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Integrative approach to pain genetics identifies pain sensitivity loci across diseases.

Ruau D, Dudley JT, Chen R, Phillips NG, Swan GE, Lazzeroni LC, Clark JD, Butte AJ, Angst MS - PLoS Comput. Biol. (2012)

Bottom Line: Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes.Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes.This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesia, Stanford University School of Medicine, Stanford, California, United States of America. druau@stanford.edu

ABSTRACT
Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10⁻¹⁰) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10⁻⁴, 1.8×10⁻⁴, and 2.2×10⁻⁴ respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates.

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Receiver Operating Characteristic (ROC) curve.The ROC curve depicts the performance of our algorithm to identify known pain genes listed in the Pain Genes Database [18]. The area under the ROC curve is 60.5%, which is significantly different from random chance. Maximum sensitivity and specificity are 56.9% and 62.7% respectively.
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pcbi-1002538-g001: Receiver Operating Characteristic (ROC) curve.The ROC curve depicts the performance of our algorithm to identify known pain genes listed in the Pain Genes Database [18]. The area under the ROC curve is 60.5%, which is significantly different from random chance. Maximum sensitivity and specificity are 56.9% and 62.7% respectively.

Mentions: The sensitivity and accuracy of this strategy for capturing genes implicated in the processing of pain was first evaluated with the aid of the Pain Gene Database (PGD) [18]. The PGD catalogs genes whose transgenic or knockout mouse counterparts have exhibited changes in pain-related phenotypes. The PGD is actively maintained and, to our knowledge, is the only pain-related gene database. Figure 1 shows the receiver operating characteristic (ROC) curve with confidence intervals. The area under the curve (AUC) was 60.5% indicating a prioritization of known pain genes from the PGD by our method.


Integrative approach to pain genetics identifies pain sensitivity loci across diseases.

Ruau D, Dudley JT, Chen R, Phillips NG, Swan GE, Lazzeroni LC, Clark JD, Butte AJ, Angst MS - PLoS Comput. Biol. (2012)

Receiver Operating Characteristic (ROC) curve.The ROC curve depicts the performance of our algorithm to identify known pain genes listed in the Pain Genes Database [18]. The area under the ROC curve is 60.5%, which is significantly different from random chance. Maximum sensitivity and specificity are 56.9% and 62.7% respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369906&req=5

pcbi-1002538-g001: Receiver Operating Characteristic (ROC) curve.The ROC curve depicts the performance of our algorithm to identify known pain genes listed in the Pain Genes Database [18]. The area under the ROC curve is 60.5%, which is significantly different from random chance. Maximum sensitivity and specificity are 56.9% and 62.7% respectively.
Mentions: The sensitivity and accuracy of this strategy for capturing genes implicated in the processing of pain was first evaluated with the aid of the Pain Gene Database (PGD) [18]. The PGD catalogs genes whose transgenic or knockout mouse counterparts have exhibited changes in pain-related phenotypes. The PGD is actively maintained and, to our knowledge, is the only pain-related gene database. Figure 1 shows the receiver operating characteristic (ROC) curve with confidence intervals. The area under the curve (AUC) was 60.5% indicating a prioritization of known pain genes from the PGD by our method.

Bottom Line: Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes.Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes.This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesia, Stanford University School of Medicine, Stanford, California, United States of America. druau@stanford.edu

ABSTRACT
Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10⁻¹⁰) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10⁻⁴, 1.8×10⁻⁴, and 2.2×10⁻⁴ respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates.

Show MeSH