Journal article
SparSNP: Fast and memory-efficient analysis of all SNPs for phenotype prediction
G Abraham, A Kowalczyk, J Zobel, M Inouye
BMC Bioinformatics | Published : 2012
Open access
Abstract
Background: A central goal of genomics is to predict phenotypic variation from genetic variation. Fitting predictive models to genome-wide and whole genome single nucleotide polymorphism (SNP) profiles allows us to estimate the predictive power of the SNPs and potentially develop diagnostic models for disease. However, many current datasets cannot be analysed with standard tools due to their large size.Results: We introduce SparSNP, a tool for fitting lasso linear models for massive SNP datasets quickly and with very low memory requirements. In analysis on a large celiac disease case/control dataset, we show that SparSNP runs substantially faster than four other state-of-the-art tools for fi..
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Awarded by Australian Research Council
Funding Acknowledgements
Thanks to David van Heel (QMUL) for supplying the celiac disease data, and to the Victorian Life Sciences Computing Initiative (VLSCI) for providing computing facilities under project VR0126. Funding: MI was supported by an NHMRC Postdoctoral Fellowship (no. 637400). 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 was made possible through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIIS.