Common biological networks underlie genetic risk for alcoholism in African- and European-American populations.

paper Primary Public

Authors: Kos, M Z; Yan, J; Dick, D M; Agrawal, A; Bucholz, K K; Rice, J P; Johnson, E O; Schuckit, M; Kuperman, S; Kramer, J; Goate, A M; Tischfield, J A; Foroud, T; Nurnberger, J; Hesselbrock, V; Porjesz, B; Bierut, L J; Edenberg, H J; Almasy, L
Year: 2013
Journal: Genes, brain, and behavior
PMID: 23607416
DOI: 10.1111/gbb.12043
PMCID: PMC3709451

Figure 1

Variance in alcohol dependence (AD) explained by genome-wide scores for African-American and European-American subjectsPolymarker scoring routines based on AD status were designed for thirteen GWAS significance thresholds (plotted against the x axis) using COGA data and applied to SAGE target samples. The y axis represents Nagelkerke’s pseudo R2, the amount of variation in AD accounted by the SAGE scores, computed for routines that are both population-matched and mismatched across the COGA and SAGE data sets.

Figure 2

Scoring analysis stratified by non-overlapping bins of score risk alleles based on (a) GWAS P-values and (b) minor allele frequenciesVariance explained was standardized by SNP counts for the respective bins.

Figure 3

Variance explained by genome-wide scoring routines for observed and simulated disease phenotypes according to MAF classThe variances explained, derived from MAF bins comprised of different score alleles, are presented for six disease models for each study population. The models represent either 100, 1,000 or 5,000 causal variants, which were randomly drawn from SNP data excluded from the original design of scoring routines, limited to either rare/uncommon markers (<5% MAF) or common markers (>5% MAF). Each model was replicated 100 times. Disease heritability was set at 0.65, with causal effect sizes fixed for all loci. Observed R2 results for AD are given as black, dotted lines.

Figure 4

Empirical P-values of four, top-ranking biological ontologies based on permuted (1,000X) Fisher’s exact tests of gene enrichment in European- and African-American samplesAllele bins, delineated by genome-wide association P-values at cumulative increments of 0.05, were annotated for gene location using UCSC hg18 coordinates.

#	Section	Preview
0	Introduction	Alcohol dependence (AD) is a complex, highly heritable disorder characterized by compulsive,…
1	Introduction	This shortfall in explained genetic variance, popularly referred to as “missing heritability”…
2	Introduction	In this paper we investigate the polygenic architecture of alcoholism by evaluating the extent to…
3	Materials and methods — Population Samples	Routines for aggregating genome-wide SNP counts into composite scores (Fig. S1) were designed using…
4	Materials and methods — Population Samples	other illicit substance abuse or dependence (although cases could). Furthermore, controls were…
5	Materials and methods — Population Samples	Developed scoring routines were applied to independent GWAS data for EA (n = 1,573) and AA (n = 841)…
6	Materials and methods — Genome-Wide Genotyping	Genotyping was performed by the Center for Inherited Disease Research (CIDR) at John Hopkins…
7	Materials and methods — Polymarker Scoring	COGA has conducted a series of analyses that evaluate the predictive utility of GWAS data for…
8	Materials and methods — Polymarker Scoring	For the design of the genome-wide scoring routines, autosomal GWAS data (n = 1,003,800) was pruned…
9	Materials and methods — Polymarker Scoring	Genome-wide association tests were conducted with the program PLINK (Purcell et al. 2007), using the…
10	Materials and methods — Variance Component Analysis of AD Liability	Using the method proposed by Yang et al. (2010), the amount of variance in AD risk that is explained…
11	Materials and methods — Variance Component Analysis of AD Liability	the matrix of genetic relationships between individuals). Using the software GCTA (Yang et al.…
12	Materials and methods — Simulation of Genome-Wide Scores for Different Disease Models	Using the program GCTA, case-control phenotypes for six disease architectures were simulated using…
13	Materials and methods — Simulation of Genome-Wide Scores for Different Disease Models	liability threshold, with the number of cases matching those in the original phenotype data. Causal…
14	Materials and methods — Gene Enrichment Analysis for Biological Ontologies	For the final analytical approach, the focus was shifted from the general, genetic architecture of…
15	Materials and methods — Gene Enrichment Analysis for Biological Ontologies	belonging to various biological ontologies (n = 507) and receptor signaling pathways (n = 227), as…
16	Results — Application of Population-Matched Scoring Routines	When target scores derived from associations in the COGA data set are used to predict case/control…
17	Results — Variance Component Analysis	To obtain a more accurate estimate of AD variance explained by genome-wide markers, we conducted…
18	Results — Application of Population-Mismatched Scoring Routines	Despite having nearly equivalent levels of AD risk variation captured by common genetic markers, EAs…
19	Results — Scoring Delineated by Association P-Value and MAF Class	To further dissect the allelic architecture of alcoholism in our two study populations, we re-ran…

Name	Type
AA	cohort
AA gene list local	cohort
AA SAGE local	cohort
AbdB local	gene
ABDB local	gene
acetaldehyde	drug
ADH1B	gene
ADH4	gene
AD liability local	phenotype
AD status	phenotype
African American	cohort
African-Americans	cohort
alcohol	phenotype
alcohol dependence	phenotype
alcoholism	phenotype
Alzheimer's disease	phenotype
Alzheimer's disease risk local	phenotype
antisocial personality disorder	phenotype
case-control phenotypes	phenotype
causal loci local	variant
causal variant	cohort
causal variants	cohort
childhood conduct disorder	phenotype
chloride transport local	gene
cocaine	phenotype
COGA sites	cohort
COGEND	cohort
Collaborative Study on the Genetics of Alcoholism (COGA)	cohort
common GWAS markers local	variant
common variants	cohort
controls	cohort
copy number variants	variant
Crohn’s disease	phenotype
disease phenotypes	phenotype
EA	cohort
EA gene list local	cohort
EP300	gene
European ancestry	cohort
European-derived risk alleles local	variant
European population	cohort
Family study of cocaine dependence	cohort
GABA	phenotype
GABAA receptor	drug
GABRA2	gene
GCTA	drug
glycine	drug
glycine and serine metabolism local	gene
Health Maintenance Organizations local	cohort
height	phenotype
homeotic (Hox) AbdB genes local	gene
HOX AbdB genes local	gene
Illicit substance abuse local	phenotype
Illicit substance dependence local	phenotype
intelligence	phenotype
MAF	gene
missing heritability	phenotype
nicotine	drug
NOD2 local	gene
Notch	gene
participants	cohort
psychiatric disorders	phenotype
Purcell et al. 2009 schizophrenia cohort local	cohort
rare risk allele local	variant
rare variant	cohort
rare variation	cohort
reward system	anatomy
risk allele	cohort
rs1229984	variant
SAGE	cohort
schizophrenia	phenotype
serine	drug
simulated disease local	phenotype
SNP	cohort
Study of Addiction: Genetics and Environment	cohort
substance abuse	phenotype
uncommon risk allele local	variant

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In this knowledge base

Title	Year	PMID
Incorporating Functional Genomic Information to Enhance Polygenic Signal and Identify Variants Involved in Gene-by-Environment Interaction for Young Adult Alcohol Problems.	2018	29121402
Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications.	2017	28240821
Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses.	2015	26110981
Genomic influences on alcohol problems in a population-based sample of young adults.	2015	25439982
The Genetics, Neurogenetics and Pharmacogenetics of Addiction.	2014	25045619

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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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Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses.	Hart AB et al.	—	2015	→
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