BACKGROUND: T2D susceptibility genes identified so far are mainly involved in beta-cell function. Studies from GWAS have identified many loci but were focused on simple clinical parameters. The missing heritability in T2D might be explained by multiple, low-frequency variants that GWAS let slip. A powerful approach to discover causal variants is sequencing the extremes of phenotypes by Next Generation Sequencing (NGS).
AIMS: To identify new low-frequency variants in GWAS loci for T2D, searching for primary defects in beta-cell insulin secretion through two stages. Stage 1: re-sequencing of 9 candidate genes from GWAS (p
METHODOLOGY: We have a large population (996 adults and 896 youths), and we aim to enroll up to 2500 subjects to amplify the power for genetic association. In all subjects measures of insulin secretion and resistance from OGTT have been calculated, including insulinogenic index (IGI30), ISI (insulin-sensitivity index), and Disposition Index (DI). NGS is performed by TruSeqCustomAmplicon library on MiSeq system (Illumina). New variants will be genotyped by Real-Time PCR.
RELEVANCE OF EXPECTED RESULTS: The novelty of this approach is the analysis of extremes of quantitative traits rather than dichotomizing the same distribution into cases and controls, comparing the low and the high ends of the normal distribution, to maximize the statistical power to discover low-frequency variants that contribute to traits. We expect that this study will deliver several results: from confirmatory gene association to newer T2D associated polymorphisms, to possible new insights into potential biological mechanisms influencing T2D pathogenesis.
So far published re-sequencing studies of candidate genes from GWAS on T2D have focused only on a small number of loci, there are only a few, and all are focused on simple parameters such as fasting glucose or insulin. There is a clear need to look to more specific and in depth phenotypic characteristics of study subjects, in order to try to explain the biological significance of the gene association. The novel powerful approach of this study is the analysis of extremes of quantitative traits rather than dichotomizing the same distribution into cases and controls, by comparing the low and the high ends of the normal distribution of a given phenotypic parameter to maximize the statistical power to discover risk variants and to detect their association to the trait. We have planned to collect around 2500 adult subjects in whom an OGTT for the diagnosis of T2D will be performed, to be able to calculate dynamic indices providing more specific and sophisticated measures of insulin-secretion. Furthermore, we do follow many of our patients, so it will be possible, if necessary, to control for clinical parameters after years.
To date, T2D GWAS and meta-analysis studies, have catalogued at least 88 susceptibility loci (Gaulton KJ, 2015; DIAGRAM Consortium, 2014; Lyssenko V, 2013; Dupuis J, 2010; Voight BF, 2010; Zeggini E, 2008). For the majority of the association signals that have arisen from GWAS to date their translation into molecular mechanisms is more challenging due to the propensity of the signals to map to intra-genic regions and the lack of known biology for the closest protein coding genes in many cases. Also, risk variants may alter any step of signaling, from glucose-stimulated insulin secretion, proinsulin conversion to incretin secretion or action. Among the many association signals arising from the GWAS, there are a few notable (e.g. KCNJ11, ABCC8, and SLC30A8) that not only map to a biologically plausible gene but the association signals include a nonsynonymous single nucleotide polymorphism (SNP) that has facilitated functional characterization (Sladek R, 2007; Florez JC, 2004; Gloyn AL, 2003). In addition, SNPs implicated by GWAS are tightly correlated with other SNPs in the region and are probably in linkage disequilibrium (LD) with the causal variant rather than causal themselves. Furthermore, these common genetic variants with MAF >5% have small to moderate effects, and explain only a small fraction of disease heritability for common disease. Thus, it has been hypothesized that low-frequency variants with MAF