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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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System-based approach to pain-gene candidate prioritization.(A) Publications annotated with the MeSH term “pain”[mh] in conjunction with a disease MeSH term were retrieved from MEDLINE. (B) The co-citation ratio (see Materials and Methods) established a disease-specific pain index (DSPI) representing the relative painfulness of a disease compared to others. (C) The disease-associated experiments publicly available from GEO and AE were retrieved and significantly differentially expressed genes were extracted in a disease-specific manner. (D) Disease gene lists were organized according to the DSPI from highest to lowest. The gene expression fold change was correlated with the disease ordering to determine significant associations between gene expression patterns and the pain index. Genes were ranked according to their Spearman rank correlation coefficient (rho) and p-values corrected for multiple hypothesis testing.
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pcbi-1002538-g005: System-based approach to pain-gene candidate prioritization.(A) Publications annotated with the MeSH term “pain”[mh] in conjunction with a disease MeSH term were retrieved from MEDLINE. (B) The co-citation ratio (see Materials and Methods) established a disease-specific pain index (DSPI) representing the relative painfulness of a disease compared to others. (C) The disease-associated experiments publicly available from GEO and AE were retrieved and significantly differentially expressed genes were extracted in a disease-specific manner. (D) Disease gene lists were organized according to the DSPI from highest to lowest. The gene expression fold change was correlated with the disease ordering to determine significant associations between gene expression patterns and the pain index. Genes were ranked according to their Spearman rank correlation coefficient (rho) and p-values corrected for multiple hypothesis testing.

Mentions: The comprehensive disease-specific pain index (DSPI) was established by ranking all 2962 diseases by their respective disease-pain ratio (Table S1). Implicit to our algorithm is the assumption that each disease provides unique qualitative information that may be diluted if weighting results by publication frequency. Thus, the only criteria for inclusion of the disease in the DSPI was to have at least one co-citation with a “pain” related MeSH term (Figure 5A–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)

System-based approach to pain-gene candidate prioritization.(A) Publications annotated with the MeSH term “pain”[mh] in conjunction with a disease MeSH term were retrieved from MEDLINE. (B) The co-citation ratio (see Materials and Methods) established a disease-specific pain index (DSPI) representing the relative painfulness of a disease compared to others. (C) The disease-associated experiments publicly available from GEO and AE were retrieved and significantly differentially expressed genes were extracted in a disease-specific manner. (D) Disease gene lists were organized according to the DSPI from highest to lowest. The gene expression fold change was correlated with the disease ordering to determine significant associations between gene expression patterns and the pain index. Genes were ranked according to their Spearman rank correlation coefficient (rho) and p-values corrected for multiple hypothesis testing.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002538-g005: System-based approach to pain-gene candidate prioritization.(A) Publications annotated with the MeSH term “pain”[mh] in conjunction with a disease MeSH term were retrieved from MEDLINE. (B) The co-citation ratio (see Materials and Methods) established a disease-specific pain index (DSPI) representing the relative painfulness of a disease compared to others. (C) The disease-associated experiments publicly available from GEO and AE were retrieved and significantly differentially expressed genes were extracted in a disease-specific manner. (D) Disease gene lists were organized according to the DSPI from highest to lowest. The gene expression fold change was correlated with the disease ordering to determine significant associations between gene expression patterns and the pain index. Genes were ranked according to their Spearman rank correlation coefficient (rho) and p-values corrected for multiple hypothesis testing.
Mentions: The comprehensive disease-specific pain index (DSPI) was established by ranking all 2962 diseases by their respective disease-pain ratio (Table S1). Implicit to our algorithm is the assumption that each disease provides unique qualitative information that may be diluted if weighting results by publication frequency. Thus, the only criteria for inclusion of the disease in the DSPI was to have at least one co-citation with a “pain” related MeSH term (Figure 5A–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