Genetic effects of BDKRB2 and KNG1 on deep venous thrombosis after orthopedic surgery and the potential mediator

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

BACKGROUND

Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.

OBJECTIVES

To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.

METHODS

Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).

RESULTS

Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.

CONCLUSIONS

The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Large-scale correlation analysis of deep venous thrombosis and gut microbiota

Objective

Although previous studies have shown that gut microbiota may be involved in the occurrence of deep venous thrombosis (DVT), the specific link between the two remains unclear. The present study aimed to explore this question from a genetic perspective.

Materials and methods

Genome-wide association study (GWAS) summary data of DVT were obtained from the UK Biobank (N = 9,059). GWAS summary data of the gut microbiota were obtained from the Flemish Gut Flora Project (N = 2,223) and two German cohorts (FoCus, N = 950; PopGen, N = 717). All the participants were of European ancestry. Linkage disequilibrium score (LDSC) regression has great potential for analyzing the heritability of disease or character traits. LDSC regression was used to analyze the genetic correlation between DVT and the gut microbiota based on the GWAS summary data obtained from previous studies. Mendelian randomization (MR) was used to analyze the genetic causal relationship between DVT and the gut microbiota. We used the random effects inverse variance weighted, MR Egger, weighted median, simple mode, and weighted mode to perform MR analysis. We performed a sensitivity analysis of the MR analysis results by examining heterogeneity and horizontal pleiotropy.

Results

Linkage disequilibrium score analysis showed that Streptococcaceae (correlation coefficient = -0.542, SE = 0.237, P = 0.022), Dialister (correlation coefficient = -0.623, SE = 0.316, P = 0.049), Streptococcus (correlation coefficient = -0.576, SE = 0.264, P = 0.029), and Lactobacillales (correlation coefficient = -0.484, SE = 0.237, P = 0.042) had suggestive genetic correlation with DVT. In addition, the MR analysis showed that Streptococcaceae had a positive genetic causal relationship with DVT (P = 0.027, OR = 1.005). There was no heterogeneity or horizontal pleiotropy in the MR analysis (P > 0.05).

Conclusion

In this study, four gut microbes (Streptococcaceae, Dialister Streptococcus, Lactobacillales) had suggestive genetic correlations with DVT, and Streptococcaceae had a positive causal relationship with DVT. Our findings provide a new research direction for the further study of and prevention of DVT.

The genetic variants in calcium signaling related genes influence anti-tuberculosis drug induced liver injury: A prospective study

Although many genetic variants related to anti-tuberculosis drug induced liver injury (ATDILI) have been identified, the prediction and personalized treatment of ATDILI have failed to achieve, indicating there remains an area for further exploration. This study aimed to explore the influence of single nucleotide polymorphisms (SNPs) in Bradykinin receptor B2 (BDKRB2), Teneurin transmembrane protein 2 (TENM2), transforming growth factor beta 2 (TGFB2), and solute carrier family 2 member 13 (SLC2A13) on the risk of ATDILI.The subjects comprised 746 Chinese tuberculosis (TB) patients. Custom-by-design 2x48-Plex SNPscanTM kit was employed to genotype 28 selected SNPs. The associations of SNPs with ATDILI risk and clinical phenotypes were analyzed according to the distributions of allelic and genotypic frequencies and different genetic models. The odds ratio (OR) with corresponding 95% confidence interval (CI) was calculated.Among subjects with successfully genotyped, 107 participants suffered from ATDILI during follow-up. In BDKRB2, patients with rs79280755 G allele or rs117806152 C allele were more vulnerable to ATDILI (PBonferronicorrection = .002 and .03, respectively). Rs79280755 increased the risk of ATDILI significantly whether in additive (OR = 3.218, 95% CI: 1.686-6.139, PBonferroni correction = .003) or dominant model (PBonferroni correction = .003), as well as rs117806152 (Additive model: PBonferroni correction = .05; dominant model: PBonferroni correction = .03). For TENM2, rs80003210 G allele contributed to the decreased risk of ATDILI (PBonferroni correction = .02), while rs2617972 A allele conferred susceptibility to ATDILI (PBonferroni correction = .01). Regarding rs2617972, significant findings were also observed in both additive (OR = 3.203, 95% CI: 1.487-6.896, PBonferroni correction = .02) and dominant model (PBonferroni correction = .02). Moreover, rs79280755 and rs117806152 in BDKRB2 significantly affected some laboratory indicators. However, no meaningful SNPs were observed in TGFB2 and SLC2A13.Our study revealed that both BDKRB2 and TENM2 genetic polymorphisms were interrogated in relation to ATDILI susceptibility and some laboratory indicators in the Western Chinese Han population, shedding a new light on exploring novel biomarkers and targets for ATDILI.