Convergent Pathways Identified for Cannabis Use Disorder Across Diverse Ancestry Populations
Peng, Q.; Wilhelmsen, K. C.; Ehlers, C. L.
Show abstract
Large disparities in the prevalence of cannabis use disorder (CUD) exist across ethnic groups in the U.S. Despite large GWAS meta-analyses identifying numerous genome-wide significant loci for CUD in European descents, little is known about other ethnic groups. While most GWAS and SNP-heritability studies focus on common genomic variants, rare and low-frequency variants, particularly those altering proteins, are known to be enriched for the heritability of complex traits and may contribute to disease in different ways across populations, either through converging or alternative pathways. In this study, we examined three populations including European Americans (EA) and two understudied populations: American Indians (AI) and Mexican Americans (MA). We focused on rare and low frequency functional variants in genes and pathways, and performed association analysis with CUD severity. We identified 10 significant loci in AI, the ARSA gene in MA, three significant pathways in MA, and one in EA associated with CUD severity. Notably, pathways related to arylsulfatases activation and heparan sulfate degradation were supported by both EA and MA, with additional evidence from AI. The integrin beta-1 cell surface interaction pathway, involved in cell adhesion, was uniquely significant in MA. Several immune-related pathways were also found, including an autoimmune condition significant in MA with evidence from EA as well, and a p38-gamma/delta mediated signaling pathway supported across all three cohorts. Although each population displayed distinct pathways linked to CUD, overlapping genes in top pathways suggested shared genetic factors, further highlighting the importance of considering diverse populations in genetic research on cannabis use disorder.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Polygenic risk scores for psychiatric, inflammatory, and cardio-metabolic traits and diseases highlight possible genetic overlaps with suicide attempt and treatment-emergent suicidal ideation 93%
- "The Heidelberg Five" Personality Dimensions: Genome-wide Associations, Polygenic Risk for Neuroticism, and Psychopathology 20 Years after Assessment 93%
- Schizophrenia Risk Alleles Often Affect The Expression of Many Genes and Each Gene May Have a Different Effect On The Risk; A Mediation Analysis. 93%
Similar papers in this journal
- Divergent gene expression in alcohol and opioid usedisorders results in consistent alterations in functional networks in the Dorsolateral Prefrontal Cortex 94%
- Drinking and smoking polygenic risk is associated with neurodevelopmental outcomes of children and young adults independently of psychopathology and substance use 94%
- Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder 93%
Similar papers in this journal
- Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry 94%
- Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals 92%
- Genome-wide association study and multi-trait analysis of opioid use disorder identifies novel associations in 639,709 individuals of European and African ancestry 92%
Similar papers in this journal
Similar papers in this journal
- Neuronal-specific methylome and hydroxymethylome analysis reveal replicated and novel loci associated with alcohol use disorder 92%
- Polygenic heterogeneity across obsessive-compulsive disorder subgroups defined by a comorbid diagnosis 91%
- Substance abuse and the risk of severe COVID-19: Mendelian randomization confirms the causal role of opioids but hints a negative causal effect for cannabinoids 91%
"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.