scholarly journals Improved Detection of Potentially Pleiotropic Genes in Coronary Artery Disease and Chronic Kidney Disease Using GWAS Summary Statistics

2020 ◽  
Vol 11 ◽  
Author(s):  
Haimiao Chen ◽  
Ting Wang ◽  
Jinna Yang ◽  
Shuiping Huang ◽  
Ping Zeng
Keyword(s):  
Coronary Artery Disease ◽  
Chronic Kidney Disease ◽  
Coronary Artery ◽  
Kidney Disease ◽  
Association Studies ◽  
Functional Enrichment ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genetic Determination ◽  
Artery Disease

The coexistence of coronary artery disease (CAD) and chronic kidney disease (CKD) implies overlapped genetic foundation. However, the common genetic determination between the two diseases remains largely unknown. Relying on summary statistics publicly available from large scale genome-wide association studies (n = 184,305 for CAD and n = 567,460 for CKD), we observed significant positive genetic correlation between CAD and CKD (rg = 0.173, p = 0.024) via the linkage disequilibrium score regression. Next, we implemented gene-based association analysis for each disease through MAGMA (Multi-marker Analysis of GenoMic Annotation) and detected 763 and 827 genes associated with CAD or CKD (FDR < 0.05). Among those 72 genes were shared between the two diseases. Furthermore, by integrating the overlapped genetic information between CAD and CKD, we implemented two pleiotropy-informed informatics approaches including cFDR (conditional false discovery rate) and GPA (Genetic analysis incorporating Pleiotropy and Annotation), and identified 169 and 504 shared genes (FDR < 0.05), of which 121 genes were simultaneously discovered by cFDR and GPA. Importantly, we found 11 potentially new pleiotropic genes related to both CAD and CKD (i.e., ARHGEF19, RSG1, NDST2, CAMK2G, VCL, LRP10, RBM23, USP10, WNT9B, GOSR2, and RPRML). Five of the newly identified pleiotropic genes were further repeated via an additional dataset CAD available from UK Biobank. Our functional enrichment analysis showed that those pleiotropic genes were enriched in diverse relevant pathway processes including quaternary ammonium group transmembrane transporter, dopamine transport. Overall, this study identifies common genetic architectures overlapped between CAD and CKD and will help to advance understanding of the molecular mechanisms underlying the comorbidity of the two diseases.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

scholarly journals Causal Inference Between Chronic Periodontitis and Chronic Kidney Disease: A Bidirectional Mendelian Randomization Analysis in a European Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Yang ◽  
Tianyi Chen ◽  
Yahong Zhu ◽  
Mingxia Bai ◽  
Xingang Li
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Causal Inference ◽  
Causal Relationship ◽  
Instrumental Variables ◽  
Chronic Periodontitis ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics

BackgroundPrevious epidemiological studies have shown significant associations between chronic periodontitis (CP) and chronic kidney disease (CKD), but the causal relationship remains uncertain. Aiming to examine the causal relationship between these two diseases, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis with multiple MR methods.MethodsFor the casual effect of CP on CKD, we selected seven single-nucleotide polymorphisms (SNPs) specific to CP as genetic instrumental variables from the genome-wide association studies (GWAS) in the GLIDE Consortium. The summary statistics of complementary kidney function measures, i.e., estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), were derived from the GWAS in the CKDGen Consortium. For the reversed causal inference, six SNPs associated with eGFR and nine with BUN from the CKDGen Consortium were included and the summary statistics were extracted from the CLIDE Consortium.ResultsNo significant causal association between genetically determined CP and eGFR or BUN was found (all p > 0.05). Based on the conventional inverse variance-weighted method, one of seven instrumental variables supported genetically predicted CP being associated with a higher risk of eGFR (estimate = 0.019, 95% CI: 0.012–0.026, p < 0.001).ConclusionEvidence from our bidirectional causal inference does not support a causal relation between CP and CKD risk and therefore suggests that associations reported by previous observational studies may represent confounding.

scholarly journals 83-Year-Old Man With Chronic Kidney Disease, Fluid Overload, and Coronary Artery Disease Develops Altered Mental Status

CHEST Journal ◽  
2016 ◽  
Vol 149 (4) ◽  
pp. e111-e114 ◽  
Author(s):  
Brett C. Bade ◽  
Sean P. Callahan ◽  
Jean Paul Higuero ◽  
Nicholas Pastis ◽  
John Terrill Huggins
Keyword(s):  
Coronary Artery Disease ◽  
Chronic Kidney Disease ◽  
Coronary Artery ◽  
Kidney Disease ◽  
Mental Status ◽  
Fluid Overload ◽  
Altered Mental Status ◽  
Artery Disease

Characterizing a common CERS2 polymorphism in a mouse model of metabolic disease and in subjects from the Utah CAD Study

2021 ◽  
Author(s):  
Rebekah J Nicholson ◽  
Annelise M Poss ◽  
J Alan Maschek ◽  
James E Cox ◽  
Paul N Hopkins ◽  
...  
Keyword(s):  
Metabolic Disease ◽  
Coronary Artery Disease ◽  
Enzyme Activity ◽  
Coronary Artery ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Serum Samples ◽  
Artery Disease ◽  
Disease Study

Abstract Context Genome-wide association studies have identified associations between a common single nucleotide polymorphism (SNP, rs267738) in CERS2 – a gene that encodes a (dihydro)ceramide synthase involved in the biosynthesis of very-long chain sphingolipids (e.g. C20-C26) – and indices of metabolic dysfunction (e.g. impaired glucose homeostasis). However, the biological consequences of this mutation on enzyme activity and its causal roles in metabolic disease are unresolved. Objective The studies described herein aimed to characterize the effects of rs267738 on CERS2 enzyme activity, sphingolipid profiles, and metabolic outcomes. Design We performed in-depth lipidomic and metabolic characterization of a novel CRISPR knock-in mouse modeling the rs267738 variant. In parallel, we conducted mass spectrometry-based, targeted lipidomics on 567 serum samples collected through the Utah Coronary Artery Disease study, which included 185 patients harboring one (n = 163) or both (n = 22) rs267738 alleles. Results In-silico analysis of the amino acid substitution within CERS2 caused by the rs267738 mutation suggested that rs267738 is deleterious for enzyme function. Homozygous knock-in mice had reduced liver CERS2 activity and enhanced diet-induced glucose intolerance and hepatic steatosis. However, human serum sphingolipids and a ceramide-based CERT1 risk score of cardiovascular disease were not significantly affected by rs267738 allele count. Conclusions The rs267738 SNP leads to a partial loss-of-function of CERS2, which worsened metabolic parameters in knock-in mice. However, rs267738 was insufficient to effect changes in serum sphingolipid profiles in subjects from the Utah Coronary Artery Disease Study.

Sign in / Sign up

Export Citation Format

Share Document