Exploring the genetic architecture of alcohol dependence in African-Americans via analysis of a genomewide set of common variants.
- Authors
- Yang, Can; Li, Cong; Kranzler, Henry R; Farrer, Lindsay A; Zhao, Hongyu; Gelernter, Joel
- Year
- 2014
- Journal
- Human genetics
- PMID
- 24297757
- DOI
- 10.1007/s00439-013-1399-8
- PMCID
- PMC3988209
Alcohol dependence (AD) is a complex psychiatric disorder that affects about 12.5 % of US adults. Genetic factors play a major role in the development of AD. We conducted a genomewide association study in 2,875 African-Americans including 1,719 AD cases and 1,156 controls. We used the Illumina Omni 1-Quad microarray, which yielded 769,498 single-nucleotide polymorphisms (SNPs) after quality control. To explore the genetic architecture of AD, we estimated the variance that could be explained by all SNPs and subsets of SNPs using two different approaches to genome partitioning. We found that 23.9 % (s.e. 9.3 %) of the phenotypic variance could be explained by using all of the common SNPs on the array. We also found a significant linear relationship between the proportion of the top SNPs used and the phenotypic variance explained by them. Based on genome partitioning of common variants, we also observed a significant linear relationship between the variance explained by a chromosome and its length. Chromosome 4, known to contain several AD risk genes, accounted for excess risk in proportion to its length. By functional partitioning, we found that the genetic variants within 20 kb of genes explained 17.5 % (s.e. 11.4 %) of the phenotypic variance. Our findings are consistent with the generally accepted view that AD is a highly polygenic trait, i.e., the genetic risk in AD appears to be conferred by multiple variants, each of which may have a small or moderate effect.
Left panel: The proportion of SNPs selected using different P-value thresholds. Right panel: Variance explained by top ranking SNPs based on different P-value thresholds. The results were obtained based on 50 random partitions of the entire data set to avoid winner’s curse.
Variance explained by each individual chromosome. Left panel: number of genes vs. explained variance. The R2 is 0.14% (P-value= 0.8685), which shows no significant association between the number of genes and the explained variance. Right panel: chromosome length vs. explained variance. The R2 is 21.2% (P-value=0.031), which reflects a significant association between chromosome length and explained variance.
Variance explained by the genic and intergenic regions. Left panel: functional partition of all SNPs. The entire genome was divided into four categories – exons (4%), introns (39%), intergenic regions, and others (e.g., downstream (DS), upstream (US) and untranslated region (UTR)). We defined the intergenic region based on the minimal distance d between a SNP and all genes. A SNP was assigned to the intergenic region if d ≥ τ (the distance threshold), otherwise it was assigned to the genic region. Given τ = 0 kb, the proportion of SNPs in each category is shown in the figure, i.e., 52% of the SNPs were in the intergenic region and the remaining SNPs were in the genic region. Given τ = 10 kb, about 9% of SNPs were partitioned to the genic region, and thus the intergenic region was reduced to 43%. Right panel: Variance explained by the genic and intergenic regions. As the distance threshold increased, more SNPs were partitioned into the genic region. The variance explained by the genic region increased quickly when τ increased from 0 kb to 20 kb, and remained almost the same when τ increased from 20 kb to 50 kb.
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| Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. | Kranzler HR et al. | — | 2019 | → |
| Shared additive genetic variation for alcohol dependence among subjects of African and European ancestry. | Brick LA et al. | — | 2019 | → |
| Accounting for heteroscedasticity and censoring in chromosome partitioning analyses. | Kemppainen P et al. | — | 2018 | → |
| Incorporating Functional Genomic Information to Enhance Polygenic Signal and Identify Variants Involved in Gene-by-Environment Interaction for Young Adult Alcohol Problems. | Salvatore JE et al. | — | 2018 | → |
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| Review: DNA methylation and alcohol use disorders: Progress and challenges. | Zhang H et al. | — | 2017 | → |
| Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses. | Hart AB et al. | — | 2015 | → |
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