Study Explores Genetic Links to Disease Risk Prediction

Overview of Genome-Wide Association Studies

A recent study from Canada highlights the uncertain connections between human genetics and disease risk prediction. Genome-wide association (GWA) studies are emerging techniques designed to identify genes associated with human diseases. These studies involve a comprehensive scan of the human genome to locate specific variations known as single nucleotide polymorphisms (SNPs). Highly significant SNPs are typically found more frequently in individuals with a disease compared to those without, indicating a potential role in the disease’s development.

Since the inception of the first GWA study in 2005, this methodology has contributed significantly to our understanding of the genetic underpinnings of numerous complex diseases, including diabetes, and various phenotypes such as body mass index and hair color.

Challenges in Predicting Disease Risk

While the contributions of GWA studies are noteworthy, the reliability of SNPs identified through these studies for predicting disease risk remains ambiguous. Comparing the effectiveness of GWA-derived SNPs to biomarkers from non-GWA studies, such as clinical, metabolomic, and proteomic analyses, presents challenges. Non-GWA studies often utilize performance metrics like receiver-operator characteristic (ROC) curves and area under the receiver-operator characteristic (AUROC) curves to evaluate the predictive power of biomarkers. These metrics assess a test’s capability to differentiate between true positives and false positives.

In contrast, GWA studies typically employ p-values and odds ratios for individual SNP markers to indicate the significance of differences between affected and unaffected individuals.

Standardizing Biomarker Reporting

To bridge the gap between these two methodologies, researchers at the University of Alberta developed a software tool aimed at standardizing biomarker reporting across GWA and non-GWA study-derived risk predictors. The team, led by Dr. David Wishart from the Department of Biological Sciences and the Department of Computing Science, introduced G-WIZ, a tool designed to compute ROC and AUROC curves using publicly available GWA study data. This tool was employed to evaluate the predictive capability of GWA-derived SNPs or combinations of SNPs for disease risk and traits, with findings published in the journal PLOS ONE.

For the analysis, multiple SNPs were aggregated to form a multi-SNP risk predictor for each GWA study. The researchers discovered that the average AUROC for these multi-SNP risk panels was low, at only 0.55. In contrast, most clinical, metabolite, or protein-based biomarkers demonstrated AUROCs exceeding 0.7.

Comparative Analysis of Biomarkers

The team compared the AUROC values derived from GWA studies with those reported in clinical, metabolomic, and proteomic studies. The results indicated that biomarkers identified through non-GWA methodologies were significantly more effective in predicting disease risk than SNP biomarkers. The research also concluded that SNP information lacked predictive power for most human conditions, suggesting a minimal genetic influence on disease risk.

Dr. Wishart stated, “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 percent at best.”

Conditions with Notable Genetic Components

The study identified five conditions with a higher genetic component: age-related macular degeneration, celiac disease, progressive supranuclear palsy, craniofacial microsomia, and black hair color.

“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,” Dr. Wishart explained.

Implications for Health Measurement

Dr. Wishart emphasized the importance of measuring metabolites, microbes, and proteins rather than genes for a more accurate assessment of health and disease propensity. He added, “This research also highlights the need to understand our environment and the safety or quality of our food, air, and water.”

References

Patron, J., Serra-Cayuela, A., Han, B., Li, C. & Wishart, D. S. Assessing the performance of genome-wide association studies for predicting disease risk. PLoS ONE 14, e0220215 (2019).
Willis, K. Your DNA is not your destiny — or a good predictor of your health. EurekAlert! (2019).
Image by Thor Deichmann from Pixabay.