The Salmon lecture

Clinical Neuroscience Research 2 (2002) 110–119 www.elsevier.com/locate/clires

The Salmon lecture Psychiatric genetics: an intellectual journey Kenneth S. Kendler a,b,* a

Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia, Virginia Commonwealth University, PO Box 980126, Richmond, VA 23298-0126, USA b Department of Human Genetics, Virginia Institute for Psychiatry and Behavioral Genetics, Medical College of Virginia, Virginia Commonwealth University, PO Box 980126, Richmond, VA 23298-0126, USA

Abstract My purpose here is to outline the main features of my scientific career in psychiatry. In so doing, I will also illustrate a number of the scientific themes that have arisen in psychiatric genetics over the last 20 years. This is a necessarily selective and, at the ripe-old age of 51, a somewhat self-indulgent exercise. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Psychiatric genetics; Research; Schizophrenia

1. First inklings I entered my Psychiatric Residency in 1977 with two clear, if somewhat contradictory goals. I wanted to be a well-trained clinical psychiatrist and I wished to be a scientist. Given my dim knowledge of psychiatric research, I could then see only one possible way to achieve these two goals – to become a biological psychiatrist. In my first several weeks of training, a key event occurred. I had been caring for a young man – we will call him Jeff – who had a classical paranoid psychosis. He demonstrated extensive ideas of reference and non-bizarre persecutory delusions with no thought disorder, hallucinations or affective deterioration. Late one afternoon, one of our senior professors, who was conducting a study of dopamine metabolites in the cerebro-spinal fluid of schizophrenic patients, asked me to write the pre-lumbar puncture orders. Being a bit full of myself as a first-year resident and probably wanting to show off my diagnostic acumen, I protested “but Dr X, Jeff doesn’t have schizophrenia. He has a paranoid psychosis”. Dr X replied “close enough”. I pondered this response for some time. My main concern was ‘how would you know?’ That is, how could we answer the question of whether Jeff’s illness was or was not ‘close enough’ to schizophrenia to be included in a study on the biology of the disorder? One approach to this problem was what I came to call the ‘Great German Professor Principle’.

That is, the answer was ‘because Kraepelin, Bleuler, or Schneider said so’. While this answer may have made the clinician in me happy, it was hardly satisfying to the scientist. I started reading widely both in the classical literature of descriptive psychiatry and, beginning with the famous article of Robins and Guze [1], what was then known about empirical approaches to the problems of psychiatric diagnosis. They proposed five phases for establishing diagnostic validity in psychiatric illness: clinical description, laboratory studies, delimitation from other disorders, follow-up study and family study. The weight of the validation process fell, according to their system, on the final two steps where the goal was to demonstrate diagnostic homogeneity over time and familial aggregation of the putative syndrome. I thought these criteria could be expanded upon – coming up with a list of antecedent validators (e.g. family studies, premorbid personality, demographic factors and precipitating factors), concurrent validators (e.g. psychological or biological test data) and predictive validators (e.g. diagnostic consistency, overall functioning over time and response to treatment) [2]. Using this framework, I tried to review all the world’s literature that addressed these questions for paranoia and schizophrenia. This review, which represented my first major psychiatric publication, concluded that Dr X was probably wrong. Paranoia (or delusional disorder as George Winokur [3] had proposed to call it) was probably a different disorder than schizophrenia.

* Tel.: 11-804-828-8590; fax: 11-804-828-1471. E-mail address: [email protected] (K.S. Kendler). 1566-2772/02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S15 66- 2772(02)0001 3-0

K.S. Kendler / Clinical Neuroscience Research 2 (2002) 110–119

2. First exposures – the joys of secondary data analysis Of all the potential validators that were available, I was particularly impressed with genetic strategies. Compared to other possible validators (with the possible exception of treatment response), genetic factors seemed to most closely reflect etiology. I had not, at the point, read widely in the psychiatric genetics literature. But, as I reviewed the scanty information available about the genetic relationship of schizophrenia and delusional disorder, it occurred to me that there must be some already collected data sets that would contain the needed information. I looked about and was most impressed with the Danish Adoption Studies of Schizophrenia [4,5]. Kety and colleagues had not been especially interested in the problem of delusional disorder. So, while still a resident, I wrote Dr Kety proposing a re-analysis of what was then the Copenhagen sample of the Danish Adoption Study. We exchanged letters and he was quite gracious. He said that he was not prepared to copy the thousands of pages of documents for me. But if I was willing to travel up to MacLean Hospital outside Boston, he would have them pulled for me. We agreed that I would stay completely blind and submit our diagnostic forms to him prior to him breaking the blind. Since it was likely to be a long and rather lonely job reviewing what I later learned to be 329 quite lengthy detailed interview protocols. I invited my friend and coresident, Dr Alan Gruenberg, to join me in the task. On some five weekends spread out over several months, we would both get up early on a Saturday morning outside New Haven and drive up to MacLean hospital. We would find a tall pile of interviews waiting for us and we worked hard for 2 straight days before heading home on Sunday night. Before we started, we outlined our code sheet. While we were reviewing these interviews, we thought it prudent to address a number of potentially interesting questions – so we coded for a rather wide range of psychiatric disorders using the then fairly new Diagnostic and Statistic Manual of Mental Disorders, Third Edition (DSM-III) criteria. I recall clearly doing the analyses using index cards and a pocket calculator. I had mastered the obscure art of x 2 analysis and that was all the statistical knowledge needed at that time. While we found that cases of schizophrenia spectrum disorder were highly concentrated in the biologic relatives of the schizophrenic adoptees, consistent with my prediction, this pattern was not seen for delusional disorder [6]. One particularly impressive result was to have a long-term impact on my interest in psychiatric genetics. Using rigorously applied DSM-III criteria, Alan and I had diagnosed 14 cases of schizotypal personality disorder in the 329 relatives. When we broke the blind, 11 of those cases occurred in the 105 biologic relatives of schizophrenia adoptees while the remaining three were found in the other 224 relatives [7]. I was stunned. The probability of such a distribution of cases occurring by chance was remote (x 2 ¼ 14.65, df ¼ 1,

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P ¼ 0:0001). I had been reading the biological psychiatry literature for a year or more at that point, but could not recall any finding nearly so robust. Along with John Strauss, Alan and I published a series of six articles in the Archives describing the results of our re-analysis of this rich data set. Since the work with the Danish Adoption Study had been so much fun and so productive, I looked around for another sample on which to work. Contact with Dr Ming Tsuang eventually led to a generous invitation for Alan and I to rereview and re-diagnose, using DSM criteria, the Iowa 500 and non-500 studies [8]. Now we had to fly to Iowa City and stay a week at a time. The work load was considerably greater than with the Danish sample – 510 index probands, 318 control probands and well over 2000 total relatives. It took us several trips and lots of meals in the hospital cafeteria before we were done. My parents had both gotten their PhDs in the Department of Psychology at the University of Iowa in the early 1940s. Indeed, I am named after their mentor, Kenneth Spence, for whom a major building is named on the University of Iowa campus. My mother used to earn extra money doing psychological evaluations of the psychiatric patients at the Iowa Psychopathic Hospital which was where the probands for the Iowa 500 and non500 studies were ascertained. Several times, in the charts we were reviewing, I came upon her clear and easily recognizable hand-written notes. When we broke blind, again the results were stunning. We diagnosed a total of 28 cases of schizophrenia in the relatives of the DSM-III schizophrenia probands and controls. Of those 28, 26 were amongst the 723 relatives of schizophrenia patients and two were amongst the 1056 relatives of controls. The P value for this finding was 0.00000008 [9]. I believe these experiences illustrate a principle of scientific research. Before I did these studies, I knew about the strong evidence for the familial aggregation of schizophrenia and schizophrenia spectrum. I had read the relevant articles. Nonetheless, when with my own and Alan’s labor – our own hands as it were – we reproduced such results, I was deeply influenced emotionally. This was a robust phenomenon.

3. Crisis and transition – biological psychiatrist or psychiatric geneticist? Had you asked me during my residency and immediate post-residency years about my research identity, I would have said with pride that I was a ‘biological psychiatrist’. During my residency at Yale, I was selected into a research track and worked on the question of when and how did plasma levels of dopamine metabolites reflect brain levels. Two years after my residency, I had a Veterans Administration (VA) career development award, was running a small laboratory and had just been funded with my first National Institute of Mental Health (NIMH) grant. I was at that time a bit embarrassed at the genetics work I was

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doing. It did not then fit into the Zeitgeist of the ‘scientific psychiatrist’. It was ‘soft’ and did not – at that time – involve real science, like getting tissues or fluids and measuring neurochemicals. Despite these successes, at the age of 32, I was increasingly dissatisfied with my career and made a switch from neuropharmacology to genetics. There were four main reasons for this critical decision. First, I had been reading in depth about the neurochemistry and neuropharmacology of the dopamine system for several years. It was also increasingly clear to me that we had no good way of obtaining accurate measures of the activity of the relevant dopamine pathways in living humans. I increasingly felt that the goal with which I had started – to understand the cause of schizophrenia through abnormalities of the brain dopamine system – was unrealistically simplistic. The idea – then quite current – that schizophrenia was ‘just too much functional dopamine at certain critical central nervous system (CNS) circuits’ was quickly losing its appeal to me as a useful paradigm. Second, I was in a lab coat killing and dissecting rodents all day. The work I was doing made no use of my clinical training. I had really liked seeing patients and was especially interested in the problems of psychiatric diagnosis. Third, I was getting much more impressive results from the genetics research than I was with the neurochemistry. Sure, I could change levels of plasma dopamine metabolites – but even with large changes in brain levels in rodents – using pharmacologic probes that could not possibly be used in humans – I was getting only modest changes in the plasma metabolites. I remember being especially discouraged when I was able to get much larger changes in plasma homovanillic acid (HVA) in humans by administering a high monoamine meal than by anything I could do to brain dopamine metabolism [10]. Finally, a key event was serendipitous. I got a catalog from a near-by college in the mail for summer school. One of the courses in genetics was taught by a well known population geneticist, Dr Lee Ehrman. I thought to myself – ‘I haven’t studied genetics since my second year in medical school. Maybe I should take a look at this’. The class was eye-opening. More importantly, looking for reading for my summer vacation, I was strolling through the library genetics section and lighted upon Douglas Falconer’s textbook on quantitative genetics [11]. I read it through with an increasing sense of wonder and excitement. I understood less than 10% of what I was reading but what I saw was that – underneath the primitive statistics I had been using – was a well-worked out, elegant, conceptually rigorous and carefully grounded scientific discipline of statistical genetics. I could dimly see that these methods might be applied to some really interesting psychiatric questions. I was hooked. I went back to my supervisor (Dr Kenneth Davis) and told him I wanted to switch careers to psychiatric genetics. He was quite generous and, given my career development

award, allowed me the flexibility to spend a year in transition commuting 1–2 days a week up to Ken Kidd’s lab at Yale, learning population and quantitative genetics and studying genetic epidemiology with Neil Risch, who was then at Columbia. It was an exciting year and I dabbled in mouse genetics [12]. It became clear, however, that I could not learn enough to ‘self-launch’ into a psychiatric genetics career. I had to move and work with specialists who were interested in working with me and training me. It was in those months that I first met Lindon Eaves – a brilliant, English statistical geneticist who much influenced my later career. From the very first, we hit it off and had a series of long conversations about possible interfaces between our interests. It was very intellectually exciting and I began to read deeply and widely in statistical genetics. My formal training in statistics was minimal and never quite up to the task, but I was blessed with a good quantitative sense and an ability to usually see the relevant overall statistical issue. This has over the years often, but not always, compensated for my lack of grit statistical and mathematical training.

4. Eaves, Birmingham and biometrical genetics So, 3 years out of residency, as an ‘ex-biological psychiatrist’ I came to the Medical College of Virginia. Lindon had the wonderful idea, in my negotiations with MCV, to request support to go back to study at Birmingham, England, one of the two great centers of British statistical genetics, and the place where he had received his genetics training. For 4 months, I took the statistical section of their year-long Masters of Science program in the genetics department. The teaching was wonderful. I read, studied and dreamed about statistical genetics without distraction. The conceptual rigor and beauty of the models – usually worked out in tractable genetic organisms like plants or fruit flies – deeply appealed to me. What has always particularly attracted me to this work is the possibility of rigorously evaluating competing hypotheses. Built firmly into the center of biometrical genetics is the idea that you advance science by carefully collecting data and then allowing different analytic models to compete with one another to try to explain the pattern of the data. Applying these methods to humans requires many compromises as none of the highly controlled genetic or environmental manipulations are possible. But, as Lindon once said, “that just means we will have to be that much smarter”. But it also means that we have to be more tolerant of ambiguity – always with the fear that some hidden bias in the data is misleading you. The first 2 years at Medical College of Virginia (MCV) after I got back were a very exciting time. I sat at a computer running statistical models some 30 h a week. Lindon was then surrounded by two of his most talented students, Andrew Heath and Nick Martin. I was the token American. They were very generous with their time as I asked lots of

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questions. It was in this scientific and interpersonal context that what I see as one of the two most important of my scientific works was conceived – the population based twin studies of psychiatric illness. The first systematic twin study of a psychiatric illness was on schizophrenia by Luxenburger and published in 1928 [13]. From then until the 1970s, well over a dozen major twin studies of psychiatric illnesses had been published. With rare exception, they all had the following key characteristics. First, twins had been found (the technical term is ascertained) through treatment facilities, almost always psychiatric hospitals or registers collecting information from more than one hospital. That is, to get into the study, the twins had to be sufficiently ill that they were hospitalized for treatment. Second, they examined severe psychiatric illness – usually schizophrenia or bipolar affective illness – although a few had studied major depression and alcoholism. Third, with the notable exception of Kallmann’s epic study of schizophrenia twins [14], sample size was almost always small, usually less than 100 pairs. Fourth, the clinical assessments were usually done by one person – usually a psychiatrist – non-blind to the zygosity and diagnosis of the cotwin. Fifth, little or no serious attempt was made to assess environmental risk factors, although some studies examined variables such as birth order or birth weight. Sixth, the statistical analysis of these studies was restricted to a comparison of the degree of similarity in identical or monozygotic twins and fraternal or dizygotic twins. A new paradigm for psychiatric twin studies emerged out of these discussions, the key participants in which were me, Andrew Heath and Lindon Eaves. Their intellectual creativity was critical to the development of this paradigm which had five key characteristics. First, it was population based. Instead of viewing twins through the lens of treatment facilities (with all the expected biases such as selection for comorbidity [15]), we wanted to study unselected twins. This was greatly facilitated by the existence at MCV of the population based Virginia Twin Registry which had been established by Walter Nance and Linda Corey in the late 1970s. Second, the conceptual and analytic approach to these studies was based on the rigorous corpus of biometrical genetics. This meant that our goal was to test a series of nested hypotheses of increasing complexity examining the role of genetic and environmental risk factors in the etiology of psychiatric and drug abuse disorders. I outline some of these issues below. Third, one of the consequences of having a clear conceptual framework within which to conduct our analyses was that it was now possible – given certain generally reasonable assumptions – to figure out the sample sizes we needed to study. Although many of the most relevant power analyses were yet to be done (e.g. ref. [16]), a key set of analyses on continuous traits [17] indicated that we needed to study much larger numbers of twins than had been attempted before in studies of psychiatric illness. (Of note, large twin studies had previously been conducted of ‘psychological’ traits including symptoms of

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depression and anxiety using mailed questionnaires [18,19]). Fourth, because such large numbers had to be assessed, we needed to borrow from psychiatric epidemiology the methods they had developed to accurately assess the history of psychiatric illness in large numbers of individuals. The old model for twin studies – where one psychiatric clinician takes a year or two off and drives around interviewing all the twins – was no longer feasible. Fifth, we took the environment seriously and indeed spent as least as much of our precious interviewing time in the evaluation of environmental risk factors as we did in the assessment of psychiatric phenotypes. This proved to be an immensely fertile paradigm. One thing became clear in our early discussions. We needed a psychiatric epidemiologist on our team. I asked several of my old colleagues who would be best to work with. They all said Ron Kessler. I still remember my ‘cold call’ to Ron describing the study and asking if he was interested. Fortunately, he was and our work together developed into a very fertile collaboration. He taught me a lot. Our first National Institute of Health (NIH) project – written in 1985 with funding started in 1986 – proposed to study 1100 pairs of adult female-female twins focusing largely on anxiety and depression. Since that time, we have conducted three further waves of interviews with this cohort of female twins and interviewed their parents. Beginning in 1992, we launched a more ambitious longitudinal study of adult male-male and male-female twins the goal of which was to complete two waves of interviews with approximately 1500 male-male monozygotic (MZ) and dyzygotic (DZ) and 1300 male-female DZ pairs. We are currently conducting a third wave follow-up with the male-male twin sample. It is not possible to here convey the full richness of the questions we have been able to ask in this expanding data set. I will list 11 issues. First, we have attempted to quantitate, using standard univariate biometrical twin analysis, the magnitude of the etiologic role of genetic and environmental risk factors in a wide range of psychiatric and substance use disorders [20–26]. Second, we have explored the genetic and environmental causes of comorbidity both among psychiatric disorders [27–30] and between psychiatric and substance use disorders [26,31,32]. Third, we have shown that a number of key putative ‘environmental’ risk factors, including stressful life events [33,34], social support [35], and parenting behavior [36] are themselves influenced by genetic factors. Fourth, we have demonstrated that using multiple occasions of assessment that model the impact of measurement error results in a substantially higher estimate of the heritability of MD [37] and phobias [38]. Fifth, applying a twin-family model to alcoholism in female-female twins and their parents, we demonstrated that parentoffspring transmission of the vulnerability to alcoholism appears to be due to genetic pathways [39]. Sixth, we incorporated directly into twin models measured rather than latent measures of environmental risk factors, demonstrat-

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ing the large potential gain in statistical power [40,41]. Seventh, we applied latent class analysis to a twin design to explore empirically the boundaries of nosologic categories [42,43]. Eighth, we developed models to incorporate multiple ratings into a twin and twin-family design that permits estimation of and ‘correction’ for biased ratings [44,45]. Ninth, we explored the validity of the key potential biases in the twin method, including selection bias, zygosity-dependent effects, pregnancy and birth complications and the equal environment assumption [46–48]. Tenth, we developed and applied models to investigate the impact of gene-environment interaction in the etiology of psychiatric disorders [49]. Eleventh, we developed and applied models to account for the ‘conditional’ nature of substance use and abuse – that is that risk factors for abuse can only be expressed conditional upon initial exposure [50,51].

5. Irish journeys When – as a Yale Resident – I was collecting all available information on schizophrenia and delusional disorder for my review paper, in a dusty section of the Yale Medical Library, I came upon a series of statistical reports from the Medico-Social Research Board at the wonderful address of 73 Lower Baggot Street in Dublin. While containing large amounts of data on discharge frequency and status, the reports had collapsed together the categories of schizophrenia (ICD 295) and paranoia (ICD 297). So, I dutifully wrote to the authors, one of whom was Dermot Walsh, inquiring whether they could generate data for me separating the two categories. They replied that they could for a certain fee and sent me a set of reprints. One of these described the threecounty case register established by the Irish government to carefully assess the long-held belief that the Irish had excess rates of madness [52]. We corresponded over the next year or so and I was so bold at one point to raise the issue of whether we might conduct a new family study of schizophrenia based upon these case registers. As with the twin work, one of my early methodologic concerns was the representativeness of samples used in psychiatric genetics research. The results of psychiatric genetic studies – be it the odds ratio of illness in the sibling of an affected versus control proband or the heritability statistic from a twin study – are all ultimately population based statistics. They are of limited value if the investigator does not have a good grasp of how the sample was obtained and how their results might be extrapolated back to the total population. While an earlier German and Scandinavian tradition sampled probands for psychiatric genetic studies using epidemiologic methods, the recent tradition in US psychiatric genetics had been exclusively to use samples of convenience. Probands were typically selected from patients treated at the institution where the investigator worked. The problem was that many of these were secondary and

tertiary referral centers and it would be quite difficult to know how typical such patients or their families were. My first visit to Ireland was when I was in Birmingham, England studying genetics. I spent a 3-day visit which included a memorable day-long drive to county Roscommon – the most westerly, rural and most hospitable site for a possible family study. The Scottish Rite funded a 2 year pilot study which then provided us the data to obtain NIMH funding for what I had hoped would be a relatively definitive family study of schizophrenia. The study included three proband groups. The schizophrenic probands represented all cases with a clinical diagnosis of schizophrenia from the Roscommon County Case Register born after 1930. We also sampled ,75% of all cases from the Register with a diagnosis of severe affective illness. The control sample was age and sex-matched to the two clinical proband groups and obtained from the population-based electoral register. As one might expect from the very rural and friendly Irish population, the interviewing team of Irish psychiatrists and research interviewers – working from June 1984 till November 1989 – were able to achieve a high level of success, interviewing 88 and 86% of the living and traceable probands and relatives, respectively. I myself conducted some 150 reliability interviews and, over the course of literally dozens of visits, spent weeks during the day driving the back roads of County Roscommon and my nights in local pubs or trying to keep warm in my ‘Bed and Breakfast’ where the central heating was always switched off after dinner. Dermot Walsh proved to be an extraordinarily capable and dedicated collaborator. The first major set of results of what we called the Roscommon Family Study were published in the Archives in a series of four papers [53–56]. We were able to robustly replicate a number of previous findings in the literature. In particular, schizophrenia was indeed strongly familial and the schizophrenia spectrum, from a familial perspective, included both schizophrenia-like personality disorders and non-schizophrenia psychotic disorders.

6. Enter molecular genetics I had had wonderful courses both in college and medical school in molecular genetics but had had no significant exposure to this area of research from the time I started my psychiatric residency until another fateful event in the library of the Dept of Genetics in Birmingham in November of 1983. I scanned the copy of Nature and came across the following article ‘A polymorphic DNA marker genetically linked to Huntington’s disease’ by Jim Gusella and colleagues. I recall sitting down and reading it carefully and wondering ‘through a glass darkly’ whether it ever might be possible to apply such methods to psychiatric diseases. In my transitional year before moving to MCV, I had studied the statistical aspects of linkage analysis and so knew something about its theoretical aspects. In the next

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year, I tried to read everything I could about both the molecular and statistical aspects of human linkage. In our first NIH application for the Roscommon Family Study, written in Nov of 1985, we asked for funds to identify, study and establish cell-lines for ten high-density pedigrees for linkage analysis. As the Roscommon Family Study reached full stride, in April 1987 we started what was to become the Irish Study of High-Density Schizophrenia Families (ISHDSF) [57]. At first, we focused on the West of Ireland, especially the counties of Roscommon, Galway and Sligo. The work went so well that we submitted a supplemental grant – bigger in budget than the original – to expand our ascertainment for high-density families to the entire island of Ireland. That, too was funded. I am pleased to say that this was one of the first major ‘cross-border’ collaborations in psychiatric research in Ireland. In addition to the dedicated work of Dr Dermot Walsh, Prof. Roy McClelland – then Chair of the Department of Psychiatry at Queen’s University, Belfast – was also essential to the success of this study. Between April 1987 and November 1992, in collaboration with the Health Research Board, Dublin and the Queen’s University, Belfast, our talented and dedicated field teams visited a total of 39 psychiatric facilities in Ireland and Northern Ireland, which together provided over 95% of in-patient care in all of Ireland. These hospitals served catchmented populations and all had community nursing services that provide follow-up care for the chronically mentally ill in the community. Because staff turnover was low, the hospital staff, and particularly the community nurses, were quite knowledgeable about patients and their families. In Ireland, where the population is considerably less mobile than in the United States, relatives in a family often live in close proximity to one another. At this point, the study center had switched from County Roscommon to Dublin. Over the 5 years of active field-work, I would visit – usually for 2–3 days – every couple of months. I grew to know the Dublin airport very well. It was while the field work for the ISHDSF was just starting, that the field experienced the first high-profile positive linkage results first for bipolar illness [58] and then for schizophrenia [59]. The latter I heard about in a particularly dramatic way. In the fall of 1988. I had a visitor from England and was driving him in to work. He mentioned, a bit informally, that ‘Gurling’s group’ had found the gene for schizophrenia on chromosome 5. I recall being stunned and having to exert self-control not to drive the car off the road! This went against everything that I understood about the disease. I did not share what I felt then was the overly optimistic assessment of the impact of molecular methods on psychiatric disorders. At that time, a number of leaders in our field made what I thought were poorly informed and naive statements about how soon we would be finding all the genes for psychiatric disorders. In 1986, in a review paper on the

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genetics of schizophrenia, trying to balance over-enthusiasm and cynicism, I wrote … there are many reasons why the detection of a major gene for schizophrenia by linkage analysis should be much more difficult than for the typical Mendelian human disorders. Probably the most important such reasons are incomplete penetrance and genetic heterogeneity [60]. A year later, in a paper with the prescient title of ‘The feasibility of linkage studies in schizophrenia’, I wrote: Much of the current enthusiasm for linkage studies in schizophrenia is based on the implicit assumption that Mendelian disorders such as Huntington’s disease are good models for schizophrenia. This assumption deserves to be challenged. Therefore, I will review what is known about the genetic etiology of two complex phenotypes that may serve as more realistic models for schizophrenia: early onset hearing loss and coronary heart disease. I concluded this essay with the following words, which still sound pretty good today: The inherent difficulties in applying linkage methods to schizophrenia should not discourage us from pursuing this line of research. However, it is best that we begin such a venture well informed of the difficulties and pitfalls. Otherwise, linkage analysis could joint the many scientific approaches to schizophrenia which have been characterized by rapid and overly enthusiastic endorsement by the psychiatric community only to be followed by disappointment and precipitous rejection. Well over 40 person-years of effort went into the field work of the ISHDSF. I was very fortunate to have a wonderful team of young Irish psychiatrists and social workers. We screened well over 1000 possible eligible families and were able to study 270 of them which contained multiple cases of schizophrenia. We performed a series of power analyses based on our sample – which was then and probably still remains the largest such sample of its type [57]. These results were sobering. These analyses assumed that schizophrenia was due to multiple different genes of moderate effect size. We showed that if we were gene hunting by linkage and had markers right near a disease locus, if that locus caused illness in 30% or more of families, we would almost certainly be able to detect it. However, if it caused illness in 20% of families or less, our chances were slim. If it caused illness in between 20 and 30% of families, then the results could break either way. When these simulations were first shown to me, after years of work, I thought to myself ‘now, how likely is it that the most common liability gene for schizophrenia causes illness in at least a third of families?’ I had to say that I could not confidently answer that question in the positive. Given the immense complexity

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of the human CNS and the terrific clinical heterogeneity of schizophrenia, I regarded it as very plausible that this disease resulted from many genes, with no one single gene being responsible for more than a modest proportion of all cases. It took 3 years of intense work in the laboratory of my colleague Richard Straub before we could begin to see the results of our linkage analysis in the ISHDSF and another three before a genome scan, involving well over 600 000 individual genotypes, was completed. Briefly, relatively strong evidence for linkage was found in four regions: 6p24–22 [61], 8p22–21 [62], 5q22–31 [63] and 10p15–11 [64]. The 6p finding – first reported by our group in 1995 – is one of the most replicated linkage findings in schizophrenia with seven other positive reports. The other three regions have each been replicated by at least two other groups. However, as is typical in linkage studies of complex diseases, many non-replications have also been published. The critical question that I deeply wanted to know is – what is the probability that these findings were true positives or false positives – due probably to the many multiple tests built into linkage studies. We still do not know the answer to that question. Enough replications have been reported that my intuition suggests that the ‘worst outcome’ model – that they are all false positives – seems unlikely. But it is hard to trust onself when so much work has been put into a study. We have been working very hard at what is called ‘fine-mapping’ trying to localize the genetic signal under our large 6p linkage peak. We may have made some progress, but time will tell if this is significant or not. An important development in the field has been the probable success of the positional cloning strategy for two other complex diseases – type II diabetes [65] and Crohn’s disease [66]. As the ISHDSF study was in full swing, I had to address another question – was it time to launch a linkage study of another disorder? In the ideal world, it would make sense to solve one problem first before moving on to others. However, our group and school had put quite a bit of resources into creating a molecular genetics lab and being up to date on and even developing some tools for the statistical analysis of linkage data on complex phenotypes. At this point, nearly all efforts world-wide in our field were focused either on schizophrenia or bipolar illness – the one major exception being the efforts of Collaborative Study on the Genetics of Alcoholism (COGA) on the genetics of alcoholism. I had, in 1993, done my first study on smoking behavior [26]. I began to review this literature and was impressed at how often this phenotype had been studied and how consistently results suggested its high heritability. This was clearly demonstrated in a literature review recently completed with my colleague Patrick Sullivan [67]. In addition, animal studies showed reliable strain differences in sensitivity to a range of actions of nicotine. I argued that genes of moder-

ate effect size were potentially more plausible in studies of drug abuse than in traditional psychiatric disorders. Many studies in animals and humans have indicated that any time a foreign substance is put into the body, genetic variation occurs in the distribution and metabolism of the substance as well as in its interaction with end-organ receptors and other down-stream effector systems. I reasoned that we should next try to tackle a drug abuse disorder. COGA was working on alcoholism. A phenotype perhaps even easier to study because of its high prevalence and relatively low level of stigma was nicotine dependence. We applied for and were funded for a study of 400 sibling pairs concordant for nicotine dependence. The results of our linkage analysis were only suggestive – no really strong linkage signals were found [68]. We have also been funded since 1997 to conduct a large affected sib-pair study of alcohol dependence in Ireland. The field work for this has proven surprisingly difficult, but with help from key Irish colleagues and a good deal of determination, we are now past 400 pairs and are funded to complete the molecular genetic work. In addition, our research group, under the leadership of Dr Patrick Sullivan, has launched a largescale linkage study of major depression and the closely associated personality trait of neuroticism.

7. The genetic epidemiologic model While much recent energy has gone into what might be described as ‘gene finding’ methods, I remain a firm believer in the continued value of genetic-epidemiologic studies. These remain an active focus of our research group. I have recently spent a good deal of time developing an empirical but broadly inclusive etiologic model for major depression – building and expanding on our previous effort [69]. While there has been much speculation that psychiatric disorders in general and major depression in particular are ‘complex multifactorial’ disorders, only a modest amount of empirical work has been done with more than a handful of putative risk factors (e.g. ref. [70]). Most of these studies have used regression techniques which provide no insight into the causal relationship between predictor variables. We have worked on models that attempt to elucidate the developmental pathways through which the risk factors lead to illness [71]. Ideally, this should be done with long-term, prospective studies in genetically informative populations. However, it will be years before such samples are available. In the meantime, some important insights can be obtained by using carefully combined prospective and retrospective designs with adult populations. Particular care must be taken to reduce the problems of inaccurate and especially biased recall of key risk factors. These models look quite complex, but I suspect do not in fact come close to the true complexity of the various etiologic pathways to disorders like major depression. Another recent focus has been on developing broad

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models for comorbidity. Given the high levels of comorbidity consistently seen for psychiatric and substance use disorders [72,73], it is likely that many risk factors for psychopathology are not disorder-specific. Indeed, this hypothesis is supported by numerous empirical studies (e.g. refs [74–76]). It is highly likely that the observed pattern of comorbidity indicates that the broad array of common psychiatric and drug use disorders may be explained most parsimoniously by a small number of underlying dimensions of liability. We have recently completed preliminary analyses involving ten common psychiatric and drug abuse syndromes in our male-male twin pairs. These analyzes stretched to the limit our computer resources – tying up our machines for weeks at a time. The pattern is revealing. We found that three broad genetic factors explain much of the observed pattern of co-occurrence of common psychiatric and drug abuse disorders. These factors reflect a broad liability to externalizing disorders, and a broad liability to internalizing disorders which in turn can be divided into two sub-factors that predispose to chronic dysphoria versus phobic-anxiety. The only robust evidence we found for the impact of family environment was on externalizing disorders. From a genetic perspective, it is increasingly clear that the DSMs have not gotten it ‘right’. As we consider progressing to gene finding methods for these common disorders, it will be important to know the structure of the genetic risk factors. Our results suggest that – in most instances – they are unlikely to be disorder-specific. The genetic epidemiologic model has and will continue to be a very fertile one. It is often the best way to rigorously evaluate hypotheses of critical clinical significance. For example, both clinical and animal work suggest that individuals with early life adversity may be more sensitive to the effects of stress experienced in adulthood. Can this be shown in a genetically informative population? Based on the work of George Brown [77], we have developed more refined assessments for stressful life events that allow us to rate the critical dimensions of humiliation, entrapment, loss and danger. Can we show that individuals with particular high risk backgrounds (based either on genetic liability or prior environmental adversity) have an especially heightened probability of illness when confronting these especially pathogenic experiences?

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expected these approaches, which proved very successful for tertiary syphilis to work for schizophrenia and manicdepressive illness. They did not. During my brief career, I think we substantially overestimated the impact of our beginning knowledge about neurotransmitter systems. Some readers will remember the time when we had the ‘one-synapse’ model – ‘schizophrenia was too much dopamine’ ‘depression was too little norepinephrine’, etc. This approach worked very well for Parkinson’s disease, but not for psychiatric disorders. We are now doing the same thing with genetics. These techniques are powerful and may be capable of producing real insight into the etiology of these disorders. But these methods will not solve these complex disorders. Psychiatric disorders are not like Huntington’s disease or cystic fibrosis. Genetic epidemiologic methods can clarify certain parts of key developmental pathways. Gene discovery, if it can be accomplished with this generation of technology, can provide biological pathways into which we can pour our scientific and pharmaceutical skills to produce important new knowledge and, especially critical, new pharmacologic tools to treat these disorders. But given what we already know about the psychiatric disorders, even when all gene effects are explained, there will remain important parts of the etiology of psychiatric disorders that are imbedded in the context of complex human experiences. How do people with shared genotypes interact in this extraordinarily intricate thing we call the human family? How, as we become adults, do our genetically mediated temperaments shape our interpersonal environments? How, given our prior experiences and genotypes, do we explain the astonishingly wide variation in how human beings respond to the same pathogenic adversities? A sign of the maturation of our field would be a wide-spread tolerance for this kind of complexity. Hopefully, this might be accompanied by a shift from our frequent disputations at the level of ‘theoretical orientation’ (e.g. biologic versus psychodynamic) toward the common playing field of empirical inquiry. The study of genes will not tell us everything we want to know about the etiology of psychiatric illness. However, no complete story of their causation will be possible without taking into account the role of genetic risk factors.

8. Final thoughts Acknowledgements Psychiatry in an immature science and is vulnerable to the perils of reductionism. We deal with a subject matter of overwhelming complexity and our tools are quite meager compared to the magnitude of the task. We have a strong, understandable but unrealistic desire to have our problems ‘solved’ by some new magnificent technology. I have read enough to know that several great psychiatrists of the late 19th century, including Kraepelin, overestimated the impact of the then recently developed neuropathologic methods. He

The work described in this paper were supported by a number of NIH grants, of which MH-40828, MH-41953, MH/AA-49492, DA-11287, MH-01277, AA-09095, and MH-45390 were the most significant. Many colleagues contributed to the work described herein. Amongst those not mentioned in the text, I acknowledge my debt and gratitude to Drs Michael Neale, Carol Prescott, Charles MacLean and Nancy Pedersen.

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