Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA

Host genetics and nutrition

Optimal nutrition is essential for health and physiological performance. Nutrition-related diseases such as obesity and diabetes are major causes of death and reduced quality of life in modern Western societies. Thanks to combining nutrigenetics and nutrigenomics, genomic nutrition allows the study of the interaction between nutrition, genetics and physiology. Currently, interrelated multi-genetic and multifactorial phenotypes are studied from a multiethnic and multi-omics approach, step by step identifying the important role of pathways, in addition to those directly related to metabolism. It allows the progressive identification of genetic profiles associated with specific susceptibilities to diet-related phenotypes, which may facilitate individualised dietary recommendations to improve health and quality of life.

Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease

Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.

Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses

BACKGROUND

While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD.

METHODS

We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055).

RESULTS

Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively.

CONCLUSION

Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Transcriptomic Analysis of Type 2 Diabetes Mellitus Combined with Lower Extremity Atherosclerotic Occlusive Disease

Background

The pathological damage mechanism of type 2 diabetes (T2D) and macroangiopathy is extremely complex, and T2D and arteriosclerosis obliterans have different biological behaviors and clinical features. To explore the mechanism of lower extremity arteriosclerosis occlusion (LEAOD) in T2D patients, we utilized RNA-seq to identify unique gene expression signatures of T2D and LEAOD through transcriptomic analysis.

Methods

We obtained blood samples and performed RNA sequencing from four patients with T2D, five of whom had LEAOD. Another six age- and gender-matched blood samples from healthy volunteers were used for control. By exploring the general and specific differential expression analysis after transcriptome sequencing, specific gene expression patterns of T2D and LEAOD were verified.

Results

Transcriptome analysis found differentially expressed genes in T2D, and T2D + LEAOD (vs normal) separately, of which 35/486 (T2D/T2D + LEAOD) were up-regulated and 1290/2970 (T2D/T2D + LEAOD) were down-regulated. A strong overlap of 571 genes across T2D, LEAOD, and coexisting conditions was mainly involved in extracellular exosomes and the transcription process. By exploring the sex difference gene expression features between T2D, T2D + LEAOD, and healthy controls, we noticed that sex chromosome-associated genes do not participate in the sexual dimorphism gene expression profiles of T2D and LEAOD. Protein-Protein Interaction Network analysis and drug target prediction provided the drug candidates to treat T2D and LEAOD.

Conclusion

This study provides some evidence at the transcript level to uncover the association of T2D with LEAOD. The screened hub genes and predicted target drugs may be therapeutic targets.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine.

Thiamine Is a Natural Peroxisome Proliferator–Activated Receptor Gamma (PPAR-γ) Activator

Background:

There has been increasing evidence for the correlation between thiamine deficiency and type 2 diabetes (T2D). T2D is a condition in which an individual’s insulin sensitivity is highly compromised. Peroxisome proliferator–activated receptor gamma (PPAR-γ) is a ligand-activated transcription factor etiologically relevant to T2D. We hypothesized that thiamine could be a PPAR-γ ligand and thus activate PPAR-γ and ameliorate T2D.

Objective:

This study aims to establish thiamine as a PPAR-γ ligand via molecular docking and dynamics simulations (MDS) and thiamine’s ability to induce adipogenesis, upregulating PPAR-γ and AP-2 genes using in vitro assays.

Methods:

Thiamine/PPAR-γ binding was studied using Schrödinger’s Glide. The bound complex was simulated in the OPLS 2005 force field using Desmond. 3T3-L1 preadipocyte cells were differentiated in the presence of thiamine and rosiglitazone and stained with Oil Red O. Nuclear protein from the differentiated cells was used to study the binding of the PPAR-γ response element (PPRE) using an ELISA-based assay. mRNA from differentiated cells was used to study the expression of genes using quantitative RT-PCR.

Results:

In silico docking shows that thiamine binds with PPAR-γ. MDS indicate that the interactions between thiamine and PPAR-γ are stable over a significant period. Thiamine induces the differentiation of 3T3-L1 preadipocytes in a dose-dependent manner and enhances the PPRE-binding activity of PPAR-γ. Thiamine treatment significantly increases the mRNA levels of PPAR-γ and AP-2 genes.

Conclusion:

Our results show that thiamine is a PPAR-γ ligand. Animal studies and clinical trials are required to corroborate the results obtained.

Exploring the Pleiotropic Genes and Therapeutic Targets Associated with Heart Failure and Chronic Kidney Disease by Integrating metaCCA and SGLT2 Inhibitors' Target Prediction

Background

Previous studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug—sodium-glucose cotransporter 2 (SGLT2) inhibitors.

Methods

The genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets.

Results

After metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors' target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease.

Conclusion

We identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.

Overweight and risk of type 2 diabetes: A prospective Chinese twin study

OBJECTIVES

This study aimed to estimate the association between overweight and type 2 diabetes mellitus (T2DM) in twins, and further to explore whether genetic and early-life environmental factors account for this association.

METHODS

This study included 31,197 twin individuals from the Chinese National Twin Registry (CNTR). Generalized estimating equation (GEE) models were applied for unmatched case-control analysis. Conditional logistic regressions were used in co-twin matched case-control analysis. Logistic regressions were fitted to examine the differences in odds ratios (ORs) from the GEE models and conditional logistic regressions. Bivariate genetic model was used to explore the genetic and environmental correlation between body mass index (BMI) and T2DM.

RESULTS

In the GEE model, overweight was associated with a higher T2DM risk (OR=2.71, 95% confidence interval (CI): 1.96∼3.73), compared with participants with normal BMI. In the multi-adjusted conditional logistic regression, the association was still significant (OR=2.60, 95% CI: 1.15∼5.87). The ORs from the unmatched and matched analyses were different (P = 0.042). Particularly, overweight could increase T2DM risk in monozygotic (MZ) twins, and the difference in ORs between the unmatched and matched designs was significant (P = 0.014). After controlling for age and sex, the positive BMI-T2DM association was partly due to a significant genetic correlation (rA= 0.31, 95% CI: 0.20∼0.41).

CONCLUSIONS

Our findings suggest that genetics and early-life environments might account for the observed overweight-T2DM association. Genetic correlation between BMI and T2DM further provides evidence for the influence of overlap genes on their association.

The association of polymorphisms in related circadian rhythm genes and clopidogrel resistance susceptibility

Previous studies have confirmed that a dynamic change in circadian rhythm will affect platelet activity, resulting in clopidogrel resistance (CR). We attempted to evaluate whether polymorphisms of related circadian rhythm genes are involved in CR in stable coronary artery disease (SCAD) patients. A sum of 204 SCAD patients met our requirements and were recruited, and 96 patients were considered to have CR. After clinical data collection and platelet function evaluation, genomic DNA was isolated from human peripheral blood, and 23 tagSNPs from related circadian rhythm genes were genotyped by GenomeLab SNPstream Genotyping System. After RNA isolation, relative expression of related gene mRNAs (CLOCK, CRY1, CACNA1C and PRKCG) was measured by real-time PCR. The results showed that polymorphisms in CRY1, CACNA1C and PRKCG changed the response to clopidogrel. And then, the rs1801260 polymorphism might lead to higher mRNA expression in CLOCK and potentially induce the occurrence of CR. Additionally, the TC genotype of rs3745406 might lower mRNA expression of PRKCG, resulting in CR. These findings support the hypothesized role of circadian rhythm genes in CR and indicate probable biomarkers for CR susceptibility, providing new insight into individualized medicine for coronary heart disease.

Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study

Motivation

Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.

Results

Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets.

Availability and implementation

https://github.com/theorod93/sCCA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Helicobacter pylori Related Diseases and Osteoporotic Fractures (Narrative Review)

Osteoporosis (OP) and osteoporotic fractures (OFs) are common multifactorial and heterogenic disorders of increasing incidence. Helicobacter pylori (H.p.) colonizes the stomach approximately in half of the world's population, causes gastroduodenal diseases and is prevalent in numerous extra-digestive diseases known to be associated with OP/OF. The studies regarding relationship between H.p. infection (HPI) and OP/OFs are inconsistent. The current review summarizes the relevant literature on the potential role of HPI in OP, falls and OFs and highlights the reasons for controversies in the publications. In the first section, after a brief overview of HPI biological features, we analyze the studies evaluating the association of HPI and bone status. The second part includes data on the prevalence of OP/OFs in HPI-induced gastroduodenal diseases (peptic ulcer, chronic/atrophic gastritis and cancer) and the effects of acid-suppressive drugs. In the next section, we discuss the possible contribution of HPI-associated extra-digestive diseases and medications to OP/OF, focusing on conditions affecting both bone homeostasis and predisposing to falls. In the last section, we describe clinical implications of accumulated data on HPI as a co-factor of OP/OF and present a feasible five-step algorithm for OP/OF risk assessment and management in regard to HPI, emphasizing the importance of an integrative (but differentiated) holistic approach. Increased awareness about the consequences of HPI linked to OP/OF can aid early detection and management. Further research on the HPI-OP/OF relationship is needed to close current knowledge gaps and improve clinical management of both OP/OF and HPI-related disorders.

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

Haplotypes of the Mutated SIRT2 Promoter Contributing to Transcription Factor Binding and Type 2 Diabetes Susceptibility

Genetic variability is an important causative factor for susceptibility and pathogenesis of type 2 diabetes (T2D). Histone deacetylase, sirtuin 2 (SIRT2), plays regulatory roles in glucose metabolism and insulin sensitivity. However, whether the SIRT2 variants or haplotypes contribute to T2D risk remain to be elucidated. In this study, we first detected three novel polymorphisms (P-MU1, P-MU2, and P-MU3) in the promoter of SIRT2 in the Chinese population. All pairwise sets of the three loci were strongly in linkage disequilibrium. Next, we constructed the haplotype block structure, and found H1-GGC and H2-CCA accounted for the most (total 91.8%) in T2D. The haplotype combination H1-H1-GGGGCC displayed a high risk for T2D (OR = 2.03, 95% CI = 1.12-3.72). By association analysis, we found the individuals carrying H1-H1-GGGGCC had significantly higher fasting plasma glucose and glycated hemoglobin. The haplotype H1-GGC presented a 6.74-fold higher promoter activity than H2-CCA, which was consistent with the correlation results. Furthermore, we clarified the mechanism whereby the C allele of both the P-MU1 and P-MU2 loci disrupted the signal transducer and activator of transcription 1 (STAT1) binding sites, leading to the attenuation of the SIRT2 transcription. Together, these data suggest that the linked haplotype GGC could be considered as a promising marker for T2D diagnosis and therapy assessment.

Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.