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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

Manhattan plots and linkage disequilibrium heatmaps for ABLIM3 and NCALD.Log10 transformed correlation association p-values with pain cold threshold for 43 and 132 SNPs located in the ABLIM3 (A) and NCALD (B) genes regions, respectively. The x-axis represents the SNPs chromosomal physical location scale. The bottom heatmap represents linkage disequilibrium (LD) pairwise r2 based on the genotyped twin cohort. Blue star indicates polymorphisms found to be significantly associated with pain cold threshold.
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pcbi-1002538-g003: Manhattan plots and linkage disequilibrium heatmaps for ABLIM3 and NCALD.Log10 transformed correlation association p-values with pain cold threshold for 43 and 132 SNPs located in the ABLIM3 (A) and NCALD (B) genes regions, respectively. The x-axis represents the SNPs chromosomal physical location scale. The bottom heatmap represents linkage disequilibrium (LD) pairwise r2 based on the genotyped twin cohort. Blue star indicates polymorphisms found to be significantly associated with pain cold threshold.

Mentions: Within the five selected genes, 251 tag SNPs were tested. Polymorphisms in ABLIM3 (rs4512126) and NCALD (rs12548828, rs7826700, and rs1075791) showed significant association with the cold pressor pain threshold after Bonferroni correction (Figure 3A–B). Linkage disequilibrium (LD) analysis of the genotyped SNPs revealed a relatively weak LD structure around these polymorphisms. The LD structure in both genes was similar between the study cohort and the HapMap CEU population for the same region (Figure S2 and S3). Interestingly, the influence of the rs4512126 loci on the cold pressor pain threshold was tested, which revealed a male specific effect for individuals with the T/T allele (Figure 4A). Males with homozygous T/T alleles exhibited a significantly higher mean pain cold threshold than all other groups (p = 0.005, 4×10−4, 0.02, 0.005, 0.01, for A/A Males, A/A Females, A/T Male, A/T Females and T/T Females, respectively). The largest effect sizes (Cohen's d) were observed between T/T Males and A/A Males and Females (0.38 and 0.39, respectively). Effect sizes between T/T Males and the other groups were below the small effect size threshold (< = 0.2) with 0.16, 0.11 and 0.17 for A/T Males, A/T Females and TT Females respectively.


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)

Manhattan plots and linkage disequilibrium heatmaps for ABLIM3 and NCALD.Log10 transformed correlation association p-values with pain cold threshold for 43 and 132 SNPs located in the ABLIM3 (A) and NCALD (B) genes regions, respectively. The x-axis represents the SNPs chromosomal physical location scale. The bottom heatmap represents linkage disequilibrium (LD) pairwise r2 based on the genotyped twin cohort. Blue star indicates polymorphisms found to be significantly associated with pain cold threshold.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002538-g003: Manhattan plots and linkage disequilibrium heatmaps for ABLIM3 and NCALD.Log10 transformed correlation association p-values with pain cold threshold for 43 and 132 SNPs located in the ABLIM3 (A) and NCALD (B) genes regions, respectively. The x-axis represents the SNPs chromosomal physical location scale. The bottom heatmap represents linkage disequilibrium (LD) pairwise r2 based on the genotyped twin cohort. Blue star indicates polymorphisms found to be significantly associated with pain cold threshold.
Mentions: Within the five selected genes, 251 tag SNPs were tested. Polymorphisms in ABLIM3 (rs4512126) and NCALD (rs12548828, rs7826700, and rs1075791) showed significant association with the cold pressor pain threshold after Bonferroni correction (Figure 3A–B). Linkage disequilibrium (LD) analysis of the genotyped SNPs revealed a relatively weak LD structure around these polymorphisms. The LD structure in both genes was similar between the study cohort and the HapMap CEU population for the same region (Figure S2 and S3). Interestingly, the influence of the rs4512126 loci on the cold pressor pain threshold was tested, which revealed a male specific effect for individuals with the T/T allele (Figure 4A). Males with homozygous T/T alleles exhibited a significantly higher mean pain cold threshold than all other groups (p = 0.005, 4×10−4, 0.02, 0.005, 0.01, for A/A Males, A/A Females, A/T Male, A/T Females and T/T Females, respectively). The largest effect sizes (Cohen's d) were observed between T/T Males and A/A Males and Females (0.38 and 0.39, respectively). Effect sizes between T/T Males and the other groups were below the small effect size threshold (< = 0.2) with 0.16, 0.11 and 0.17 for A/T Males, A/T Females and TT Females respectively.

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