Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

Genome-Wide Association Analysis of Rapid Decline in Lung Function: Analysis From the Korean Genome and Epidemiology Study

BACKGROUND

A rapid decline in forced expiratory volume in 1 second (FEV1) is considered an important phenotype of the development of chronic obstructive pulmonary disease (COPD). However, the associations between specific genetic variants (single-nucleotide polymorphisms; SNPs) and this phenotype remain uncertain.

METHODS

We enrolled 6,516 individuals from the Korean Genome and Epidemiology Study (KoGES). A rapid decline in FEV1 was defined as an annual decrease of FEV1 ≥ 60 mL/year. A multivariable logistic regression model was used to assess the associations between SNP variants and the rapid decline in FEV1. Considering the significant impact of smoking on lung function, a subgroup analysis based on smoking history was also conducted.

RESULTS

A genome-wide association analysis of the rapid decline in FEV1 identified 15 association signals (P < 5.0 × 10-8). Among the 15 nucleotide variants, rs9833533 and rs1496255 have been previously reported to be associated with lung function development. In the subgroup analysis, rs16951883 (adjusted odds ratio [aOR], 3.24; P = 5.87 × 10-8) was the most significant SNP associated with rapid decline in FEV1 among never smokers, followed by rs41476549, rs16840064, and rs1350110. Conversely, among ever smokers, rs10959478 (aOR, 4.74; P = 8.27 × 10-7) showed the highest significance, followed by rs6805861, rs9833533, and rs16906215.

CONCLUSION

We identified 15 nucleotide variants linked to a rapid decline in FEV1, including two SNPs previously reported to be associated with lung function development. Additional SNPs, which were associated with COPD, may be found using novel phenotypes.

Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.

Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population

Network analysis has become a crucial tool in genetic research, enabling the exploration of associations between genes and diseases. Its utility extends beyond genetics to include the assessment of environmental factors. Unipartite network analysis is commonly used in genomics to visualize initial insights and relationships among variables. Syndromic diseases, such as metabolic syndrome, are characterized by the simultaneous occurrence of various signs, symptoms, and clinicopathological features. Metabolic syndrome encompasses hypertension, diabetes, obesity, and dyslipidemia, and both genetic and environmental factors contribute to its development. Given that relevant data often consist of distinct sets of variables, a more intuitive visualization method is needed. This study applied multipartite network analysis as an effective method to understand the associations among genetic, environmental, and disease components in syndromic diseases. We considered three distinct variable sets: genetic factors, environmental factors, and disease components. The process involved projecting a tripartite network onto a two-mode bipartite network and then simplifying it into a one-mode network. This approach facilitated the visualization of relationships among factors across different sets and within individual sets. To transition from multipartite to unipartite networks, we suggest both sequential and concurrent projection methods. Data from the Korean Association Resource (KARE) project were utilized, including 352,228 SNPs from 8840 individuals, alongside information on environmental factors such as lifestyle, dietary, and socioeconomic factors. The single-SNP analysis step filtered SNPs, supplemented by reference SNPs reported in a genome-wide association study catalog. The resulting network patterns differed significantly by sex: demographic factors and fat intake were crucial for women, while alcohol consumption was central for men. Indirect relationships were identified through projected bipartite networks, revealing that SNPs such as rs4244457, rs2156552, and rs10899345 had lifestyle interactions on metabolic components. Our approach offers several advantages: it simplifies the visualization of complex relationships among different datasets, identifies environmental interactions, and provides insights into SNP clusters sharing common environmental factors and metabolic components. This framework provides a comprehensive approach to elucidate the mechanisms underlying complex diseases like metabolic syndrome.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

Genetic Polymorphisms in a Familial Hypercholesterolemia Population from North-Eastern Europe

(1) Background: Familial hypercholesterolemia (FH) is one of the most prevalent inherited metabolic disorders. The purpose of the study was to investigate the role in cardiovascular disease (CVD) of PAI-1, ACE, ApoB-100, MTHFR A1298C, and C677T. (2) Methods: From a group of 1499 patients, we included 52 patients diagnosed with FH phenotype and 17 patients in a control group. (3) Results: Most of the FH patients had multiple comorbidities compared to the control group, such as atherosclerosis (48.1% vs. 17.6%), atherosclerotic cardiovascular disease (ASCVD 32.7% vs. 11.8%), and metabolic syndrome (MetS, 40.4% vs. 11.8%). In total, 66.7% of the FH patients had PAI-1 4G/5G genotype and MetS. Between 4G/5G and 4G/4G, a statistically significant difference was observed (p = 0.013). FH patients with ApoB R3500Q polymorphism were correlated with ASCVD (p = 0.031). Both MTHFR C677T and A1298C polymorphisms had a significant correlation with gender, alcohol consumption, and smoking status. ACE polymorphism was associated with ATS in FH patients, statistically significant differences being observed between heterozygous and homozygous D genotype (p = 0.036) as well as between heterozygous and homozygous I genotype (p = 0.021). (4) Conclusions: A link between these polymorphisms was demonstrated in the FH group for ATS, ASCVD, and MetS.

A Genome-Wide Association Study of a Korean Population Identifies Genetic Susceptibility to Hypertension Based on Sex-Specific Differences

Genome-wide association studies have expanded our understanding of the genetic variation of hypertension. Hypertension and blood pressure are influenced by sex-specific differences; therefore, genetic variants may have sex-specific effects on phenotype. To identify the genetic factors influencing the sex-specific differences concerning hypertension, we conducted a heterogeneity analysis of a genome-wide association study (GWAS) on 13,926 samples from a Korean population. Using the Illumina exome chip data of the population, we performed GWASs of the male and female population independently and applied a statistical test that identified heterogeneous effects of the variants between the two groups. To gain information about the biological implication of the genetic heterogeneity, we used gene set enrichment analysis with GWAS catalog and pathway gene sets. The heterogeneity analysis revealed that the rs11066015 of ACAD10 was a significant locus that had sex-specific genetic effects on the development of hypertension. The rs2074356 of HECTD4 also showed significant genetic heterogeneity in systolic blood pressure. The enrichment analysis showed significant results that are consistent with the pathophysiology of hypertension. These results indicate a sex-specific genetic susceptibility to hypertension that should be considered in future genetic studies of hypertension.

Genetics of Body Fat Distribution: Comparative Analyses in Populations with European, Asian and African Ancestries

Preferential fat accumulation in visceral vs. subcutaneous depots makes obese individuals more prone to metabolic complications. Body fat distribution (FD) is regulated by genetics. FD patterns vary across ethnic groups independent of obesity. Asians have more and Africans have less visceral fat compared with Europeans. Consequently, Asians tend to be more susceptible to type 2 diabetes even with lower BMIs when compared with Europeans. To date, genome-wide association studies (GWAS) have identified more than 460 loci related to FD traits. However, the majority of these data were generated in European populations. In this review, we aimed to summarize recent advances in FD genetics with a focus on comparisons between European and non-European populations (Asians and Africans). We therefore not only compared FD-related susceptibility loci identified in three ethnicities but also discussed whether known genetic variants might explain the FD pattern heterogeneity across different ancestries. Moreover, we describe several novel candidate genes potentially regulating FD, including NID2, HECTD4 and GNAS, identified in studies with Asian populations. It is of note that in agreement with current knowledge, most of the proposed FD candidate genes found in Asians belong to the group of developmental genes.

A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study

A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH₄ > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.