Pathway based analysis of genotypes in relation to alcohol dependence.
- Authors
- Reimers, M A; Riley, B P; Kalsi, G; Kertes, D A; Kendler, K S
- Year
- 2012
- Journal
- The pharmacogenomics journal
- PMID
- 21468025
- DOI
- 10.1038/tpj.2011.10
- PMCID
- PMC3134610
We introduce a method for detecting variants in several genes of related function with small effect on a phenotype of interest. Our method uses logistic regression to test whether multiple alleles within a functional set have significantly higher than expected predictive value, even though none individually may have strong individual effects. We illustrate this method by testing seven gene sets (including 48 genes), from a study with 1350 single nucleotide polymorphisms in 130 addiction candidate genes studied in a sample of 575 alcohol dependence (AD) cases and 530 controls. We conclude that AD is related to variation in genes participating in Glutamate and γ-amino butyric acid signaling, as has been reported elsewhere, and in stress response pathways, but not with genes in several other systems implicated in other drugs of abuse.
Upper Left Effect sizes (as odds-ratios) for SNPs selected in the GABA gene set. Three SNPs for GABA occur in the same gene (GABRG3), but the markers are not in LD (r2 < 0.25 by construction). Upper Right. Odds ratios for SNPs in the glutamate receptor gene set. Lower Left Odds ratios for selected SNP’s in the Norepinephrine gene set. Lower Right. Odds ratios for SNP’s selected in the CRH gene set.
LLM interpretation
This figure consists of four bar charts showing the effect sizes (odds ratios) for selected SNPs across four gene sets: Adrenergic receptors, GABA signaling, Glutamate signaling, and Corticotropin releasing hormone. The y-axis represents the odds ratio, ranging from 1.0 to 1.6, while the x-axis lists specific SNPs grouped by their associated genes. Each chart displays varying odds ratios for the individual SNPs, with values generally ranging between 1.1 and 1.5.
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External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Susceptibility to extinction and reinstatement of ethanol-induced conditioned place preference is related to differences in astrocyte cystine-glutamate antiporter content. | Amaral VCS et al. | — | 2021 | → |
| Flying Together: <i>Drosophila</i> as a Tool to Understand the Genetics of Human Alcoholism. | Lathen DR et al. | — | 2020 | → |
| NMDA receptor GluN2A subunit deletion protects against dependence-like ethanol drinking. | Jury NJ et al. | — | 2018 | → |
| Ethanol-Associated Changes in Glutamate Reward Neurocircuitry: A Minireview of Clinical and Preclinical Genetic Findings. | Bell RL et al. | — | 2016 | → |
| Common single nucleotide variants underlying drug addiction: more than a decade of research. | Bühler KM et al. | — | 2015 | → |
| Drosophila and Caenorhabditis elegans as Discovery Platforms for Genes Involved in Human Alcohol Use Disorder. | Grotewiel M et al. | — | 2015 | → |
| Gene-based and pathway-based genome-wide association study of alcohol dependence. | Zuo L et al. | — | 2015 | → |
| Monoacylglycerol lipase (MGLL) polymorphism rs604300 interacts with childhood adversity to predict cannabis dependence symptoms and amygdala habituation: Evidence from an endocannabinoid system-level analysis. | Carey CE et al. | — | 2015 | → |
| The effects of single nucleotide polymorphisms in glutamatergic neurotransmission genes on neural response to alcohol cues and craving. | Bach P et al. | — | 2015 | → |
| Individual differences underlying susceptibility to addiction: Role for the endogenous oxytocin system. | Buisman-Pijlman FT et al. | — | 2014 | → |
| Identifying gene networks underlying the neurobiology of ethanol and alcoholism. | Wolen AR et al. | — | 2012 | → |
| The genetics of addiction-a translational perspective. | Agrawal A et al. | — | 2012 | → |