Journal article
Performance and Robustness of Penalized and Unpenalized Methods for Genetic Prediction of Complex Human Disease
G Abraham, A Kowalczyk, J Zobel, M Inouye
Genetic Epidemiology | WILEY | Published : 2013
DOI: 10.1002/gepi.21698
Abstract
A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic-net penalized support-vector machine models, a mixed-effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false-positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome-wide SNP profiles across eight complex diseases within..
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Awarded by Victorian Life Sciences Computation Initiative
Awarded by NHMRC Biomedical Australian Training Fellowship
Awarded by Wellcome Trust
Funding Acknowledgements
Thanks to David A. van Heel (Queen Mary University of London) for supplying the celiac disease data, to Peter Visscher (University of Queensland) and Naomi Wray (University of Queensland) for useful discussions, to Laurent Jacob (University of California, Berkeley) for discussion of correlation effects on the lasso, and to Jian Yang (University of Queensland) for help with GCTA. This work was supported by the Australian Research Council, and by the NICTA Victorian Research Laboratory. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications, and the Digital Economy, and the Australian Research Council through the ICT Centre of Excellence program. This work utilized the computing resources of the Victorian Life Sciences Computation Initiative (project VR0126). M. I. was supported by an NHMRC Biomedical Australian Training Fellowship (no. 637400). This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113 and 085475.