title: Even modest prediction accuracy of genomic models can have large clinical utility.
abstract: Whole Genome Prediction (WGP) jointly fits thousands of SNPs into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, and 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. We provide an illustration of this using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy (R (2) = 0.07) to assess the clinical utility of WGP despite these limitations. Prevention of Type 2 Diabetes (T2DM) post-surgery was considered the major outcome. Treating only patients above predefined threshold of predicted weight loss in our 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, our 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.
authors.affiliation: Department of Health Behavior, Nutrition Obesity Research Center, Office of Energetics, University of Alabama at Birmingham Birmingham, AL, USA.
authors.forename: Emily J
authors.initials: EJ
authors.lastname: Dhurandhar
authors.affiliation: Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA.
authors.forename: Ana I
authors.initials: AI
authors.lastname: Vazquez
authors.affiliation: Weis Center for Research, Institute of Obesity, Geisinger Health System Danville, PA, USA.
authors.forename: George A
authors.initials: GA
authors.lastname: Argyropoulos
authors.affiliation: School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
authors.forename: David B
authors.initials: DB
authors.lastname: Allison
keywords: clinical application; genomics; methods; prediction; statistical genetics