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
Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Queensland, Australia.
Alcohol Clin Exp Res. 2009 Apr;33(4):729-39. doi: 10.1111/j.1530-0277.2008.00890.x. Epub 2009 Jan 22.
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?
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.
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.
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.
先前的研究已经在为酒精中毒研究选取的具有信息价值的样本中发现了基因对酒精使用有影响的证据。然而，越来越多的全基因组关联研究（GWAS）使用的样本并非根据酒精消费情况选取（即根据其他特征和精神病理学形式选取），但这些研究仍将酒精消费作为一个风险因素进行评估。在这样的样本中，期望能够识别出导致酒精消费差异的基因是否合理呢？
采用一种探索性方法来确定，使用为多种不同目的收集的样本对酒精消费量大进行连锁分析，是否能够重复先前的研究结果。通过电话访谈从社区样本中收集消费的数量和频率测量数据。这些测量数据以及基因分型数据可用于5441名个体（5067对近似独立的同胞对）。其中1533名个体的数据是在两次约间隔8.2年的时间收集的，提供了两个数据集，分别最大限度地收集了较年轻或较年长时的数据。进行分析以解决年龄和较高水平的酒精消费是否影响结果这一问题。将较年轻和较年长的完整样本中的连锁结果进行比较，并与分别剔除了酒精消费量分布较低端约10%、20%和40%饮酒者的样本进行比较。
年龄有差异的样本以及保留的轻度饮酒者百分比不同，连锁峰也有所不同。较大的峰（对数优势分数>2.0）通常出现在先前连锁研究中确定的区域和/或包含酒精中毒候选基因的区域，包括AGT、CARTPT、OPRD1、PIK3R1和PDYN。
结果表明，将酒精消费作为其他状况的协变量进行评估的全基因组关联研究，在识别导致与消费相关差异的基因方面会取得一些成功。然而，样本特征，如参与者年龄和性状分布，可能会对遗传信号的强度产生重大影响。这些结果可为即将开展的适用相同限制条件的全基因组关联研究提供参考。
