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

Abstract

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.

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.

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).

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).

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)).