Mini ReviewGenome-wide association studies in aging-related processes such as diabetes mellitus, atherosclerosis and cancer
Introduction
Association studies in genetic epidemiology investigate whether in a study population certain genetic variants (polymorphisms) are associated with a certain phenotype. This phenotype can be of quantitative (e.g. blood glucose concentrations) or qualitative nature (e.g. diabetes mellitus). It requires a sufficient number of unrelated individuals with the quantitative phenotype measured or a case–control sample with subjects having the disease (cases) or who are free of the disease (controls). In the past the majority of the association studies were candidate gene studies, meaning that a gene was selected because it was biologically plausible to be involved in the pathogenesis of a disease. To mention an example: there is strong evidence that bilirubin has antioxidative properties and low levels are associated with coronary heart disease. A linkage scan identified a large region on chromosome 2 to harbor a gene related to bilirubin levels (Kronenberg et al., 2002). This region includes besides many other genes a gene for the enzyme uridine diphosphate glycosyltransferase 1 (UGT1A1) which is involved in the bilirubin glucoronidation. Due to the involvement of this enzyme in the metabolism of bilirubin, UGT1A1 promoted to a candidate gene which we finally showed in the prospective Framingham Heart Study to be associated with coronary heart disease (Lin et al., 2006).
Compared to this hypothesis-driven approach an hypothesis-free approach can identify new candidate genes without making any biological assumptions. It therefore allows to identify genes involved in pathways which until now are unknown to be involved in a certain phenotype. A new and powerful approach are genome-wide association studies (GWAS) which investigate hundreds of thousands of genetic variants across the entire human genome whether they are associated with the phenotype. The genetic marker usually applied for this analysis is the single nucleotide polymorphism (SNP). More than 10 millions are known throughout the human genome and a typical GWAS investigates 100,000–500,000 SNPs and more recently even a million SNPs. This requires high throughput DNA microarray technology. The first genome-wide association studies have caused a major enthusiasm since they have identified previously unknown genetic risk factors for complex diseases such as myocardial infarction, diabetes mellitus, obesity, age-related macular degeneration, cancer and inflammatory bowel disease. There are many studies in progress and the first large meta-analysis GWAS consortia are forming to identify even very small gene effects.
Section snippets
Design of a GWAS
A GWAS investigating e.g. 500,000 SNPs has the major problem that about 25,000 false positive associations will be identified if the conventional significance level of p < 0.05 is applied. Since 500,000 statistical tests are performed, one might apply the conventional Bonferroni’s correction which results in a necessary significance level of p = 0.0000001 (10−7) which is also called the threshold of genome-wide significance. Several considerations on the study design resulted in pros and cons and
GWAS on type 2 diabetes mellitus
Type 2 diabetes mellitus (T2DM) is considered as the geneticist’s nightmare (Frayling, 2007). However, recent advances by GWAS revealed several surprises resulting in a major progress. Besides the five already known gene regions, six GWAS (Saxena et al., 2007, Frayling et al., 2007, Sladek et al., 2007, Scuteri et al., 2007, Steinthorsdottir et al., 2007, Scott et al., 2007, The Wellcome Trust Case Control Consortium, 2007, Zeggini et al., 2007) have identified six new gene regions which
GWAS on coronary artery disease
In the past hundreds of candidate gene association studies reported genes to be associated with coronary artery disease (CAD). Many of these associations were not replicated. It is therefore surprising and reassuring, that within a few weeks four genome-wide association studies identified the same gene region to be associated with CAD (The Wellcome Trust Case Control Consortium, 2007, Samani et al., 2007, McPherson et al., 2007, Helgadottir et al., 2007). It is located on chromosome 9p21 and
Breast cancer
Since known susceptibility genes account for less than 25% of the familial breast cancer risk, the search for other genes which probably contribute moderate risks for the disease is reasonable. A large UK study conducted a two-stage GWAS in 4398 breast cancer cases and 4316 controls, followed by a third stage in which 30 SNPs were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. The consortium observed five novel independent loci exhibiting strong and consistent
Lessons we learn from GWAS
First of all, the most exciting perspective we learn from the first GWAS is the fact that the method is working since many of these GWAS re-identify already well-known genes for diseases. And even more important, they identify unknown gene regions which we would have never interconnected with the disease using the methods in place before GWAS were introduced. This will bring regulatory and metabolic pathways to our attention which were unknown to be related to a particular complex disease.
Conclusions
GWAS are a new and very powerful tool to identify genetic contributors to aging-related phenotypes. Former candidate gene studies identified mostly the so-called “low-hanging fruits” which strongly increase the risk for a disease two- and more-fold. Sufficiently powered GWAS will probably result in the identification of “middle- and high-hanging fruits” which increase the risk for a complex disease only by 10–50%. The formation of GWAS consortia with the joint analysis of several GWAS will
Acknowledgements
This work was supported by the “Genomics of Lipid-associated Disorders – GOLD” of the “Austrian Genome Research Programme GEN-AU” to F. Kronenberg.
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