Integration of genetic and genomic data to establish direct links between regulatory variation, genomic annotation and regional genes One powerful approach within this category attempts to identify variants that affect the expression level of nearby genes, so-called cis-expression quantitative trait loci (cis-eQTLs). In cases where the association signal overlaps a cis-eQTL in a disease-relevant tissue, this can reveal both the target gene and the direction of effect for the risk variant. While many cis-eQTLs are shared across tissues, others appear to show more restricted effects that are specific to one or more tissues [
82]. Since physiological characterisations of carriers of type 2 diabetes risk variants have implied a central role for islet dysfunction in disease susceptibility, several cis-eQTL studies have focused on pancreatic islets [
83,
84]. Though the power to detect associations has been limited by the availability of islets from donors, the approach has successfully highlighted candidate effector transcripts with previously unknown roles in disease pathogenesis [
85,
86]; this is the case for the poorly characterised
ZMIZ1 gene that was identified in a recent study [
86]. In vitro work subsequently confirmed a role for
ZMIZ1 in islet function following functional studies.
Intersecting human genetics with genomic annotation can also be used to define common regulatory themes that underlie causal mechanisms at multiple loci. A recent study, for example, demonstrated an enrichment of islet and liver binding sites for the forkhead box protein A2 (FOXA2) transcription factor among type 2 diabetes association signals [
87]. These results suggest a shared role of FOXA2 across a subset of risk loci and highlight the potential to identify specific causal variants. In one instance, at the
MTNR1B locus, where the association signal has been collapsed to a single variant through fine-mapping, the FOXA2 binding event was shown to be a marker for binding of another transcription factor, neurogenic differentiation 1 (NEUROD1). It was found that the risk allele creates a NEUROD1 binding site, leading to increased expression of
MTNR1B in beta cells. This is in line with a cis-eQTL that was previously identified for this variant in islets, and adds support to a mechanism for this non-coding risk allele being mediated via elevated melatonin receptor 1B (MTRN1B; encoded by
MTNR1B) activity [
86,
88].
Interestingly, a different direction of effect for the
MTRN1B gene has been proposed by coding loss-of-function variants, which have also been associated with elevated risk of type 2 diabetes [
89]. One potential explanation is suggested by the observation that the regulatory variant appears to exhibit tissue-specificity in its activity [
87]. It is thus possible that the discrepancy between coding and non-coding variants could reflect differences between global and local roles of
MTRN1B. Other explanations are possible and it remains to be seen whether increased
MTNR1B transcript levels translate into higher protein expression. MTNR1B, a G-protein-coupled receptor, has received considerable attention as a potentially ‘druggable’ target. Addressing the inconsistencies in genetic data will thus provide insights into the suitability of MTNR1B as a drug target and inform any potential therapeutic strategies.