Summary: A new study reports diseases such as cancers, Alzheimer’s disease, and diabetes may have a genetic contribution of 5-10% at most.
Source: University of Alberta
In most cases, your genes have less than five percent to do with your risk of developing a particular disease, according to new research by University of Alberta scientists.
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,” said David Wishart, professor in the University of Alberta’s Department of Biological Sciences and the Department of Computing Science and co-author on the study. “The vast majority of diseases, including many cancers, diabetes, and Alzheimer’s disease, have a genetic contribution of 5 to 10 percent 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 percent.
“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,” explained Wishart.
Wishart and his research collaborators suggest that measuring metabolites, chemicals, proteins, or the microbiome provides a much more accurate measure of human disease risk and are also more accurate for diagnosis. The findings fly in the face of many modern gene testing businesses models, which suggest that gene testing can accurately predict someone’s risk for disease.
“The bottom line is that if you want to have an accurate measure of your health, your propensity for disease or what you can do about it, it’s better to measure your metabolites, your microbes or your proteins–not your genes,” added Wishart. “This research also highlights the need to understand our environment and the safety or quality of our food, air, and water.”
University of Alberta
Katie Willis – University of Alberta
The image is in the public domain.
Original Research: Open access
“Assessing the performance of genome-wide association studies for predicting disease risk”. Jonas Patron, Arnau Serra-Cayuela, Beomsoo Han, Carin Li, David Scott Wishart.
PLOS ONE doi:10.1371/journal.pone.0220215.
Assessing the performance of genome-wide association studies for predicting disease risk
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 curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS.