In order to investigate the age specificity of the genetic and endophenotypic factors noted above on the early onset of alcohol use and dependence, we studied adolescents and young adults drawn from the Collaborative Studies on the Genetics of Alcoholism sample . Because we wanted to understand the processes which lead from non-drinking to regular drinking to alcohol dependence we used both the onset of regular alcohol use and of alcohol dependence as dependent variables. As we noted above, more severe cases of alcohol dependence in adults were found associated with earlier ages of onset of drinking and are more likely to be the result of genetic factors, thus we hypothesized that specific genetic and related neurophysiological endophenotypes would have a greater predictive power in those with the earliest ages of onset. Since the principal objective is to determine whether there are age-varying effects of the predictive variables, survival analysis using standard Cox proportional hazards models in which effects are age invariant is not appropriate. In addition, such models cannot account for differential effects on survival which are the result of unmeasured heterogeneity in the sample . DTSA provides an alternative model which avoids these problems and which can be implemented with logistic regression methods. By dividing subjects into groups based upon age of onset, a single logistic regression model can be applied to estimate the probability of those at risk in each age group of becoming alcohol dependent as a function of the predictive variables . The functional form of the model can be set to determine age-specific effects and/or age-independent effects,vertical grow rack systems and use age-invariant and/or age-dependent covariates.
A weighted model was employed to enable the use of all members of multi-member families . The output of a DTSA calculation is the same as the output from a logistic regression calculation. Each DTSA model had the following structure: the outcomes, or dependent variables were either alcohol dependence or regular alcohol use. Regular alcohol use was defined as consumption at least once a month for 6 or more consecutive months. In all cases four distinct age ranges were used: under 16, 16 and 17, 18 and 19, over 19. These age groups were determined by the fact that ages of onset were whole numbers of years, that the numbers of those who became alcohol dependent be about the same in each group, and that there be at least 50 subjects in each group who became alcohol dependent to provide a reasonable degree of statistical reliability in the calculations. The covariates were a genotype from a CHRM2 SNP, ERO power from one of the leads, family type , number of parents who smoke, gender, and scores on principal components 1 and 2 derived from the stratification analysis of the sample genome . The CHRM2 SNPs analyzed here, rs978437, rs7800170, rs1824024, rs2061174, and rs2350786 include the three most significant of those for alcohol dependence with comorbid drug dependence in Dick et al. as well as two others that appear to be in a range of significance indicated by that table. From preliminary statistical screening of the genotypic distributions in the sample, a recessive model was employed which contrasted major allele homozygotes with those who were not. The electrophysiological phenotypes used in the analysis were found to be significant in previous studies ; these studies showed reduced amplitudes in alcoholics and in those offspring at high risk. The number of parents who smoke were selected in part because the Kaplan–Meier curves with different values showed considerable variation for a discussion of the effects of parental smoking on adolescent behavior.
DTSA results were calculated for the entire sample. Our fourth item for investigation, whether the influence of these SNPs would be greater in a behaviorally defined sub-sample comprising a putatively more genetically vulnerable group was suggested by the results of Dick et al. and King and Chassin . Given the prevalence of various substance abuse categories in the sample and the number of subjects in each category who become alcohol dependent during the age range of the study, the broad criterion of the use of an illicit drug regardless of age of onset or frequency of use was employed to define the more genetically vulnerable group. This sub-sample will be called the ‘‘illicit drug use’’ sub-sample. Unlike the definition of illicit drug use in Dick et al. , this definition does not categorize regular use of cannabis as illicit drug use. Since more than half the sample are characterized as regular users of cannabis at some time during the age range of the study , regular use of cannabis can not be considered a practice that violates norms of agerelated behavior or involves enhanced risk taking, and thus not an element of ‘‘externalizing psychopathology’’. We note that 90 % of cannabis dependent subjects who are also alcohol dependent are included in the sub-sample, so although our criterion does not span regular cannabis use we are probably picking up those more genetically vulnerable cannabis dependent subjects and thus paralleling the group used in Dick et al. . For the regular alcohol use outcome, there were a sufficient number of illicit drug non-users who became regular users of alcohol to provide a sub-sample to contrast with the illicit drug use sub-sample. Since about 75 % of the alcohol dependent subjects were members of the illicit drug use sub-sample, there were too few alcohol dependent subjects with no illicit drug use to provide a contrasting sub-sample. However some inferences about the significance of illicit drug use for the onset of alcohol dependence can be drawn from the differences between the DTSA results for the entire sample and the results for the illicit drug use sub-sample.
Since regular alcohol use is a necessary condition of alcohol dependence, it could not be used as a covariate in the DTSA calculation of the onset of alcohol dependence. In order to investigate the duration of the transition from regular alcohol use to alcohol dependence as a function of the age of onset of alcohol dependence, the third item for investigation, logistic regression analyses of the onset of alcohol dependence as the outcome in each of the age ranges, restricted to the sample of those who are regular users of alcohol within that age range, were carried out. All covariates used in the DTSA calculations were used with duration of drinking as an additional covariate. Although those who become alcohol dependent are removed from the sample at each age range, this is not a survival analysis method because new regular users of alcohol are added to the sample at each age range. However, the results of these tests can be compared to the DTSA results for the illicit drug use sub-sample to examine the effect of including all alcohol dependent subjects in the sample,vertical grow racks cost as opposed to a restricted sub-sample as found in the illicit drug use sub-sample. In order to investigate the duration of the transition from regular alcohol use to alcohol dependence as a function of the age of onset of regular alcohol use, both Fisher’s exact test and the Cochran-Armitage trend test were applied to the distribution in each of the first three age ranges of the proportion of those who became alcohol dependent in the same or subsequent age range for those who became regular users of alcohol in that age range.We investigated whether there were age-related trends in the genotypic distributions which underlie the results of the DTSA for the SNP covariates and the rapidity of the transition from regular alcohol use to alcohol dependence. Two separate Cochran-Armitage trend tests were carried out on genotypic distributions of the SNPs of the illicit drug use sub-sample. Given the use of the recessive genetic model in the DTSA tests, subjects in the illicit drug use sub-sample were divided into two genotypic groups, those who had two copies of the major allele and those who did not. The first trend test was of the genotypic distribution of those who became alcohol dependent as a function of age of onset of alcohol dependence, comparing those who had two copies of the major allele with those who did not. The null hypothesis is that the relative effect of having a particular genotype does not vary linearly between ages of onset; that is, that the ratio of different genotypes of those who become alcohol dependent does not display a linear trend between ages of onset. To test whether there was trend in the genotypic distributions as a function of the rapidity of the transition from regular alcohol use to alcohol dependence, a second trend test was carried out. This test was of the genotypic distribution of those who began regular alcohol use in the youngest age range and became alcohol dependent at any age as a function of age of onset of alcohol dependence, comparing those who had two copies of the major allele with those who did not. The null hypothesis is that the ratio of different genotypes of those who become alcohol dependent does not show a trend between different time spans from the onset of regular alcohol use to the onset of alcohol dependence. We restricted our analysis to those who became regular alcohol users in the youngest age range in order to obtain results for those who might take a relatively long time to develop alcohol dependence.Significant CHRM2 SNP association were noted for the onset of alcohol dependence and were found only in the those with age of onset younger than 16.
These results were obtained both in the entire sample and the illicit drug sub-sample. In all cases with significant results, occurrence of the major allele was the risk factor. No CHRM2 SNPs were found to be significant predictors of the onset of regular alcohol use for any age range. In comparing the entire sample with the sub-sample, the CHRM2 effects are greater in the illicit drug use sub-sample than in the sample as a whole. In particular, restricting the sample to those most genetically vulnerable enables two more SNPs to become significant at the 0.05 level. If the risk of the onset of alcohol dependence as a function of genotype were as great in the drug non-users as in the illicit drug use sub-sample, and taking into account the lower rate of regular alcohol use in the drug non-users, there would be almost twice as many alcohol dependent subjects among the drug non-users as in fact there are.The significance of family type alcoholic family or community family and number of parents who smoke was greatest in the younger age ranges. Effects are measured in changes in logit from baseline. When significant, SNP effects were about 1.0 for having two copies of the risk allele in the recessive genetic models, and the delta ERO effect was about 0.5 per standard deviation. When significant, the parental smoking effect was about 0.2 per smoker, the family type effect ranged from 1.0 to almost 2.0, and the gender effect ranged from about 0.5 to 1.0. In the logistic regression analyses used to investigate the duration of the transition from regular alcohol use to alcohol dependence as a function of the age of onset of alcohol dependence, genotype was not significant in any age range in both linear and quadratic models for duration. In the linear model for duration, modeled as log, delta ERO values at Fz are significant in the youngest age range, and both Fz and Cz ERO values are significant in the oldest age range. ERO results are consistent with those obtained in the DTSA models. Duration was significant in the three youngest age ranges. In the quadratic model for duration, modeled as the sum of log and log2 , the Fz and Cz ERO values are significant only in the oldest age range. The effect of duration of drinking was significant in the three youngest age ranges with an overall U-shape in the two youngest age ranges. Since the beta value for the log term is negative and the beta value for the log2 term is positive, the rising part of the U-shape masks the Fz ERO effect in the youngest age range .