Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

Marees, Andries T.; Gamazon, Eric R.; Gerring, Zachary F.; Vorspan, Florence; Fingal, Josh; Brink, Wim van den; Smit, Dirk J.A.; Verweij, Karin J. H.; Kranzler, Henry R.; Sherva, Richard; Farrer, Lindsay A.; Gelernter, Joel; Derks, Eske M.

doi:10.1016/j.drugalcdep.2019.107703

Cited by 20 publications

(22 citation statements)

References 40 publications

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“…Additionally, 78 of the 93 SNPs have published gene information [36,37]. Many of them are located in the well-known gene cluster related to smoking-associated diseases and behaviors [30]. These SNPs are shown in different colors in Fig 4. Among the 15 SNPs without published gene information, 3 SNPs (1 in the 18 th block and 2 in the 37 th block) have been discovered as associated SNPs for smoking cessation and chronic obstructive pulmonary disease (COPD) [38][39][40].…”

Section: Results By Dense Ld Blockmentioning

confidence: 99%

“…The results showed that a genomic region on chromosome 15 (15:78,700,000 to 79,230,000) had the strongest signal for CPD (see S1 Appendix for the genome-wide Manhattan plot). These loci coincided with the well-known cluster of genes IREB2, CHRNA3, CHRNA5, CHRNB4, HYKK, and PSMA4 that have been discussed in several GWASs [28][29][30][31]. To further identify causal variants, we applied our fine-mapping approach for an in-depth evaluation of this genomic region, covering 5.3 Mb and 1733 SNPs.…”

Section: Ukbb Datamentioning

confidence: 88%

“…The simulation results show that it can effectively improve the sensitivity and false-positive error rate in the causal variant selection by improving variable selection accuracy with dense LD structure [24][25][26][27]. We applied this approach in UKBB sample to focus on a gene cluster on chromosome 15 centered around IREB2, CHRNA3, CHRNA5, CHRNB4, HYKK, and PSMA4 genes that is associated with smoking severity measured as with a cigarettes per day -CPD trait [28][29][30][31]. Our fine-mapping analysis prioritized 93 variants with high statistical significance (p < 10 −50 ), and most of them having large effect sizes (> 0.75).…”

Section: Introductionmentioning

confidence: 99%

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Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence

Hatch

et al. 2020

Preprint

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Fine-mapping is an analytical step to perform causal prioritization of the polymorphic variants on a trait-associated genomic region observed from genome-wide association studies (GWAS). The prioritization of causal variants can be challenging due to the linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. We propose a novel ℓ0 graph norm shrinkage algorithm to select causal variants from dense LD blocks consisting of highly correlated SNPs that may not be proximal or contiguous. We extract dense LD blocks and perform regression shrinkage to calculate a prioritization score to select a parsimonious set of causal variants. Our approach is computationally efficient and allows performing fine-mapping on thousands of polymorphisms. We demonstrate its application using a large UK Biobank (UKBB) sample related to nicotine addiction. Our results suggest that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Simulations were used to evaluate and compare the performance of our method and existing fine-mapping algorithms. The results demonstrated that our method outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate regarding causal variant selection. The application of this method to smoking severity trait in UKBB sample replicated previously reported loci and suggested the causal prioritization of genetic effects on nicotine dependency.Author summaryDisentangling the complex linkage disequilibrium (LD) pattern and selecting the underlying causal variants have been a long-term challenge for genetic fine-mapping. We find that the LD pattern within GWAS loci is intrinsically organized in delicate graph topological structures, which can be effectively learned by our novel ℓ0 graph norm shrinkage algorithm. The extracted LD graph structure is critical for causal variant selection. Moreover, our method is less constrained by the width of GWAS loci and thus can fine-map a massive number of correlated SNPs.

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Section: Results By Dense Ld Blockmentioning

confidence: 99%

Section: Ukbb Datamentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence

Hatch

et al. 2020

Preprint

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“…Increased expression of the most strongly associated gene, PSMA4 , was significantly associated with schizophrenia, cigarettes per day, and smoking cessation. The gene PSMA4 , located within the 15q25.1 gene cluster, has previously been associated with nicotine dependence and lung cancer (29), and we recently linked its expression in multiple GTEx brain tissues to cigarettes per day and smoking cessation (30). PSMA4 has also been identified as one of six “high confidence” genes in schizophrenia, based on probabilistic fine mapping approaches and observed expression profiles (31).…”

Section: Discussionmentioning

confidence: 99%

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

Zf¹,

Jg²,

Gamazon

et al. 2021

Preprint

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Genome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

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“…Forward genetic approaches can be employed both in study of human subjects and animal models for cocaine use and related traits. Human studies have had some success to date (Cabana-Domínguez et al, 2019; Gelernter et al, 2014; Marees et al, 2020); however, a large proportion of the genetic variants that contribute to heritable influence on cocaine use disorder remain undiscovered. Approaches in laboratory animal populations offer complementary methods with several key advantages.…”

Section: Introductionmentioning

confidence: 99%

Extreme Phenotypic Diversity in Operant Responding for an Intravenous Cocaine or Saline Infusion in the Hybrid Mouse Diversity Panel

Bagley

Smith

et al. 2021

Preprint

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Cocaine self-administration is complexly determined trait, and a substantial proportion of individual differences in cocaine use is determined by genetic variation. Cocaine intravenous self-administration (IVSA) procedures in laboratory animals provide opportunities to prospectively investigate neurogenetic influences on the acquisition of voluntary cocaine use. Large and genetically diverse mouse populations, like the Hybrid Mouse Diversity Panel (HMDP), have been developed for forward genetic approaches that can reveal genetic variants that influence traits like cocaine IVSA. This population enables high resolution and well-powered genome wide association studies, as well as the discovery of genetic correlations. Here, we provide information on cocaine (or saline - as a control) IVSA in 65 strains of the HMDP. We found cocaine IVSA to be substantially heritable in this population, with strain-level intake ranging for near zero to >25 mg/kg/session. Though saline IVSA was also found to be heritable, a very modest genetic correlation between cocaine and saline IVSA indicates that operant responding for the cocaine reinforcer was influenced by a substantial proportion of unique genetic variants. These data indicate that the HMDP is suitable for forward genetic approaches for the analysis of cocaine IVSA, and this project has also led to the discovery of reference strains with extreme cocaine IVSA phenotypes, revealing them as polygenic models of risk and resilience to cocaine reinforcement. This is part of an ongoing effort to characterize genetic and genomic variation that moderates cocaine IVSA, which may, in turn, provide a more comprehensive understanding of cocaine risk genetics and neurobiology.

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Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

Cited by 20 publications

References 40 publications

Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence

Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

Extreme Phenotypic Diversity in Operant Responding for an Intravenous Cocaine or Saline Infusion in the Hybrid Mouse Diversity Panel

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