Journal of Child Psychology and Psychiatry 55:10 (2014), pp 1105–1106
doi:10.1111/jcpp.12308
Commentary: Response to commentary by Rutter on Munafo et al. (2014) 1
,1,2,3 Stanley Zammit,4,5 and Jonathan Flint6 Marcus R. Munafo
MRC Integrative Epidemiology Unit, University of Bristol, Bristol; 2UK Centre for Tobacco and Alcohol Studies, University of Bristol, Bristol; 3School of Experimental Psychology, University of Bristol, Bristol; 4School of Social and Community Medicine, University of Bristol, Bristol; 5MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff and University, Cardiff; 6Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
Rutter’s commentary (Rutter, 2014) on our article (Munaf o et al., 2014) provides us the opportunity to clarify some issues that he (and therefore, we suspect, others) may have misunderstood. First, we do not dispute the existence of gene– environment (G9E) interactions. Rutter (2014) believes we are mounting a ‘one-sided attack on G9E in relation to life events’. This is simply not so. We have no argument against the existence of G9E in the aggregate. Everyone is familiar with the idea that when two people encounter the same environmental stressor the results can be very different, and it makes sense to attribute this variation in part to differences in genetic makeup. Twin and family data provide formal tests of this idea, and confirm its importance (Cadoret, Yates, Troughton, Woodworth, & Stewart, 1995; Cloninger, Sigvardsson, Bohman, & von Knorring, 1982; Kendler & Karkowski-Shuman, 1997). There are also convincing data from other species for the existence of G9E (Caligari & Mather, 1975; Crabbe, Wahlsten, & Dudek, 1999; Henderson, 1976; Valdar et al., 2006). Indeed we fully concur with the three points Rutter makes at the end of his commentary as the main messages for practitioners. But those points have nothing to do with the intent of our article. So what are we disputing? Even after the most cursory survey of the literature one cannot avoid the conclusion that the strength of evidence for the effects of G9E at a single locus (be it the serotonin transporter or the monoamine oxidase-A gene) is contested. We want to understand why there is still disagreement (after more than 10 years), and what could be done to resolve the issue. For clarity, from this point on we use the terms locus-specific G9E and genome-wide G9E. Our concern is that the results supporting the existence of locus-specific G9E for psychiatric phenotypes in human studies are not robust. Second, Rutter’s commentary displays a common misunderstanding that the choice of statistical model can be determined in terms of biological plausibility. Our knowledge of biological mechanisms underlying multifactorial complex diseases does not allow us to choose one model over another to test specific hypotheses about underlying pathological processes. Indeed, the example we provide in Table 1 of our article demonstrates why evidence of G9E cannot be interpreted in terms of biological
mechanisms. The fact that one can reach opposing conclusions from the same data by using different models does not mean, as Rutter’s commentary suggests, that it is invalid or impossible to compare such models. It simply demonstrates why inferences about biological mechanisms based on patterns of statistical interaction are inherently flawed. The pattern of interaction observed in single gene disorders such as phenylketonuria is an exception to this. However, contrary to Rutter’s interpretation, a biological interaction of this nature would show evidence of statistical interaction under any statistical model used (i.e., show model-independence). The preference for additive models expressed by Kendler and Gardner (Kendler & Gardner, 2010), as cited in Rutter commentary, is based on the fact that evidence of greater than additive effects tells us that some individuals only developed the outcome because they were exposed to both G and E (i.e., that both factors co-participate in the same causal model of disease). Although this is confusingly described as ‘biological interaction’, it does not tell us anything about underlying biological mechanisms – a point emphasised by Kendler himself (Kendler & Gardner, 2010) – and in fact can also be inferred from lack of evidence of G9E under multiplicative models. Third, running through the commentary is the assumption that genome-wide association studies (GWAS) of psychiatric disease have failed (‘there are few successes of GWAS in the field of mental disorders, schizophrenia being a partial exception to this’), and that successful genetic mapping requires the inclusion of locus-specific G9E (particularly for studies of major depression). While it would certainly be interesting to include environmental measures in genetic analysis, we don’t think it is either necessary, or currently practical. We now know that the genetic architecture of psychiatric disease is highly polygenic. For all psychiatric conditions that have been examined, including major depression, bipolar disorder, schizophrenia, autism, and attention deficit hyperactivity disorder (Cross-Disorder Group of the Psychiatric Genomics Consortium et al., 2013), and Tourette syndrome and obsessional compulsive disorder (Davis et al., 2013), between one third and a half of the heritability is due to the combined direct effects of
© 2014 Association for Child and Adolescent Mental Health. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA
1106
Commentary: Marcus R. Munaf o, Stanley Zammit, and Jonathan Flint
many segregating sequence variants distributed across the genome, each making a small individual contribution. This means that genome-wide significant effects can be detected by increasing sample size. The recent success with schizophrenia illustrates this well (Schizophrenia Working Group of the Psychiatric Genetics Consortium, 2014), and in this respect progress is no different than for other non-psychiatric complex traits. Finding 8 loci contributing to the risk of hypertension required a sample size of 34,433, with follow up in a further 113,250 subjects (Newton-Cheh et al., 2009). Success does not require locus-specific G9E analysis – it just needs a larger sample size. Of course, it’s possible that the sample size needed will be very large indeed – 50,000, for example, for major depression (Flint & Kendler, 2014; Wray et al., 2012) – and that including locus-specific G9E might reduce the necessary sample size. However, genotyping costs continue to fall, as does the cost of collecting diagnostic data on a large scale (Perlis et al., 2012), while the costs of the in-depth phenotyping required to assess environmental effects have not declined. Unless locus-specific G9E effects are large enough that they allow a very substantial reduction in sample size (and so far there is no evidence that this is so), then it is more efficient to identify loci that robustly contribute a main effect before proceeding to test for locus specific G9E. In other words, we should focus efforts on detecting main effects. In our view, attempts to resurrect candidate genes that have not provided robust evidence of a main effect by appealing to putative locus-specific G9E effects is a retrograde step. Our understanding of the genetic architecture of complex traits has moved on. In contrast to the chequered history of candidate gene studies, unbiased genome-wide approaches are revealing insights into the neurobiology of complex traits. As genome-wide association studies identify main effects of individual loci, these can then be interrogated further in order to understand the mechanisms they underpin, and to explore potential environmental effect modifiers.
J Child Psychol Psychiatr 2014; 55(10): 1105–6
Marcus R. Munaf o, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol, UK; Email: [email protected]
the genesis of aggressivity and conduct disorders. Archives of General Psychiatry, 52, 916–924. Caligari, P.D., & Mather, K. (1975). Genotype–environment interaction. III. Interactions in Drosophila melanogaster. Proceedings of the Royal Society of London B Biological Sciences, 191, 387–411. Cloninger, C.R., Sigvardsson, S., Bohman, M., & von Knorring, A.L. (1982). Predisposition to petty criminality in Swedish adoptees. II. Cross-fostering analysis of gene-environment interaction. Archives of General Psychiatry, 39, 1242–1247. Crabbe, J.C., Wahlsten, D., & Dudek, B.C. (1999). Genetics of mouse behavior: interactions with laboratory environment. Science, 284, 1670–1672. Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee, S.H., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., Perlis, R.H., ... & Wray, N.R. (2013). Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics, 45, 984–994. Davis, L.K., Yu, D., Keenan, C.L., Gamazon, E.R., Konkashbaev, A.I., Derks, E.M., ... & Scharf, J.M. (2013). Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture. PLoS Genetics, 9, e1003864. Flint, J., & Kendler, K.S. (2014). The genetics of major depression. Neuron, 81, 484–503. Henderson, N.D. (1976). Short exposures to enriched environments can increase genetic variability of behavior in mice. Developmental Psychobiology, 9, 549–553. Kendler, K.S., & Gardner, C.O. (2010). Interpretation of interactions: guide for the perplexed. British Journal of Psychiatry, 197, 170–171. Kendler, K.S., & Karkowski-Shuman, L. (1997). Stressful life events and genetic liability to major depression: Genetic control of exposure to the environment. Psychological Medicine, 27, 539–547. Munaf o, M.R., Zammit, S., & Flint, J. (2014). Practitioner Review: A critical perspective on gene–environment interaction models – what impact should they have on clinical perceptions and practice? Journal of Child Psychology and Psychiatry, 55, 1092–1101. Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M.D., Bochud, M., Coin, L., ... & Munroe, P.B. (2009). Genome-wide association study identifies eight loci associated with blood pressure. Nature Genetics, 41, 666–676. Perlis, R.H., Iosifescu, D.V., Castro, V.M., Murphy, S.N., Gainer, V.S., Minnier, J., ... & Smoller, J.W. (2012). Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychological Medicine, 42, 41–50. Rutter, M. (2014). Commentary: G 9 E in child psychiatry and psychology: a broadening of the scope of enquiry as prompted by the Munaf o et al. (2014). Journal of Child Psychology and Psychiatry, 55, 1102–1104. Schizophrenia Working Group of the Psychiatric Genetics Consortium. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421–427. Valdar, W., Solberg, L.C., Gauguier, D., Cookson, W.O., Rawlins, J.N., Mott, R., & Flint, J. (2006). Genetic and environmental effects on complex traits in mice. Genetics, 174, 959–984. Wray, N.R., Pergadia, M.L., Blackwood, D.H., Penninx, B.W., Gordon, S.D., Nyholt, D.R., ... & Sullivan, P.F. (2012). Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Molecular Psychiatry, 17, 36–48.
References
Accepted for publication: 16 July 2014 Published online: 6 August 2014
Acknowledgements This Response is in reply to the invited commentary by Rutter (2014). The authors have declared that they have no competing interests in relation to this article, but see also the acknowledgement section in Munaf o et al. (2014).
Correspondence
Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G., & Stewart, M.A. (1995). Genetic-environmental interaction in
© 2014 Association for Child and Adolescent Mental Health.
doi:10.1111/jcpp.12308
Commentary: Response to commentary by Rutter on Munafo et al. (2014) 1
,1,2,3 Stanley Zammit,4,5 and Jonathan Flint6 Marcus R. Munafo
MRC Integrative Epidemiology Unit, University of Bristol, Bristol; 2UK Centre for Tobacco and Alcohol Studies, University of Bristol, Bristol; 3School of Experimental Psychology, University of Bristol, Bristol; 4School of Social and Community Medicine, University of Bristol, Bristol; 5MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff and University, Cardiff; 6Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
Rutter’s commentary (Rutter, 2014) on our article (Munaf o et al., 2014) provides us the opportunity to clarify some issues that he (and therefore, we suspect, others) may have misunderstood. First, we do not dispute the existence of gene– environment (G9E) interactions. Rutter (2014) believes we are mounting a ‘one-sided attack on G9E in relation to life events’. This is simply not so. We have no argument against the existence of G9E in the aggregate. Everyone is familiar with the idea that when two people encounter the same environmental stressor the results can be very different, and it makes sense to attribute this variation in part to differences in genetic makeup. Twin and family data provide formal tests of this idea, and confirm its importance (Cadoret, Yates, Troughton, Woodworth, & Stewart, 1995; Cloninger, Sigvardsson, Bohman, & von Knorring, 1982; Kendler & Karkowski-Shuman, 1997). There are also convincing data from other species for the existence of G9E (Caligari & Mather, 1975; Crabbe, Wahlsten, & Dudek, 1999; Henderson, 1976; Valdar et al., 2006). Indeed we fully concur with the three points Rutter makes at the end of his commentary as the main messages for practitioners. But those points have nothing to do with the intent of our article. So what are we disputing? Even after the most cursory survey of the literature one cannot avoid the conclusion that the strength of evidence for the effects of G9E at a single locus (be it the serotonin transporter or the monoamine oxidase-A gene) is contested. We want to understand why there is still disagreement (after more than 10 years), and what could be done to resolve the issue. For clarity, from this point on we use the terms locus-specific G9E and genome-wide G9E. Our concern is that the results supporting the existence of locus-specific G9E for psychiatric phenotypes in human studies are not robust. Second, Rutter’s commentary displays a common misunderstanding that the choice of statistical model can be determined in terms of biological plausibility. Our knowledge of biological mechanisms underlying multifactorial complex diseases does not allow us to choose one model over another to test specific hypotheses about underlying pathological processes. Indeed, the example we provide in Table 1 of our article demonstrates why evidence of G9E cannot be interpreted in terms of biological
mechanisms. The fact that one can reach opposing conclusions from the same data by using different models does not mean, as Rutter’s commentary suggests, that it is invalid or impossible to compare such models. It simply demonstrates why inferences about biological mechanisms based on patterns of statistical interaction are inherently flawed. The pattern of interaction observed in single gene disorders such as phenylketonuria is an exception to this. However, contrary to Rutter’s interpretation, a biological interaction of this nature would show evidence of statistical interaction under any statistical model used (i.e., show model-independence). The preference for additive models expressed by Kendler and Gardner (Kendler & Gardner, 2010), as cited in Rutter commentary, is based on the fact that evidence of greater than additive effects tells us that some individuals only developed the outcome because they were exposed to both G and E (i.e., that both factors co-participate in the same causal model of disease). Although this is confusingly described as ‘biological interaction’, it does not tell us anything about underlying biological mechanisms – a point emphasised by Kendler himself (Kendler & Gardner, 2010) – and in fact can also be inferred from lack of evidence of G9E under multiplicative models. Third, running through the commentary is the assumption that genome-wide association studies (GWAS) of psychiatric disease have failed (‘there are few successes of GWAS in the field of mental disorders, schizophrenia being a partial exception to this’), and that successful genetic mapping requires the inclusion of locus-specific G9E (particularly for studies of major depression). While it would certainly be interesting to include environmental measures in genetic analysis, we don’t think it is either necessary, or currently practical. We now know that the genetic architecture of psychiatric disease is highly polygenic. For all psychiatric conditions that have been examined, including major depression, bipolar disorder, schizophrenia, autism, and attention deficit hyperactivity disorder (Cross-Disorder Group of the Psychiatric Genomics Consortium et al., 2013), and Tourette syndrome and obsessional compulsive disorder (Davis et al., 2013), between one third and a half of the heritability is due to the combined direct effects of
© 2014 Association for Child and Adolescent Mental Health. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA
1106
Commentary: Marcus R. Munaf o, Stanley Zammit, and Jonathan Flint
many segregating sequence variants distributed across the genome, each making a small individual contribution. This means that genome-wide significant effects can be detected by increasing sample size. The recent success with schizophrenia illustrates this well (Schizophrenia Working Group of the Psychiatric Genetics Consortium, 2014), and in this respect progress is no different than for other non-psychiatric complex traits. Finding 8 loci contributing to the risk of hypertension required a sample size of 34,433, with follow up in a further 113,250 subjects (Newton-Cheh et al., 2009). Success does not require locus-specific G9E analysis – it just needs a larger sample size. Of course, it’s possible that the sample size needed will be very large indeed – 50,000, for example, for major depression (Flint & Kendler, 2014; Wray et al., 2012) – and that including locus-specific G9E might reduce the necessary sample size. However, genotyping costs continue to fall, as does the cost of collecting diagnostic data on a large scale (Perlis et al., 2012), while the costs of the in-depth phenotyping required to assess environmental effects have not declined. Unless locus-specific G9E effects are large enough that they allow a very substantial reduction in sample size (and so far there is no evidence that this is so), then it is more efficient to identify loci that robustly contribute a main effect before proceeding to test for locus specific G9E. In other words, we should focus efforts on detecting main effects. In our view, attempts to resurrect candidate genes that have not provided robust evidence of a main effect by appealing to putative locus-specific G9E effects is a retrograde step. Our understanding of the genetic architecture of complex traits has moved on. In contrast to the chequered history of candidate gene studies, unbiased genome-wide approaches are revealing insights into the neurobiology of complex traits. As genome-wide association studies identify main effects of individual loci, these can then be interrogated further in order to understand the mechanisms they underpin, and to explore potential environmental effect modifiers.
J Child Psychol Psychiatr 2014; 55(10): 1105–6
Marcus R. Munaf o, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol, UK; Email: [email protected]
the genesis of aggressivity and conduct disorders. Archives of General Psychiatry, 52, 916–924. Caligari, P.D., & Mather, K. (1975). Genotype–environment interaction. III. Interactions in Drosophila melanogaster. Proceedings of the Royal Society of London B Biological Sciences, 191, 387–411. Cloninger, C.R., Sigvardsson, S., Bohman, M., & von Knorring, A.L. (1982). Predisposition to petty criminality in Swedish adoptees. II. Cross-fostering analysis of gene-environment interaction. Archives of General Psychiatry, 39, 1242–1247. Crabbe, J.C., Wahlsten, D., & Dudek, B.C. (1999). Genetics of mouse behavior: interactions with laboratory environment. Science, 284, 1670–1672. Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee, S.H., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., Perlis, R.H., ... & Wray, N.R. (2013). Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics, 45, 984–994. Davis, L.K., Yu, D., Keenan, C.L., Gamazon, E.R., Konkashbaev, A.I., Derks, E.M., ... & Scharf, J.M. (2013). Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture. PLoS Genetics, 9, e1003864. Flint, J., & Kendler, K.S. (2014). The genetics of major depression. Neuron, 81, 484–503. Henderson, N.D. (1976). Short exposures to enriched environments can increase genetic variability of behavior in mice. Developmental Psychobiology, 9, 549–553. Kendler, K.S., & Gardner, C.O. (2010). Interpretation of interactions: guide for the perplexed. British Journal of Psychiatry, 197, 170–171. Kendler, K.S., & Karkowski-Shuman, L. (1997). Stressful life events and genetic liability to major depression: Genetic control of exposure to the environment. Psychological Medicine, 27, 539–547. Munaf o, M.R., Zammit, S., & Flint, J. (2014). Practitioner Review: A critical perspective on gene–environment interaction models – what impact should they have on clinical perceptions and practice? Journal of Child Psychology and Psychiatry, 55, 1092–1101. Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M.D., Bochud, M., Coin, L., ... & Munroe, P.B. (2009). Genome-wide association study identifies eight loci associated with blood pressure. Nature Genetics, 41, 666–676. Perlis, R.H., Iosifescu, D.V., Castro, V.M., Murphy, S.N., Gainer, V.S., Minnier, J., ... & Smoller, J.W. (2012). Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychological Medicine, 42, 41–50. Rutter, M. (2014). Commentary: G 9 E in child psychiatry and psychology: a broadening of the scope of enquiry as prompted by the Munaf o et al. (2014). Journal of Child Psychology and Psychiatry, 55, 1102–1104. Schizophrenia Working Group of the Psychiatric Genetics Consortium. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421–427. Valdar, W., Solberg, L.C., Gauguier, D., Cookson, W.O., Rawlins, J.N., Mott, R., & Flint, J. (2006). Genetic and environmental effects on complex traits in mice. Genetics, 174, 959–984. Wray, N.R., Pergadia, M.L., Blackwood, D.H., Penninx, B.W., Gordon, S.D., Nyholt, D.R., ... & Sullivan, P.F. (2012). Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Molecular Psychiatry, 17, 36–48.
References
Accepted for publication: 16 July 2014 Published online: 6 August 2014
Acknowledgements This Response is in reply to the invited commentary by Rutter (2014). The authors have declared that they have no competing interests in relation to this article, but see also the acknowledgement section in Munaf o et al. (2014).
Correspondence
Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G., & Stewart, M.A. (1995). Genetic-environmental interaction in
© 2014 Association for Child and Adolescent Mental Health.
