The association between vitamin D receptor polymorphisms and tissue-specific insulin resistance in human obesity.

BACKGROUND/OBJECTIVES: To investigate (1) the association of four VDR polymorphisms (TaqI/rs731236, ApaI/rs7975232, FokI/rs10735810, and Bsml/rs1544410) with markers of adiposity and tissue-specific insulin resistance at baseline, after weight loss and weight maintenance; (2) the effect of the VDR polymorphisms in the SAT transcriptome in overweight/obese Caucasians of the DiOGenes cohort. METHODS: We included 553 adult obese individuals (mean BMI 34.8 kg/m2), men (n = 197) and women (n = 356) at baseline, following an 8-week weight loss intervention and 26 weeks weight maintenance. Genotyping was performed using an Illumina 660W-Quad SNP chip on the Illumina iScan Genotyping System. Tissue-specific IR was determined using Hepatic Insulin Resistance Index (HIRI), Muscle Insulin Sensitivity Index (MISI), and Adipose Tissue Insulin Resistance Index (Adipo-IR). Expression quantitative trait loci (eQTL) analysis was performed to determine the effect of SNPs on SAT gene expression. RESULTS: None of the VDR polymorphisms were associated with HIRI or MISI. Interestingly, carriers of the G allele of VDR FokI showed higher Adipo-IR (GG + GA 7.8 ± 0.4 vs. AA 5.6 ± 0.5, P = 0.010) and higher systemic FFA (GG + GA: 637.8 ± 13.4 vs. AA: 547.9 ± 24.7 µmol/L, P = 0.011), even after adjustment with age, sex, center, and FM. However, eQTL analysis showed minor to no effect of these genotypes on the transcriptional level in SAT. Also, VDR polymorphisms were not related to changes in body weight and IR as result of dietary intervention (P > 0.05 for all parameters). CONCLUSIONS: The VDR Fokl variant is associated with elevated circulating FFA and Adipo-IR at baseline. Nevertheless, minor to no effect of VDR SNPs on the transcriptional level in SAT, indicating that putative mechanisms of action remain to be determined. Finally, VDR SNPs did not affect dietary intervention outcome in the present cohort.

Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits.

Genome-wide association studies have shed light on the association between natural genetic variation and cardiovascular traits. However, linking a cardiovascular trait associated locus to a candidate gene or set of candidate genes for prioritization for follow-up mechanistic studies is all but straightforward. Genomic technologies based on next-generation sequencing technology nowadays offer multiple opportunities to dissect gene regulatory networks underlying genetic cardiovascular trait associations, thereby aiding in the identification of candidate genes at unprecedented scale. RNA sequencing in particular becomes a powerful tool when combined with genotyping to identify loci that modulate transcript abundance, known as expression quantitative trait loci (eQTL), or loci modulating transcript splicing known as splicing quantitative trait loci (sQTL). Additionally, the allele-specific resolution of RNA-sequencing technology enables estimation of allelic imbalance, a state where the two alleles of a gene are expressed at a ratio differing from the expected 1:1 ratio. When multiple high-throughput approaches are combined with deep phenotyping in a single study, a comprehensive elucidation of the relationship between genotype and phenotype comes into view, an approach known as systems genetics. In this review, we cover key applications of systems genetics in the broad cardiovascular field.

Bioinformatic interrogation of expression array data to identify nutritionally regulated genes potentially modulated by DNA methylation.

DNA methylation occurs at CpG dinucleotide sites within the genome and is recognised as one of the mechanisms involved in regulation of gene expression. CpG sites are relatively underrepresented in the mammalian genome, but occur densely in regions called CpG islands (CGIs). CGIs located in the promoters of genes inhibit transcription when methylated by impeding transcription factor binding. Due to the malleable nature of DNA methylation, environmental factors are able to influence promoter CGI methylation patterns and thus influence gene expression. Recent studies have provided evidence that nutrition (and other environmental exposures) can cause altered CGI methylation but, with a few exceptions, the genes influenced by these exposures remain largely unknown. Here we describe a novel bioinformatics approach for the analysis of gene expression microarray data designed to identify regulatory sites within promoters of differentially expressed genes that may be influenced by changes in DNA methylation.