The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans
Toikumo, S. I.; Vickers-Smith, R. A.; Jinwala, Z.; Xu, H.; Saini, D.; Hartwell, E. E.; Pavicic Venegas, M. V.; Sullivan, K. A.; Xu, K.; Jacobson, D. A.; Gelernter, J.; Rentsch, C. T.; Stahl, E.; Cheatle, M.; Zhou, H. T.; Waxman, S.; Justice, A. C.; Kember, R. L.; Kranzler, H.
Show abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sexual dimorphism in a neuronal mechanism of spinal hyperexcitability across rodent and human models of pathological pain 94%
- Genome-wide association study of febrile seizures identifies seven new loci implicating fever response and neuronal excitability genes 92%
- Medullary kappa-opioid receptor neurons inhibit pain and itch through a descending circuit 92%
Similar papers in this journal
- Genome-wide analysis of binge-eating disorder identifies the first three risk loci and implicates iron metabolism 95%
- Large scale genome-wide association study in a Japanese population identified 45 novel susceptibility loci for 22 diseases 94%
- Genome-wide analyses of 200,453 individuals yields new insights into the causes and consequences of clonal hematopoiesis 94%
Similar papers in this journal
- Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain at different anatomic sites 92%
- Composite trait Mendelian Randomization reveals distinct metabolic and lifestyle consequences of differences in body shape 92%
- Capturing additional genetic risk from family history for improved polygenic risk prediction 91%
Similar papers in this journal
- A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients 90%
- Infiltrating lipid-rich macrophage subpopulations identified as a regulator of increasing prostate size in human benign prostatic hyperplasia 90%
- A Ligand-Centered Framework for γδ T Cell Activation in Colorectal Cancer Revealed by Single-Cell and Transformer-Based Perturbation 90%
Similar papers in this journal
- Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19 94%
- Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals 94%
- Identification of 64 new risk loci for major depression, refinement of the genetic architecture and risk prediction of recurrence and comorbidities 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.