A common variant association study in ethnic Saudi Arabs reveals novel susceptibility loci for hypertriglyceridemia

Association of rs2954029 and rs6982502 Variants with Coronary Artery Disease by HRM Technique: A GWAS Replication Study in an Iranian Population

BACKGROUND

Genome-wide association studies (GWAS) have been the primary tool for an unbiased study of the genetic background of coronary artery disease (CAD). They have identified a list of single-nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD). In this study, we aimed to replicate the association of rs2954029 and rs6982502, a GWAS identified SNP, to CAD in an Iranian population.

METHODS

A sample of 285 subjects undergoing coronary angiography, including 134 CAD patients and 151 healthy. The genotype determination of rs2954029 and rs6982502 SNPs performed using the high-resolution melting analysis (HRM) technique.

RESULTS

Our results revealed that the TT genotype of rs2954029 (p= 0.009) and rs6982502 (p< 0.001) were significantly higher in CAD patients compared with controls. Binary logistic regression showed that rs6982502 and rs2954029 increase the risk of CAD incidence (2.470 times, p= 0.011, 95% CI= [1.219-4.751], and 2.174 times, p= 0.033, 95% CI= [1.066-4.433] respectively). After adjusting for confounders, we found that rs6982502 and rs2954029 are significantly associated with CAD risk.

CONCLUSION

These data showed that the TT genotype of rs2954029 and rs6982502 is associated with the risk of CAD in a hospital-based sample of the Iranian population, which has replicated the result of recent GWAS studies.

Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population

While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.

CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information

MOTIVATION

Genome-wide association studies (GWASs) have been successful in identifying many genetic variants associated with complex traits. However, the mechanistic links between these variants and complex traits remain elusive. A scientific hypothesis is that genetic variants influence complex traits at the organismal level via affecting cellular traits, such as regulating gene expression and altering protein abundance. Although earlier works have already presented some scientific insights about this hypothesis and their findings are very promising, statistical methods that effectively harness multilayered data (e.g. genetic variants, cellular traits and organismal traits) on a large scale for functional and mechanistic exploration are highly demanding.

RESULTS

In this study, we propose a collaborative mixed model (CoMM) to investigate the mechanistic role of associated variants in complex traits. The key idea is built upon the emerging scientific evidence that genetic effects at the cellular level are much stronger than those at the organismal level. Briefly, CoMM combines two models: the first model relating gene expression with genotype and the second model relating phenotype with predicted gene expression using the first model. The two models are fitted jointly in CoMM, such that the uncertainty in predicting gene expression has been fully accounted. To demonstrate the advantages of CoMM over existing methods, we conducted extensive simulation studies, and also applied CoMM to analyze 25 traits in NFBC1966 and Genetic Epidemiology Research on Aging (GERA) studies by integrating transcriptome information from the Genetic European in Health and Disease (GEUVADIS) Project. The results indicate that by leveraging regulatory information, CoMM can effectively improve the power of prioritizing risk variants. Regarding the computational efficiency, CoMM can complete the analysis of NFBC1966 dataset and GERA datasets in 2 and 18 min, respectively.

AVAILABILITY AND IMPLEMENTATION

The developed R package is available at https://github.com/gordonliu810822/CoMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association between lncRNA and GCKR gene in type 2 diabetes mellitus

OBJECTIVE

To screen long non-coding RNA (lncRNA) related to glucokinase regulatory protein gene (GCKR), its differential expression was analyzed in patients with Type 2 diabetes mellitus (T2DM) and control samples. The correlation of lncRNA with GCKR was verified and its potential value as a molecular marker of T2DM was assessed.

METHODS

Lymphocyte RNA was extracted from five patients with T2DM and five patients with non-T2DM. The expression profiles of circulating lncRNAs and mRNAs were obtained by microarray. Bioinformatics analysis was used to screen lncRNAs associated with the GCKR gene in 127 patients with T2DM and 130 patients with non-T2DM were selected. The expression levels of the GCKR gene and lncRNA (ENST00000588707.1 and TCONS_00004187) in the T2DM group and control group were verified by real-time PCR. Additionally, a correlation analysis was conducted. The value of circulating ENST00000588707.1 and TCONS_00004187 as biomarkers for the diagnosis of T2DM was performed by receiver operating characteristic curve analysis.

RESULTS

We identified 68 lncRNAs and 74 mRNAs differentially expressed from the expression profile. Compared with the control group, the expression levels of the GCKR gene and lncRNA ENST00000588707.1 and TCONS_00004187 in the T2DM group were significantly lower (P < 0.05). The correlation analysis revealed that ENST00000588707.1 and TCONS_00004187 were correlated with GCKR gene expression and glycolipid metabolism (P < 0.05). ROC analysis showed that the area under the curve value of ENST00000588707.1 between T2DM patients and non-T2DM patients was 0.816 (95% CI: 0.764-0.869, sensitivity 72.0%, specificity 80.3%) and the AUC value of TCONS_00004187 was 0.826 (95% CI: 0.774-0.879, sensitivity 81.6%, specificity 61.3%).

CONCLUSION

lncRNA ENST0000588707.1 and TCONS_00004187 could serve as potential biomarkers for T2DM, which could involve in glycolipid metabolism by regulating the GCKR gene.

Pragmatic Analysis of Dyslipidemia Involvement in Coronary Artery Disease: A Narrative Review

Background

Dyslipidemia is the main factor involved in the occurrence and progression of coronary artery disease.

Objective

The research strategy is aimed at analyzing new data on the pathophysiology of dyslipidemia involvement in coronary artery disease, the modalities of atherogenic risk estimation and therapeutic advances.

Methods

Scientific articles published in PubMed from January 2017 to February 2018 were searched using the terms “dyslipidemia” and “ischemic heart disease”.

Results

PCSK9 contributes to the increase in serum levels of low-density lipoprotein-cholesterol and lipoprotein (a). The inflammation is involved in the progression of hyperlipidemia and atherosclerosis. Hypercholesterolemia changes the global cardiac gene expression profile and is thus involved in the increase of oxidative stress, mitochondrial dysfunction, and apoptosis initiated by inflammation. Coronary artery calcifications may estimate the risk of coronary events. The cardio-ankle vascular index evaluates the arterial stiffness and correlates with subclinical coronary atherosclerosis. The carotid plaque score is superior to carotid intima-media thickness for risk stratification in patients with familial hypercholesterolemia and both can independently predict coronary artery disease. The lipoprotein (a) and familial hypercholesterolemia have a synergistic role in predicting the risk of early onset and severity of coronary atherosclerosis. A decrease in atherosclerotic coronary plaque progression can be achieved in patients with plasma LDL-cholesterol levels below 70 mg/dL. A highly durable RNA interference therapeutic inhibitor of PCSK9 synthesis could be a future solution.

Conclusion

The prophylaxis and treatment of coronary artery disease in a dyslipidemic patient should be based on a careful assessment of cardio-vascular risk factors and individual metabolic particularities, so it may be personalized.