Scientific article

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities

CollaboratorsPollak, Pierre
Published inHuman mutation, vol. 33, no. 12, p. 1708-1718
Publication date2012

The success of genome-wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top-validated single-nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1-5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross-validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ~0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ~0.56; heritability ~38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni-tuebingen.de/software/MACLEAPS/.

  • Area Under Curve
  • Bipolar Disorder/diagnosis/genetics
  • Case-Control Studies
  • Computer Simulation
  • Diabetes Mellitus, Type 1/diagnosis/genetics
  • Diabetes Mellitus, Type 2/diagnosis/genetics
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study/methods
  • Humans
  • Models, Genetic
  • Parkinson Disease/diagnosis/genetics
  • Polymorphism, Single Nucleotide
  • ROC Curve
  • Risk
  • Software
  • Support Vector Machines
Citation (ISO format)
MITTAG, Florian et al. Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities. In: Human mutation, 2012, vol. 33, n° 12, p. 1708–1718. doi: 10.1002/humu.22161
Main files (1)
Article (Published version)
ISSN of the journal1059-7794

Technical informations

Creation01/13/2015 1:55:00 PM
First validation01/13/2015 1:55:00 PM
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