Can we identify genes for alcohol consumption in samples ascertained for heterogeneous purposes?

paper Cited Public

Authors: Hansell, Narelle K; Agrawal, Arpana; Whitfield, John B; Morley, Katherine I; Gordon, Scott D; Lind, Penelope A; Pergadia, Michele L; Montgomery, Grant W; Madden, Pamela A F; Todd, Richard D; Heath, Andrew C; Martin, Nicholas G
Year: 2009
Journal: Alcoholism, clinical and experimental research
PMID: 19183129
DOI: 10.1111/j.1530-0277.2008.00890.x
PMCID: PMC3164813

BACKGROUND: Previous studies have identified evidence of genetic influence on alcohol use in samples selected to be informative for alcoholism research. However, there are a growing number of genome-wide association studies (GWAS) using samples unselected for alcohol consumption (i.e., selected on other traits and forms of psychopathology), which nevertheless assess consumption as a risk factor. Is it reasonable to expect that genes contributing to variation in alcohol consumption can be identified in such samples? METHODS: An exploratory approach was taken to determine whether linkage analyses for heaviness of alcohol consumption, using a sample collected for heterogeneous purposes, could replicate previous findings. Quantity and frequency measures of consumption were collected in telephone interviews from community samples. These measures, and genotyping, were available for 5,441 individuals (5,067 quasi-independent sibling pairs). For 1,533 of these individuals, data were collected on 2 occasions, about 8.2 years apart, providing 2 datasets that maximize data collected at either a younger or an older age. Analyses were conducted to address the question of whether age and heavier levels of alcohol consumption effects outcome. Linkage results were compared in the younger and older full samples, and with samples in which approximately 10, 20, and 40 of drinkers from the lower end of the distribution of alcohol consumption were dropped. RESULTS: Linkage peaks varied for the age differentiated samples and for percentage of light drinkers retained. Larger peaks (LOD scores >2.0) were typically found in regions previously identified in linkage studies and/or containing proposed candidate genes for alcoholism including AGT, CARTPT, OPRD1, PIK3R1, and PDYN. CONCLUSIONS: The results suggest that GWAS assessing alcohol consumption as a covariate for other conditions will have some success in identifying genes contributing to consumption-related variation. However, sample characteristics, such as participant age, and trait distribution, may have substantial effects on the strength of the genetic signal. These results can inform forthcoming GWAS where the same restrictions apply.

Figure 1

Linkage plots for Quantity × Frequency, Quantity, and Frequency with full and truncated samples (100%, 90%, 80%, & 60%), for the older and younger datasets, are shown for the 22 autosomal chromosomes and the X chromosome.

LLM interpretation

This figure consists of 12 linkage plots organized into three main sections: "Quantity x Frequency," "Quantity," and "Frequency," each showing results for four sample sizes (100%, 90%, 80%, and 60%). The x-axes represent the 22 autosomal chromosomes and the X chromosome, while the y-axes show LOD scores. Each plot compares "Older" (red line) and "Younger" (blue line) datasets, displaying varying peaks of genetic linkage across the chromosomes.

Figure 2

Linkage on chromosome 6 for Quantity × Frequency with full and truncated samples (100%, 90%, 80%, 60%), using the younger dataset, and with the location of the candidate gene NRN1 (neuritin 1) indicated.

LLM interpretation

This line graph shows the LOD score across the map distance (Cm) of chromosome 6 for the "Quantity x Frequency" trait in a younger dataset. Four sample sizes are compared (100%, 90%, 80%, and 60%), with the 100% and 90% samples showing a prominent peak in LOD score (reaching approximately 3.25) between 10 and 30 Cm. The candidate gene *NRN1* is indicated by an arrow at approximately 12 Cm, coinciding with the primary peak of the full and 90% sample datasets.

Figure 3

Linkage on chromosome 1 for Quantity × Frequency with full and truncated samples (100%, 90%, 80%, 60%), using the younger dataset, and showing the location of candidate genes STX12 (syntaxin 12-binding protein), OPRD1 (opioid receptor, delta-1), DHX9 (DEAH (Asp-Glu-Ala_His) box Polypeptide 9), and AGT (angiotensinogen).

LLM interpretation

This line graph shows the LOD score across the map distance (Cm) of chromosome 1 for the "Quantity x Frequency" trait in a younger dataset. Four lines represent different sample sizes (100%, 90%, 80%, and 60%), with the 80% sample showing the highest peak LOD score (approximately 2.75) near the AGT gene. Two primary peaks are visible: one between 30–50 Cm near the STX12 and OPRD1 genes, and another between 180–240 Cm near the DHX9 and AGT genes.

Figure 4

Linkage on chromosome 3 for quantity and frequency measures with full and/or truncated samples (100%, 60%), using the older dataset, and showing the location of the candidate genes TRH (thyrotropin-releasing hormone) and SOX2 (SRY (sex determining region Y) – box 2).

LLM interpretation

This line graph shows LOD scores across the map distance (cM) of chromosome 3 for four different dataset conditions: Quantity 100%, Quantity 60%, Frequency 100%, and Quantity x Frequency 60%. The "Quantity 100%" group exhibits the highest peak (LOD score ~2.7) near the TRH gene location, while the "Quantity x Frequency 60%" group shows a prominent peak (LOD score ~2.5) near the SOX2 gene location. The x-axis represents map distance in centimorgans (cM), and the y-axis represents the LOD score.

Figure 5

Linkage on chromosome 5 for Frequency with full and truncated samples (100%, 90%, 80%, 60%), using the older dataset and showing the genes CARTPT (cocaine- and amphetamine-regulated transcript (CART) prepropeptide) and PIK3R1 ((phosphoinositide-e-kinase, regulatory subunit 1 (alpha)).

LLM interpretation

This line graph shows the LOD score across the map distance (Cm) of chromosome 5 for four different sample sizes (100%, 90%, 80%, and 60%) using an older dataset. The x-axis represents map distance in centimorgans, and the y-axis represents the LOD score. A prominent peak is observed around 80-90 Cm, which increases in magnitude as the sample percentage decreases, with the 60% sample reaching the highest LOD score of approximately 2.5 near the labeled genes *CARTPT* and *PIK3R1*.

#	Section	Preview
20	MATERIALS AND METHODS — Zygosity Determination and Genotyping	For each chromosome, all genotypes for that chromosome were merged into a data structure after…
21	MATERIALS AND METHODS — Zygosity Determination and Genotyping	For the 5441 individuals with alcohol phenotypes, the number of unique autosomal markers per…
22	MATERIALS AND METHODS — Statistical Analyses	Pedigree-wide regression analyses were conducted for the 22 autosomal chromosomes using…
23	MATERIALS AND METHODS — Statistical Analyses	Multipoint linkage analyses were examined for the older and younger datasets. The position and order…
24	MATERIALS AND METHODS — Statistical Analyses	Preliminary analyses were performed using SPSS (version 15.0 for Windows, 2006). Quantity ×…
25	MATERIALS AND METHODS — Statistical Analyses	Normalized residuals (i.e. mean 0, variance 1) were used for all variables to facilitate analyses in…
26	RESULTS	The plotted results of univariate multipoint linkage analyses are shown in Figure 1 for the older…
27	RESULTS — Quantity × Frequency	This, the primary outcome measure, provided the best linkage results, peaking with a LOD of 3.3 on…
28	RESULTS — Quantity × Frequency	A number of regions of linkage activity were found on chromosome 1 (Figure 3). The first peaked with…
29	RESULTS — Quantity × Frequency	A peak with a LOD of 2.5 was observed on chromosome 3 at marker GATA92B06 for the older dataset in…
30	RESULTS — Quantity	The most notable peak for Quantity, with a LOD of 2.7, was found on chromosome 3 at D3S1267 (between…
31	RESULTS — Quantity	In the younger dataset, a peak with a LOD of 2.1 was found on chromosome 1 at marker D1S2667…
32	RESULTS — Frequency	Peaks found for Frequency echoed those found for Quantity × Frequency on chromosomes 1 and 6.…
33	RESULTS — Frequency	A linkage peak with a LOD of 2.5 was found on chromosome 5 at marker GATA141B10, for the older…
34	DISCUSSION	Our study took an exploratory approach to examine the feasibility of identifying genes influencing…
35	DISCUSSION	Linkage peaks with LOD scores greater than 2.0 were found on chromosomes 1p, 1q, 3q, 5q, 6p, 20p,…
36	DISCUSSION	Some linkage peaks detected in previous studies have coincided with candidate genes, most obviously…
37	DISCUSSION	In general, linkage found in the current study was stronger when the dataset maximised data…
38	DISCUSSION	Linkage results were also found to vary when comparing the full sample to truncated samples in which…
39	DISCUSSION	Convergent findings suggest that the current analyses were successful in identifying chromosomal…

Name	Type
Actual number of drinks recorded local	phenotype
ADH1B	gene
age	phenotype
Age2 local	phenotype
AGT	gene
alcohol	phenotype
alcohol dependence	phenotype
alcoholism	phenotype
alcohol-related phenotypes	phenotype
Alcohol Use	phenotype
amelogenin local	gene
ATA109H09 local	variant
ATA28B11 local	variant
ATA85B10 local	variant
Australian Genome Research Facility local	cohort
Australian Twin Registry (ATR) local	cohort
blood group local	phenotype
British ancestry local	phenotype
cancer	phenotype
CARTPT	gene
Categorical drinking response local	phenotype
Children of twins local	cohort
chromosome 1 local	variant
Chromosome 18 local	variant
Chromosome 20 local	variant
Chromosome 3 local	variant
chromosome 6 local	variant
Chromosome 6 local	variant
Chronic health conditions	phenotype
Cohort_1642_parents local	cohort
Cohort_5441_individuals local	cohort
Collaborative Study on the Genetics of Alcoholism (COGA)	cohort
combined cohort	cohort
Complete twin pairs local	cohort
consumption	phenotype
control	cohort
controls	cohort
D1S1612 local	variant
D1S199 local	variant
D1S225 local	variant
D1S2667 local	variant
D1S2697 local	variant
D1S491 local	variant
D2S1612 local	variant
D3S1267 local	variant
D3S1292 local	variant
D3S2460 local	variant
D6S344 local	variant
D6S470 local	variant
drinkers	phenotype
drinking	phenotype
drinks per week	phenotype
DXS6789 local	variant
DXS6800 local	variant
DXS8029 local	variant
DZ pair local	cohort
European ancestry	cohort
Family History of Substance Abuse Samples local	cohort
Finnish Genome Centre local	cohort
first two phases of telephone interviews local	cohort
frequency	phenotype
full sample	cohort
GABRA2	gene
GATA141B10 local	variant
GATA152F04 local	variant
GATA88F03 local	variant
GATA92B06 local	variant
Gemini Genomics local	cohort
general population	cohort
genome scans local	cohort
GRR local	drug
heavy drinking	phenotype
hypertension	phenotype
intermediate samples local	cohort
Irish ancestry local	phenotype
Lange-Goradia genotype elimination algorithm local	drug
light drinkers	phenotype
Low bone density local	phenotype
Low consumption local	phenotype
low consumption levels local	phenotype
Marshfield Clinic's Mammalian Genotyping Service local	cohort
Merlin	drug
most concentrated sample local	cohort
Non-twin siblings local	cohort
older cohort	cohort
older cohort (28–90 years) local	cohort
older dataset local	cohort
Older dataset local	cohort
Oprd1	cohort
outlier	phenotype
Parental pairs local	cohort
parents	cohort
Pdyn	gene
pedtool local	drug
phase three telephone interview participants local	cohort
PIK3R1	gene
QISP local	phenotype
quantity	phenotype
Quantity × Frequency local	phenotype
Quantity × Frequency score local	phenotype
Quasi-independent sibling pairs (QISPs) local	cohort
Queensland Institute of Medical Research	cohort
Relpair	drug
samples	cohort
SE30 local	variant
Sequana Therapeutics local	cohort
sex	phenotype
sibling pair local	phenotype
smoking	phenotype
study cohort	cohort
sub-sample local	cohort
Truncated sample 60% local	cohort
Truncated sample 80% local	cohort
Truncated sample 90% local	cohort
Twin cohort	cohort
University of Leiden local	cohort
Unselected Samples local	cohort
voluntary alcohol consumption	phenotype
X chromosome	drug
younger cohort	cohort
younger cohort (23–39 years) local	cohort
younger dataset local	cohort
Younger dataset local	cohort
younger twin cohort local	cohort
zygosity	phenotype

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Title	Year	PMID
Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples.	2018	29405378
Association of OPRD1 polymorphisms with heroin dependence in a large case-control series.	2014	22500942
Alcohol consumption in men is influenced by qualitatively different genetic factors in adolescence and adulthood.	2013	23282961
ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure.	2013	23303482
Linkage analysis of alcohol dependence symptoms in the community.	2010	19860796
Alcohol consumption indices of genetic risk for alcohol dependence.	2009	19576574

Title	Authors	Journal	Year	Link
Delta Opioid Pharmacology in Relation to Alcohol Behaviors.	Alongkronrusmee D et al.	—	2018	→
Effects of high alcohol intake, alcohol-related symptoms and smoking on mortality.	Whitfield JB et al.	—	2018	→
Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples.	Savage JE et al.	—	2018	→
Association of OPRD1 polymorphisms with heroin dependence in a large case-control series.	Nelson EC et al.	—	2014	→
Alcohol consumption in men is influenced by qualitatively different genetic factors in adolescence and adulthood.	Edwards AC et al.	—	2013	→
ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure.	Nelson EC et al.	—	2013	→
The dynorphin/κ-opioid receptor system and its role in psychiatric disorders.	Tejeda HA et al.	—	2012	→
Linkage analysis of alcohol dependence symptoms in the community.	Hansell NK et al.	—	2010	→
Alcohol consumption indices of genetic risk for alcohol dependence.	Grant JD et al.	—	2009	→
Genetical genomic determinants of alcohol consumption in rats and humans.	Tabakoff B et al.	—	2009	→

Can we identify genes for alcohol consumption in samples ascertained for heterogeneous purposes?

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