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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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Related in: MedlinePlus

Genes with significant correlation between expression rank fold-change and disease-specific pain index (DSPI).(A, B) DLG4 and CHRNA4 are known pain genes listed in the Pain Gene Database. (C–G) ABLIM3, PDE2A, NAALAD2, CREB1 and NCALD were selected for further investigation through genotyping in an independent human cohort. X-axis represents disease ordered according to the DSPI. Y-axis displays the rank fold change. Solid line indicates linear regression fit. Rho Spearman correlation coefficient and uncorrected correlation p-values are shown. The curved dashed line represents the 95% confidence interval of the linear regression performed against the DSPI.
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pcbi-1002538-g002: Genes with significant correlation between expression rank fold-change and disease-specific pain index (DSPI).(A, B) DLG4 and CHRNA4 are known pain genes listed in the Pain Gene Database. (C–G) ABLIM3, PDE2A, NAALAD2, CREB1 and NCALD were selected for further investigation through genotyping in an independent human cohort. X-axis represents disease ordered according to the DSPI. Y-axis displays the rank fold change. Solid line indicates linear regression fit. Rho Spearman correlation coefficient and uncorrected correlation p-values are shown. The curved dashed line represents the 95% confidence interval of the linear regression performed against the DSPI.

Mentions: We evaluated the significance of the association of the 3812 genes with the DSPI using a threshold-based estimated false discovery rate. Forty-seven genes were significantly associated with the DSPI (pFDR<0.01; Table 2). Among the 47 genes, two genes, DLG4 (PSD-95) and CHRNA4, were referenced in the PGD [19], [20]. DLG4 and CHRNA4 were both found to have expression changes in 13 of 121 diseases that were positively correlated with pain indices (Figure 2A–B).


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)

Genes with significant correlation between expression rank fold-change and disease-specific pain index (DSPI).(A, B) DLG4 and CHRNA4 are known pain genes listed in the Pain Gene Database. (C–G) ABLIM3, PDE2A, NAALAD2, CREB1 and NCALD were selected for further investigation through genotyping in an independent human cohort. X-axis represents disease ordered according to the DSPI. Y-axis displays the rank fold change. Solid line indicates linear regression fit. Rho Spearman correlation coefficient and uncorrected correlation p-values are shown. The curved dashed line represents the 95% confidence interval of the linear regression performed against the DSPI.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002538-g002: Genes with significant correlation between expression rank fold-change and disease-specific pain index (DSPI).(A, B) DLG4 and CHRNA4 are known pain genes listed in the Pain Gene Database. (C–G) ABLIM3, PDE2A, NAALAD2, CREB1 and NCALD were selected for further investigation through genotyping in an independent human cohort. X-axis represents disease ordered according to the DSPI. Y-axis displays the rank fold change. Solid line indicates linear regression fit. Rho Spearman correlation coefficient and uncorrected correlation p-values are shown. The curved dashed line represents the 95% confidence interval of the linear regression performed against the DSPI.
Mentions: We evaluated the significance of the association of the 3812 genes with the DSPI using a threshold-based estimated false discovery rate. Forty-seven genes were significantly associated with the DSPI (pFDR<0.01; Table 2). Among the 47 genes, two genes, DLG4 (PSD-95) and CHRNA4, were referenced in the PGD [19], [20]. DLG4 and CHRNA4 were both found to have expression changes in 13 of 121 diseases that were positively correlated with pain indices (Figure 2A–B).

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
Related in: MedlinePlus