This is an age of genetics, in which the costs of obtaining and working with genetic data have dropped by orders of magnitude, while the capabilities of the tools and technologies have expanded to a similar degree. Give the scientific community a hammer, and a great many parts of the field start to look like a nail. Thus there are innumerable studies of genetics and longevity, genetics and specific age-related diseases, and so forth. There is considerable interest in trying to find out whether there is a genetic contribution to survival to extreme old age, and then using this information to develop therapies.
What the data tells us, however, is that we all age in pretty much the same way. The underlying processes of damage and reactions to damage are the same in everyone. The risk of age-related disease is not all that influenced by genetics for the vast majority of people and vast majority of conditions. Long-lived lineages of humans are a tiny, tiny fraction of the population, and may well exist for cultural rather than genetic reasons. Only a tiny number of genetic variants have been reliably correlated with longevity, and the effect sizes in each case are small, the variants adding only modestly to the odds of living longer.
What has by far the largest effect on variations in human aging? Firstly lifestyle, largely exercise and diet, and secondly environment, largely exposure to pathogens, particularly persistent viral infections. This will remain the case until the first rejuvenation therapies are widely adopted, at which point whether or not one uses them will become the largest contributing cause to variation in aging. Genetics is an enormously valuable branch of the sciences, but not as a direct path to human longevity.
In most cases, your genes have less than five per cent to do with your risk of developing a particular disease, according to new research. In the largest meta-analysis ever conducted, scientists have examined two decades of data from studies that examine the relationships between common gene mutations, also known as single nucleotide polymorphisms (SNPs), and different diseases and conditions. And the results show that the links between most human diseases and genetics are shaky at best. "Simply put, DNA is not your destiny, and SNPs are duds for disease prediction. The vast majority of diseases, including many cancers, diabetes, and Alzheimer's disease, have a genetic contribution of 5 to 10 per cent at best."
The study also highlights some notable exceptions, including Crohn's disease, celiac disease, and macular degeneration, which have a genetic contribution of approximately 40 to 50 per cent. "Despite these rare exceptions, it is becoming increasingly clear that the risks for getting most diseases arise from your metabolism, your environment, your lifestyle, or your exposure to various kinds of nutrients, chemicals, bacteria, or viruses."
To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic (ROC) curve (AUROC).
Our results indicate that the average AUROC for a typical GWAS-derived biomarker profile is low, just 0.55 with a standard deviation of 0.05. This is significantly lower than what we expected given that (the few) published AUROCs typically report a range between 0.62-0.67. The fact that published GWAS AUROCs tend to be high (~0.65) and unpublished GWAS AUROCs tend to be low (~0.55), suggests that one reason for the paucity of published GWAS AUROCs is that many AUROCs for SNP biomarker profiles are either uninterestingly low (less than 0.55), or not statistically different from those generated by a random predictor.
The GWAS-ROCS Database is a freely available electronic database containing the largest and most comprehensive set of SNP-derived AUROCs. All of the data is either directly from, or derived from, studies accessible through PubMed or GWAS Central - an open-access online repository of summary-level genome-wide association study (GWAS) data. The database currently houses 579 simulated populations (corresponding to 219 different conditions) and SNP data (odds ratio, risk allele frequency, and p-values) for 2886 unique SNPs. Each study simulation record (GR-Card) contains information detailing the original study as well as simulated population data (e.g. ROC curves, AUROCs, SNP-heritability scores) determined from careful population modelling to recreate individual-level GWAS data. All GWAS-ROCS data is downloadable and is intended for applications in genomics, biomarker discovery, and general education.