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Dhurandhar explores using prediction accuracy of genomic models toward establishing personalized medicine

December 8, 2014

Whole Genome Prediction (WGP) jointly fits thousands of single nucleotide polymorphisms (SNPs) into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, as well as for their associations with that trait. To date, WGP has offered only modest prediction accuracy, but in some cases even modest prediction accuracy may be useful.

Emily J. Dhurandhar, PhD, assistant professor in the Department of Health Behavior—in collaboration with Ana I. Vázquez, PhD, assistant professor in the Department of Biostatistics, Section on Statistical Genetics; and David B. Allison, PhD, distinguished professor and director of the Office of Energetics and Nutrition Obesity Research Center (NORC)—recently provided an illustration using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy to assess the clinical utility of WGP, despite these limitations.

Prevention of type 2 diabetes (T2DM) postsurgery was considered the major outcome. Dr. Dhurandhar and her team found that treating only patients above a predefined threshold of predicted weight loss in the simulation—in the realistic context of finite resources for the surgery—significantly reduced lifetime risk of T2DM in the treatable population by selecting those most likely to succeed. Thus, their example illustrates how WGP may be clinically useful in some situations and, even with moderate accuracy, may provide a clear path for turning personalized medicine from theory to reality.

To read “Even Modest Prediction Accuracy of Genomic Models Can Have Large Clinical Utility,” published in October 2014 in the journal Frontiers in Genetics, click here.