Association between Phosphatase Related Gene Variants  and Coronary Artery Disease: Case-Control Study  and Meta-Analysis

Han, Xuelei; Zhang, Lijun; Zhang, Zhiqiang; Zhang, Zengtang; Wang, Jian-Chun; Jun, Yang; Niu, Jiamin

doi:10.3390/ijms150814058

Cited by 7 publications

(4 citation statements)

References 67 publications

(74 reference statements)

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“…These observations are in line with the results discussed by Han Xia et al 2014, where the phosphatase-related single nucleotide polymorphism, rs12526453 was significantly associated with coronary artery disease (CAD) risk in multiple populations. [23] rs10757274 and rs10757278 were seen to increase the levels of the risk marker, PLAC ( P < .05), thus, these polymorphisms can be considered as major risk factors contributing to the risk of developing CVDs as described earlier. [24,25] Nevertheless, the comparative analysis also revealed that the SNPs, rs1801133, rs1549758, rs1799983, rs5082, and rs5186 were found to be significantly associated with cardioprotective markers such as HDL and ApoA1.…”

Section: Discussionmentioning

confidence: 82%

Influence of genetic polymorphisms on serum biomarkers of cardiac health

et al. 2023

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide which is why early risk prediction is crucial. Discrete Polygenic risk score (PRS) measurement using saliva or dried blood spot samples collected at home poses a convenient means for early CVD risk assessment. The present study assessed the effects of 28 disease-associated single nucleotide polymorphisms (SNPs) on 16 serological cardiac markers and also aggregated the risk alleles into a PRS to evaluate its applicability in CVD-risk prediction. The study assessed genetic and serological markers in 184 individuals. The association between serological markers and individual genetic variants was evaluated using a two-tailed t test while the associations of serum markers with the PRS was analyzed using the Pearson correlation. The comparative analysis of genotypes revealed statistically significant associations between serum markers and CVD-associated SNPs with Apo B: Apo A-1, LDL Direct, Apo B, sdLDL, hsCRP, Lp(a), NT-proBNP, and PLAC levels being significantly associated with the risk alleles of the SNPs, rs12526453, rs5186, rs10911021, rs1801131, rs670, rs10757274, and rs10757278. Increased PLAC levels were associated with rs10757274 and rs10757278 (P < .05). The SNPs, rs1801133, rs1549758, rs1799983, rs5082, and rs5186 were significantly associated with an increase in the cardioprotective markers, HDL and ApoA1 (P < .05). Furthermore, the PRS was associated with increasing levels of several serum cardiac markers (r2 > 0.6). Significant correlations were observed between high PRSs and NT-proBNP and ox-LDL levels with the r2 values being 0.82 (95% CI = 0.13–0.99; P = .03) and 0.94 (95% CI = 0.63–0.99; P = .005), respectively. The present study reports that SNPs have differential effects on serum markers with rs12526453, rs5186, rs10911021, rs1801131, rs670, rs10757274, and rs10757278 showing significant associations with elevated marker levels, which are indicators of deteriorating cardiac health. A unified PRS using several SNPs was also associated with an increase in serum markers levels, especially, NT-proBNP and ox-LDL. Genetic assessment via a convenient at-home collection to calculate the PRS can serve as an effective predictive tool for early CVD-risk assessment. This may help identify the risk groups that may require increased serological monitoring.

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Section: Discussionmentioning

confidence: 82%

Influence of genetic polymorphisms on serum biomarkers of cardiac health

et al. 2023

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“…COL5A2 was also reported as a novel candidate marker for the identification and treatment of ischemic cardiovascular disease ( 34 ). PTPN11 was reported to contain a CAD risk variant ( 35 ). SMAD3 was reported to be associated with CAD and suggested to be a useful biomarker for diagnosis and risk stratification ( 36 ).…”

Section: Evaluation Of Gwab Using Disease Gwas Datamentioning

confidence: 99%

GWAB: a web server for the network-based boosting of human genome-wide association data

Shim

Bang

Yang

et al. 2017

Nucleic Acids Research

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During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10−8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

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“…I have read with interest the recent paper by Han and coworkers [ 1 ] on the putative effects of a PHACTR1 variant in the context of coronary artery disease. The authors conclude to a significant risk-enhancing role of rs12526453 on the grounds of 19 earlier case-control studies.…”

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confidence: 99%

“…Thus, cases and controls have been muddled [ 2 ], fictitious cases and controls have been introduced (e.g., from a genome-wide association study phase that did not include rs12526453 [ 3 ]), odds ratios have been altered [ 4 , 5 ] and quantitative traits have been arbitrarily dichotomized post hoc to fit in a case-control design [ 6 ]. Other inconsistencies include discrepant measures of effect size (e.g., Table 5 and Figure 3 [ 1 ]), contradictory numbers in article retrieval (see Figure 1 [ 1 ]), missing data points (see Figure 5 [ 1 ]) and utterly erratic referencing.…”

mentioning

confidence: 99%