title: High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans.
abstract: BACKGROUND
  With complex traits and diseases having potential genetic contributions of thousands of genetic factors, and with current genotyping arrays consisting of millions of single nucleotide polymorphisms (SNPs), powerful high-dimensional statistical techniques are needed to comprehensively model the genetic variance. Machine learning techniques have many advantages including lack of parametric assumptions, and high power and flexibility.
  
  
  RESULTS
  We have applied three machine learning approaches: Random Forest Regression (RFR), Boosted Regression Tree (BRT) and Support Vector Regression (SVR) to the prediction of warfarin maintenance dose in a cohort of African Americans. We have developed a multi-step approach that selects SNPs, builds prediction models with different subsets of selected SNPs along with known associated genetic and environmental variables and tests the discovered models in a cross-validation framework. Preliminary results indicate that our modeling approach gives much higher accuracy than previous models for warfarin dose prediction. A model size of 200 SNPs (in addition to the known genetic and environmental variables) gives the best accuracy. The R(2) between the predicted and actual square root of warfarin dose in this model was on average 66.4% for RFR, 57.8% for SVR and 56.9% for BRT. Thus RFR had the best accuracy, but all three techniques achieved better performance than the current published R(2) of 43% in a sample of mixed ethnicity, and 27% in an African American sample. In summary, machine learning approaches for high-dimensional pharmacogenetic prediction, and for prediction of clinical continuous traits of interest, hold great promise and warrant further research.
authors: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
authors: Erdal
authors: E
authors: Cosgun
authors: Nita A
authors: NA
authors: Limdi
authors: Christine W
authors: CW
authors: Duarte
mesh_terms: D001741:Black or African American / Q000235:genetics*; D000925:Anticoagulants / Q000008:administration & dosage*; D001185:Artificial Intelligence; D004305:Dose-Response Relationship, Drug; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008954:Models, Biological; D010597:Pharmacogenetics / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D014859:Warfarin / Q000008:administration & dosage*
chemical_list: D000925:Anticoagulants; D014859:Warfarin