Researchers here propose an interesting use for genetic data obtained from the many centenarians now known to the scientific community from past studies of genetics and longevity:
In the last ten years the scientific community has devoted a consistent effort to identify the genetic basis of the most common age-related diseases, as they represent one of the most important public health and socio-economical burden all over the world and particularly in Western Countries. This challenge was mainly faced up by genome wide association studies (GWASs) based on microarray technology that allows the simultaneous analyses of hundred thousands of single nucleotide polymorphisms (SNPs), within the framework of the "common variant common disease" theory.
So far, more than 1,000 published GWASs reported significant associations of ~4,000 SNPs for more than 200 traits/diseases. GWASs of age-related, chronic human diseases often suffer from a lack of power to detect modest effects, which can to some extent explain why the identified genetic effects comprise only a small fraction of the estimated trait heritability. These limitations can be overcome simply by ever increasing sample size in order to achieve the necessary statistical power to detect variants with small effects, which is not always feasible.
Here we propose an alternative approach of including healthy centenarians as a more homogeneous and extreme control group. As a proof of principle we focused on type 2 diabetes (T2D) and assessed genotypic associations of 31 SNPs associated with T2D, diabetes complications and metabolic diseases and SNPs of genes relevant for telomere stability and age-related diseases. We hypothesized that the frequencies of risk variants are inversely correlated with decreasing health and longevity. We performed association analyses comparing diabetic patients and non-diabetic controls followed by association analyses with extreme phenotypic groups (T2D patients with complications and centenarians). Results drew attention to rs7903146 (TCF7L2 gene) that showed a constant increase in the frequencies of risk genotype (TT) from centenarians to diabetic patients who developed macro-complications and the strongest genotypic association was detected when diabetic patients were compared to centenarians. We conclude that robust and biologically relevant associations can be obtained when extreme phenotypes, even with a small sample size, are compared.