Gene-gene interactions lead to higher risk for development of type 2 diabetes in a Chinese Han population: a prospective nested case-control study

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.

Relationship between GCKR gene rs780094 polymorphism and type 2 diabetes with albuminuria

BACKGROUND

Diabetic kidney disease is one of the common complications of type 2 diabetes (T2D). There are no typical symptoms in the early stage, and the disease will progress to moderate and late stage when albuminuria reaches a high level. Treatment is difficult and the prognosis is poor. At present, the pathogenesis of diabetic kidney disease is still unclear, and it is believed that it is associated with genetic and environmental factors.

AIM

To explore the relationship between the glucokinase regulatory protein (GCKR) gene rs780094 polymorphism and T2D with albuminuria.

METHODS

We selected 252 patients (126 males and 126 females) with T2D admitted to our hospital from January 2020 to October 2020, and 66 healthy people (44 females and 22 males). According to the urinary albumin/creatinine ratio, the subjects were divided into group I (control), group II (T2D with normoalbuminuria), group III (T2D with microalbuminuria), and group IV (T2D with macroalbuminuria). Additionly, the subjects were divided into group M (normal group) or group N (albuminuria group) according to whether they developed albuminuria. We detected the GCKR gene rs780094 polymorphism (C/T) of all subjects, and measured the correlation between GCKR gene rs780094 polymorphism (C/T) and T2D with albuminuria.

RESULTS

Gene distribution and genotype distribution among groups I-IV accorded with the Hardy-Weinberg equilibrium. Genotype frequency was significantly different among the four groups (P = 0.048, χ2 = 7.906). T allele frequency in groups II, III, and IV was significantly higher than that in group I. Logistic regression analysis of the risk factors for T2D with albuminuria showed that the CT + TT genotype (odds ratio = 1.710, 95% confidence interval: 1.172-2.493) was a risk factor.

CONCLUSION

CT + TT genotype is a risk factor for T2D with albuminuria. In the future, we can assess the risk of individuals carrying susceptible genes to delay the onset of T2D.

Comprehensive analysis of key m5C modification-related genes in type 2 diabetes

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear.

Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.

Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells.

Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.

Personalized nutrition: A review of genotype-based nutritional supplementation

Nutritional disorders have become a major public health issue, requiring increased targeted approaches. Personalized nutrition adapted to individual needs has garnered dramatic attention as an effective way to improve nutritional balance and maintain health. With the rapidly evolving fields of genomics and nutrigenetics, accumulation of genetic variants has been indicated to alter the effects of nutritional supplementation, suggesting its indispensable role in the genotype-based personalized nutrition. Additionally, the metabolism of nutrients, such as lipids, especially omega-3 polyunsaturated fatty acids, glucose, vitamin A, folic acid, vitamin D, iron, and calcium could be effectively improved with related genetic variants. This review focuses on existing literatures linking critical genetic variants to the nutrient and the ways in which these variants influence the outcomes of certain nutritional supplementations. Although further studies are required in this direction, such evidence provides valuable insights for the guidance of appropriate interventions using genetic information, thus paving the way for the smooth transition of conventional generic approach to genotype-based personalized nutrition.

Glucose metabolism-related gene polymorphisms as the risk predictors of type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease characterized by elevated blood glucose. Multiple environmental and genetic factors can increase the risk of T2DM and its complications, and genetic polymorphisms are no exception. This review is mainly focused on the related genes involved in glucose metabolic, including G6PC2, GCK, GCKR and OCT3. In this review, we have summarized the results reported globally and found that the genetic variants of GCK and OCT3 genes is a risk factor for T2DM while G6PC2 and GCKR genes are controversial in different ethnic groups. Hopefully, this summary could possibly help researchers and physicians understand the mechanism of T2DM so as to diagnose and even prevent T2DM at early time.

Association between Adiponectin Gene Polymorphism and Environmental Risk Factors of Type 2 Diabetes Mellitus among the Chinese Population in Hohhot

Objective

The aim of this study was to investigate the association between adiponectin gene polymorphisms rs10937273, rs1501299, rs182052, rs2241767, and rs266729 and environmental risk factors of type 2 diabetes mellitus (T2DM) in Hohhot. The study explored different models of gene-environment interactions, aimed at providing approaches for the prevention and control of T2DM in combination with the characteristics of the local population.

Methods

A case-control study was conducted including 406 Chinese participants, comprising 203 cases and 203 controls from various hospitals. Adiponectin (ADIPOQ) gene polymorphisms rs10937273, rs1501299, rs182052, rs2241767, and rs266729 were detected using an improved multiple ligation detection reaction technique. Generalized multifactor dimensionality reduction (GMDR) and logistic regression were conducted to analyze the associations between adiponectin gene polymorphisms and T2DM, as well as the interactions between adiponectin gene polymorphisms and environmental factors.

Results

ADIPOQ gene polymorphisms rs10937273, rs1501299, rs182052, rs2241767, and rs266729 were associated with type 2 diabetes. Based on the haplotype of the five adiponectin gene single-nucleotide polymorphism (SNP) loci, we found that G-G-A-A-C was a susceptible haplotype of T2DM (P < 0.05). Interaction analyses demonstrated associations between rs1501299 and central obesity (consistency = 80%, P = 0.011) and between rs266729 and rs182052 and central obesity (consistency = 70%, P = 0.011).

Conclusions

Our findings indicate that there is an interaction between the ADIPOQ gene and central obesity, which provides new insights into the prevention and treatment of T2DM.

Association of GCKR rs780094 polymorphism with circulating lipid levels in type 2 diabetes and hyperuricemia in Uygur Chinese

To investigate the relationship between a GCKR rs780094 polymorphism and lipid profiles in the Xinjiang Uygur population in China. 980 type 2 diabetes mellitus (T2DM) patients, 1017 hyperuricemia (HUA) and 1185 healthy controls were included in this study. After genotyping of rs780094 by Sequenom Mass ARRAY system, chi-square test and logistic regression analysis were used for association analysis as well as a genotype-phenotype analysis. We found that the serum concentration of TC (P<0.001) was significantly higher and HDL-C (P<0.001) was lower in T2DM than in control participants. Subjects with HUA had a significantly higher TG (P=0.003) and lower HDL-C (P<0.001) than control participants. Additionally, under the recessive model, rs780094 was shown to be associated with the risk of HUA (P=0.015, OR=1.311), particularly in males (P=0.047, OR=1.330). Subsequent interaction analysis between rs780094 and lipid parameters showed that the TG level was positively correlated with HUA in the rs780094- AA+AG carriers (P=0.005). The TC concentrations showed to be associated with T2DM in the rs780094- AA+AG carriers (P<0.001). The association between lipid parameters and gender showed that significantly higher TG levels (P<0.001) and lower HDL-C levels (P<0.001) were observed in female HUA. Higher LDL-C levels were found in male HUA (P=0.015). Moreover, statistically higher TC levels and lower HDL-C levels were found both in male and female T2DM cases (TC: male: P<0.001, female: P=0.014. HDL-C: male: P<0.001, female: P<0.001.).To conclude, our results demonstrated that different genotypes of rs780094 had different effects on blood lipids in HUA and T2DM patients in a Uygur population. Gender was also one of the factors influencing blood lipid levels.