Chronic pain is reportedly associated with the transient receptor potential canonical 3 (TRPC3) gene. The present study examined the genetic associations between the single-nucleotide polymorphisms (SNPs) of the TRPC3 gene and chronic pain. The genomic samples from 194 patients underwent linkage disequilibrium (LD) analyses of 29 SNPs within and around the vicinity of the TRPC3 gene. We examined the associations between the SNPs and the susceptibility to chronic pain by comparing the genotype distribution of 194 patients with 282 control subjects. All SNP genotype data were extracted from our previous whole-genome genotyping results. Twenty-nine SNPs were extracted, and a total of four LD blocks with 15 tag SNPs were observed within and around the TRPC3 gene. We further analyzed the associations between these tag SNPs and chronic pain. The rs11726196 SNP genotype distribution of patients was significantly different from the control subjects even after multiple-testing correction with the number of SNPs. The TT + TG genotype of rs11726196 is often carried by chronic pain patients, suggesting a causal role for the T allele. These results contribute to our understanding of the genetic risk factors for chronic pain.

Considerable individual differences have been widely observed in the sensitivity to opioids. We conducted a genome-wide association study (GWAS) in patients with cancer pain to identify potential candidate single-nucleotide polymorphisms (SNPs) that contribute to individual differences in opioid analgesic requirements in pain treatment by utilizing whole-genome genotyping arrays with more than 650,000 markers. The subjects in the GWAS were 428 patients who provided written informed consent and underwent treatment for pain with opioid analgesics in a palliative care unit at Higashi-Sapporo Hospital. The GWAS showed two intronic SNPs, rs1283671 and rs1283720, in the ANGPT1 gene that encodes a secreted glycoprotein that belongs to the angiopoietin family. These two SNPs were strongly associated with average daily opioid requirements for the treatment of pain in both the additive and recessive models (p < 5.0000 × 10−8). Several other SNPs were also significantly associated with the phenotype. In the gene-based analysis, the association was significant for the SLC2A14 gene in the additive model. These results indicate that these SNPs could serve as markers that predict the efficacy of opioid analgesics in cancer pain treatment. Our findings may provide valuable information for achieving satisfactory pain control and open new avenues for personalized pain treatment.