetaminophen biomarkers, including unchanged acetaminophen and acetaminophen glucuronide, were measured in neonatal cord blood samples from the Boston Birth Cohort. Asthma subgroups were defined on the basis of physician diagnoses of asthma and other allergic conditions (atopic dermatitis and allergic rhinitis). Multinomial regressions were used to evaluate the associations between acetaminophen biomarkers and asthma subgroups, adjusting for multiple confounders, including potential indications for maternal acetaminophen use such as maternal fever.

RESULTS

The study included 142 children with asthma and at least 1 other allergic condition, 55 children with asthma but no other allergic condition, and 613 children free of asthma. Detection of acetaminophen in cord blood, reflecting maternal exposure to acetaminophen shortly before delivery, was associated with 3.73 times the odds of developing asthma without allergic comorbidities (95% CI: 1.79-7.80, P = .0004). In contrast, the detection of acetaminophen in cord blood was not associated with an elevated risk of asthma with allergic comorbidities. Analysis of acetaminophen glucuronide yielded consistent results.

CONCLUSION

In a prospective birth cohort, cord blood acetaminophen biomarkers were associated with an increased risk of childhood asthma without allergic comorbidities, but were not associated with childhood asthma with allergic comorbidities.

Using human genetics to understand the phenotypic association between chronotype and breast cancer

Little is known regarding the shared genetic influences underlying the observed phenotypic association between chronotype and breast cancer in women. Leveraging summary statistics from the hitherto largest genome-wide association study conducted in each trait, we investigated the genetic correlation, pleiotropic loci, and causal relationship of chronotype with overall breast cancer, and with its subtypes defined by the status of oestrogen receptor. We identified a negative genomic correlation between chronotype and overall breast cancer (r g  = -0.06, p = 3.00 × 10-4), consistent across oestrogen receptor-positive (r g  = -0.05, p = 3.30 × 10-3) and oestrogen receptor-negative subtypes (r g  = -0.05, p = 1.11 × 10-2). Five specific genomic regions were further identified as contributing a significant local genetic correlation. Cross-trait meta-analysis identified 78 loci shared between chronotype and breast cancer, of which 23 were novel. Transcriptome-wide association study revealed 13 shared genes, targeting tissues of the nervous, cardiovascular, digestive, and exocrine/endocrine systems. Mendelian randomisation demonstrated a significantly reduced risk of overall breast cancer (odds ratio 0.89, 95% confidence interval 0.83-0.94; p = 1.30 × 10-4) for genetically predicted morning chronotype. No reverse causality was found. Our work demonstrates an intrinsic link underlying chronotype and breast cancer, which may provide clues to inform management of sleep habits to improve female health.

A comprehensive genome-wide cross-trait analysis of sexual factors and uterine leiomyoma

Age at first sexual intercourse (AFS) and lifetime number of sexual partners (NSP) may influence the pathogenesis of uterine leiomyoma (UL) through their associations with hormonal concentrations and uterine infections. Leveraging summary statistics from large-scale genome-wide association studies conducted in European ancestry for each trait (NAFS = 214,547; NNSP = 370,711; NUL = 302,979), we observed a significant negative genomic correlation for UL with AFS (rg = -0.11, P = 7.83×10-4), but not with NSP (rg = 0.01, P = 0.62). Four specific genomic regions were identified as contributing significant local genetic correlations to AFS and UL, including one genomic region further identified for NSP and UL. Partitioning SNP-heritability with cell-type-specific annotations, a close clustering of UL with both AFS and NSP was identified in immune and blood-related components. Cross-trait meta-analysis revealed 15 loci shared between AFS/NSP and UL, including 7 novel SNPs. Univariable two-sample Mendelian randomization (MR) analysis suggested no evidence for a causal association between genetically predicted AFS/NSP and risk of UL, nor vice versa. Multivariable MR adjusting for age at menarche or/and age at natural menopause revealed a significant causal effect of genetically predicted higher AFS on a lower risk of UL. Such effect attenuated to null when age at first birth was further included. Utilizing participant-level data from the UK Biobank, one-sample MR based on genetic risk scores yielded consistent null findings among both pre-menopausal and post-menopausal females. From a genetic perspective, our study demonstrates an intrinsic link underlying sexual factors (AFS and NSP) and UL, highlighting shared biological mechanisms rather than direct causal effects. Future studies are needed to elucidate the specific mechanisms involved in the shared genetic influences and their potential impact on UL development.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Genetic contribution of reproductive traits to risk of uterine leiomyomata: a large-scale, genome-wide, cross-trait analysis

BACKGROUND

Although phenotypic associations between female reproductive characteristics and uterine leiomyomata have long been observed in epidemiologic investigations, the shared genetic architecture underlying these complex phenotypes remains unclear.

OBJECTIVE

We aimed to investigate the shared genetic basis, pleiotropic effects, and potential causal relationships underlying reproductive traits (age at menarche, age at natural menopause, and age at first birth) and uterine leiomyomata.

STUDY DESIGN

With the use of large-scale, genome-wide association studies conducted among women of European ancestry for age at menarche (n=329,345), age at natural menopause (n=201,323), age at first birth (n=418,758), and uterine leiomyomata (ncases/ncontrols=35,474/267,505), we performed a comprehensive, genome-wide, cross-trait analysis to examine systematically the common genetic influences between reproductive traits and uterine leiomyomata.

RESULTS

Significant global genetic correlations were identified between uterine leiomyomata and age at menarche (rg, -0.17; P=3.65×10-10), age at natural menopause (rg, 0.23; P=3.26×10-07), and age at first birth (rg, -0.16; P=1.96×10-06). Thirteen genomic regions were further revealed as contributing significant local correlations (P<.05/2353) to age at natural menopause and uterine leiomyomata. A cross-trait meta-analysis identified 23 shared loci, 3 of which were novel. A transcriptome-wide association study found 15 shared genes that target tissues of the digestive, exo- or endocrine, nervous, and cardiovascular systems. Mendelian randomization suggested causal relationships between a genetically predicted older age at menarche (odds ratio, 0.88; 95% confidence interval, 0.85-0.92; P=1.50×10-10) or older age at first birth (odds ratio, 0.95; 95% confidence interval, 0.90-0.99; P=.02) and a reduced risk for uterine leiomyomata and between a genetically predicted older age at natural menopause and an increased risk for uterine leiomyomata (odds ratio, 1.08; 95% confidence interval, 1.06-1.09; P=2.30×10-27). No causal association in the reverse direction was found.

CONCLUSION

Our work highlights that there are substantial shared genetic influences and putative causal links that underlie reproductive traits and uterine leiomyomata. The findings suggest that early identification of female reproductive risk factors may facilitate the initiation of strategies to modify potential uterine leiomyomata risk.

Infant Bronchiolitis Endotypes and the Risk of Developing Childhood Asthma: Lessons From Cohort Studies

Severe bronchiolitis (i.e., bronchiolitis requiring hospitalization) during infancy is a heterogeneous condition associated with a high risk of developing childhood asthma. Yet, the exact mechanisms underlying the bronchiolitis-asthma link remain uncertain. Birth cohort studies have reported this association at the population level, including only small groups of patients with a history of bronchiolitis, and have attempted to identify the underlying biological mechanisms. Although this evidence has provided valuable insights, there are still unanswered questions regarding severe bronchiolitis-asthma pathogenesis. Recently, a few bronchiolitis cohort studies have attempted to answer these questions by applying unbiased analytical approaches to biological data. These cohort studies have identified novel bronchiolitis subtypes (i.e., endotypes) at high risk for asthma development, representing essential and enlightening evidence. For example, one distinct severe respiratory syncytial virus (RSV) bronchiolitis endotype is characterized by the presence of Moraxella catarrhalis and Streptococcus pneumoniae, higher levels of type I/II IFN expression, and changes in carbohydrate metabolism in nasal airway samples, and is associated with a high risk for childhood asthma development. Although these findings hold significance for the design of future studies that focus on childhood asthma prevention, they require validation. However, this scoping review puts the above findings into clinical context and emphasizes the significance of future research in this area aiming to offer new bronchiolitis treatments and contribute to asthma prevention.

Independent association of general and central adiposity with risk of gallstone disease: observational and genetic analyses

Background

General obesity is a well-established risk factor for gallstone disease (GSD), but whether central obesity contributes additional independent risk remains controversial. We aimed to comprehensively clarify the effect of body fat distribution on GSD.

Methods

We first investigated the observational association of central adiposity, characterized by waist-to-hip ratio (WHR), with GSD risk using data from UK Biobank (N=472,050). We then explored the genetic relationship using summary statistics from the largest genome-wide association study of GSD (ncase=43,639, ncontrol=506,798) as well as WHR, with and without adjusting for body mass index (BMI) (WHR: n=697,734; WHRadjBMI: n=694,649).

Results

Observational analysis demonstrated an increased risk of GSD with one unit increase in WHR (HR=1.18, 95%CI=1.14-1.21). A positive WHR-GSD genetic correlation (r g =0.41, P=1.42×10-52) was observed, driven by yet independent of BMI (WHRadjBMI: r g =0.19, P=6.89×10-16). Cross-trait meta-analysis identified four novel pleiotropic loci underlying WHR and GSD with biological mechanisms outside of BMI. Mendelian randomization confirmed a robust WHR-GSD causal relationship (OR=1.50, 95%CI=1.35-1.65) which attenuated yet remained significant after adjusting for BMI (OR=1.17, 95%CI=1.09-1.26). Furthermore, observational analysis confirmed a positive association between general obesity and GSD, corroborated by a shared genetic basis (r g =0.40, P=2.16×10-43), multiple novel pleiotropic loci (N=11) and a causal relationship (OR=1.67, 95%CI=1.56-1.78).

Conclusion

Both observational and genetic analyses consistently provide evidence on an association of central obesity with an increased risk of GSD, independent of general obesity. Our work highlights the need of considering both general and central obesity in the clinical management of GSD.

Epidemiological and Genetic Analyses of Schizophrenia and Breast Cancer

BACKGROUND AND HYPOTHESIS

While the phenotypic association between schizophrenia and breast cancer has been observed, the underlying intrinsic link is not adequately understood. We aim to conduct a comprehensive interrogation on both phenotypic and genetic relationships between schizophrenia and breast cancer.

STUDY DESIGN

We first used data from UK Biobank to evaluate a phenotypic association and performed an updated meta-analysis incorporating existing cohort studies. We then leveraged genomic data to explore the shared genetic architecture through a genome-wide cross-trait design.

STUDY RESULTS

Incorporating results of our observational analysis, meta-analysis of cohort studies suggested a significantly increased incidence of breast cancer among women with schizophrenia (RR = 1.30, 95% CIs = 1.14-1.48). A positive genomic correlation between schizophrenia and overall breast cancer was observed (rg = 0.12, P = 1.80 × 10-10), consistent across ER+ (rg  = 0.10, P = 5.74 × 10-7) and ER- subtypes (rg = 0.09, P = .003). This was further corroborated by four local signals. Cross-trait meta-analysis identified 23 pleiotropic loci between schizophrenia and breast cancer, including five novel loci. Gene-based analysis revealed 27 shared genes. Mendelian randomization demonstrated a significantly increased risk of overall breast cancer (OR = 1.07, P = 4.81 × 10-10) for genetically predisposed schizophrenia, which remained robust in subgroup analysis (ER+: OR = 1.10, P = 7.26 × 10-12; ER-: OR = 1.08, P = 3.50 × 10-6). No mediation effect and reverse causality was found.

CONCLUSIONS

Our study demonstrates an intrinsic link underlying schizophrenia and breast cancer, which may inform tailored screening and management of breast cancer in schizophrenia.

Investigating the shared genetic architecture between frailty and insomnia

Background

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia.

Methods

Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes.

Results

Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified.

Conclusion

This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Genome-wide association study of preserved ratio impaired spirometry (PRISm)

BACKGROUND

Preserved ratio impaired spirometry (PRISm) is defined as a forced expiratory volume in 1 s (FEV1) <80% predicted and FEV1/forced vital capacity ≥0.70. PRISm is associated with respiratory symptoms and comorbidities. Our objective was to discover novel genetic signals for PRISm and see if they provide insight into the pathogenesis of PRISm and associated comorbidities.

METHODS

We undertook a genome-wide association study (GWAS) of PRISm in UK Biobank participants (Stage 1), and selected single nucleotide polymorphisms (SNPs) reaching genome-wide significance for replication in 13 cohorts (Stage 2). A combined meta-analysis of Stage 1 and Stage 2 was done to determine top SNPs. We used cross-trait linkage disequilibrium score regression to estimate genome-wide genetic correlation between PRISm and pulmonary and extrapulmonary traits. Phenome-wide association studies of top SNPs were performed.

RESULTS

22 signals reached significance in the joint meta-analysis, including four signals novel for lung function. A strong genome-wide genetic correlation (rg) between PRISm and spirometric COPD (rg=0.62, p<0.001) was observed, and genetic correlation with type 2 diabetes (rg=0.12, p=0.007). Phenome-wide association studies showed that 18 of 22 signals were associated with diabetic traits and seven with blood pressure traits.

CONCLUSION

This is the first GWAS to successfully identify SNPs associated with PRISm. Four of the signals, rs7652391 (nearest gene MECOM), rs9431040 (HLX), rs62018863 (TMEM114) and rs185937162 (HLA-B), have not been described in association with lung function before, demonstrating the utility of using different lung function phenotypes in GWAS. Genetic factors associated with PRISm are strongly correlated with risk of both other lung diseases and extrapulmonary comorbidity.

Genetic Correlation, Shared Loci, and Causal Association Between Sex Hormone-Binding Globulin and Bone Mineral Density: Insights From a Large-Scale Genomewide Cross-Trait Analysis

Although the impact of sex hormones on bone metabolism is well-documented, effect of their primary modulator, sex hormone-binding globulin (SHBG), remains inconclusive. This study aims to elucidate the genetic overlap between SHBG and heel estimated bone mineral density (eBMD), a widely-accepted tool for osteoporosis management and fracture risk assessment. Using summary statistics from large-scale genomewide association studies conducted for SHBG (N = 370,125), SHBG adjusted for body mass index (SHBGa, N = 368,929), and eBMD (N = 426,824), a comprehensive genomewide cross-trait approach was performed to quantify global and local genetic correlations, identify pleiotropic loci, and infer causal associations. A significant overall inverse genetic correlation was found for SHBG and eBMD (rg  = -0.11, p = 3.34 × 10-10 ), which was further supported by the significant local genetic correlations observed in 11 genomic regions. Cross-trait meta-analysis revealed 219 shared loci, of which seven were novel. Notably, four novel loci (rs6542680, rs8178616, rs147110934, and rs815625) were further demonstrated to colocalize. Mendelian randomization identified a robust causal effect of SHBG on eBMD (beta = -0.22, p = 3.04 × 10-13 ), with comparable effect sizes observed in both men (beta = -0.16, p = 1.99 × 10-6 ) and women (beta = -0.19, p = 2.73 × 10-9 ). Replacing SHBG with SHBGa, the observed genetic correlations, pleiotropic loci and causal associations did not change substantially. Our work reveals a shared genetic basis between SHBG and eBMD, substantiated by multiple pleiotropic loci and a robust causal relationship. Although SHBG has been implicated in preventing and screening aging-related diseases, our findings support its etiological role in osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Using human genetics to understand the epidemiological association between obesity, serum urate, and gout

OBJECTIVES

We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout.

METHODS

We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N = 806 834), waist-to-hip ratio (WHR; N = 697 734), WHR adjusted for BMI (WHRadjBMI; N = 694 649), serum urate (N = 288 649), and gout (Ncases = 13 179 and Ncontrols = 750 634).

RESULTS

Positive overall genetic correlations were observed for BMI (rg = 0.27, P = 6.62 × 10-7), WHR (rg = 0.22, P = 6.26 × 10-7) and WHRadjBMI (rg = 0.07, P = 6.08 × 10-3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene-tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI-gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR-gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance.

CONCLUSION

Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits.

Understanding the relationship between circulating lipids and risk of chronic kidney disease: a prospective cohort study and large-scale genetic analyses

BACKGROUND

This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR).

METHODS

We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460).

RESULTS

There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60-0.95; TG: HR = 1.08, 95%CI = 1.02-1.13) and global genetic correlations (HDL-C: [Formula: see text] = - 0.132, P = 1.00 × 10-4; TG: [Formula: see text] = 0.176; P = 2.66 × 10-5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85-0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed.

CONCLUSIONS

Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD.

A large-scale genome-wide cross-trait analysis for the effect of COVID-19 on female-specific cancers

Little is known regarding the long-term adverse effects of COVID-19 on female-specific cancers, nor the shared genetic influences underlying these conditions. We performed a comprehensive genome-wide cross-trait analysis to investigate the shared genetic architecture between COVID-19 (infection, hospitalization, and critical illness) with three female-specific cancers (breast cancer (BC), epithelial ovarian cancer (EOC), and endometrial cancer (EC)). We identified significant genome-wide genetic correlations with EC for both hospitalization (r g  = 0.19, p = 0.01) and critical illness (r g  = 0.29, p = 3.00 × 10-4). Mendelian randomization demonstrated no valid association of COVID-19 with any cancer of interest, except for suggestive associations of genetically predicted hospitalization (ORIVW = 1.09, p = 0.04) and critical illness (ORIVW = 1.06, p = 0.04) with EC risk, none withstanding multiple correction. Cross-trait meta-analysis identified 20 SNPs shared between COVID-19 with BC, 15 with EOC, and 5 with EC; and transcriptome-wide association studies revealed multiple shared genes. Findings support intrinsic links underlying these complex traits, highlighting shared mechanisms rather than causal associations.

Impact of gallstone disease on the risk of stroke and coronary artery disease: evidence from prospective observational studies and genetic analyses

BACKGROUND

Despite epidemiological evidence associating gallstone disease (GSD) with cardiovascular disease (CVD), a dilemma remains on the role of cholecystectomy in modifying the risk of CVD. We aimed to characterize the phenotypic and genetic relationships between GSD and two CVD events - stroke and coronary artery disease (CAD).

METHODS

We first performed a meta-analysis of cohort studies to quantify an overall phenotypic association between GSD and CVD. We then investigated the genetic relationship leveraging the largest genome-wide genetic summary statistics. We finally examined the phenotypic association using the comprehensive data from UK Biobank (UKB).

RESULTS

An overall significant effect of GSD on CVD was found in meta-analysis (relative risk [RR] = 1.26, 95% confidence interval [CI] = 1.19-1.34). Genetically, a positive shared genetic basis was observed for GSD with stroke ([Formula: see text]=0.16, P = 6.00 × 10-4) and CAD ([Formula: see text]=0.27, P = 2.27 × 10-15), corroborated by local signals. The shared genetic architecture was largely explained by the multiple pleiotropic loci identified in cross-phenotype association study and the shared gene-tissue pairs detected by transcriptome-wide association study, but not a causal relationship (GSD to CVD) examined through Mendelian randomization (MR) (GSD-stroke: odds ratio [OR] = 1.00, 95%CI = 0.97-1.03; GSD-CAD: OR = 1.01, 95%CI = 0.98-1.04). After a careful adjustment of confounders or considering lag time using UKB data, no significant phenotypic effect of GSD on CVD was detected (GSD-stroke: hazard ratio [HR] = 0.95, 95%CI = 0.83-1.09; GSD-CAD: HR = 0.98, 95%CI = 0.91-1.06), further supporting MR findings.

CONCLUSIONS

Our work demonstrates a phenotypic and genetic relationship between GSD and CVD, highlighting a shared biological mechanism rather than a direct causal effect. These findings may provide insight into clinical and public health applications.

Epigenome-wide association analysis of infant bronchiolitis severity: a multicenter prospective cohort study

Bronchiolitis is the most common lower respiratory infection in infants, yet its pathobiology remains unclear. Here we present blood DNA methylation data from 625 infants hospitalized with bronchiolitis in a 17-center prospective study, and associate them with disease severity. We investigate differentially methylated CpGs (DMCs) for disease severity. We characterize the DMCs based on their association with cell and tissues types, biological pathways, and gene expression. Lastly, we also examine the relationships of severity-related DMCs with respiratory and immune traits in independent cohorts. We identify 33 DMCs associated with severity. These DMCs are differentially methylated in blood immune cells. These DMCs are also significantly enriched in multiple tissues (e.g., lung) and cells (e.g., small airway epithelial cells), and biological pathways (e.g., interleukin-1-mediated signaling). Additionally, these DMCs are associated with respiratory and immune traits (e.g., asthma, lung function, IgE levels). Our study suggests the role of DNA methylation in bronchiolitis severity.

Migraine, chronic kidney disease and kidney function: observational and genetic analyses

Epidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We aimed to help avoid unnecessary interventions in individuals with migraine through the investigation of phenotypic and genetic relationships underlying migraine, CKD, and kidney function. We first evaluated phenotypic associations using observational data from UK Biobank (N = 255,896). We then investigated genetic relationships leveraging genomic data in European ancestry for migraine (Ncase/Ncontrol = 48,975/540,381), CKD (Ncase/Ncontrol = 41,395/439,303), and two traits of kidney function (estimated glomerular filtration rate [eGFR, N = 567,460] and urinary albumin-to-creatinine ratio [UACR, N = 547,361]). Observational analyses suggested no significant association of migraine with the risk of CKD (HR = 1.13, 95% CI = 0.85-1.50). While we did not find any global genetic correlation in general, we identified four specific genomic regions showing significant for migraine with eGFR. Cross-trait meta-analysis identified one candidate causal variant (rs1047891) underlying migraine, CKD, and kidney function. Transcriptome-wide association study detected 28 shared expression-trait associations between migraine and kidney function. Mendelian randomization analysis suggested no causal effect of migraine on CKD (OR = 1.03, 95% CI = 0.98-1.09; P = 0.28). Despite a putative causal effect of migraine on an increased level of UACR (log-scale-beta = 0.02, 95% CI = 0.01-0.04; P = 1.92 × 10-3), it attenuated to null when accounting for both correlated and uncorrelated pleiotropy. Our work does not find evidence supporting a causal association between migraine and CKD. However, our study highlights significant biological pleiotropy between migraine and kidney function. The value of a migraine prophylactic treatment for reducing future CKD in people with migraine is likely limited.

Assessing causal relationships between gut microbiota and asthma: evidence from two sample Mendelian randomization analysis

Background

Accumulating evidence has suggested that gut microbiota dysbiosis is commonly observed in asthmatics. However, it remains unclear whether dysbiosis is a cause or consequence of asthma. We aimed to examine the genetic causal relationships of gut microbiota with asthma and its three phenotypes, including adult-onset asthma, childhood-onset asthma, and moderate-severe asthma.

Methods

To elucidate the causality of gut microbiota with asthma, we applied two sample Mendelian randomization (MR) based on the largest publicly available genome-wide association study (GWAS) summary statistics. Inverse variance weighting meta-analysis (IVW) was used to obtain the main estimates; and Weighted median, MR-Egger, Robust Adjusted Profile Score (MR-RAPS), Maximum likelihood method (ML), and MR pleiotropy residual sum and outlier (MR-PRESSO) methods were applied in sensitivity analyses. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.

Results

In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically predicted Barnesiella and RuminococcaceaeUCG014 were positively correlated with the risk of asthma, while the association between genetically predicted CandidatusSoleaferrea and asthma was negative. And for the three phenotypes of asthma, genetically predicted Akkermansia reduced the risk of adult-onset asthma, Collinsella and RuminococcaceaeUCG014 increased the risk of childhood-onset asthma, and FamilyXIIIAD3011group, Eisenbergiella, and Ruminiclostridium6 were correlated with the risk of moderate-severe asthma (all P<0.05). The reverse MR analysis didn't find evidence supporting the reverse causality from asthma and its three phenotypes to the gut microbiota genus.

Conclusion

This study suggested that microbial genera were causally associated with asthma as well as its three phenotypes. The findings deepened our understanding of the role of gut microbiota in the pathology of asthma, which emphasizes the potential of opening up a new vista for the prevention and diagnosis of asthma.

Investigating the shared genetic architecture between schizophrenia and body mass index

Evidence for reciprocal comorbidity of schizophrenia (SCZ) and body mass index (BMI) has grown in recent years. However, little is known regarding the shared genetic architecture or causality underlying the phenotypic association between SCZ and BMI. Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) on each trait, we investigated the genetic overlap and causal associations of SCZ with BMI. Our study demonstrated a genetic correlation between SCZ and BMI, and the correlation was more evident in local genomic regions. The cross-trait meta-analysis identified 27 significant SNPs shared between SCZ and BMI, most of which had the same direction of influence on both diseases. Mendelian randomization analysis showed the causal association of SCZ with BMI, but not vice versa. Combining the gene expression information, we found that the genetic correlation between SCZ and BMI is enriched in six regions of brain, led by the brain frontal cortex. Additionally, 34 functional genes and 18 specific cell types were found to have an impact on both SCZ and BMI within these regions. Taken together, our comprehensive genome-wide cross-trait analysis suggests a shared genetic basis including pleiotropic loci, tissue enrichment, and shared function genes between SCZ and BMI. This work provides novel insights into the intrinsic genetic overlap of SCZ and BMI, and highlights new opportunities and avenues for future investigation.

Investigating the relationship between depression and breast cancer: observational and genetic analyses

BACKGROUND

Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC.

METHODS

We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER - : N = 127,442).

RESULTS

Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95-1.26). A positive genetic correlation between depression and overall BC was observed ([Formula: see text] = 0.08, P = 3.00 × 10-4), consistent across ER + ([Formula: see text] = 0.06, P = 6.30 × 10-3) and ER - subtypes ([Formula: see text] = 0.08, P = 7.20 × 10-3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04-1.19), but risk of BC was not causally associated with risk of depression.

CONCLUSIONS

Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.

Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders

BACKGROUND

Genetic factors contribute to individual differences in the severity of coronavirus disease 2019 (COVID-19). A portion of genetic predisposition can be captured using polygenic risk scores (PRS). Relatively little is known about the associations between PRS and COVID-19 severity or post-acute COVID-19 in community-dwelling individuals.

METHODS

Participants in this study were 983 World Trade Center responders infected for the first time with SARS-CoV-2 (mean age at infection = 56.06; 93.4% male; 82.7% European ancestry). Seventy-five (7.6%) responders were in the severe COVID-19 category; 306 (31.1%) reported at least one post-acute COVID-19 symptom at 4-week follow-up. Analyses were adjusted for population stratification and demographic covariates.

FINDINGS

The asthma PRS was associated with severe COVID-19 category (odds ratio [OR] = 1.61, 95% confidence interval: 1.17-2.21) and more severe COVID-19 symptomatology (β = .09, p = .01), independently of respiratory disease diagnosis. Severe COVID-19 category was also associated with the allergic disease PRS (OR = 1.97, [1.26-3.07]) and the PRS for COVID-19 hospitalization (OR = 1.35, [1.01-1.82]). PRS for coronary artery disease and type II diabetes were not associated with COVID-19 severity.

CONCLUSION

Recently developed polygenic biomarkers for asthma, allergic disease, and COVID-19 hospitalization capture some of the individual differences in severity and clinical course of COVID-19 illness in a community population.

A sex- and site-specific relationship between body mass index and osteoarthritis: evidence from observational and genetic analyses

OBJECTIVE

We primarily aimed to investigate whether there are phenotypic and genetic links underlying body mass index (BMI) and overall osteoarthritis (OA). We then intended to explore whether the relationships differ across sexes and sites.

METHOD

We first evaluated the phenotypic association between BMI and overall OA using data from the UK Biobank. We then investigated the genetic relationship leveraging summary statistics of the hitherto largest genome-wide association studies performed for BMI and overall OA. Finally, we repeated all analyses in a sex- (female, male) and site- (knee, hip, spine) specific manner.

RESULTS

Observational analysis suggested an increased hazard of diagnosed OA per 5 kg/m² increment in BMI (hazard ratio = 1.38, 95% confidence interval (CI) = 1.37-1.39). A positive overall genetic correlation was observed for BMI and OA (r_g = 0.43, P = 4.72 × 10^-133), corroborated by 11 significant local signals. Cross-trait meta-analysis identified 34 pleiotropic loci shared between BMI and OA, of which seven were novel. Transcriptome-wide association study revealed 29 shared gene-tissue pairs, targeting nervous, digestive, and exo/endocrine systems. Mendelian randomization demonstrated a robust BMI-OA causal relationship (odds ratio = 1.47, 95% CI = 1.42-1.52). A similar pattern of effects was observed in sex- and site-specific analyses, with BMI affecting OA comparably in both sexes and most strongly in the knee.

CONCLUSION

Our work demonstrates an intrinsic relationship underlying BMI and overall OA, reflected by a pronounced phenotypic association, significant biological pleiotropy, and a putative causal link. Stratified analysis further reveals that the effects are distinct across sites and comparable across sexes.

SNPs, adipokynes and adiposity in children with asthma

OBJECTIVES

Asthma and obesity are complex disorders influenced by environmental and genetic factors. We performed an integrative review of genetic polymorphisms and adipokines effects in children and adolescents with asthma and obesity.

DATA SOURCES

Articles focused on these issues were collected from SciELO, PubMed, LILACS, Embase and ScienceDirect electronic databases, in 2009-2020 period.

STUDY SELECTIONS

22 articles were selected, including clinical trials, analyses approaches, case-control studies, meta-analysis and Mendelian randomization studies.

RESULTS

Leptin concentrations were higher in obesity and asthma. The high value of BMI and Leptin indicated severe asthma. Adiponectin may be reduced in obese children. The high value of BMI and low level of Adiponectin may indicate severe asthma. Some linkage of PRKCA gene, asthma and BMI was observed. FTO T allele rs62048379 was positively associated with overweight/obesity, related to protein and PUFA:SFA ratio intake and influences the choice of more energy-dense foods. FTO rs9939609 effects are more pronounced among children with insufficient vitamin D levels.

CONCLUSION

Leptin may be a potential predictor for asthma control in children. BMI and Adiponectin could have certain predictive value for asthma. FTO gene was related to a higher mean BMI Z-score and accelerated developmental age per allele. Strong genetic heterogeneity influencing on asthma and obesity susceptibilities is evident and related to distinct genetic features. GWAS with childhood obesity in asthma contributed to greater insights, mainly on later childhood. Standardized definitions for asthma and overweight/obesity in studies approaching adipokines and SNPs would provide stronger evidence in deciding the best management.

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.

Relationship among genetic variants, obesity traits and asthma in the Taiwan Biobank

BACKGROUND AND OBJECTIVE

Obesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma.

METHODS

This is a matched case-control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the 2000-2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM_2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model.

RESULTS

We found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01-1.13)), waist circumference (OR=1.10 (1.04-1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03-1.15)) and general-central obesity (OR=1.06 (1.00-1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM_2.5 exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity.

CONCLUSIONS

Obese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.

Obesity-related biomarkers underlie a shared genetic architecture between childhood body mass index and childhood asthma

Obesity and asthma are both common diseases with high population burden worldwide. Recent genetic association studies have shown that obesity is associated with asthma in adults. The relationship between childhood obesity and childhood asthma, and the underlying mechanisms linking obesity to asthma remain to be clarified. In the present study, leveraging large-scale genetic data from UK biobank and several other data sources, we investigated the shared genetic components between body mass index (BMI, n = 39620) in children and childhood asthma (ncase = 10524, ncontrol = 373393). We included GWAS summary statistics for nine obesity-related biomarkers to evaluate potential biological mediators underlying obesity and asthma. We found a genetic correlation (Rg = 0.10, P = 0.02) between childhood BMI and childhood asthma, whereas the genetic correlation between adult BMI (n = 371541) and childhood asthma was null (Rg = -0.03, P = 0.21). Genomic structural equation modeling analysis further provided evidence that the genetic effect of childhood BMI on childhood asthma (standardized effect size 0.17, P = 0.009) was not driven by the genetic component of adult BMI. Bayesian colocalization analysis identified a shared causal variant rs12436181 that was mapped to gene AMN using gene expression data in lung tissue. Mendelian randomization showed that the odds ratio of childhood asthma for one standard deviation higher of childhood BMI was 1.13 (95% confidence interval: 0.96-1.34). A systematic survey of obesity-related biomarkers showed that IL-6 and adiponectin are potential biological mediators linking obesity and asthma in children. This large-scale genetic study provides evidence that unique childhood obesity pathways could lead to childhood asthma. The findings shed light on childhood asthma pathogenic mechanisms and prevention.

Microbial dysbiosis and childhood asthma development: Integrated role of the airway and gut microbiome, environmental exposures, and host metabolic and immune response

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.

A genome-wide cross-trait analysis identifies shared loci and causal relationships of type 2 diabetes and glycaemic traits with polycystic ovary syndrome

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.

The causal relationship between allergic diseases and heart failure: Evidence from Mendelian randomization study

Background

Emerging evidence shows allergic diseases, such as atopic dermatitis and asthma, are risk factors of heart failure. However, the causal relationship between allergic diseases and heart failure is not clear.

Methods

We performed a two-sample Mendelian randomization analysis between allergic diseases and heart failure using summary statistics of genome-wide association studies from large GWAS consortia, with total sample size of 1.2 million. Independent instrumental variables for asthma and atopic dermatitis (P<1×10−5) were used as the exposure. We applied five models for the Mendelian randomization analysis. Finally, we performed the sensitivity analyses to assess the robustness of the results.

Results

We have identified 55 independent single nucleotide polymorphisms (SNPs) for asthma 54 independent SNPs for atopic dermatitis as our instrumental variables. The inverse variance-weighted (IVW) analysis showed asthma was significantly associated with increased risk of heart failure (ORIVW = 1.04, 95% CI, 1.01–1.07, P = 0.03). The Mendelian randomization analysis using the other four models also showed consistent results with the IVW analysis. Similarly, atopic dermatitis was also significantly associated with an increased risk of heart failure (ORIVW = 1.03, 95% CI, 1.01–1.06, P = 0.01), consistent with the other four models. The sensitivity analysis showed no evidence of horizontal pleiotropy or results were driven by single SNPs.

Conclusion

Our study identified asthma and atopic dermatitis as a causal risk factor for heart failure and suggest inflammatory pathogenesis as a key factor contributing to the underlying mechanism. These findings emphasize the importance of asthma and allergy control in the prevention and management of heart failure.

Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis

Little is known regarding the shared genetic architecture or causality underlying the phenotypic association observed for uterine leiomyoma (UL) and breast cancer (BC). Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) conducted in each trait, we investigated the genetic overlap and causal associations of UL with BC overall, as well as with its subtypes defined by the status of estrogen receptor (ER). We observed a positive genetic correlation between UL and BC overall (rg = 0.09, p = 6.00 × 10-3), which was consistent in ER+ subtype (rg = 0.06, p = 0.01) but not in ER- subtype (rg = 0.06, p = 0.08). Partitioning the whole genome into 1,703 independent regions, local genetic correlation was identified at 22q13.1 for UL with BC overall and with ER+ subtype. Significant genetic correlation was further discovered in 9 out of 14 functional categories, with the highest estimates observed in coding, H3K9ac, and repressed regions. Cross-trait meta-analysis identified 9 novel loci shared between UL and BC. Mendelian randomization demonstrated a significantly increased risk of BC overall (OR = 1.09, 95% CI = 1.01-1.18) and ER+ subtype (OR = 1.09, 95% CI = 1.01-1.17) for genetic liability to UL. No reverse causality was found. Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis, pleiotropic loci, as well as a putative causal relationship between UL and BC, highlighting an intrinsic link underlying these two complex female diseases.

Asthma and the Missing Heritability Problem: Necessity for Multiomics Approaches in Determining Accurate Risk Profiles

Asthma is ranked among the most common chronic conditions and has become a significant public health issue due to the recent and rapid increase in its prevalence. Investigations into the underlying genetic factors predict a heritable component for its incidence, estimated between 35% and 90% of causation. Despite the application of large-scale genome-wide association studies (GWAS) and admixture mapping approaches, the proportion of variants identified accounts for less than 15% of the observed heritability of the disease. The discrepancy between the predicted heritable component of disease and the proportion of heritability mapped to the currently identified susceptibility loci has been termed the ‘missing heritability problem.’ Here, we examine recent studies involving both the analysis of genetically encoded features that contribute to asthma and also the role of non-encoded heritable characteristics, including epigenetic, environmental, and developmental aspects of disease. The importance of vertical maternal microbiome transfer and the influence of maternal immune factors on fetal conditioning in the inheritance of disease are also discussed. In order to highlight the broad array of biological inputs that contribute to the sum of heritable risk factors associated with allergic disease incidence that, together, contribute to the induction of a pro-atopic state. Currently, there is a need to develop in-depth models of asthma risk factors to overcome the limitations encountered in the interpretation of GWAS results in isolation, which have resulted in the missing heritability problem. Hence, multiomics analyses need to be established considering genetic, epigenetic, and functional data to create a true systems biology-based approach for analyzing the regulatory pathways that underlie the inheritance of asthma and to develop accurate risk profiles for disease.

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

Shared Genetic Basis and Causal Relationship Between Television Watching, Breakfast Skipping and Type 2 Diabetes: Evidence From a Comprehensive Genetic Analysis

Background

Epidemiological investigations have established unhealthy lifestyles, such as excessive leisurely sedentary behavior (especially TV/television watching) and breakfast skipping, increase the risk of type 2 diabetes (T2D), but the causal relationship is unclear. We aimed to understand how single nucleotide variants contribute to the co-occurrence of unhealthy lifestyles and T2D, thereby providing meaningful insights into disease mechanisms.

Methods

Combining summary statistics from genome-wide association studies (GWAS) on TV watching (N = 422218), breakfast skipping (N = 193860) and T2D (N = 159208) in European pedigrees, we conducted comprehensive pairwise genetic analysis, including high-definition likelihood (HDL-method), cross-phenotype association studies (CPASSOC), GWAS-eQTL colocalization analysis and transcriptome-wide association studies (TWAS), to understand the genetic overlap between them. We also performed bidirectional two-sample Mendelian randomization (MR) analysis for causal inference using genetic instrumental variables, and two-step MR mediation analysis was used to assess any effects explained by body mass index, lipid traits and glycemic traits.

Results

HDL-method showed that T2D shared a strong genetic correlation with TV watching (rg = 0.26; P = 1.63×10-29) and skipping breakfast (rg = 0.15; P =2.02×10-6). CPASSOC identifies eight independent SNPs shared between T2D and TV watching, including one novel shared locus. TWAS and CPASSOC showed that shared genes were enriched in lung, esophageal, adipose, and thyroid tissues and highlighted potential shared regulatory pathways for lipoprotein metabolism, pancreatic β-cell function, cellular senescence and multi-mediator factors. MR showed TV watching had a causal effect on T2D (βIVW = 0.629, PIVW = 1.80×10-10), but no significant results were observed between breakfast skipping and T2D. Mediation analysis provided evidence that body mass index, fasting glucose, hemoglobin A1c and high-density lipoprotein are potential factors that mediate the causal relationship between TV and T2D.

Conclusions

Our findings provide strong evidence of shared genetics and causation between TV watching and T2D and facilitate our identification of common genetic architectures shared between them.