title: Machine learning derived risk prediction of anorexia nervosa.
abstract: BACKGROUND
  Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role.
  
  
  METHODS
  In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects.
  
  
  RESULTS
  Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values.
  
  
  CONCLUSIONS
  To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.
authors.affiliation: The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. guoy@email.chop.edu.
authors.forename: Yiran
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wei
authors.affiliation: The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.|Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA.
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Keating
authors.affiliation: The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. hakonarson@email.chop.edu.|Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA. hakonarson@email.chop.edu.
authors.forename: Hakon
authors.initials: H
authors.lastname: Hakonarson
mesh_terms: D000293:Adolescent; D000328:Adult; D000856:Anorexia Nervosa / Q000235:genetics*; D030541:Databases, Genetic; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D015203:Reproducibility of Results; D012307:Risk Factors; D018401:Sample Size