bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/549899

Cannabis use, depression and self-harm: phenotypic and genetic relationships

Hodgson, K.·Coleman, J. R.·Hagenaars, S. P.·Purves, K. L.·Choi, S. W. L.·O'Reilly, P. F.·Breen, G. F.·Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,·Lewis, C. M.
View original source ↗Export metadata ↓

Abstract

Background and AimsThe use of cannabis has previously been linked to both depression and self-harm, however the role of genetics in this relationship are unclear. We aimed to examine the phenotypic and genetic relationships between these traits. DesignGenetic and cross-sectional phenotypic data collected through UK Biobank, together with consortia genome-wide association study summary statistics. These data were used to assess the phenotypic and genetic relationship between cannabis use, depression and self harm. SettingUK, with additional international consortia data ParticipantsN=126,291 British adults aged between 40 and 70 years, recruited into UK Biobank MeasurementsGenome-wide genetic data, phenotypic data on lifetime history of cannabis use, depression and self-harm. FindingsIn UK Biobank, cannabis use is associated with increased likelihood of depression (OR=1.64, 95% CI=1.59-1.70, p=1.19x10-213) and self-harm (OR=2.85, 95% CI=2.69-3.01, p=3.46x10-304). The strength of this phenotypic association is stronger when more severe trait definitions of cannabis use and depression are considered. Additionally, significant genetic correlations are seen between cannabis use and depression using consortia summary statistics (rg=0.289, SE=0.036, p=1.45x10-15). Polygenic risk scores for cannabis use and depression both explain a small but significant proportion of variance in cannabis use, depression and self harm within a UK Biobank target sample. However, two-sample Mendelian randomisation analyses were not significant. ConclusionsCannabis use is both phenotypically and genetically associated with depression and self harm. Future work dissecting the causal mechanism linking these traits may have implications for cannabis users.

Source connections

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hodgson, K., Coleman, J. R., Hagenaars, S. P., Purves, K. L., Choi, S. W. L., O'Reilly, P. F., Breen, G. F., Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,, Lewis, C. M.. 2019-02-14. Cannabis use, depression and self-harm: phenotypic and genetic relationships. https://doi.org/10.1101/549899

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Transportability of mouse gene knockout models to human complex phenotypes

Mouse gene knockout studies are a valuable resource for understanding human gene function and the mechanisms underlying disease. However, translating insights to humans requires causal similarities between mouse and human phenotypes. A mouse model is transportable if gene perturbations that impact the mouse phenotype also affect (in orthologous genes) the corresponding human phenotype of interest, with consistent directionality. To assess the transportability of mouse models to human complex traits we combined laboratory mouse gene perturbation data from the high-throughput International Mouse Phenotyping Consortium with predicted loss-of-function data from the UK Biobank within the Gene Perturbation Analysis for Transportability (GPAT) framework. Applying GPAT to 30 matched mouse-human phenotype pairs, we found evidence of transportability for 12 mouse phenotypes including biochemical (e.g. total cholesterol), cellular (e.g. lymphocyte counts) and anthropometric (e.g. length/height) phenotypes. In contrast, there was limited evidence for others such as fat mass and body mass index in the context of the murine experimental conditions. We also found some evidence of transportability of mouse glucose measurements to diabetes mellitus in humans. Genome-wide perturbation effects on matched phenotypes were on average positively correlated between mice and humans (meta-analysis GPAT P = 2.3x10-27). However, the degree of correlation was generally modest and heterogeneous across phenotypes with some gene perturbations strongly associated in both species in opposite directions. Our framework illustrates how perturbation data can be used to quantitatively validate experimental models for understanding human disease aetiology and highlights the need for a range of orthogonal model systems, which may include challenges and perturbations to the mouse knockouts, to interrogate the mechanistic basis of human genetic associations.

genetics↗
bioRxiv2026

LRCQ: phenome-wide heritability enrichment of genomic loci from GWAS summary statistics

Some genetic variants are associated with many traits, but a variant's GWAS statistics are also inflated by the effects of variants in LD with it and by confounding shared by all variants in a study. Existing measures of per-variant pleiotropy, such as counts of significant associations or multi-trait association tests, do not separate these sources. Stratified LD score regression separates them for one trait at a time and only through predefined annotations. We present LRCQ (LD score regression on {chi}2 across many GWAS), which estimates for each locus its phenome-wide heritability enrichment: the variance of its causal effects across a set of traits, relative to an average locus. LRCQ regresses squared Z-scores from 30 or more GWAS jointly on squared LD, with per-trait LDSC intercepts and heritabilities fitted in earlier stages. Its pooled least-squares solution has a closed form whose cost does not grow with the number of traits. Its standard errors account for sample overlap and genetic correlation between GWAS. Squared Z-scores identify enrichment at the resolution of LD clumps, not single SNPs, so LRCQ reports clump-level estimates ("omni-loci"). These estimates can be aggregated over any functional annotation or gene set chosen after the analysis. In simulations with real UK Biobank LD and 30 to 1,000 traits, LRCQ was unbiased for its clump-level estimand. When GWAS share samples its test statistics are strongly right-skewed: normal-reference p-values were up to 10-fold anti-conservative at P < 0.001, and skewness-corrected p-values from the exact third cumulant restored type-I error and FDR control. Model-based standard errors were calibrated once the enrichment estimates plugged into them kept the polygenic background. It ranked enriched loci better than mean {chi}2, LD-normalised {chi}2, multi-trait omnibus tests and significant-association counts in 13 of 16 scenarios. Its main vulnerability was a misspecified LDSC intercept. Applied to 444 Pan-UK Biobank European-ancestry GWAS, LRCQ found strongly non-uniform phenome-wide enrichment: the SD of true clump-mean enrichment was 8.3, against a genome mean of 1. It identified 70 omni-loci (99 LD clumps) at FDR 5%, including CRP, CETP, APOB, AKTIP, SMOX, the SHBG region, CASZ1 and HNF1A. These ranked only moderately with mean {chi}2 and poorly with counts of significant associations. Gene-level phenome-wide enrichment rose with loss-of-function constraint. Phenome-wide heritability was 3-5-fold enriched in coding, conserved, 5' UTR and promoter sequence, and depleted in repressed chromatin. LRCQ turns hundreds of biobank GWAS into an LD-corrected map of where phenome-wide genetic variance lies, and complements its companion method LRCP, which estimates genetic-effect correlations between loci.

genetics↗