@misc{10481/49963,
year = {2016},
url = {http://hdl.handle.net/10481/49963},
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.},
organization = {This work was supported by the Spanish Secretary of Research, Development
and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III
[PI13/02714]and [PI13/01527] and the Andalusian Research Program under
project P08-TIC-03717 with the help of the European Regional Development
Fund (ERDF). The authors are very grateful to the reviewers, as they believe that
their comments have helped to substantially improve the quality of the paper.},
publisher = {Biomed Central},
title = {A comparison of genomic profiles of complex diseases under different models},
doi = {10.1186/s12920-015-0157-2},
author = {Potenciano, Victor and Abad Grau, María Del Mar and Alcina Madueño, Antonio and Matesanz, Fuencisla},
}