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An integrative method for scoring candidate genes from association studies: application to warfarin dosing.

Tatonetti NP, Dudley JT, Sagreiya H, Butte AJ, Altman RB - BMC Bioinformatics (2010)

Bottom Line: A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects.Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response.Our method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu

ABSTRACT

Background: A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.

Results: We applied our method to a published warfarin GWAS data set comprising 181 individuals. We find that our method can increase the power of the GWAS to identify both VKORC1 and CYP2C9 as warfarin pharmacogenes, where the original analysis had only identified VKORC1. Additionally, we find that our method can be used to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) responders.

Conclusions: Our method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.

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Related in: MedlinePlus

Low Dose Classification ROC Curve. Receiver Operating Characteristic Curve for the low dose classification algorithms. Two classifiers were trained, the first, dotted line, on all 20 genes for which the gene-scores significantly distinguish low-dose and non-low-dose patients (AUROC=0.886, p≤0.05, Table 3), and the second, dashed line, on only those genes that were significant after multiple hypothesis testing correction (AUROC=0.721, p≤0.001, Table 3). Both classifiers have empirical p-value significance of less than 0.01 when tested using bootstrapping.
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Figure 1: Low Dose Classification ROC Curve. Receiver Operating Characteristic Curve for the low dose classification algorithms. Two classifiers were trained, the first, dotted line, on all 20 genes for which the gene-scores significantly distinguish low-dose and non-low-dose patients (AUROC=0.886, p≤0.05, Table 3), and the second, dashed line, on only those genes that were significant after multiple hypothesis testing correction (AUROC=0.721, p≤0.001, Table 3). Both classifiers have empirical p-value significance of less than 0.01 when tested using bootstrapping.

Mentions: A logistic regression classification model was trained on VKORC1 and UBE3A gene-scores and evaluated with 10-fold cross validation (Figure 1). The AUROC was 0.721 (significance p-value < 0.01). A second logistic regression classification model was trained on the 20 genes that had a p-value ≤ 0.05 (Figure 1). The AUROC of this classifier was 0.886 (significance p-value < 0.01).


An integrative method for scoring candidate genes from association studies: application to warfarin dosing.

Tatonetti NP, Dudley JT, Sagreiya H, Butte AJ, Altman RB - BMC Bioinformatics (2010)

Low Dose Classification ROC Curve. Receiver Operating Characteristic Curve for the low dose classification algorithms. Two classifiers were trained, the first, dotted line, on all 20 genes for which the gene-scores significantly distinguish low-dose and non-low-dose patients (AUROC=0.886, p≤0.05, Table 3), and the second, dashed line, on only those genes that were significant after multiple hypothesis testing correction (AUROC=0.721, p≤0.001, Table 3). Both classifiers have empirical p-value significance of less than 0.01 when tested using bootstrapping.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967750&req=5

Figure 1: Low Dose Classification ROC Curve. Receiver Operating Characteristic Curve for the low dose classification algorithms. Two classifiers were trained, the first, dotted line, on all 20 genes for which the gene-scores significantly distinguish low-dose and non-low-dose patients (AUROC=0.886, p≤0.05, Table 3), and the second, dashed line, on only those genes that were significant after multiple hypothesis testing correction (AUROC=0.721, p≤0.001, Table 3). Both classifiers have empirical p-value significance of less than 0.01 when tested using bootstrapping.
Mentions: A logistic regression classification model was trained on VKORC1 and UBE3A gene-scores and evaluated with 10-fold cross validation (Figure 1). The AUROC was 0.721 (significance p-value < 0.01). A second logistic regression classification model was trained on the 20 genes that had a p-value ≤ 0.05 (Figure 1). The AUROC of this classifier was 0.886 (significance p-value < 0.01).

Bottom Line: A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects.Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response.Our method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu

ABSTRACT

Background: A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.

Results: We applied our method to a published warfarin GWAS data set comprising 181 individuals. We find that our method can increase the power of the GWAS to identify both VKORC1 and CYP2C9 as warfarin pharmacogenes, where the original analysis had only identified VKORC1. Additionally, we find that our method can be used to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) responders.

Conclusions: Our method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.

Show MeSH
Related in: MedlinePlus