title: A comparison of genomic profiles of complex diseases under different models.
abstract: BACKGROUND
  Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task.
  
  
  RESULTS
  We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium.
  
  
  CONCLUSIONS
  In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.
authors.affiliation: Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain. vpoten@correo.ugr.es.
authors.forename: Víctor
authors.initials: V
authors.lastname: Potenciano
authors.affiliation: Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain. mabad@ugr.es.
authors.forename: María Mar
authors.initials: MM
authors.lastname: Abad-Grau
authors.affiliation: Instituto de Parasitología y Biología Molecular, CSIC, Granada, Spain. pulgoso@ipbln.csic.es.
authors.forename: Antonio
authors.initials: A
authors.lastname: Alcina
authors.affiliation: Instituto de Parasitología y Biología Molecular, CSIC, Granada, Spain. lindo@ipbln.csic.es.
authors.forename: Fuencisla
authors.initials: F
authors.lastname: Matesanz
mesh_terms: D019540:Area Under Curve; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015203:Reproducibility of Results