Newswise — Genetic epidemiology is the study of how genetic factors may influence health. Twin and adoption studies have shown that about half of the risk of alcohol use disorders (AUDs) is due to differences in the genotypes that people carry, yet few specific genes that play a causal role have been identified. Scientists believe AUDs are highly polygenic – involving many genes – likely thousands. However, genetic risk for AUD may differ among populations. This study evaluated whether the genetic risk for AUDs differed among four population samples.
Researchers analyzed independent samples drawn from four major research projects for this study, including two population-based studies and two clinically ascertained samples. The population-based samples were from unselected birth cohorts: the Avon Longitudinal Study of Parents and Children (n=4,304) and the FinnTwin12 (n=1,135). The clinically derived samples were from families densely affected with AUDs, identified from treatment-seeking patients: the Collaborative Study on the Genetics of Alcoholism (n=2,097), and the Irish Affected Sib Pair Study of Alcohol Dependence (n=706). AUD symptoms were identified with clinical interviews, and participants of European ancestry were genotyped using genome-wide genotyping arrays. “Genetic risk scores” were obtained for each sample and then tested to see how well they predicted AUD outcomes within and across the four samples.
There were differences in the genetic architecture of AUD between the population-based and the clinically ascertained samples of individuals. The authors proposed that characteristics such as treatment-seeking status may reflect more severe alcohol dependence and/or co-occurring illnesses, which may be influenced by different sets of genes than those contributing to risk of heavy alcohol use and alcohol-related problems in the general population. They recommended that additional studies be conducted to identify genetic variants that contribute to AUD and that these include very large samples. Finally, the samples’ characteristics should be taken into account to reduce genetic heterogeneity.