Dissecting ancestry genomic background in substance dependence genome-wide association studies.

paper Cited Public

Authors: Polimanti, Renato; Yang, Can; Zhao, Hongyu; Gelernter, Joel
Year: 2015
Journal: Pharmacogenomics
PMID: 26267224
DOI: 10.2217/pgs.15.91
PMCID: PMC4632979

#	Section	Preview
0	Materials & methods — Identification of relevant genes via GWAS	To find alleles potentially associated with SD traits, we searched Medline in November 2013 using…
1	Materials & methods — Identification of interacting genes/proteins	We used several interaction/pathway tools to identify genes and proteins interacting with the…
2	Materials & methods — Population information & genomic data for ancestry analysis	Phase 1 of the 1000 Genomes (1KG) Project was used to obtain genotypic data. Specifically, we…
3	Materials & methods — Functional annotation analysis	We used three different tools for functional annotation analysis. To distinguish between variants…
4	Materials & methods — Identification of ancestry-related differences in common & rare variants	To identify the ancestry-related differences in GWAS-relevant genes and their interacting partners,…
5	Materials & methods — Identification of ancestry-related differences in common & rare variants	To identify the allele ΔF for common variants in the ancestry groups, we used the method proposed…
6	Materials & methods — Identification of ancestry-related differences in common & rare variants	For RVs, we analyzed the occurrences of functional RVs among all of the ancestry groups and used an…
7	Materials & methods — Analysis in GWAS data of the effect of common variants with ΔF>0.10	To verify the effect of common variants with high ΔF on genome-wide significant associations, we…
8	Materials & methods — Analysis in GWAS data of the effect of common variants with ΔF>0.10	of ten: PDLIM5 rs10031423, ADH1B rs1693457 and ADH1C rs6846835. Specifically, PDLIM5 rs10031423…
9	Materials & methods — Analysis in GWAS data of the effect of common variants with ΔF>0.10	this threshold in order to exclude those variants with minimal allele ΔF among human populations.…
10	Materials & methods — Analysis in GWAS data of the effect of common variants with ΔF>0.10	each ancestry group, applying the following equations:…
11	Results	Considering the results of our gene–gene/protein–protein interaction’s investigation, we…
12	Results	The ΔF analysis of common variants (n = 51,079) indicated that allelic differences are greater in…
13	Results	In the analysis of RVs, we observed that the distribution of r values differed significantly between…
14	Results	Considering the rΔF values for each analyzed gene, we observed only two significant outcomes for…
15	Results	As mentioned above, we hypothesized that allele ΔFs in variants with functional impact may explain…
16	Results	variants for European and African ancestries (Supplementary Figure 5). In ADH1C, extreme ΔF values…
17	Results	As noted above, due to the high LD present among the ten genome-wide significant variants considered…
18	Results	In EAs PDLIM5 rs10031423 showed a significant association with AD symptom counts. In EAs, we…
19	Results	are all located on chromosome 4, as rs10031423. Among these variants, rs6853490 (z = −20.085)…

Name	Type
1KG Phase 1 local	cohort
1KG population local	cohort
AA	cohort
ADH1B	gene
ADH1C	gene
admixed-American ancestry local	cohort
admixed-American group local	cohort
admixed Americans local	cohort
AD symptom count	phenotype
African	cohort
African American	cohort
African group local	cohort
alcohol	phenotype
alcohol dependence	phenotype
Alcohol Dependence Symptoms	phenotype
ALDH2	gene
allele ΔF local	variant
Alzheimer's disease	phenotype
American group local	cohort
Amerindian ancestry	cohort
ancestry	phenotype
ancestry groups	cohort
ancestry-related differences local	phenotype
APBB2 local	gene
ARHGAP10 local	gene
Asian	cohort
Asian group local	cohort
ASW	cohort
brain tissue	anatomy
C15orf53	gene
CEU	cohort
CHB	cohort
Chinese study population local	cohort
Chrna3	gene
CHRNA5	gene
Chrnb3	gene
CHS	cohort
cis-regulatory variants local	variant
CLM	gene
cocaine	phenotype
common variants	cohort
Ctbp2	gene
drug	drug
drug dependence	phenotype
DSCAML1	gene
DSM-IV cocaine dependence symptom counts local	phenotype
DSM-IV nicotine dependence symptom counts local	phenotype
DSM-IV opioid dependence symptom counts local	phenotype
EAs	cohort
European ancestry	cohort
European population	cohort
FIN	cohort
functional rare variants local	variant
functional RVs local	variant
GBR	cohort
gene	gene
genome-wide relevant genes local	gene
GSS local	gene
GWAS-relevant alleles local	variant
GWAS-relevant genes local	gene
Hdac1	gene
Htr1a	gene
IBS	cohort
JPT	cohort
Kcnb2	gene
Kcnc1	gene
Kcnd2	gene
KCNG2	gene
KIAA0040	gene
local ancestry-related variant local	variant
LWK	cohort
METAP1	gene
MXL	cohort
NALCN	gene
NCK2 local	gene
nicotine	drug
nicotine dependence	phenotype
non-African population local	cohort
Non-Asian population local	cohort
nonfunctional variants local	variant
OD	phenotype
opioid	drug
opioid dependence	phenotype
PARVA local	gene
PDLIM5	gene
PKNOX2	gene
population	cohort
PUR	cohort
rare variant	cohort
risk allele	cohort
rs10031423 local	variant
rs1229984	variant
rs12333476 local	variant
rs12639887 local	variant
rs1693457	variant
rs2452594 local	variant
rs4147541	variant
rs6846835 local	variant
rs6853490 local	variant
SAGE	cohort
SAGE dataset	cohort
SERINC2	gene
substance dependence phenotype local	phenotype
substance use	phenotype
THSD7B	gene
TSI	cohort
Wright’s fixation index local	drug
Yale-Penn	cohort
Yale–Penn	cohort
Yale–Penn dataset local	cohort
YRI	cohort
ZEB1 local	gene
ΔF local	drug

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In this knowledge base

Title	Year	PMID
ADH1B: From alcoholism, natural selection, and cancer to the human phenome.	2018	28349588
Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1.	2017	28115739
The genetic epidemiology of substance use disorder: A review.	2017	28938182

External

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Cross-ancestry genome-wide association studies identified heterogeneous loci associated with differences of allele frequency and regulome tagging between participants of European descent and other ancestry groups from the UK Biobank.	De Lillo A et al.	—	2021	→
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