Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype

Abstract

Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related physiological data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples.

Keywords: ADH1B; GWAS; Gene expression; Insulin resistance; Mexican Americans; Obesity; Transcriptomics; Type 2 diabetes; VAGES; eQTL.

Fig. 1

Simplified depiction of the relationship between complex diseases, gene expression and the genome. Both GWAS and transcriptomics seek to deduce the underlying causal genetic relationships between DNA sequence variation and physiological variation. Transcript profiling analyzes a more direct and greatly simplified connection between the genome and complex disease pathology than GWAS. eQTL explicitly analyzes the relationship between DNA sequence variation and gene expression although their extension to phenotypic analysis can be performed in a similar manner to GWAS. It should be noted that the relationships in each case, between nucleotides and clinical traits, are correlative rather than causal. For clarity, the role of active non-coding RNAs is not included although their role is implicit. Their incompletely understood function would presumably mimic that of the relatively well-understood protein coding RNAs. Furthermore, their concentration can be accurately assayed by the same transcriptomic approaches used for protein-coding RNAs and their similar transcript variants.