Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders.

Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.

The effect of SGLT2 inhibition on prostate cancer: Mendelian randomization and observational analysis using electronic healthcare and cohort data.

We evaluated the effect of sodium-glucose cotransporter 2 (SGLT2) inhibition on prostate cancer by evidence triangulation. Using Mendelian randomization, we found that genetically proxied SGLT2 inhibition reduced the risk of overall (odds ratio = 0.56, 95% confidence interval [CI] = 0.38 to 0.82; 79,148 prostate cancer cases and 61,106 controls), advanced, and early-onset prostate cancer. Using electronic healthcare data (nSGLT2i = 24,155; nDPP4i = 24,155), we found that the use of SGLT2 inhibitors was associated with a 23% reduced risk of prostate cancer (hazard ratio = 0.77, 95% CI = 0.61 to 0.99) in men with diabetes. Using data from two prospective cohorts (n4C = 57,779; nUK_Biobank = 165,430), we found little evidence to support the association of HbA1c with prostate cancer, implying a non-glycemic effect of SGLT2 inhibition on prostate cancer. In summary, this study provides multiple layers of evidence to support the beneficial effect of SGLT2 inhibition on reducing prostate cancer risk. Future trials are warranted to investigate whether SGLT2 inhibitors can be recommended for prostate cancer prevention.

Differential network knockoff filter with application to brain connectivity analysis.

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

MRSL: a causal network pruning algorithm based on GWAS summary data.

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

The causal relationship between autoimmune thyroid disorders and telomere length: A Mendelian randomization and colocalization study.

This study aimed to evaluate whether there is a causal relationship between autoimmune thyroid disorders (AITDs) and telomere length (TL) in the European population and whether there is reverse causality. In this study, Mendelian randomization (MR) and colocalization analysis were conducted to assess the potential causal relationship between AITDs and TL using summary statistics from large-scale genome-wide association studies, followed by analysis of the relationship between TL and thyroid stimulating hormone and free thyroxine (FT4) to help interpret the findings. The inverse variance weighted (IVW) method was used to estimate the causal estimates. The weighted median, MR-Egger and leave-one-out methods were used as sensitivity analyses. The IVW method results showed a significant causal relationship between autoimmune hyperthyroidism and TL (ß = -1.93 × 10-2 ; p = 4.54 × 10-5 ). There was no causal relationship between autoimmune hypothyroidism and TL (ß = -3.99 × 10-3 ; p = 0.324). The results of the reverse MR analysis showed that genetically TL had a significant causal relationship on autoimmune hyperthyroidism (IVW: odds ratio (OR) = 0.49; p = 2.83 × 10-4 ) and autoimmune hypothyroidism (IVW: OR = 0.86; p = 7.46 × 10-3 ). Both horizontal pleiotropy and heterogeneity tests indicated the validity of our bidirectional MR study. Finally, colocalization analysis suggested that there were shared causal variants between autoimmune hyperthyroidism and TL, further highlighting the robustness of the results. In conclusion, autoimmune hyperthyroidism may accelerate telomere attrition, and telomere attrition is a causal factor for AITDs.

Conditional transcriptome-wide association study for fine-mapping candidate causal genes.

Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.

Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome.

BACKGROUND: The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored. METHODS: Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test. RESULTS: A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020). CONCLUSIONS: For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.

Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics.

Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.

Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases.

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Association Between Educational Attainment and Thyroid Function: Results From Mendelian Randomization and the NHANES Study.

CONTEXT: Many observational studies have reported on the association between educational attainment (EA) and thyroid function, but the causal relationship remains unclear. OBJECTIVE: We aimed to obtain causal effects of EA on thyroid function and to quantify the mediating effects of modifiable risk factors. METHODS: Two-sample mendelian randomization (MR) was performed by using summary statistics from large genome-wide association studies (GWAS) to assess the effect of EA on thyroid function, including hypothyroidism, hyperthyroidism, thyrotropin (TSH), and free thyroxine (FT4). A multivariable analysis was conducted to assess the mediating role of smoking and help to explain the association between EA and thyroid function. Similar analysis was further performed using data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2002. RESULTS: In MR analysis, EA was causally associated with TSH (ß = .046; 95% CI, 0.015-0.077; P = 4.00 × 10-3), rather than hypothyroidism, hyperthyroidism, and FT4. Importantly, smoking could serve as a mediator in the association between EA and TSH, in which the mediating proportion was estimated to be 10.38%. After adjusting for smoking in the multivariable MR analysis, the ß value of EA on TSH was attenuated to 0.030 (95% CI, 0.016-0.045; P = 9.32 × 10-3). Multivariable logistic regression model in NHANES suggested a dose-response relationship between TSH (quartile [Q]4 vs Q1: odds ratio = 1.33; 95% CI, 1.05-1.68; P for trend = .023) and EA. Smoking, systolic blood pressure, and body mass index partially mediated the association between EA and TSH, with the proportion of the mediation effects being 43.82%, 12.28%, and 6.81%, respectively. CONCLUSION: There is a potentially causal association between EA and TSH, which could be mediated by several risk factors, such as smoking.

Association of antihypertensive drugs with fracture and bone mineral density: A comprehensive drug-target Mendelian randomization study.

Background: Observational studies have investigated the associations between antihypertensive drugs and fracture risk as well as bone mineral density (BMD), but yielding controversial results. Methods: In this study, a comprehensive drug-target Mendelian randomization (MR) analysis was conducted to systematically examine the associations between genetic proxies for eight common antihypertensive drugs and three bone health-related traits (fracture, total body BMD [TB-BMD], and estimated heel BMD [eBMD]). The main analysis used the inverse-variance weighted (IVW) method to estimate the causal effect. Multiple MR methods were also employed to test the robustness of the results. Results: The genetic proxies for angiotensin receptor blockers (ARBs) were associated with a reduced risk of fracture (odds ratio [OR] = 0.67, 95% confidence interval [CI]: 0.54 to 0.84; P = 4.42 × 10-4; P-adjusted = 0.004), higher TB-BMD (ß = 0.36, 95% CI: 0.11 to 0.61; P = 0.005; P-adjusted = 0.022), and higher eBMD (ß = 0.30, 95% CI: 0.21 to 0.38; P = 3.59 × 10-12; P-adjusted = 6.55 × 10-11). Meanwhile, genetic proxies for calcium channel blockers (CCBs) were associated with an increased risk of fracture (OR = 1.07, 95% CI: 1.03 to 1.12; P = 0.002; P-adjusted = 0.013). Genetic proxies for potassium sparing diuretics (PSDs) showed negative associations with TB-BMD (ß = -0.61, 95% CI: -0.88 to -0.33; P = 1.55 × 10-5; P-adjusted = 1.86 × 10-4). Genetic proxies for thiazide diuretics had positive associations with eBMD (ß = 0.11, 95% CI: 0.03 to 0.18; P = 0.006; P-adjusted = 0.022). No significant heterogeneity or pleiotropy was identified. The results were consistent across different MR methods. Conclusions: These findings suggest that genetic proxies for ARBs and thiazide diuretics may have a protective effect on bone health, while genetic proxies for CCBs and PSDs may have a negative effect.

Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study.

Background & purpose: Obesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM. Patients & methods: We analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk. Results: There were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively. Conclusion: Metabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies-With Applications to Studies of Breast Cancer.

Transcriptome-wide association studies (TWASs) aim to detect associations between genetically predicted gene expression and complex diseases or traits through integrating genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies. Most current TWAS methods analyze one gene at a time, ignoring the correlations between multiple genes. Few of the existing TWAS methods focus on survival outcomes. Here, we propose a novel method, namely a COx proportional hazards model for NEtwork regression in TWAS (CoNet), that is applicable for identifying the association between one given network and the survival time. CoNet considers the general relationship among the predicted gene expression as edges of the network and quantifies it through pointwise mutual information (PMI), which is under a two-stage TWAS. Extensive simulation studies illustrate that CoNet can not only achieve type I error calibration control in testing both the node effect and edge effect, but it can also gain more power compared with currently available methods. In addition, it demonstrates superior performance in real data application, namely utilizing the breast cancer survival data of UK Biobank. CoNet effectively accounts for network structure and can simultaneously identify the potential effecting nodes and edges that are related to survival outcomes in TWAS.

The effects of obesity and metabolic abnormalities on severe COVID-19-related outcomes after vaccination: A population-based study.

Breakthrough SARS-CoV-2 infections of vaccinated individuals are being reported globally, resulting in an increased risk of hospitalization and death among such patients. Therefore, it is crucial to identify the modifiable risk factors that may affect the protective efficacy of vaccine use against the development of severe COVID-19 and thus to initiate early medical interventions. Here, in population-based studies using the UK Biobank database and the 2021 National Health Interview Survey (NHIS), we analyzed 20,362 participants aged 50 years or older and 2,588 aged 18 years or older from both databases who tested positive for SARS-COV-2, of whom 33.1% and 67.7% received one or more doses of vaccine, respectively. In the UK Biobank, participants are followed from the vaccination date until October 18, 2021. We found that obesity and metabolic abnormalities (namely, hyperglycemia, hyperlipidemia, and hypertension) were modifiable factors for severe COVID-19 in vaccinated patients (all p < 0.05). When metabolic abnormalities were present, regardless of obesity, the risk of severe COVID-19 was higher than that of metabolically normal individuals (all p < 0.05). Moreover, pharmacological interventions targeting such abnormalities (namely, antihypertensive [adjusted hazard ratio (aHR) 0.64, 95% CI 0.48-0.86; p = 0.003], glucose-lowering [aHR 0.55, 95% CI 0.36-0.83; p = 0.004], and lipid-lowering treatments [aHR 0.50, 95% CI 0.37-0.68; p < 0.001]) were significantly associated with a reduced risk for this outcome. These results show that more proactive health management of patients with obesity and metabolic abnormalities is critical to reduce the incidence of severe COVID-19 after vaccination.

Association between Regional Body Muscle Mass and Non-Alcoholic Fatty Liver Disease: An Observational Study Using Data from the REACTION Study.

Background and aims: Regional muscle distribution is associated with abdominal obesity and metabolic syndrome. However, the relationship between muscle distribution and nonalcoholic fatty liver disease (NAFLD) remains unclear. This study was to determine the relationship between regional muscle distribution and the risk and severity of NAFLD. Methods: This cross-sectional study ultimately included 3161 participants. NAFLD diagnosed by ultrasonography was classified into three groups (non, mild, and moderate/severe). We estimated the regional body muscle mass (lower limbs, upper limbs, extremities, and trunk) through multifrequency bioelectrical impedance analysis (BIA). The relative muscle mass was defined as the muscle mass adjusted for the body mass index (BMI). Results: NAFLD participants accounted for 29.9% (945) of the study's population. Individuals with a higher lower limb, extremity, and trunk muscle mass had a lower risk of NAFLD (p < 0.001). Patients with moderate/severe NAFLD had a lower muscle mass of the lower limbs and trunk than patients with mild NAFLD (p < 0.001), while the muscle mass of the upper limbs and extremities did not differ significantly between the two groups. Moreover, similar results were found for both sexes and among different age groups. Conclusions: A higher muscle mass of the lower limbs, extremities, and trunk was negatively associated with the risk of NAFLD. A lower muscle mass of the limbs and trunk was inversely associated with the severity of NAFLD. This study provides a new theoretical basis for the development of individualized exercise prescriptions for the prevention of NAFLD in non-NAFLD patients.

Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis.

Importance: Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. Objective: To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. Design, Setting, and Participants: This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. Main Outcomes and Measures: The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. Results: Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). Conclusions and Relevance: These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.

Multi-trajectories of systolic and diastolic hypertension and coronary heart disease in middle-aged and older adults.

Objective: This study aimed to investigate multi-trajectories of systolic and diastolic hypertension and assess their association with the risk of coronary heart disease (CHD) in middle-aged and older Chinese adults. Methods: The study cohort comprised 4,102 individuals aged 40-75 years with records of at least four systolic blood pressure (SBP) and diastolic blood pressure (DBP). A group-based multi-trajectory model was adopted to identify multi-trajectories of systolic and diastolic hypertension, followed by a logistic model to assess the independent associations between these trajectories and CHD risk. The multinomial logistic model was used to evaluate the impact of baseline covariates on trajectory groups. Results: Six distinct trajectories for systolic and diastolic hypertension were identified which represent distinct stages of hypertension and were characterized as low-stable, low-increasing, medium-decreasing, medium-increasing-decreasing, isolated systolic hypertension phase, and high-decreasing. Compared with the low-stable group, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were 2.23 (1.34-3.70) for the medium-increasing-decreasing group and 1.87 (1.12-3.11) for the high-decreasing group after adjustment for baseline covariates. Compared with the low-increasing group, the ORs and 95% CIs were 1.88 (1.06-3.31) for the medium-increasing-decreasing group. Age, gender, drinking, body mass index (BMI), triglyceride (TG), and fasting plasma glucose (FPG) were independent predictors for trajectory groups 4 and 6. Conclusion: Novel, clinically defined multi-trajectories of systolic and diastolic hypertension were identified. Middle-aged and older adults with medium-increasing-decreasing or high-decreasing blood pressure trajectories are potentially critical periods for the development of CHD. Preventing adverse changes in hypertension status and reducing the high risk of CHD is necessary for people in distinct trajectory groups.

Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 1.73 × 10-7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

Using machine learning to identify gene interaction networks associated with breast cancer.

BACKGROUND: Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes. METHODS: The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes. RESULTS: Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways. CONCLUSION: We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.