Human pain genetics database: a resource dedicated to human pain genetics research.
Journal Article (Journal Article)
The Human Pain Genetics Database (HPGDB) is a comprehensive variant-focused inventory of genetic contributors to human pain. After curation, the HPGDB currently includes 294 studies reporting associations between 434 distinct genetic variants and various pain phenotypes. Variants were then submitted to a comprehensive analysis. First, they were validated in an independent high-powered replication cohort by testing the association of each variant with 10 different pain phenotypes (n = 1320-26,973). One hundred fifty-five variants replicated successfully (false discovery rate 20%) in at least one pain phenotype, and the association P values of the HPGDB variants were significantly lower compared with those of random controls. Among the 155 replicated variants, 21 had been included in the HPGDB because of their association with analgesia-related and 13 with nociception-related phenotypes, confirming analgesia and nociception as pathways of vulnerability for pain phenotypes. Furthermore, many genetic variants were associated with multiple pain phenotypes, and the strength of their association correlated between many pairs of phenotypes. These genetic variants explained a considerable amount of the variance between different pairs of pain phenotypes, indicating a shared genetic basis among pain phenotypes. In addition, we found that HPGDB variants show many pleiotropic associations, indicating that genetic pathophysiological mechanisms are also shared among painful and nonpainful conditions. Finally, we demonstrated that the HPGDB data set is significantly enriched for functional variants that modify gene expression, are deleterious, and colocalize with open chromatin regions. As such, the HPGDB provides a validated data set that represents a valuable resource for researchers in the human pain field.
- Meloto, CB; Benavides, R; Lichtenwalter, RN; Wen, X; Tugarinov, N; Zorina-Lichtenwalter, K; Chabot-Doré, A-J; Piltonen, MH; Cattaneo, S; Verma, V; Klares, R; Khoury, S; Parisien, M; Diatchenko, L
- April 2018
Volume / Issue
- 159 / 4
Start / End Page
- 749 - 763
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)
- United States