Genetic approach to the study of heterogeneity of affective disorders

Journal of Affective Elsevier

105

Disorders. 12 (1987) 105-113

JAD 00434

Genetic approach to the study of heterogeneity of affective disorders M. Gasperini,

A. Orsini, C. Bussoleni,

Instiiute of Clinical Psychiatry,

F. Macciardi

and E. Smeraldi

University of Milan, School of Medicine, Milan, Iialy

(Received 24 July 1986) (Revised, received 7 July 1986) (Accepted 18 December 1986)

In the present paper we compared the results of the application of segregation analysis, under two different single major locus (SML) transmission hypotheses, a dominant one with sex effect and a recessive one, to the families of 202 probands with major depression, recurrent and bipolar disorder. In the first analysis we considered only secondary cases with major affective disorders (bipolar disorders and major depression, recurrent), in the second one we included as affected phenotypes also relatives with atypical depression, dysthymic and cyclothymic disorders. Results indicated that considering spectrum disorders greatly modified familial segregation patterns.

Key words: Affective

spectrum

disorder;

Segregation

Introduction Whereas involvement of a specific genetic susceptibility in the etiopathogenesis of affective disorders is generally recognized, there is little information about whether the heredity system involved is always the same whatever the clinical manifestations. The unreliability of markers for genetic homogeneity is one of the most serious limitations of genetic linkage studies: we cannot successfully describe the main locus, since we do not have Address for correspondence: Prof. Enrico Smeraldi, Settore Didattico Ospedale S. Paolo, Istituto di Clinica Psichiatrica, Via A. di Rudini 8, 20143, Milan, Italy.

0165-0327/87/$03.50

0 1987 Elsevier Science Publishers

analysis;

Lithium

outcome;

Onset class

reliable biological parameters to test. Segregation analyses of each pedigree could not of themselves solve the question, because there is no way to generalize the results to the families as a whole. The multiple threshold models are also generally inadequate, the data obtained fit both in the antithetical hypotheses of a single major locus (SML) and a multifactorial polygenic model (MFP), maybe because they do not take into account information concerning the second-degree relatives. In previous studies we developed a strategy to generate subgroups of families as homogeneous as possible for the mode of transmission of affective disorders: we tested the pedigree of each family

B.V. (Biomedical

Division)

106 against two experimentally derived SML transmission hypotheses, a dominant one with a sex effect and a recessive one, and we calculated the log-likelihood ratios. This ratio does not indicate whether or not the mode of transmission involved is an SML one, but only whether or not the distribution of secondary cases in the family is more similar to the first or to the second model. Applying this strategy to a great number of informative families we obtained a distribution of log-likelihood ratios (Smeraldi 1985) significantly different from the normal distribution which would be obtained only if a single genetic mechanism underlies the susceptibility to the disease. We recognized at least 3 higher-frequency peaks: the peak of positive ratios represents a group of families with ratios consistent with dominant transmission, the families of the peak around zero may have a susceptibility system different from the recessive vs. dominant division, and the peak of negative ratios represents a group of families with a susceptibility system that fits the recessive model of transmission. Before applying these results to linkage analyses, we thought we should see whether this type of distribution also applies to intermediate clinical phenotypes which may be specifically linked to the same genetic susceptibility that leads to the spectrum of affective disorders. The distribution mentioned above was obtained restrictively, considering only secondary cases with major affective disorders (bipolar disorders and major depressions, recurrent) to be affected, while several experimental data (Perris et al. 1982; Hirschfeld et al. 1985; Orsini et al. in preparation) show that diagnostic classes that include dysthymic disorder, cyclothymic disorder and atypical depression, when occurring in informative families, could be incomplete forms (intermediate phenotypes) of the same disease, i.e., that from a genetic point of view they are clinical manifestations of the same affected genotype. The aim of the present study was to see what kind of log-likelihood ratio distribution we would obtain by including these other diagnoses in relatives as if they all belong to a single diagnostic spectrum of major affective disorders.

Material

and methods

Sample In the present study we examined 202 probands (97 men and 105 women), all diagnosed according to DSM-III criteria (American Psychiatric Association 1980) as having major depression, recurrent (61 men and 37 women) or bipolar disorder (36 men and 68 women), selected from the affective patients followed in the Institute of Clinical Psychiatry of the University of Milan. We selected only patients on whom complete information about second-degree relatives was available. The clinical and epidemiological information on the families of our patients (including firstand second-degree relatives), such as sex, current age, presence of affective disorders and age at onset, was collected by direct interviews with our probands and their relatives, as in our previous paper (Smeraldi et al. 1984a). We included in this study 984 first-degree and 3250 second-degree relatives. We considered to have major affective disorders those relatives that met DSM-III criteria for major depression, recurrent and bipolar disorders (208 cases). We diagnosed an affective spectrum disorder when the relatives met the DSM-III criteria for dysthymic disorder, cyclothymic disorder and atypical depression (70 cases). We collected information about outcome on long-term lithium treatment for 83 probands who had been on this therapy for at least 3 years. We considered as non-relapsed those patients who had no major affective episodes while on the above therapy regimen (59 patients) and as relapsed those who did (24 patients). For details about the treatment regimen see Smeraldi et al. (1984a). Segregation analyses We submitted our data to genetic analyses by the LIPED program, using the version of Morton and Kidd (1980). We used a linear function for age-correction within the age at onset ranges that were determined for our population in a previous work (Smeraldi et al. 1983) since the affective disorders have a wide range of age at onset. We tested the segregation patterns for each family by both of the two different hypothetical parametric sets (see Table 1) related to the single

107 TABLE

l-4 = 0.15

1

PARAMETRIC ANALYSES

SETS APPLIED

Dominant Penetrance Males Females

AA

Penetrance Males Females

Aa

Penetrance Males Females

aa

q-Value

0 0

IN OUR

model

SEGREGATION

Recessive model

0 0 CURVE

0.923 0.936

0 0.001

1 1

0.908 0.987

0.010

0.071

major locus (SML) genetic structure, the first one indicative of a recessive model and the second one indicative of a dominant model as already applied in previous works (Smeraldi et al. 1984a,b). We calculated the log-likelihood ratios (loglikelihood recessive model/log-likelihood dominant model), one for each family. These indicate the fits of the two genetic hypotheses. First we used as affected phenotypes only the relatives with diagnoses of major affective disorders. The next step was to see what the segregation pattern of each family would be with the same parametric sets as in the first analysis plus relatives with diagnoses of affective spectrum disorders. We then calculated these log-likelihood ratios. Comparing the two log-likelihood ratio distributions, we recognized three peaks of higher frequencies separated by classes of lowest frequency around -0.50 and +0.90 log-likelihood ratios. The distributions of the log-likelihood ratio values are graphed in Fig. 1 (only major affective disorders included) and Fig. 2 (including affective spectrum disorders), subdivided into classes of frequency with limits of - 3.375 and + 3.225 and 0.15 intervals. The figures and the parameters of the curves were obtained with the SPSS program (Nie et al. 1975). Statistical analyses

Data collected for our 202 probands with log-

PARAMETERS

MEAN KURT0818 STANDARD DEVIATION SKEWNESS

Fig. 1. Familial log-likelihood affective spectrum disorders.

ratios

= I

ml7 .OS7 1.184 = -505 q

distribution

excluding

likelihood ratio values grouped into the three peaks of Fig. 2 (affective spectrum disorders included) were subdivided according to the indicated segregation structures of their families into probands with log-likelihood ratio ‘type 1’ (33 subjects), ‘type 2’ (64 subjects) and ‘type 3’ (105 subjects). In a first analysis we used the number of relatives with affective spectrum disorders as the dependent variable, weighted for the number of relatives in the family. The independent variables were the number of relatives with major affective disorders and the type of familial log-likelihood ratio. These data were analyzed by the TABSUR program (Morabito and Marubini 1980) and by the method of Cox (1970). In a second analysis we used the number of suicides in relatives as the dependent variable, weighted for the total number of relatives in the family. In the first step the independent variables were the type of familial log-likelihood ratio and the classes of age at onset of the disease in the

CURVE

PARAMETERS

MEAN KURTOSIS STANDARD DEVIATION SKEWNESS

Fig. 2. Familial log-likelihood affective spectrum disorders.

ratios

= .7S7 = ,100 = 1.247 = -.SlS

distribution

including

108 proband according to those used by Weissman et al. (1984). In the second step the independent variables were the number of relatives with major affective disorders and the classes of age at onset of the disease in the proband. Cox’s method (1970) was used. All analyses were performed on the Sperry Univac 1100/90 Computer of the Milan University. Results

Before submitting our data to segregation analysis to evaluate the relationship between major affective disorders (major depression, recurrent and bipolar disorder) and affective spectrum disorders (dysthymic disorder, cyclothymic disorder and atypical depression) from a genetic point of view, we carried out an exploratory descriptive analysis of the weight of such diagnoses in our families as a whole and divided according to some characteristics of the proband. In our total sample of 202 families we found 70 relatives with dysthymic disorders, cyclothymic disorders and atypical depressions in a subset of 49 families, including 960 first- and second-degree relatives. We investigated the relationships between the presence of affective spectrum disorders in the pedigrees and several clinical and epidemiological features of their probands. With reference to the polarity of the clinical form in the probands of these families, there were more diagnoses of major depression, recurrent vs. those of bipolar disorders (UP = 59.2% vs. BP = 40.8%) than in the total sample (UP = 48.5%, BP = 51.5%). The probands of these families with secondary cases with spectrum disorders are mainly female (females 63.3%. males 36.7%) while the ratio between the proband sexes in the total sample is nearly balanced (females 52%, males 48%). A large number of the probands in this subset of families (53.1% vs. 41.5% for the sample as a whole) has been under treatment with lithium salts for long enough to provide information about their outcome on treatment. The percentage of lithium responders (good lithium outcome 69.2%) overlaps that for the total sample (71.2%). Segregation

From

analysis

the results

of the segregation

analysis

applied to these 49 families two observations could be made: (a) With the two SML hypotheses of transmission of the disease, dominant and recessive, the values of the log-likelihoods ranged between - 1.51 and - 13.19 for the dominant model and between - 2.51 and - 11.68 for the recessive one when we excluded the diagnoses of affective spectrum disorders. When the relatives with affective spectrum disorders were included, the log-likelihood values ranged between -1.51 and - 13.72 and between - 2.51 and - 12.68 for the two models. (b) Using the log-likelihood values estimated for each family according to the two different models of transmission, we calculated the log-likelihood ratios (recessive model log-likelihood/ dominant model log-likelihood). Comparing the log-likelihood ratio values estimated after excluding or including the affective spectrum disorders, we observed that 27 families always fell within the same peak of the distribution, 15 families shifted towards the left hand peak, changing the type of ratio, and only 7 shifted to the right. Clearly, the inclusion of affective spectrum disorders does not cause a single shift towards only one of the two genetic hypotheses of transmission. This finding could be due to the type of relationship of the affected relative in the pedigree. It is possible that if the subject with an affective spectrum disorder is placed in a position with a high probability of being affected according to the SML hypothesis of transmission, the family will shift towards the dominant model, while if his position indicates that he has few chances of being affected, the family will shift towards the recessive model. In this case, occurrence of the illness may be independent of the expression of the underlying genetic susceptibility. We investigated whether a relationship existed between the number of secondary cases with affective spectrum disorders in each family and the observed shifts in the log-likelihood ratio distribution. We found that 69% of the families with more than one relative with these disorders underwent a shift that changed the type of ratio, while only 36% of the families with one relative with a diagnosis of affective spectrum disorder changed group. In this case too, the main factor determining the shifts could be the position in the pedigrees

109 of the secondary affective cases. Summarizing, the distribution obtained after including the secondary cases with affective spectrum disorders (Fig. 2) is quite similar to the one from which they were excluded; we were still not dealing with a normal distribution (for details see the parameters of the distribution in Fig. 2). Statistical analyses The consistency of the distribution makes it possible to test the relationship between the classes of ratio generated with each genetic strategy and the presence of affective spectrum disorders. As a matter of fact, pedigrees with and without affective spectrum disorders were present in all three familial groups selected by the segregation analysis. On the other hand, we also surveyed the effects of classifying such diagnoses as affected phenotypes in each family on the evaluation of the familial log-likelihood ratio and therefore on the favoured model of transmission. We carried out a statistical analysis to test for the existence of different relationships between major affective disorders and affective spectrum disorders in the secondary cases of the three groups of families. For this purpose we thought it necessary to take into account the size of our families, since the number of relatives is a factor that might strongly influence the possibility of finding secondary cases in pedigrees. Therefore we weighted our dependent variable, i.e., the number of relatives with affective spectrum disorders, for the number of relatives in each family, so that we could consider the variable to be an index of risk exposure. We feel that only in this way differently sized families can be compared although each family included some second-degree relatives. Table 2 lists our data arranged according to the variables. The effects of ‘number of relatives with major depressions, recurrent and bipolar disorders’ and ‘type of log-likelihood ratio’ on the dependent variable, morbidity risk for affective spectrum disorders, were determined. Table 3 shows the results of this analysis, indicating the significance of the variable ‘number of relatives with major affective disorders’ for the ‘number of relatives with affective spectrum disorders’ (first step of the analysis). However when

TABLE 2 POSITIVE OR NEGATIVE FAMILY HISTORY FOR MAJOR AFFECTIVE DISORDERS AND AFFECTIVE SPECTRUM DISORDERS ACCORDING TO THE THREE TYPES OF FAMILIAL LOG-LIKELIHOOD RATIO In brackets: the familial mean sizes. Family history

Type of log-likelihood ratio 1

No affective spectrumdisorders No major 0 affective disorders Presence of major affective disorders

21 (16.1)

2

3

4 (14.2)

58 (17.6)

37 (17.3)

$0)

Presence of affective spectrum disorders No major 2 6 affective disorders (13.5) (15.7) Presence of major 17 affective disorders 10 (19.6) (19.0)

7 (23.1)

(2l.4)

we took into account the type of familial log-likelihood ratio, the significance of the variable ‘number of relatives with major affective disorders’ disappeared and we observed a greater statistical significance of the variable type of familial loglikelihood ratio. That is, the more relatives with affective spectrum disorders the more relatives with major affective disorders when they alone are considered, but the relationship is even stronger for the families that match the recessive model of transmission (type 1 log-likelihood ratio). Afterwards we used an analogous system to analyze our data, this time including the number

TABLE 3 STATISTICAL SIGNIFICANCE DICTING THE FREQUENCY TRUM DISORDERS Variables

Beta

Number of relatives with major affective disorders -0.1366 Type of ratio

OF VARIABLES IN PREOF AFFECTIVE SPEC-

Z

Beta

Z

-2.2711

-0.0629

- 1.0177

0.3930

3.5250

110 of suicides in relatives in relation to the number of secondary cases with major affective disorders and the type of familial log-likelihood ratio (for these data see Table 4). As one can see in Table 4, the observed frequencies of suicides in relatives are not homogeneously distributed among the three groups: incidence was higher in the group of families with a preferential recessive model of transmission. Therefore, we analyzed the relationship between the number of secondary cases of suicides and some variables that are more or less expressions of a genetic susceptibility to the disease, such as the type of familial segregation (mode of inheritance), the total number of secondary cases in the pedigree and the proband age at onset of the disease divided in classes according to Weissman et al. (1984). Tables 5 and 6 show the results of this analysis: in this model we also corrected the number of suicides (dependent variable) for the number of relatives in the pedigree. We analyzed separately the correlation between the number of suicides and the number of affected relatives or type of ratio, since we thought that these last two overlapped. Table 5 shows the statistical significance of the variables ‘type of ratio’ and ‘classes of age at onset’. It is important to note that ‘type of ratio’ is statistically significant whether or not the analysis includes the variable ‘age at onset’. Table 6 reports the statistical significance of the variables ‘number of relatives with major affective disorders’ and ‘classes of age at onset’ of the disease in the proband. We observed a higher frequency of suicides in the families that fit the SML recessive model of

TABLE

4

FREQUENCY OF SUICIDES AND TIVE DISORDERS IN FAMILIES SEGREGATION STRUCTURES

OF MAJOR AFFECWITH DIFFERENT

Type of log-likelihood

Number of suicides Number of relatives with maJor affective disorders

ratio

1

2

3

12

11

7

67

75

65

(17.91%)

(14.67%)

(10.77%)

TABLE

5

STATISTICAL SIGNIFICANCE DICTING THE FREQUENCY

OF VARIABLES OF SUICIDES

Variables

Beta

Z

Type of ratio

0.2654

2.7486

Classes of age at onset

TABLE

Beta

IN PRE-

Z

0.3006

3.0860

- 0.1780

~ 2.3373

6

STATISTICAL SIGNIFICANCE DICTING THE FREQUENCY

OF VARIABLES OF SUICIDES

Variables

Beta

Z

Beta

Z

Number of relatives with maJor affective disorders

~ 0.3215

-4.4107

-0.3408

-4.5630

-0.1714

-2.2746

Classes of age at onset

IN PRE-

transmission and in the families with more relatives with major affective disorders. The highest incidence of suicides was also found in the relatives of probands with later ages at onset. This finding cannot be interpreted only as an expression of differential genetic loads, since both suicides and early onset are linked to greater severity of the disease. It could also be due to the higher mean ages of the relatives of probands with later onset, which implies a greater chance of diagnosing them as affected. Discussion

One important outcome of this study is the recognition of significant effect of spectrum diagnoses on the pattern of the segregation analyses. Using spectrum disorders to detect secondary cases of depressive disease greatly modified-the familial segregation patterns of our affective probands. This indicated the great importance of using intermediate phenotypes to have more precise indications of susceptibility to use in segregation studies (Goldin et al. 1983). Recent studies have provided interesting indications for new approaches to study depressive illness. Attention should be given to the situational major depressive disorders (Hirschfeld et al.

111 1985) since they share identical familial backgrounds with major depressive disorders, in addition to similar clinical pictures and require the same social supports, even though they have earlier onset and fewer hospitalisations. They might be viewed and studied as indicators of non-situational depression. In the same way, criteria for personality disorders, according to the second axis of DSM-III (American Psychiatric Association 1980), could be useful to identify some overlapping traits with affective disorders among personality disorders. A sample of patients with some of the personality disorders associated with affective disorders has recently been compared with a sample of affective patients without personality disorders for several clinical and biological characteristics that might be specific for more severe illness. There were no differences between the two groups of patients in the Dexamethasone Suppression Test (DST), in response to treatment and in familial risk for depression. The group of patients with personality disorders plus depression seems to share some things with Akiskal’s character spectrum disorders patients (Akiskal 1983) and with Winokur’s depression spectrum disease patients (Winokur 1972). Several investigators have suggested that low-grade or atypical affective disorders might masquerade as personality disorders (Liebowitz and Klein 1979; Akiskal et al. 1980). Perhaps a broader concept of the depressive spectrum, including personality disorders, needs to be considered and investigated when doing segregation analyses. It is possible that looking for other psychiatric disorders to include in the spectrum might cause us to review the classification of some disorders, such as panic disorders, and including them in depressive illness. Many reports in the literature have suggested that such disorders are closely linked with depression. This is supported by several similarities in clinical and pharmacological profiles and in familial risks for the disease. Since for genetic studies it is of great interest to study the segregation of susceptibility to the disease and not the segregation of the disease, we used segregation analyses to identify the familial structures of susceptibility whatever the phenotypical manifestations. In this sense, purely clinical diagnoses would be provided with external

validity criteria and the study of the susceptibility mode of transmission allowed us to group disorders with symptomatological similarities but with different characteristics, for instance recurrence and duration. We have found that some affective spectrum disorders belonging to the diagnostic first and second axes of DSM-III (American Psychiatric Association 1980) share the specific susceptibility system for the major affective disorders. We have observed (Table 3) that the incidence of dysthymic disorders, cyclothymic disorders and atypical depressions is significantly related to the presence in families of such other diagnoses as major depression, recurrent and bipolar disorders and that the affective spectrum disorders are more frequent in those families that fit the dominant model of transmission. Furthermore, the statistical analyses showed that the subdivision obtained by applying genetic models is a more specific detector of families with differential risks for affective spectrum disorders than a purely diagnostic subdivision. Our data also confirm the genetic heterogeneity of the disease. We found a distribution of familial segregation structures that clearly differs from normality and includes three peaks of higher frequency. The identification of these three subgroups of families, which may share common genetic backgrounds within each peak and have different modes of transmission of the disease susceptibility among the three peaks, is a starting point for looking for genetic or biological markers of the disease, and indicate that it will be useful in linkage studies. Nevertheless the genetic reclassification of our families needs further validation. Among the available information about our probands and their relatives two kinds of data may be useful to indicate the reality of the genetic heterogeneity observed: these are the outcome after long-term lithium therapy and the incidence of suicides among secondary cases. In a previous study taking into account only major affective disorders (Smeraldi 1985) we analyzed the frequencies of good outcome on lithium preventive treatment in the three subgroups of probands identified by segregation strategy: we found that patients in the three groups did not differ in the rates of relapse.

112 In the present study we observed that the rates of non-relapsed patients under lithium treatment in families with and without affective spectrum disorders were not significantly different. However, it is rather difficult to define patients as good or poor responders to preventive lithium therapy because of differences in spontaneous relapsing rates and it is even more difficult to collect reliable information about the outcome on lithium treatment of their relatives. Moreover, since lithium salts therapy is not generally suitable for the affective spectrum disorders, we did not use the outcome on lithium treatment as an external parameter to validate our genetically obtained subdivision. In our previous paper (Smeraldi 1985) we also observed that different familial segregation patterns were related to differential risks for suicides among secondary affective cases. This finding was confirmed in the present study and suicides may be a clinical criterion of severity linked to the severity at the point of the genetic load. It was of great interest to us that the heterogeneity observed in rates of suicides is also significantly related to other variables that are phenotypical manifestations of susceptibility to the disease. We observed the highest frequencies of suicides among secondary affective cases of families with a type of ratio indicating a recessive model of transmission. Furthermore the highest rates of suicides were found in the families with the highest incidence of secondary cases of major affective disorders and whose probands had a later onset of the disease. We adopted classes of age at onset in agreement with Weissman et al. (1984) because they have been already tested and validated in another population against some epidemiological variables. The differences in suicide risks might be associated with monoaminergic abnormalities in a subgroup of subjects with affective disorders in whom the serotonin abnormality seems to be a true susceptibility factor. In the liierature, low post-probenecid CSF 5HIAA responses have been reported in depressed patients with the highest rates of suicides (Van Praag 1982) and in patients with higher frequencies of secondary affective cases (Van Praag and De Haan 1979; Sedvall et al. 1980). Another relevant but debated biological marker

for affective disorders is the response in the Dexamethasone Suppression Test (DST). The literature reports many interesting studies of associations between DST results and several clinical and epidemiological characteristics of the depressed patients. Nevertheless, although differences have been found between suppressor and non-suppressor patients with regard to age, life events, suicide attempts, premorbid personality disorders, rate of treated alcoholism and antisocial personality in their first-degree relatives, there was no positive association between DST results and family history of depression (Zimmerman et al. 1986). References Akiskal, H.S., Dysthymic disorder - Psychopathology of proposed chronic depressive subtypes, Am. J. Psychiatry, 140 (1983) 11-20. Akiskal, H.S., Rosenthal, T.L., Haykal, R.F.. Lemmi, H., Rosenthal. R.H. and Scott-Strauss, A., Characterological depressions - Clinical and sleep EEG findings separating ‘subaffective dysthymias’ from character spectrum disorders. Arch. Gen. Psychiatry, 37 (1980) 777-783. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 3rd edn., American Psychiatric Association, Washington. DC, 1980. Cox, D.R., Analysis of Binary Data. Chapman and Hall. New York, 1970. Goldin. L.R., Gershon, E.S., Targum, S.D., Sparkes, R.S. and McGinnis, M., Segregation and linkage studies in families of patients with bipolar, unipolar and schizoaffective mood disorders, Am. J. Hum. Genet., 35 (1983) 274-287. Hirschfeld, R.M.A., Klerman, G.L., Andreasen, N.C.. Clayton, P.J. and Keller, M.B.. Situational major depressive disorders, Arch. Gen. Psychiatry, 42 (1985) 1109-1114. Liebowitz. M.R. and Klein, D.F., Hysteroid dysphoria, Psychiatr. Clin. N. Am., 2 (1979) 555-575. Morabito, A. and Marubini, E., TABSUR: a program featured to aid statistical analysis in cancer clinical research. In: Analyse des Don&es. INRIA. Le Chesnay, Naples, 1980. Morton. L.A. and Kidd. K.K., The effects of variable age of onset and diagnostic criteria on the estimates of linkage An example using manic-depressive illness and color blindness. Sot. Biol., 27 (1980) l-10. Nie, N.H.. Hull. C.H., Jenkins. J.G., Steinbrenner. K. and Bent, D.H., SPSS Statistical Package for the Social Sciences, 2nd edn., McGraw-Hill, New York, 1975. Orsini, A., Gasperini, M., Smeraldi, E., et al.. Genetic study of affective disorders in Italian and Swedish populations. In preparation. Perris. C.. Perris, H., Ericsson, U. and Von Knorring, L.. The genetics of depressions. A family study of unipolar and neurotic reactive depressed patients, Arch. Psychiatr. Nervenkr.. 232 (1982) 1377155.

113 Sedvall, G., Frye, B., Gulberg, B. et al., Relationships in healthy volunteers between concentration of monoamine metabolites in cerebrospinal fluid and family history of psychiatric morbidity, Br. J. Psychiatry, 136 (1980) 366. Smeraldi, E.. Genetic aspects in affective disorders. Paper presented at EMRC Workshop on ‘Prediction of Course and Outcome in Depressive Illness: Needed Areas for Research’, Cork, Ireland, 28-30 August, 1985. Smeraldi, E.. Gasperini, M., Macciardi, F., Bussoleni, C. and Morabito, A., Factors affecting the distribution of age of onset in affective patients, J. Psychiatr. Res., 17 (1983) 309-317. Smeraldi, E., Petroccione, A., Gasperini, M., Macciardi, F.. Orsini, A. and Kidd, K.K., Oucomes on lithium treatment as a tool for genetic studies in affective disorders, J. Affect. Disord., 6 (1984a) 139-151.

Smeraldi, E., Petroccione, A., Gasperini, M.. Macciardi. F. and Orsini, A., The search for genetic homogeneity in affective disorders, J. Affect. Disord.. 7 (1984b) 99-107. Van Praag, H.M. and De Haan, S., Central serotonin metabolism and frequency of depression, Psychiatr. Res.. 1 (1979) 219. Weissman, M.M., Wickramaratne, P., Merikangas, K.R., Leckman. J.F., Prusoff, B.A., Caruso, K.A., Kidd, K.K. and Gammon, G.D., Onset of major depression in early adulthood: increased familial loading and specificity, Arch. Gen. Psychiatry, 41 (1984) 1136-1143. Winokur, G., Depression spectrum disease - Description and family study, Compr. Psychiatry, 13 (1972) 3-8. Zimmerman, M., Coryell, W. and Pfohl, B., The validity of Dexamethasone Suppression Test as a marker for endogenous depression, Arch. Gen. Psychiatry. 43 (1986) 347-355.