Gene–gene and gene–environment interactions
Gene–gene interactions, or epistasis, have been suggested as a possible explanation for difficulties in replicating genetic association in complex diseases . The standard statistical methods used in association studies are usually limited to analysis of single marker effects and thereby do not account for interactions between markers. Previous attempts to study epistasis in complex diseases have focused on interactions between candidate regions [69,70]. However, the recent abundance of GWAS data has made a comprehensive search across the genome more feasible.
Some studies have attempted to account for epistasis in GWAS using a two-step approach in which significant SNPs are tested against each other or against all other SNPs in the study with variable results [71,72]. The main problem when studying epistasis is power, since interaction between loci with modest effects is difficult to detect without extremely large sample sizes. However, a study using simulated data has shown that power could actually increase when testing all pair-wise combinations of SNPs in GWAS settings despite the penalty for multiple testing; especially when no marginal effects were present .Thorough studies in diabetes addressing epistasis using this approach are missing.
Further, a recent paper by Eric Lander and coworkers provided compelling evidence that gene–gene interaction can also contribute to missing heritability by causing “phantom heritability” that inflates the estimated narrow sense heritability of the trait .
Gene–environment interactions are equally difficult to study but are likely to play an important role in T2DM development. The epidemic of T2DM only dates back 50 years, and it is quite obvious that during this period only the environment, not the genes have changed. However, the genetic architecture determines our response to the environment. Genetic variants could affect specific metabolic processes to make an individual more susceptible to the harmful effects of a poor diet but also personality traits that make an individual more or less likely to overconsume and live a sedentary lifestyle. It will however be a formidable task to identify the environmental triggers for most of the genetic variants increasing susceptibility to diabetes as this will require very large studies with precise information on diet, exercise, energy expenditure, and so on.
The environment can also influence the expression of the genome, and ultimately the phenotype, via the epigenome. Even though the DNA sequence is not changed, the phenotype is altered by epigenetic modifications of gene expression by mechanisms including methylation of DNA, posttranslational modification of histones, or activation of microRNAs. These modifications have the potential to be stable and heritable across cell divisions [75,76]. Changes to the phenotype can be at the level of the cell, tissue, or whole organism.
It is tempting to speculate that environmental factors such as diet and exercise can change the level of DNA methylation and thereby cause changes in gene expression, but evidence that DNA methylation contributes to the increase in T2DM is still lacking. Epigenetic mechanisms may, however, play a role in progression of the disease by inducing glucotoxicity in islets and predispose to diabetic complications. Elevated glucose is a prerequisite to this condition and it is well established that cells can memorize changes in glucose concentrations. For example, two large studies, the UKPDS and DCCT studies, showed that an initial good metabolic control was associated with reduced frequency of diabetic complications decades later.The advanced “metabolic memory” hypothesis suggests that this is because glucose can induce histone modifications in endothelial cells that can be remembered long after .
As previously mentioned, the risk of T2DM in offspring is greater if the mother has T2DM compared to if the father is affected. The reasons for this parent-of-origin effect are unknown but one potential explanation could be distorted parent-of-origin transmission of risk alleles which is often associated with DNA methylation and imprinting. Variants in both KCNQ1 and KLF14 show stronger effects on T2DM when the risk allele is transmitted from the mother than from the father [34,78]; in some instances the paternal allele can even be protective making it almost impossible to detect association in a traditional case-control study.
Epigenetic mechanisms can also act in utero. If this intrauterine programming results in a reduced β-cell mass, it could predispose to diabetes later in life when the insulin requirements increase as a consequence of obesity and insulin resistance.
GWAS on their own provide limited insights into the molecular mechanisms driving disease. To reach an understanding of disease pathogenesis, it is important to analyze the GWAS data in the context of complementary types of follow-up analyses such as related protein module analysis, expression profiling under conditions relevant for the disease, and analysis of genotype-phenotype associations . For example, by combining GWAS information with metabolomics it has been possible to identify strong associations between SNPs and metabolic reactions that otherwise would have been missed . Network or pathway-based approaches, including enrichment in pre-defined pathways by, for example, KEGG  (www.genome.jp) and Gene Ontology (GO) (www.geneontology.org), have also been used to identify disease genes for various diseases [79,82–85].Thus, an integrative approach of several data types is likely to discover disease genes that would not be identified by the use of classical GWAS approaches.This was also illustrated in a recent study of human islets identifying novel candidate genes for T2DM based upon expression differences and co-expression with known T2DM genes as well as protein–protein interaction analyses . Integration of GWAS data with such data could, thus, facilitate a systems-based understanding of the pathogenic mechanisms.
The technical revolution in the field of genetics has allowed identification of numerous genetic variants that associate with T2DM. Yet, the dissection of the genetics of T2DM is still in its infancy, so far only explaining a small proportion of the total heritability of diabetes. In spite of this, it has already greatly contributed to our understanding of disease mechanisms by identifying pathways that could not be linked to diabetes by existing hypothetical models, even though many genetic findings are very recent and have yet to make their contribution to our knowledge about diabetes pathogenesis. Diabetes is probably a much more diverse disease than the current subdivision into T1DM and T2DM implies and a more precise subdivision into subgroups may both facilitate the investigation of T2DM genetics and pave the way for more individualized treatment. A holistic systems biology approach will also be required to obtain a complete picture of how genetic variation leads to diabetes. The rapid technology development during the past years holds promises that this will be possible in a not too distant future.