Evaluation of risk prediction updates from commercial genome-wide scans

Abstract

Purpose: Commercial internet-based companies offer genome-wide scans to predict the risk of common diseases and personalize nutrition and lifestyle recommendations. These risk estimates are updated with every new gene discovery.

Methods: To assess the benefits of updating risk information in commercial genome-wide scans, we compared type 2 diabetes risk predictions based on TCF7L2 alone, 18 polymorphisms alone, and 18 polymorphisms plus age, sex, and body mass index. Analyses were performed using data from the Rotterdam study, a prospective, population-based study among individuals aged 55 years and older. Data were available from 5297 participants.

Results: The actual prevalence of type 2 diabetes in the study population was 20%. Predicted risks were below average for carriers of the TCF7L2 CC genotype (predicted risk 17.6%) and above average for the CT and TT genotypes (20.8% and 28.0%). Adding the other 17 polymorphisms caused 34% of participants to be reclassified (i.e., switched between below and above average): 24% of the CC carriers changed to increased risk, 52% and 6% of the CT and TT carriers changed to decreased risk. Including information on age, sex, and body mass index caused 29% to change categories (27%, 31%, and 19% for CC, CT, and TT carriers, respectively). In total, 39% of participants changed categories once when risk factors were updated, and 11% changed twice, i.e., back to their initial risk category.

Conclusion: Updating risk factors may produce contradictory information about an individual's risk status over time, which is undesirable if lifestyle and nutritional recommendations vary accordingly.