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A Family Study of Alcohol Dependence
Coaggregation of Multiple Disorders in Relatives of Alcohol-Dependent Probands
John I. Nurnberger, Jr, MD, PhD;
Ryan Wiegand, MS;
Kathleen Bucholz, PhD;
Sean O’Connor, MD;
Eric T. Meyer, MA;
Theodore Reich, MD ;
John Rice, PhD;
Marc Schuckit, MD;
Lucy King, MD;
Theodore Petti, MD, MPH;
Laura Bierut, MD;
Anthony L. Hinrichs;
Samuel Kuperman, MD;
Victor Hesselbrock, PhD;
Bernice Porjesz, PhD
Arch Gen Psychiatry. 2004;61:1246-1256.
ABSTRACT
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Background Alcohol dependence tends to aggregate within families. We analyzed data from the family collection of the Collaborative Study on the Genetics of Alcoholism to quantify familial aggregation using several different criterion sets. We also assessed the aggregation of other psychiatric disorders in the same sample to identify areas of possible shared genetic vulnerability.
Design Age-corrected lifetime morbid risk was estimated in adult first-degree relatives of affected probands and control subjects for selected disorders. Diagnostic data were gathered by semistructured interview (the Semi-Structured Assessment for the Genetics of Alcoholism), family history, and medical records. Rates of illness were corrected by validating interview and family history reports against senior clinicians’ all sources best estimate diagnoses. Sex, ethnicity, comorbidity, cohort effects, and site of ascertainment were also taken into account.
Results Including data from 8296 relatives of alcoholic probands and 1654 controls, we report lifetime risk rates of 28.8% and 14.4% for DSM-IV alcohol dependence in relatives of probands and controls, respectively; respective rates were 37.0% and 20.5% for the less stringent DSM-III-R alcohol dependence, 20.9% and 9.7% for any DSM-III-R diagnosis of nonalcohol nonnicotine substance dependence, and 8.1% and 5.2% for antisocial personality disorder. Rates of specific substance dependence were markedly increased in relatives of alcohol-dependent probands for cocaine, marijuana, opiates, sedatives, stimulants, and tobacco. Aggregation was also seen for panic disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and major depression.
Conclusions The risk of alcohol dependence in relatives of probands compared with controls is increased about 2-fold. The aggregation of antisocial personality disorder, drug dependence, anxiety disorders, and mood disorders suggests common mechanisms for these disorders and alcohol dependence within some families. These data suggest new phenotypes for molecular genetic studies and alternative strategies for studying the heterogeneity of alcohol dependence.
INTRODUCTION
Alcohol consumption and alcohol dependence are influenced by genetic factors in humans and experimental animals. The heritability of alcohol consumption is estimated at 35% to 40% in twin studies.1-3 Twin studies of alcohol dependence have generally shown a ratio of monozygotic concordance–dizygotic concordance of about 2:1, with differences in the absolute value of the concordance related to diagnostic criteria.4-8 A study of 133 adoptees was performed by Goodwin and Schulsinger9; adopted-away sons of alcoholics had an 18% rate of alcohol dependence (similar to that in sons of alcoholics raised in their biological families) and adopted-away sons of control subjects had a 5% rate of alcohol dependence. Bohman10 studied 2324 adoptees, finding 39% alcohol dependence in sons of alcoholic fathers and 20% alcohol dependence in sons of controls; the comparable figures for daughters were 20% and 6%. Family studies were reviewed by Cotton11 in 1979, including data on 6251 relatives of alcoholics and 4083 relatives of controls. Rates of illness in fathers in the 2 groups were 27% and 5%, respectively; respective rates in mothers were 5% and 1%.
Characteristically, women have had lower rates of alcohol dependence than men, in epidemiologic and family and twin studies.12-13 The question arises whether risk for alcohol dependence is passed on comparably by female and male relatives in families with cases of alcohol dependence. Kaij and Dock14 found that grandsons of alcoholics had equal risk whether they were the son of a son of an alcoholic or the son of a daughter of an alcoholic. Likewise, Cloninger et al15 reported as much alcohol dependence in relatives of female alcoholics as in relatives of male alcoholics. This suggests that the 2 sexes are equivalent in genetic load for alcohol dependence and that differential expression of the illness in the 2 sexes is related to nongenetic factors.
We may distinguish between comorbidity (disorders occurring together in an individual) and coaggregation (disorders occurring together in families). Certain psychiatric disorders have been reported to be more prevalent in persons with alcohol dependence compared with controls (comorbid disorders), including child conduct disorder and adult antisocial personality disorder (ASPD),10, 16 depression,17-20 and anxiety disorders.18, 21 Other disorders have been noted in relatives (coaggregating disorders), including drug abuse or dependence,22 somatization in female relatives,15 and attention-deficit/hyperactivity disorder in juvenile offspring.23
Our goal was to perform a family study using modern diagnostic criteria and a structured assessment for multiple disorders on Axis I and ASPD on Axis II. The purpose was to identify disorders aggregating in relatives of persons with alcohol dependence and, thus, specify areas of possible shared genetic vulnerability factors. We studied 1269 probands with alcohol dependence ascertained through treatment facilities without regard to family history (a subset of high-density families were later studied for genetic linkage12, 24). They and their relatives are compared with the relatives of 232 probands ascertained at 6 centers to represent a population control sample. In all, 8296 adult first-degree relatives of alcohol-dependent probands were compared with 1654 adult controls.
METHODS
The Collaborative Study on the Genetics of Alcoholism began in 1989 with the participation of 6 centers (Indiana University School of Medicine; University of Iowa; University of Connecticut; State University of New York Health Sciences Center at Brooklyn; University of California, San Diego; and Washington University). A semistructured interview (the Semi-Structured Assessment for the Genetics of Alcoholism [SSAGA]25-26) was designed to assess alcohol dependence by multiple criteria and other major psychiatric disorders. The study was approved by institutional review boards at each site. Probands with alcohol dependence by DSM-III-R criteria (American Psychiatric Association, 1987) and definite alcoholism by the criteria of Feighner et al27 were systematically ascertained from consecutive admissions to treatment facilities and invited to participate in the study. This ascertainment method was specifically designed to support a family study. Inclusion criteria included the availability of 4 first-degree relatives, at least 2 of whom were living in the catchment area of one of the participating sites. Probands and relatives were required to be English speaking. Exclusion criteria included habitual intravenous drug use (>30 times in a lifetime or within 6 months of ascertainment), known human immunodeficiency virus–positive status, or a terminal illness not related to alcoholism. These families were designated as stage 1 and form the data set for the analyses in this report. A subset of families (designated as stage 2) contained at least 2 additional first-degree relatives with alcohol dependence; members of these families were included in analyses of genetic linkage and electrophysiological features (Rice et al28 provide a summary). Control families were identified from various source populations, including motor vehicle registrants, dental clinic attendees, and parents of college students. Such families were required to have spouses and 3 children older than 13 years willing to participate. Because controls were selected to represent a subset of the general population, they were not excluded if they met the criteria for alcohol dependence or any other psychiatric disorder. Probands, spouses, and first-degree relatives of those aged 18 years and older were then invited to participate (children and adolescents in these families were assessed using age-appropriate instruments and will be the subject of a separate report). A comparison of demographic characteristics in the relative and control groups is shown in Table 1. Differences in age, sex, and ethnicity were accounted for in the multivariate analyses presented later.
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Table 1. Description of the Sample of Relatives and Controls, With a Detailed Description of Those Directly Interviewed*
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Participants provided informed consent and were personally interviewed with the SSAGA and the Family History Assessment Module (FHAM). Medical records were sought for those with a history of psychiatric treatment. Certain disorders, including posttraumatic stress disorder (PTSD), nicotine dependence, and attention-deficit/hyperactivity disorder, were only assessed using a revised version of the SSAGA prepared in 1997 and, thus, the denominators for these disorders are smaller than for the other disorders. All diagnoses were assessed on a lifetime basis. Disorders judged to be organic (ie, direct effects of substance use) were not included in these analyses. Original analyses separated control probands from control relatives; later analyses combined control probands and control relatives (referred to generically as "controls" from this point on), and results were essentially the same. Interview instruments and procedures were the same for relatives of alcohol-dependent probands and controls. Conference calls and continuously updated procedure manuals were used to standardize interview technique and scoring norms across sites.
Assessment folders were developed for each subject, including the interview and information from relatives and medical records, where available. A subset of subjects (1929 of 9950 subjects, or 19.4% of the sample studied herein) were diagnosed by best estimate procedures. This included all members of the stage 2 families included in the genetic linkage studies and a subset of controls. Probands with alcohol dependence were also diagnosed by best estimate procedures, but their data are not included herein because of their ascertainment as affected persons. The best estimate process involved senior clinician assessment (J.I.N., S.O., T.R., M.S., L.K., T.P., L.B., S.K., and V.H.) of all diagnostic material to assign lifetime diagnoses, blind to proband or relative status. The best estimate process was then used to validate information from the SSAGA and FHAM. Considering the best estimate as the gold standard, diagnoses based on algorithmic extraction of information from the SSAGA were classified as true positive, true negative, false positive, and false negative. These functions were used to adjust rates of diagnoses in interviewed subjects not included in the best estimate process (4324 of 9718 subjects, or 44.5%), using the following function: Total number of affected subjects = [SSAGA-positive subjects x (true positives/total positives)] + [SSAGA-negative subjects x (false negatives/total negatives)].
By using a similar procedure, information from the FHAM was validated, to assign diagnoses to subjects who did not participate in the SSAGA interview (3497 of 9718 subjects, or 36.0%). As an example, 75.9% of subjects implicated as having DSM-III-R alcohol dependence by 1 first-degree relative in fact were assigned that diagnosis in the best estimate process, 87.7% of subjects implicated by 2 relatives received that diagnosis by best estimate, and 93.7% of subjects implicated by 3 relatives received that diagnosis by best estimate. In this way, the results of the best estimate process were generalized to all first-degree relatives of alcohol-dependent probands and control probands.
Because the total subject group (and the group of best-estimated subjects) included many more relatives of alcoholics than controls (8296/9718), the validation of SSAGA and FHAM was differentially reflective of relationships among self-report, relatives’ reports, and clinician judgment in families of an alcoholic proband. However, we have tested the premise that information from relatives is related to self-report (by SSAGA) similarly in families of alcohol-dependent probands and controls. In fact, comparison of the proportion of SSAGA-affected subjects among groups of relatives and controls separated by number of implications of illness by family members did not differ significantly for any condition used in the analysis, with a single exception: subjects with 3 or more implications of DSM-IV alcohol dependence by family members were more likely to be affected by SSAGA among relatives of alcohol-dependent probands (66%) than among controls (37%). Because only a few controls were in this category, correction for this difference would reduce the estimated prevalence of alcohol dependence in controls only from 14.4% to 14.0%. The diagnostic criteria are DSM-III-R unless otherwise noted. We have observed the standard DSM convention that subjects diagnosed as having substance dependence are not also diagnosed as having substance abuse, even though most would meet the symptomatic criteria for abuse as well.
Age correction was performed for 6 disorders: alcohol dependence by DSM-III-R and DSM-IV, major depression, mania, drug dependence (any diagnosis of nonalcohol nonnicotine substance dependence), and ASPD. For these disorders, data from all directly interviewed affected subjects in the data set were used to generate an age-of-onset function. The group of subjects at risk was then divided by decade, and the size of the unaffected portion of each group adjusted by the proportion of affected subjects, with onset by the median of that decade, producing the age-adjusted number of subjects at risk (a modified Stromgren procedure, as in the studies by Johnson and Leeman29 and Gershon and colleagues30). The age of onset was considered to be the year that a subject met the full criteria for the disorder. For ASPD (presumed to be a continuous trait that begins, by definition, during childhood), the age of onset was considered to be the age at the time of the first symptom and, thus, age-corrected data for this group of adult subjects were identical to the raw data. We also studied Kaplan-Meier survival curves using the Cox proportional hazards regression model for these disorders (ie, alcohol dependence, major depression, mania, drug dependence, and ASPD). Rates of illness in relatives of alcohol-dependent probands were compared with rates of illness in controls using the  2 test. Relative risk (RR) estimates were calculated with their 95% confidence intervals (CIs).
A multivariate logistic regression model was used to account for the effects of sex, ethnicity, site of ascertainment, birth cohort, comorbidity in the proband, and comorbid alcoholism in the relative. Cohort effects were assessed by dividing relatives into groups by decade of birth. Familial effects (the effect of the variable number of first-degree relatives in families) were controlled using a random effects odds ratio method and a marginal odds ratio method.
RESULTS
Table 2 shows the prevalence of various psychiatric disorders in first-degree relatives of probands with Collaborative Study on the Genetics of Alcoholism–determined alcoholism (DSM-III-R alcohol dependence plus Feighner et al27 definite alcoholism) and in the sample of controls. Many disorders seem to cluster in families with an alcohol-dependent proband, including alcohol dependence itself (by 4 criterion systems), other forms of substance dependence, ASPD, several anxiety disorders, major depression, and dysthymia. Diagnoses that did not seem to cluster in relatives include anorexia, bulimia, mania, and several forms of substance abuse, including DSM-IV alcohol abuse. Somatization disorder was too infrequently diagnosed for an accurate comparison. Attention-deficit/hyperactivity disorder was assessed only in those subjects interviewed after 1997, and that subgroup does not show a significant aggregation. Table 3 corrects the raw data for all interviewed subjects, based on the SSAGA false-negative and false-positive rates (see the "Methods" section). This correction lowers the rate of DSM-IV alcohol abuse and increases the rate of DSM-IV alcohol dependence, among other changes. Versions of Table 2 and Table 3 in which probands are diagnosed by DSM-IV or International Classification of Diseases, 10th Revision (ICD-10) are available at the following Web site (http://ipr.iupui.edu/coga/research.html); in general, RRs are quite comparable to those presented herein, and there is no case in which a disorder shows significant familial aggregation in one version of the table but not others. Controlling for the effect of family size did not change the pattern of familial aggregation (data not shown). Marginal odds ratios were significant for each disorder in Table 2, with P < .05.
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Table 2. Prevalence and Relative Risk of Particular Disorders in Adult First-Degree Relatives of Probands With Alcohol Dependence Compared With Control Subjects
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Table 3. Corrected Prevalence of Psychiatric Disorders in Interviewed First-Degree Relatives of Probands With Alcohol Dependence and Control Subjects*
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For certain disorders, we were able to systematically include information from relatives in assigning diagnoses, because detailed information was provided in the FHAM. These included alcohol dependence, drug dependence, mania, depression, and ASPD. These data have also been age corrected. Results are presented in Table 4. Adjusted rates of DSM-III-R alcohol dependence are 37.0% and20.5% in relatives of probands and controls, respectively (RR, 1.8; 95% CI, 1.6-2.0). Comparable figures for DSM-IV alcohol dependence are 28.8% and 14.4% (RR, 2.0; 95% CI, 1.8-2.3); for any form of DSM-III-R nonalcohol nonnicotine substance dependence, 20.9% and 9.7% (RR, 2.2; 95% CI, 1.9-2.5); for primary major depression, 19.5% and 18.0% (RR, 1.1; 95% CI, 1.0-1.2); for mania, 1.2% and 0.9% (RR, 1.3; 95% CI, 0.7-2.3); and for ASPD, 8.1% and 5.2% (RR, 1.6; 95% CI, 1.3-2.0). These prevalence figures represent the best estimate of rates of illness for the full complement of 8296 adult first-degree relatives of alcohol-dependent probands and 1654 adult controls. Relative risk estimates in siblings alone (data not shown) were generally comparable to those in all first-degree relatives; the RR for ASPD in siblings of 2.0 (95% CI, 1.5-2.7) was somewhat higher than that in first-degree relatives generally. Cox proportional hazards regression ratios for these disorders (interviewed relatives only) are 2.9 (P < .001) for DSM-III-R alcohol dependence, 3.2 (P < .001) for DSM-IV alcohol dependence, 3.2 (P < .001) for drug dependence, 1.1 (P=.08) for major depression, 1.7 (P=.16) for mania, and 2.5 (P < .001) for ASPD.
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Table 4. Age-Corrected Prevalence of Selected Disorders in Interviewed First-Degree Relatives of Alcohol-Dependent Probands Compared With Interviewed Controls and in All First-Degree Relatives Compared With All Controls*
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Prevalence estimates were also determined separately by sex (data not shown). Generally, similar patterns are seen in RRs for male and female relatives, with an exception being an RR of 1.8 (95% CI, 1.4-2.4) for ASPD in men compared with an RR of 1.2 (95% CI, 0.8-1.8) in women. As expected, the absolute prevalence rates in male relatives exceed those in female relatives for DSM-III-R alcohol dependence (48.0% vs 26.3%), DSM-IV alcohol dependence (37.1% vs 20.7%), drug dependence (26.0% vs 15.9%), and ASPD (11.7% vs 4.6%). Prevalence rates in female relatives exceed those in male relatives for depression (22.8% vs 16.0%). Prevalence rates for alcohol dependence for each sex are plotted by age in Figure 1; significant effects of sex, and study vs control status, are seen. Rates of alcohol dependence in female relatives of alcohol-dependent probands are not different from rates in male controls. Prevalence rates for all coaggregating disorders are shown by sex in Figure 2; significant effects of sex, and study vs control status, are seen. In addition to having a higher chance of having any of the coaggregating disorders, relatives have more disorders (mean ± SD, 1.3 ± 1.2 vs 0.6 ± 0.9 for men [Wilcoxon z = 12.6, P < .001]; and mean ± SD, 0.9 ± 1.1 vs 0.4 ± 0.7 for women [Wilcoxon z = 10.7, P < .001]).
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Figure 1. The development of DSM-III-R alcohol dependence in male and female relatives of alcohol-dependent probands and control subjects is shown. A significant effect of sex and relative vs control status (hazard ratio, 2.73; P < .001 [for both variables]) is seen.
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Figure 2. The development of any coaggregating DSM-III-R disorder (alcohol dependence, nonnicotine drug dependence, antisocial personality disorder, mood disorder, or anxiety disorder) in male and female relatives of alcohol-dependent probands and control subjects is shown. A significant effect of sex (hazard ratio, 1.55; P < .001) and relative vs control status (hazard ratio, 1.58; P < .001) is seen.
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Noting the relatively high prevalence of ASPD in the control group, we examined symptom counts for antisocial behavior in relatives and controls for 29 variables (to assess whether the ASPD-diagnosed controls were as symptomatic as the ASPD-diagnosed relatives of alcohol-dependent probands). Semi-Structured Assessment for the Genetics of Alcoholism–positive relatives of probands (n = 325) showed a mean ± SD of 18.2% ± 9.2% of possible symptoms and SSAGA-positive controls (n = 37) showed a mean ± SD of 21.3% ± 8.6% of possible symptoms. Semi-Structured Assessment for the Genetics of Alcoholism–negative relatives of probands showed a mean ± SD of 11.9% ± 3.1% of symptoms and SSAGA-negative controls showed a mean ± SD of 11.0% ± 3.2% of symptoms. Subjects with false-negative SSAGA results (compared with best estimate results in the same person) showed 14.8%±6.5% of symptoms, whereas subjects with true-negative SSAGA results showed 11.5%±3.0% of symptoms. If we make the conservative assumption that SSAGA-negative subjects with a score of 18% or more are actually false negatives (only 2.3% of true negatives have that many symptoms), the prevalence estimate of 3.6% in controls using raw data (Table 2) would increase to 5.4%; this tends to support our final estimate of 5.3% in controls.
Because cohort effects have been reported for alcoholism and depression,28, 31-33 we controlled our data for cohort effects (linear and quadratic) by dividing subjects into decade of birth (Table 5). We also controlled for sex, ethnicity, ascertainment site, comorbidity in probands, and comorbidity in relatives. These analyses include results for interviewed subjects only (because the estimates for all relatives include projected data). Most disorders studied continue to show aggregation with these effects accounted for, including alcohol dependence (by DSM-III-R, DSM-IV, Feighner et al,27 and ICD-10), alcohol abuse (by DSM-III-R), all forms of substance dependence except for opiate dependence, ASPD, major depression, obsessive-compulsive disorder, panic disorder, and PTSD. Thus, these disorders are found in increased rates in relatives of alcoholic probands, independent of whether the proband has the disorder or whether the relative has comorbid alcoholism. If we rerun the analysis without controlling for comorbidity (data not shown), the categories of any anxiety disorder and opiate dependence are significantly aggregated as well, suggesting that in these cases it is a comorbid disorder (alcohol dependence plus any anxiety disorder or alcohol dependence plus opiate dependence) that is familial. The correction for comorbidity applied herein is a conservative one, testing the hypothesis that the pure form of one (noncomorbid) disorder is related to the pure form of the other. This underestimates the common genetic variance represented by familial comorbid illness, which is substantial.
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Table 5. Data for Familial Aggregation of Various Disorders in Relatives of Alcohol-Dependent Probands*
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COMMENT
Alcohol dependence diagnosed by any of 4 criterion sets is clearly familial. The risk ratio we report herein is somewhat lower than has been reported in other family studies (those reviewed by Nurnberger and Gershon34 and Merikangas and Risch35). This is primarily related to our control values, which range (Table 3) from 10.76% (ICD-10) to 21.88% (Feighner et al27). Relative risk increases modestly, progressing from the more inclusive criteria of Feighner et al to the more restrictive criteria of ICD-10. The high rates in controls are also seen in the raw data (15.93% for DSM-III-R and 16.92% for Feighner et al), and are slightly increased by age correction, correction for SSAGA false negatives and false positives, and inclusion of uninterviewed relatives. Of course, these are lifetime diagnoses and quite often do not represent an active drinking problem. The best approximations we have for the true rate of DSM-IV alcohol dependence in controls come from the data summarized in Table 4 (14.4% in all relatives). This is comparable to the SSAGA-corrected rate of 13.56% in Table 3 (our best estimate of true prevalence in interviewed relatives only).
It may be argued that the present set of controls has been selected for health, in that the participation of spouse and children were required. However, the rate of DSM-III-R alcohol dependence we report in controls, based on direct interview (17.3% [Table 4]), is similar to the rate reported by Kessler et al (14.1%) in the National Comorbidity Survey.20 Rates of major depression, drug dependence, mania, and ASPD (17.5%, 7.6%, 0.5%, and 3.3%, respectively) are also generally comparable to National Comorbidity Survey rates (17.1%, 7.5%, 1.6%, and 3.5%, respectively). The alcohol-dependent probands, with 4 participating first-degree relatives, may also be relatively healthy because they represent affected persons with some family ties. In that regard, the substantially increased rates of illness in family members are even more remarkable.
We may also ask about the true RR of alcohol dependence in relatives of alcoholic probands compared with controls. It seems that it is safe (and conservative) to estimate an RR of about 2. Higher rates are seen with ICD-10 and DSM-IV criteria in Table 2, but they are decreased when best estimate procedures are used. Estimates of RR in siblings alone (the Risch  ) also give an estimate of about 2 (data not shown).
Alcohol abuse (DSM-IV) does not cluster in families of alcohol-dependent probands. Semi-Structured Assessment for the Genetics of Alcoholism–corrected rates are 4.0% and 3.5% for relatives of probands and controls, respectively, for the DSM-III-R definition of abuse, and 12.6% and 11.2%, respectively, for the DSM-IV definition. Cocaine abuse, opiate abuse, sedative abuse, and stimulant abuse also show no difference between familial groups in SSAGA-corrected rates. Rates of abuse are low in relation to rates of dependence. In controls, corrected data show 6-fold more subjects with DSM-III-R alcohol dependence than abuse; the comparable value forDSM-IV alcohol dependence and abuse is 1.2-fold; for cocaine, 2-fold; for marijuana, 3-fold; for opiates, 2-fold; for sedatives, 2-fold; and for stimulants, 2-fold. In relatives of alcohol-dependent probands, the values are generally higher (eg, 2-fold for DSM-IV and 9-fold for DSM-III-R alcohol dependence vs alcohol abuse). It may be that abuse is underdiagnosed using the present criteria, that dependence is overly diagnosed, or that both are true. On the other hand, it may be more accurate to think of these disorders as truly independent of each other. These issues have also been discussed by Grant and colleagues.36-40
In contrast to abuse, all forms of nonalcohol substance dependence show aggregation in relatives of alcoholic probands, including cocaine (RR, 3.1), marijuana (RR, 1.8), opiates (RR, 2.5), sedatives (RR, 2.0), stimulants (RR, 2.7), and tobacco (RR, 2.2). In fact, the RR for any form of drug dependence excluding tobacco is 2.3, which is equal to or greater than the RR for alcohol dependence by any definition. This is consistent with studies showing evidence for a generalized genetic predisposition to substance dependence41-44 as well as specific factors related to alcohol dependence. Support for specific genetic factors for substance dependence other than alcohol would require probands with other forms of substance dependence (which is beyond the scope of this study).
The excess of ASPD diagnoses in relatives of alcoholic probands is consistent with many previous studies. The prevalence of ASPD in controls in this study is relatively high. Estimates in these data vary from 3.3% in interviewed relatives to 5.2% in all relatives to 6.2%, applying corrections for SSAGA false negatives and false positives. However, estimates of ASPD in relatives of alcoholic probands are consistently higher (7.1%, 8.1%, and 8.8%, respectively).
Individual anxiety disorders that remain modestly but significantly aggregated after controlling for multiple factors include obsessive-compulsive disorder, panic disorder, and PTSD. The rates of some of these disorders in relatives were determined as part of previous reports,45-46 and do not seem substantially different in the present (expanded) data set. The excess of anxiety disorders in relatives cannot, in general, be explained by the presence of an anxiety disorder in the proband (except for the category of any anxiety disorder). Previous studies of PTSD have shown ambiguous results in the assessment of the familial relationship with alcohol dependence.47-48
There is a modest excess of major depression (odds ratio, 1.35) in relatives of alcoholics after controlling for multiple factors. In a separate analysis, the rate of comorbid major depression was not elevated in alcoholic probands, although the rate of secondary depression (depression in the context of heavy drinking or other organic precipitants) was elevated.49 Secondary depression also seemed to be elevated in relatives of alcoholic probands in that study, and comorbid alcoholism and depression aggregated in families. There is no evidence for aggregation of mania in relatives of alcoholic probands (Table 4). Comorbid mania does seem to be elevated in alcoholic subjects themselves in the Collaborative Study on the Genetics of Alcoholism data set.50 Comorbid alcoholism and mania are also more likely to appear in relatives of comorbid (alcohol-dependent and manic) probands.50
Attention-deficit/hyperactivity disorder23 and bulimia51 have been reported in some previous studies to be related to alcohol dependence. We cannot confirm a relationship in this population.
The addition of family history data on uninterviewed relatives resulted in minor adjustments in prevalence estimates (the exception being DSM-IV alcohol dependence in controls), suggesting that the diagnostic profile of uninterviewed relatives was fairly similar to that of the interviewed relatives.
An excess of men among subjects diagnosed as having externalizing disorders and an excess of women among those diagnosed as having depression would be expected from previous studies.
Variation in rates of illness by ascertainment site may reflect different sources for controls at different sites (eg, motor vehicle records vs dental clinics). Our multivariate analysis considered site as a confounding variable and still revealed significant coaggregation of multiple disorders.
Family studies by their nature include environmental effects, genetic-environmental interactions, and genetic effects. One complex effect is that of assortative mating, which is known to occur in families with cases of alcohol dependence.52-53 These effects may never be completely controlled for; analysis of sibling pairs, however, eliminates the effect of multigenerational assortative mating and shows generally similar results to analysis of all first-degree relatives.
Family studies may suggest new phenotypes for genetic linkage and association studies. It would be useful to consider ASPD or the combination of ASPD and alcohol dependence as a genetic phenotype. Linkage studies of habitual smoking in this sample have been reported,54 as have linkage studies of alcoholism and/or depression.49 Because evidence in the present analysis suggests that anxiety disorders (specifically, panic disorder, PTSD, and any anxiety disorder) aggregate in relatives of alcoholics independent of comorbidity, it would seem useful to test anxiety as a possible alternate phenotype within families with alcohol dependence.
Another, and perhaps more general, role of family studies is to define the familial/genetic relationships between disorders. A disorder more common in relatives than controls may share specific genetic vulnerability factors with the illness in the proband. In combination with twin studies, we may think of the genetic spectrum of alcohol dependence as including not only ASPD but also multiple forms of drug dependence and some forms of depressive and anxiety disorders. This familial coaggregation is distinct in origin and significance from comorbidity (multiple disorders in the same person), which may result from secondary effects of one disorder on another. Coaggregation in families is more likely to represent shared genetic variance.
AUTHOR INFORMATION
Correspondence: John I. Nurnberger, Jr, MD, PhD, Institute of Psychiatric Research, Indiana University School of Medicine, 791 Union Dr, Indianapolis, IN 46202-4887 (jnurnber{at}iupui.edu).
Submitted for Publication: August 12, 2003; final revision received April 23, 2004; accepted April 30, 2004.
Funding/Support: This national collaborative study is supported by grant U10AA08403 from the National Institute on Alcohol Abuse and Alcoholism, Bethesda, Md.
Previous Presentation: This study was presented in part at the Research Society for Alcoholism meeting; June 29, 2002; San Francisco, Calif, and June 23, 2003; Ft Lauderdale, Fla.
Acknowledgment: The Collaborative Study on the Genetics of Alcoholism (principal investigator: H. Begleiter, coprincipal investigators: L. Bierut, H. Edenberg, V. Hesselbrock, B. Porjesz)includes 9 different centers where data collection, analysis, and storage occur. The 9 sites and principal investigators and coinvestigators are as follows: University of Connecticut, Hartford (V. Hesselbrock); Indiana University School of Medicine, Indianapolis (H. Edenberg; J. Nurnberger, Jr; P. M. Conneally; and T. Foroud); University of Iowa, Iowa City (R. Crowe and S. Kuperman); State University of New York Health Sciences Center at Brooklyn (B. Porjesz and H. Begleiter); Washington University, St Louis, Mo (L. Bierut, J. Rice, and A. Goate); University of California at San Diego (M. Schuckit); Howard University, Washington, DC (R. Taylor); Rutgers University, Piscataway, NJ (J. Tischfield); and Southwest Foundation, San Antonio, Tex (L. Almasy). Lisa Neuhold serves as the National Institute on Alcohol Abuse and Alcoholism staff collaborator. This national collaborative study is supported by National Institutes of Health grant U10AA08403 from the National Institute on Alcohol Abuse and Alcoholism.
In memory of Dr Reich, coprincipal investigator of the Collaborative Study on the Genetics of Alcoholism since its inception and one of the founders of modern psychiatric genetics, we acknowledge his immeasurable and fundamental scientific contributions to the Collaborative Study on the Genetics of Alcoholism and the field.
Author Affiliations: Institute of Psychiatric Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis (Drs Nurnberger, O’Connor, and King and Messrs Wiegand and Meyer); and Departments of Psychiatry, Washington University, St Louis, Mo (Drs Bucholz, Reich, Rice, and Bierut and Mr Hinrichs), University of California, San Diego (Dr Schuckit), Robert Wood Johnson Medical School, Piscataway, NJ (Dr Petti), University of Iowa, Iowa City (Dr Kuperman), University of Connecticut, Hartford (Dr Hesselbrock), and State University of New York, Brooklyn (Dr Porjesz).
Deceased.
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