Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus

Prevalence of uncoupling protein one genetic polymorphisms and their relationship with cardiovascular and metabolic health

Contribution of UCP1 single nucleotide polymorphisms (SNPs) to susceptibility for cardiometabolic pathologies (CMP) and their involvement in specific risk factors for these conditions varies across populations. We tested whether UCP1 SNPs A-3826G, A-1766G, Ala64Thr and A-112C are associated with common CMP and their risk factors across Armenia, Greece, Poland, Russia and United Kingdom. This case-control study included genotyping of these SNPs, from 2,283 Caucasians. Results were extended via systematic review and meta-analysis. In Armenia, GA genotype and A allele of Ala64Thr displayed ~2-fold higher risk for CMP compared to GG genotype and G allele, respectively (p<0.05). In Greece, A allele of Ala64Thr decreased risk of CMP by 39%. Healthy individuals with A-3826G GG genotype and carriers of mutant allele of A-112C and Ala64Thr had higher body mass index compared to those carrying other alleles. In healthy Polish, higher waist-to-hip ratio (WHR) was observed in heterozygotes A-3826G compared to AA homozygotes. Heterozygosity of A-112C and Ala64Thr SNPs was related to lower WHR in CMP individuals compared to wild type homozygotes (p<0.05). Meta-analysis showed no statistically significant odds-ratios across our SNPs (p>0.05). Concluding, the studied SNPs could be associated with the most common CMP and their risk factors in some populations.

Exploring effective approaches for haplotype block phasing

BACKGROUND

Knowledge of phase, the specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations. One approach for detecting such mutations is through phased haplotype association analysis. While the accuracy of methods for phasing genotype data has been widely explored, there has been little attention given to phasing accuracy at haplotype block scale. Understanding the combined impact of the accuracy of phasing tool and the method used to determine haplotype blocks on the error rate within the determined blocks is essential to conduct accurate haplotype analyses.

RESULTS

We present a systematic study exploring the relationship between seven widely used phasing methods and two common methods for determining haplotype blocks. The evaluation focuses on the number of haplotype blocks that are incorrectly phased. Insights from these results are used to develop a haplotype estimator based on a consensus of three tools. The consensus estimator achieved the most accurate phasing in all applied tests. Individually, EAGLE2, BEAGLE and SHAPEIT2 alternate in being the best performing tool in different scenarios. Determining haplotype blocks based on linkage disequilibrium leads to more correctly phased blocks compared to a sliding window approach. We find that there is little difference between phasing sections of a genome (e.g. a gene) compared to phasing entire chromosomes. Finally, we show that the location of phasing error varies when the tools are applied to the same data several times, a finding that could be important for downstream analyses.

CONCLUSIONS

The choice of phasing and block determination algorithms and their interaction impacts the accuracy of phased haplotype blocks. This work provides guidance and evidence for the different design choices needed for analyses using haplotype blocks. The study highlights a number of issues that may have limited the replicability of previous haplotype analysis.

Correlation Between PPARGC1A Gene Rs8192678 G>A Polymorphism and Susceptibility To Type-2 Diabetes

Objective

To systematically investigate the correlation between the G>A polymorphism of the peroxisome proliferator-activated receptor γ coactivator 1α (PPARGC1A or PGC-1alpha) gene rs8192678 locus and the susceptibility to type-2 diabetes mellitus (T2DM).

Methods

The inclusion and exclusion criteria and retrieval strategies of original literatures were formulated. Then, subjects and free words “PPARGC1A”,”gene polymorphism”, and “T2DM” were retrieved from the PubMed, EMBASE, and Cochrane Library databases. Case-control studies on the G>A polymorphism of the PPARGC1A gene rs8192678 locus and susceptibility to T2DM were included for the meta-analysis.

Results

The number of cases in the T2DM group and control group was 5,607 and 7,596, respectively. The meta-analysis revealed that the PPARGC1A gene rs8192678 locus G>A polymorphism is associated with susceptibility to T2DM. There are differences in each group of genetic models, of which three groups of genetic models are highly significant. In the allele model, OR=1.249, 95% CI: 1.099-1.419, and P=0.001. In the dominant inheritance model, OR=1.364, 95% CI: 1.152-1.614, and P=0.000. In the additive inheritance model, OR=0.828, 95% CI: 0.726-0.945, and P=0.005. And one group is significant, in the recessive inheritance model, OR=1.187, 95% CI: 1.021-1.381, and P=0.026.

Conclusion

In Western Asian, South Asian, European and African populations, the A allele of the PPARGC1A gene rs8192678 locus may be one of the risk factors for T2DM.

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

Background

The purpose of this study was to evaluate the effect of single-nucleotide polymorphisms (SNPs) of the GCKR and G6PC2 genes on risk for type 2 diabetes and the SNP-SNP and haplotype-based interactions between these genes.

Methods

Subjects of this nested case-control study were selected from a prospective cohort residing in the rural area of Luoyang city in China. Cases (n = 538) were individually matched with controls. Six SNPs in the GCKR and G6PC2 genes were selected and genotyped using an SNPscan™ kit. Stratified Cox proportional hazards regression models were used to generate odds ratios (ORs) and 95% confidence intervals (CI) for different genotype models for the risk of T2DM. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the interactions between two genes with among six SNPs. The linkage disequilibrium (LD) analysis and the haplotype analysis were carried out by SHEsis online.

Results

We found that the C allele of rs780094 was associated with increased risk for T2DM in Han Chinese population. However, the rs492594-C allele in G6PC2 was associated with a decreased risk of T2DM. We also found a significant SNP-SNP interaction between rs2293572 and rs492594, and the CCCCGC and CGCCCA haplotypes significantly increased the risk of T2DM, however, the CCCCCA haplotype had lower susceptibility to T2DM.

Conclusion

The results suggest that the GCKR and G6PC2 genes may contribute to the risk of T2DM independently and/or in an interactive manner in the Han Chinese population.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0813-6) contains supplementary material, which is available to authorized users.

Linking Metabolic Disease With the PGC-1α Gly482Ser Polymorphism

Peroxisome proliferator-activated receptor γ coactivator 1-α (PGC-1α) is a highly conserved transcriptional coactivator enriched in metabolically active tissues including liver, adipose, pancreas, and muscle. It plays a role in regulating whole body energy metabolism and its deregulation has been implicated in type 2 diabetes (T2D). A single nucleotide variant of the PPARGC1A gene (rs8192678) is associated with T2D susceptibility, relative risk of obesity and insulin resistance, and lower indices of β cell function. This common polymorphism is within a highly conserved region of the bioactive protein and leads to a single amino acid substitution (glycine 482 to serine). Its prevalence and effects on metabolic parameters appear to vary depending on factors including ethnicity and sex, suggesting important interactions between genetics and cultural/environmental factors and associated disease risk. Interestingly, carriers of the serine allele respond better to some T2D interventions, illustrating the importance of understanding functional impacts of genetic variance on PGC-1α when targeting this pathway for personalized medicine. This review summarizes a growing body of literature surrounding possible links between the PGC-1α Gly482Ser single nucleotide polymorphism and diabetes, with focus on key clinical findings, affected metabolic systems, potential molecular mechanisms, and the influence of geographical or ethnic background on associated risk.