Multi-ancestry genome-wide association study of cannabis use

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Multi-ancestry genome-wide association study of cannabis use Nov 20, 2023 21:20:54 GMT

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Post by Admin on Nov 20, 2023 21:20:54 GMT

Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications

Abstract
As recreational use of cannabis is being decriminalized in many places and medical use widely sanctioned, there are growing concerns about increases in cannabis use disorder (CanUD), which is associated with numerous medical comorbidities. Here we performed a genome-wide association study of CanUD in the Million Veteran Program (MVP), followed by meta-analysis in 1,054,365 individuals (ncases = 64,314) from four broad ancestries designated by the reference panel used for assignment (European n = 886,025, African n = 123,208, admixed American n = 38,289 and East Asian n = 6,843). Population-specific methods were applied to calculate single nucleotide polymorphism-based heritability within each ancestry. Statistically significant single nucleotide polymorphism-based heritability for CanUD was observed in all but the smallest population (East Asian). We discovered genome-wide significant loci unique to each ancestry: 22 in European, 2 each in African and East Asian, and 1 in admixed American ancestries. A genetically informed causal relationship analysis indicated a possible effect of genetic liability for CanUD on lung cancer risk, suggesting potential unanticipated future medical and psychiatric public health consequences that require further study to disentangle from other known risk factors such as cigarette smoking.

Main
Cannabis is a psychoactive substance with a long history of use and dependence. Recently within the United States, 37 states have approved what is termed medical cannabis use, and 19 states, 2 territories and the District of Columbia allow possession of cannabis for recreational purposes. In Europe, only Malta has fully legalized recreational cannabis, although many other countries have decriminalized possession of small amounts of cannabis and have enabled medical allowances. It was recently legalized in Thailand but remains prohibited in many parts of Asia, the Middle East and South America. The status in many of these places may be subject to change in the near future. More than a third of individuals who use cannabis develop cannabis use disorders (CanUD), and evidence regarding the impact of legalization on escalating use and use disorders is mixed1,2. Substantial negative health outcomes associated with chronic cannabis use include various cancers associated with inhaling combustion products3, declines in cognitive capacity and motivation and increased schizophrenia (SCZ) risk4,5. Individual and societal complications that result from CanUD include decreased productivity and accidents related to intoxication6. The full range of risks and negative outcomes associated with cannabis use and CanUD may not be appreciated widely. Considering the gradually increasing permissiveness surrounding its use, understanding various sources of risk that influence CanUD is both necessary and timely.

In this Article, we combined genome-wide genotype data from the Million Veteran Program (MVP) with expanded samples from iPSYCH27,8 and Mass General Brigham (MGB) BioBank9 and meta-analyzed these with the Psychiatric Genomics Consortium (PGC)/deCODE/iPSYCH1 study7,10. MVP, one of the largest biobanks in the world11, has enabled a substantial increase in power for genomic discovery by doubling the number of cases of European (EUR) ancestry available. By increasing sample numbers, we substantially increased the number of discovered loci and confirmed previous findings7,10. We also leveraged the ancestral diversity of the MVP to expand analyses of African ancestry individuals (AFR) and conducted genome-wide association studies (GWAS) analyses in Admixed American (AMR) and East Asian (EAS) ancestries. Linkage disequilibrium (LD) score regression (LDSC) can quantify variance explained by genetics and identify overlap between traits. This method is sufficient for EUR ancestries but not appropriate for some non-European and admixed ancestries. To solve this problem, we used cohort-derived covariate LDSC12 to calculate single nucleotide polymorphism (SNP)-based heritability in these populations, finding similar results among all ancestries. We conducted a transcriptome-wide association study (TWAS), which leverages annotations based on variant associations to changes in gene expression, in adult and fetal brain tissue to identify significant expression quantitative trait loci (eQTLs), using stratified LDSC to show enriched SNP-based heritability in fetal but not adult cortex. We also conducted Mendelian randomization (MR) analyses—an approach that uses genetic variations identified by GWAS as instruments to obtain an unbiased estimate of the effect of a trait of interest (here, CanUD) on outcomes—to examine causal relationships with chronic pain, lung cancer, physical activity and SCZ. Finally, we performed genomic structural equation modeling (gSEM)—a multivariate method for analyzing GWAS summary statistics to examine joint genetic architecture of traits—to understand the genomic relationships between cannabis use traits and other psychiatric and substance use disorder (SUD) traits. This work builds upon a decade of progress in the field7,10,13,14,15,16,17,18.

www.nature.com/articles/s41588-023-01563-z

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Multi-ancestry genome-wide association study of cannabis use Nov 21, 2023 21:28:13 GMT

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Post by Admin on Nov 21, 2023 21:28:13 GMT

Results
GWAS
We assembled a total sample of 886,025 EUR participants across five datasets (Table 1; 42,281 cases and 843,744 controls) for GWAS meta-analysis of CanUD and identified 22 independent genome-wide significant (GWS) loci in this population. In the AFR meta-analysis of 123,208 participants across three cohorts (19,065 cases and 104,143 controls), we identified two GWS loci. In a cohort of 38,289 participants assigned using the broad AMR ancestry references (which include individuals recruited from several Latin American populations) in the MVP cohort (2,774 cases and 35,515 controls) we found one GWS locus, and in EAS ancestry references we identified two GWS loci. The lead signal for EUR was near CHRNA2 (rs56372821, P = 7.3 × 10−14), which encodes cholinergic receptor nicotinic alpha 2 subunit, consistent with prior GWAS7,10; the lead SNP was identical to one prior study7. Findings for AFR include a SNP in an intron of SLC36A2 (rs573117193, P = 4.9 × 10−8), which encodes a pH-dependent proton-coupled amino acid transporter for glycine, alanine and proline. The lead SNP in AMR was rs9815757 (P = 4.4 × 10−8). The lead SNP in EAS (rs78561048, P = 6.7 × 10−9) is intronic to SEMA6D, which encodes semaphorin 6D (Fig. 1 and Table 2). Several variants showed concordant direction of effect across all four stratified ancestral groups. Five additional loci were discovered in the multi-ancestry analysis: rs7003100 (intergenic), rs7029483 (130 kb upstream of MTND2P8), rs2627197 (intronic to ENO4), rs34438449 (40 kb downstream of MIR5007) and rs147144681 (intronic to CHRNA3).

Fig. 1: Stacked Manhattan plots depicting CanUD GWAS results from four ancestries tested.

From top to bottom, 2 loci were identified in EAS ancestry, 1 for AMR, 2 for AFR, and 22 for EUR (red dots). Nearby genes are shown for orientation. P values were calculated with bidirectional Wald’s test. The field standard GWS threshold of P < 5 × 10−8 (horizontal red line) was used to determine significant associations. Other colors indicate different chromosomes. Chr, chromosome.

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Multi-ancestry genome-wide association study of cannabis use Nov 23, 2023 21:49:58 GMT

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Post by Admin on Nov 23, 2023 21:49:58 GMT

LDSC
Intergroup comparisons between EUR CanUD cohorts (MVP, PGC/deCODE, iPSYCH2) included in the meta-analysis yielded high genetic correlation, with rG ranging between 0.71 and 0.87. Comparative analysis of CanUD and cannabis use traits with a range of psychiatric and nonpsychiatric traits revealed striking differences, with CanUD showing far stronger overlap with pathological and negative traits (Fig. 2). The largest magnitude difference was in educational attainment, which showed a positive correlation with cannabis use but a negative correlation with CanUD. Covariate LDSC was used to calculate SNP-based heritability within each ancestral group. Significant SNP-based heritability was identified for the three larger ancestries: EUR h2 = 6.7% (standard error (s.e.) = 0.017), AFR h2 = 8.1% (s.e. = 0.013), and AMR h2 = 18.0% (s.e. = 0.042). There was high variance and a high point estimate in AMR. LDSC was used to calculate genetic correlation between cannabis use dependence cohorts included in this meta-analysis and also within MVP phenotype definitions (Supplementary Table 1). Genetic correlations were calculated for 1,335 traits (Fig. 2 and Supplementary Fig. 2). The strongest observed positive correlations were related to smoking initiation and alcohol dependence, while the strongest negative correlations were with ages of first intercourse and smoking cessation.

Fig. 2: Genetic correlations.

Comparison of genetic correlations between CanUD and cannabis use18. Left axis depicts the range of rG between 1 and −1. Red bars and blue bars depict the rG point estimate per trait for CanUD and cannabis use, respectively. Black error bars represent the standard error. Right axis displays the absolute magnitude of the Z statistic for the difference in rG between CanUD and cannabis use (gold line). SNP-based heritability for each comparison trait is included in the table. EA, educational attainment; ReExp, re-experience; AUDIT-C, Alcohol Use Disorders Identification Test-Consumption; TDI, Townsend deprivation index.

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Multi-ancestry genome-wide association study of cannabis use Nov 26, 2023 0:27:03 GMT

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Post by Admin on Nov 26, 2023 0:27:03 GMT

Cross-ancestry genetic correlation
Genetic correlations were calculated against available traits using POPCORN19 for CanUD in African ancestry and a selection of traits represented in Fig. 2. When compared to the same traits in EUR, there is no significant difference across ancestries (Supplementary Fig. 3).

Mendelian randomization
Multi-site chronic pain had a unidirectional causal effect on CanUD (inverse variance-weighted (IVW) β = 0.46, P = 2.90 × 10−5). There was a bidirectional causal effect of CanUD and SCZ (SCZ→CanUD IVW β = 0.17, P = 2.07 × 10−5, CanUD→SCZ IVW β = 0.17, P = 0.01). CanUD showed a unidirectional effect on lung cancer (IVW β = 0.18, P = 0.006) (Supplementary Fig. 1 and Supplementary Tables 5–13).

Conditional analysis
For EUR, we performed a multi-trait conditional and joint analysis (mtCOJO) of CanUD conditioned on two smoking traits from the GWAS and Sequencing Consortium of Alcohol and Nicotine use study to investigate potential confounding effects20. Two different datasets were used: smoking initiation and cigarettes per day. Individual runs were performed for the two cigarette smoking traits. A proportion of 18 of 22 original lead SNPs remained in the dataset following conditioning on smoking initiation (meaning they matched with variants in the conditioning data). For two out of four remaining SNPs, there were proxy SNPs in LD with each lead SNP showing GWS P values. Only rs545943750 and rs184064410 were excluded after conditioning due to missingness in the smoking data, leaving 20 of 22 lead loci from the CanUD GWAS available in the conditional analysis. All 20 remained GWS following conditioning. The results were similar with CanUD conditioning on cigarettes per day, with the same 20 lead loci remaining GWS after conditioning. Conditional analysis with smoking initiation or cigarettes per day did not substantially alter the magnitude of the lead CHRNA2 association (Pcond = 2.14 × 10-14). We used these summary statistics conditioned on cigarette smoking initiation to re-test the causal relationship between CanUD and lung cancer, and while the signal attenuated, it was still significant (IVW β = 0.2, P = 0.0025). The conditional analysis with cigarettes per day, however, removed the effect of CanUD on lung cancer (P = 0.79).

Multi-trait analysis of GWAS
Considering the high genetic correlation of CanUD with alcohol use disorder (AUD) and the Fagerström Test for Nicotine Dependence (FTND), we conducted an multi-trait analysis of GWAS (MTAG) analysis that identified 34 lead SNPs at 26 genomic risk loci, including four novel loci compared to the EUR meta-analysis, at P < 5 × 10−8 for CanUD (Supplementary Fig. 5 and Supplementary Table 14) when combined with AUD and FTND. The GWAS-equivalent sample size for CanUD was 200,762, augmenting the meta-analysis effective sample size of 161,053 by 20%. Ten genomic risk loci were significant (or in LD with significant variants) in both the GWAS and MTAG analyses. The remaining 16 significant variants were LD independent. The effect size of eight of the 26 significant SNPs in the MTAG analysis was significantly smaller than those obtained from the original GWAS (Supplementary Table 15), suggesting specificity to CanUD.

Transcriptome-wide association study
In TWAS analyses, 59 and 25 genes were detected (P < 2.5 × 10−6) using adult and fetal brain frontal cortex expression, respectively, with six genes in common (Fig. 3a). We tested these genes by permutation test, leaving 44 and 17 genes using adult and fetal models, with two genes in common (Fig. 3a). For the remaining genes within 1 Mb of one another, we applied gene-level probabilistic fine-mapping. In the end, we detected 36 and 15 genes using the adult and fetal models, which form 90% credible sets (with 90% estimated probability of containing the causal variant) that explain the corresponding genetic associations (Fig. 3a, b). These sets contained only one gene in common: DALR Anticodon Binding Domain Containing 3 (DALRD3) (Fig. 3a, b). The observed gene associations included four distinct GWAS loci: 3p21.31 (gene detected in adult and fetal brain cortex: DALRD3), 5q12.1 (fetal: ERCC8), 11q23.2 (adult: RP11-629G13.1) and 16q22.2 (adult: PHLPP2). Protein functions of these genes are described in the Discussion below. The remaining set of genes identifies 38 candidate novel genetic loci associated with CanUD, with potential underlying transcriptomic mechanisms in either adult or fetal brain cortex (Supplementary Table 3).

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Multi-ancestry genome-wide association study of cannabis use Dec 1, 2023 19:02:28 GMT

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Fig. 3: TWAS and tissue enrichment of the EUR CanUD GWAS variants.

a, Number of TWAS gene–trait associations for adult and fetal brain frontal cortex, across significance thresholds. b, Miami plot of TWAS Z scores across analyses with adult (top) and fetal (bottom) brain frontal cortex. Genes are colored red if the gene passes transcriptome-wide significance, green if the gene additionally passes permutation testing and blue if the gene is estimated to be in the 90% credible set at the locus. All genes in blue are labeled. The horizontal line indicates threshold statistical significance following multiple testing correction. c, Scatterplot of standardized effect sizes (TWAS Z scores) across adult and fetal brain frontal cortex. A total of 10,722 genes were tested in adult tissue, while 2,293 genes were tested in fetal tissue. The dashed line indicates the expected relationship if no enrichment for adult or fetal brain. d, Enrichment ratio point estimate of proportion of SNP-based heritability explained by annotation and proportion of SNPs in annotation. Error bar shows a 95% Wald-type confidence interval. Bars marked with an asterisk indicate an enrichment ratio >1 at Benjamini–Hochberg adjusted P < 0.05.

Partitioned SNP-based heritability
Standardized TWAS effect sizes estimated using adult and fetal brain frontal cortex expression models showed moderate correlation (Spearman’s ρ = 0.54, P < 2.2 × 10−16; Fig. 3c). Accordingly, we next estimated the SNP-based heritability enrichment in adult and fetal brain cortex eQTL. Using LDSC, we estimated enrichment ratios for SNP-based heritability using different windows around expression SNPs for expression genes. We detected significant enrichments only for fetal brain frontal cortex expression SNPs at windows of 0 bp, 50 bp and 100 bp. In general, fetal brain frontal cortex eQTLs were far more enriched for CanUD trait heritability than adult brain cortex eQTLs (Fig. 3d).

gSEM
Using exploratory factor analysis (EFA), a four-factor model fit the data best, with the cumulative variance explained being 0.789, distributed relatively evenly across the four factors, with each accounting for between 22.7% and 29.2% of the overall variance explained (factor 1 of 0.23, factor 2 of 0.19, factor 3 of 0.18 and factor 4 of 0.18). Each of the four factors had high sums of square (SS) loadings (factor 1 SS of 3.5, factor 2 SS of 2.9, factor 3 SS of 2.8 and factor 4 SS of 2.7).

Using confirmatory factor analysis (CFA) to evaluate the four-factor model that allowed all factors to intercorrelate had a comparative fit index of 0.913, a standardized root mean square residual of 0.068, a chi-squared value of 1397.5 and an Akaike information criterion of 1483.5. Traits loading most strongly on factor 1 included ‘Unable to work’ (loading of 1.06 ± 0.04), Townsend deprivation index (loading of 0.56 ± 0.03), chronic pain (loading of 0.50 ± 0.04) and FTND (loading of 0.45 ± 0.07). Traits loading most strongly on factor 2 included number of sex partners (loading of 0.91 ± 0.02), cannabis use (loading of 0.70 ± 0.03) and initiation of regular smoking (loading of 0.58 ± 0.03). Psychiatric traits loaded most strongly on factor 3 and included major depressive disorder (MDD) (loading of 0.95 ± 0.02), post-traumatic stress disorder (PTSD) checklist score (PCL) total (loading of 0.88 ± 0.04), generalized anxiety disorder symptoms (loading of 0.86 ± 0.03), suicide attempt (loading of 0.59 ± 0.05) and SCZ (loading of 0.29 ± 0.02). SUD traits loaded most strongly on factor 4 and included CanUD (loading of 0.96 ± 0.03), opioid use disorder (loading of 0.85 ± 0.05) and AUD (loading of 0.81 ± 0.03). There were moderate correlations between factors 2 and 4 (r = 0.65), factors 1 and 3 (r = 0.64), factors 1 and 4 (r = 0.52) and factors 3 and 4 (r = 0.53). All correlations and loadings are summarized in Fig. 4.