Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population

Genetic variation in TAS2R38 bitterness receptor is associated with body composition in Korean females

Bitterness-receptor gene TAS2R38 is associated with taste sensitivity and dietary behaviour, and is known to play a critical role in adiposity. However, evidence regarding body composition from a large cohort is lacking. This study aimed to ascertain whether TAS2R38 rs10246939 C > T bitterness genetic variation is associated with body composition in Korean individuals. The TAS2R38 rs10246939 genotypes, anthropometric measurements, and body composition of 1,843 males and 1,801 females from the Korean Genome and Epidemiology Study were analysed. Findings suggested that there was a significant difference in body fat components by TAS2R38 genotype. Furthermore, the bitterness genotype exhibited a positive association with adiposity markers in females. The TT genotype showed greater body mass index, body fat percentage, and degree of obesity than those with the C allele. However, such an association was not observed in males. In conclusion, this study suggests that TAS2R38 rs10246939 is associated with fat tissue markers in Korean females.

Whole-genome sequencing study to identify candidate markers indicating susceptibility to type 2 diabetes in Bama miniature pigs

BACKGROUND

Hundreds of single-nucleotide polymorphism (SNP) sites have been found to be potential genetic markers of type 2 diabetes mellitus (T2DM). However, SNPs related to T2DM in minipigs have been less reported. This study aimed to screen the T2DM-susceptible candidate SNP loci in Bama minipigs so as to improve the success rate of the minipig T2DM model.

METHODS

The genomic DNAs of three Bama minipigs with T2DM, six sibling low-susceptibility minipigs with T2DM, and three normal control minipigs were compared by whole-genome sequencing. The T2DM Bama minipig-specific loci were obtained, and their functions were annotated. Meanwhile, the Biomart software was used to perform homology alignment with T2DM-related loci obtained from the human genome-wide association study to screen candidate SNP markers for T2DM in Bama miniature pigs.

RESULTS

Whole-genome resequencing detected 6960 specific loci in the minipigs with T2DM, and 13 loci corresponding to 9 diabetes-related genes were selected. Further, a set of 122 specific loci in 69 orthologous genes of human T2DM candidate genes were obtained in the pigs. Collectively, a batch of T2DM-susceptible candidate SNP markers in Bama minipigs, covering 16 genes and 135 loci, was established.

CONCLUSIONS

Whole-genome sequencing and comparative genomics analysis of the orthologous genes in pigs that corresponded to the human T2DM-related variant loci successfully screened out T2DM-susceptible candidate markers in Bama miniature pigs. Using these loci to predict the susceptibility of the pigs before constructing an animal model of T2DM may help to establish an ideal animal model.

Sex differences in the influence of type 2 diabetes (T2D)-related genes, parental history of T2D, and obesity on T2D development: a case-control study

BACKGROUND

This study investigated the effect of sex and age at type 2 diabetes (T2D) diagnosis on the influence of T2D-related genes, parental history of T2D, and obesity on T2D development.

METHODS

In this case-control study, 1012 T2D cases and 1008 healthy subjects were selected from the Diabetes in Mexico Study database. Participants were stratified by sex and age at T2D diagnosis (early, ≤ 45 years; late, ≥ 46 years). Sixty-nine T2D-associated single nucleotide polymorphisms were explored and the percentage contribution (R²) of T2D-related genes, parental history of T2D, and obesity (body mass index [BMI] and waist-hip ratio [WHR]) on T2D development was calculated using univariate and multivariate logistic regression models.

RESULTS

T2D-related genes influenced T2D development most in males who were diagnosed early (R² = 23.5%; females, R² = 13.5%; males and females diagnosed late, R² = 11.9% and R² = 7.3%, respectively). With an early diagnosis, insulin production-related genes were more influential in males (76.0% of R²) while peripheral insulin resistance-associated genes were more influential in females (52.3% of R²). With a late diagnosis, insulin production-related genes from chromosome region 11p15.5 notably influenced males while peripheral insulin resistance and genes associated with inflammation and other processes notably influenced females. Influence of parental history was higher among those diagnosed early (males, 19.9%; females, 17.5%) versus late (males, 6.4%; females, 5,3%). Unilateral maternal T2D history was more influential than paternal T2D history. BMI influenced T2D development for all, while WHR exclusively influenced males.

CONCLUSIONS

The influence of T2D-related genes, maternal T2D history, and fat distribution on T2D development was greater in males than females.

Relative contributions of the host genome, microbiome, and environment to the metabolic profile

BACKGROUND

Metabolic syndrome is as a well-known risk factor for cardiovascular disease, which is associated with both genetic and environmental factors. Recently, the microbiome composition has been shown to affect the development of metabolic syndrome. Thus, it is expected that the complex interplay among host genetics, the microbiome, and environmental factors could affect metabolic syndrome.

OBJECTIVE

To evaluate the relative contributions of genetic, microbiome, and environmental factors to metabolic syndrome using statistical approaches.

METHODS

Data from the prospective Korean Association REsource project cohort (N = 8476) were used in this study, including single-nucleotide polymorphisms, phenotypes and lifestyle factors, and the urine-derived microbial composition. The effect of each data source on metabolic phenotypes was evaluated using a heritability estimation approach and a prediction model separately. We further experimented with various types of metagenomic relationship matrices to estimate the phenotypic variance explained by the microbiome.

RESULTS

With the heritability estimation, five of the 11 metabolic phenotypes were significantly associated with metagenome-wide similarity. We found significant heritability for fasting glucose (4.8%), high-density lipoprotein cholesterol (4.9%), waist-hip ratio (7.7%), and waist circumference (5.6%). Microbiome compositions provided more accurate estimations than genetic factors for the same sample size. In the prediction model, the contribution of each source to the prediction accuracy varied for each phenotype.

CONCLUSION

The effects of host genetics, the metagenome, and environmental factors on metabolic syndrome were minimal. Our statistical analysis suffers from a small sample size, and the measurement error is expected to be substantial. Further analysis is necessary to quantify the effects with better accuracy.