Quantitative trait linkage studies of diabetes-related traits

Complexity of type 2 diabetes mellitus data sets emerging from nutrigenomic research: a case for dimensionality reduction?

Nutrigenomics promises personalized nutrition and an improvement in preventing, delaying, and reducing the symptoms of chronic diseases such as diabetes. Nutritional genomics is the study of how foods affect the expression of genetic information in an individual and how an individual's genetic makeup affects the metabolism and response to nutrients and other bioactive components in food. The path to those promises has significant challenges, from experimental designs that include analysis of genetic heterogeneity to the complexities of food and environmental factors. One of the more significant complications in developing the knowledge base and potential applications is how to analyze high-dimensional datasets of genetic, nutrient, metabolomic (clinical), and other variables influencing health and disease processes. Type 2 diabetes mellitus (T2DM) is used as an illustration of the challenges in studying complex phenotypes with nutrigenomics concepts and approaches.

The quest for cardiovascular health in the genomic era: nutrigenetics and plasma lipoproteins

Nutrigenetics and nutrigenomics are promising multidisciplinary fields that focus on studying the interactions between nutritional factors, genetic factors and health outcomes. Their goal is to achieve more efficient individual dietary intervention strategies aimed at preventing disease, improving quality of life and achieving healthy aging. Our studies, and those of many other investigators, using population-based and intervention studies have found evidence for interactions between dietary factors, genetic variants and biochemical markers of CVD. Now, the characterization of individuals who may respond better to one type of dietary recommendation than another can be begun. Thus, a low-fat low-cholesterol strategy may be particularly efficacious in lowering the plasma cholesterol levels of those subjects carrying the apoE4 allele at the APOE gene. HDL-cholesterol (HDL-C) levels are also modulated by dietary, behavioural and genetic factors. It has been reported that the effect of PUFA intake on HDL-C concentrations is modulated by an APOA1 genetic polymorphism. Thus, subjects carrying the A allele at the –75 G/A polymorphism show an increase in HDL-C with increased intakes of PUFA, whereas those homozygotes for the more common G allele have the expected lowering of HDL-C levels with increased intake of PUFA. Variability at the hepatic lipase gene is also associated with interactions between intake of fat and HDL-C concentrations that could shed some light on the different abilities of certain ethnic groups to adapt to new nutritional environments. This knowledge should lead to successful dietary recommendations partly based on genetic factors that may help to reduce cardiovascular risk more efficiently than the current universal recommendations.

Application of nutrigenomic concepts to Type 2 diabetes mellitus

The genetic makeup that individuals inherit from their ancestors is responsible for variation in responses to food and susceptibility to chronic diseases such as Type 2 diabetes mellitus (T2DM). Common variations in gene sequences, such as single nucleotide polymorphisms, produce differences in complex traits such as height or weight potential, food metabolism, food-gene interactions, and disease susceptibilities. Nutritional genomics, or nutrigenomics, is the study of how foods affect the expression of genetic information in an individual and how an individual's genetic makeup affects the metabolism and response to nutrients and other bioactive components in food. Since both diet and genes alter one's health and susceptibility to disease, identifying genes that are regulated by diet and that cause or contribute to chronic diseases could result in the development of diagnostic tools, individualized intervention, and eventually strategies for maintaining health. Translating this research through clinical studies promises contributions to the development of personalized medicine that includes nutritional as well as drug interventions. Reviewed here are the key nutrigenomic concepts that help explain aspects of the development and complexity of T2DM.

EIF4A2 is a positional candidate gene at the 3q27 locus linked to type 2 diabetes in French families

One of the most replicated loci influencing type 2 diabetes-related quantitative traits (quantitative trait loci [QTL]) is on chromosome 3q27 and modulates both type 2 diabetes-and metabolic syndrome-associated phenotypes. A QTL for type 2 diabetes age of onset (logarithm of odds [LOD] score = 3.01 at D3S3686, P = 0.0001) was identified in a set of French families. To assess genetic variation underlying both age-of-onset QTL and our previous type 2 diabetes linkage in a 3.87-Mb interval, we explored 36 single nucleotide polymorphisms (SNPs) in two biologically relevant candidate genes for glucose homeostasis, kininogen (KNG1), and eukaryotic translation initiation factor 4alpha2 (EIF4A2). Analysis of 148 families showed significant association of a frequent SNP, rs266714, located 2.47 kb upstream of EIF4A2, with familial type 2 diabetes (family-based association test, P = 0.0008) and early age of onset (P = 0.0008). This SNP also contributes to both age-of-onset QTL (1.13 LOD score decrease P = 0.02) and type 2 diabetes linkage (genotype identical-by-descent sharing test, P = 0.02). However, no association was observed in three independent European diabetic cohorts. EIF4A2 controls specific mRNA translation and protein synthesis rate in pancreatic beta-cells, and our data indicates that EIF4A2 is downregulated by high glucose in rat beta-INS832/13 cells. The potential role of EIF4A2 in glucose homeostasis and its putative contribution to type 2 diabetes in the presence of metabolic stress will require further investigation.

Genetic epidemiology of diabetes

Conventional genetic analysis focuses on the genes that account for specific phenotypes, while traditional epidemiology is more concerned with the environmental causes and risk factors related to traits. Genetic epidemiology is an alliance of the 2 fields that focuses on both genetics, including allelic variants in different populations, and environment, in order to explain exactly how genes convey effects in different environmental contexts and to arrive at a more complete comprehension of the etiology of complex traits. In this review, we discuss the epidemiology of diabetes and the current understanding of the genetic bases of obesity and diabetes and provide suggestions for accelerated accumulation of clinically useful genetic information.

The role of HNF4A variants in the risk of type 2 diabetes

Genes influence susceptibility to type 2 diabetes mellitus (T2DM), and both positional cloning and candidate gene approaches have been used to identify these genes. Linkage analysis has generated evidence for T2DM-predisposing variants on chromosome 20q in studies of Caucasians, Asians, and Africans, and fine-mapping recently identified a likely susceptibility gene, hepatocyte nuclear factor 4-alpha (HNF4A). Rare loss-of-function mutations in HNF4A cause maturity-onset diabetes of the young and now common noncoding variants have been found to be associated with T2DM.

Genetics and functional genomics of type 2 diabetes mellitus

Genome-wide studies of transcription in the skeletal muscle of type 2 diabetic patients have identified coordinated changes in the expression of genes involved in oxidative phosphorylation, and have underlined the central role of the oxidative-phosphorylation regulator, PCG1alpha. These findings help unravel the complex pathogenesis and inheritance of polygenic type 2 diabetes mellitus.