Machine learning derived risk prediction of anorexia nervosa

BMC Medical Genomics
Yiran GuoHakon Hakonarson

Abstract

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. 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. 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. To our knowledge, this is...Continue Reading

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Citations

Mar 16, 2017·Translational Psychiatry·P D NegraesV Duvvuri
Oct 17, 2019·International Journal of Sports Physiology and Performance·Gil RodasAlejandro Lucia
May 1, 2021·Journal of Gastroenterology and Hepatology·Yongjia LiuXingxiang He

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Methods Mentioned

BETA
genotyping

Software Mentioned

R
R package
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Anorexia Nervosa

Anorexia nervosa (AN) is a psychiatric condition characterized by severe weight loss and secondary problems associated with malnutrition. Here is the latest research on AN.