SparSNP: fast and memory-efficient analysis of all SNPs for phenotype prediction.

SparSNP: fast and memory-efficient analysis of all SNPs for phenotype prediction.
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DOI:
10.1186/1471-2105-13-88
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发表时间:
2012-05-10
期刊:
影响因子:
3
通讯作者:
Inouye M
Inouye M
中科院分区:
生物学4区
文献类型:
--
作者:
Abraham G;Kowalczyk A;Zobel J;Inouye M

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基因组学的一个中心目标是从遗传变异中预测表型变异。将预测模型拟合到全基因组和全基因组单核苷酸多态性(SNP)谱中,使我们能够估计SNP的预测能力,并有可能开发疾病的诊断模型。然而,许多当前的数据集由于其庞大的规模而无法用标准工具进行分析。我们介绍了SparSNP,一个快速拟合大量SNP数据集的套索线性模型的工具,并且内存要求非常低。在对大型乳糜泻病例/对照数据集的分析中,我们表明SparSNP在拟合大规模惩罚模型时比其他四种最先进的工具运行得快得多。SparSNP是仅有的两个能够成功地将模型拟合到整个乳糜泻数据集的工具之一,而且它在这方面表现出色。与其他工具相比,SparSNP生成的模型在交叉验证中的预测性能优于或等于预测性能。基因组数据集的规模正在迅速增加,现有的模型拟合方法由于其令人望而却步的时间或内存要求而不切实际。这项研究表明,SparSNP是基因组分析工具包的重要补充。SparSNP可在http://www.genomics.csse.unimelb.edu.au/SparSNP上获得
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. 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 fitting large scale penalised models. SparSNP was one of only two tools that could successfully fit models to the entire celiac disease dataset, and it did so with superior performance. Compared with the other tools, the models generated by SparSNP had better than or equal to predictive performance in cross-validation. Genomic datasets are rapidly increasing in size, rendering existing approaches to model fitting impractical due to their prohibitive time or memory requirements. This study shows that SparSNP is an essential addition to the genomic analysis toolkit. SparSNP is available at http://www.genomics.csse.unimelb.edu.au/SparSNP
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