Purpose of Review
The following review provides some description of the movement in cross-disorder psychiatric genomics toward addressing both comorbidity and polygenicity.
We attempt to show how dimensional approaches to the phenotype have led to further addressing the problem of comorbidity of psychiatric diagnoses. And we also attempt to show how a dimensional approach to the genome, with different statistical methods from traditional genome-wide association analyses, has begun to resolve the problem of massive polygenicity.
Cross-disorder research, of any area in psychiatry, arguably has the most potential to inform clinical diagnosis, early detection and prevention strategies, and pharmacological treatment research. Future research might leverage what we now know to inform developmental studies of risk and resilience.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
General effects of co-heritability between specific disorders found in these analyses have withstood a recent, more sophisticated statistical approach to controlling for linkage disequilibrium, LD Score Regression (7.Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Consortium R, et al. An Atlas of Genetic Correlations across Human Diseases and Traits2015 2015-01-01 00:00:00.).
It is important to note, however, that “synaptic function” is a very broad category in the biological pathway literature, and that psychiatric diagnoses are likely all related to synaptic function to some degree. In addition, there may be unique forms of ASD and psychosis corresponding to earlier onset or increased genetic risk, and these regions could be associated with specific forms of illness that are not broadly generalizable.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T, et al. Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet. 2011;89(5):607–18.
Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7. Finding genetic effects on massively polygenic disorders has required genome-wide association studies (GWAS) with tens of thousands of samples. This set of schizophrenia findings from the PGC is a major boon for psychiatric genetics, and the polygenic weights from these samples provide important information about biological mechanisms affecting liability.
Cross-Disorder Group of the Psychiatric Genomics C. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9.
Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537–51.
Delude CM. Deep phenotyping: the details of disease. Nature. 2015;527(7576):S14–5.
International Schizophrenia C, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–52.
Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Consortium R, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41. The field is quickly developing and validating strategies to examine the additive effects of genetic variants across genes, pathways, and larger swathes of the genome. This important application involved polygenic profile scoring of traits using GWAS summary statistics, deriving genomic relatedness matrices to calculate heritability estimates from common genetic variants, and modeling the linkage disequilibrium in the sample in order to obtain more accurate estimates of genetic risk.
Krapohl E, Euesden J, Zabaneh D, Pingault JB, Rimfeld K, von Stumm S, et al. Phenome-wide analysis of genome-wide polygenic scores. Mol Psychiatry. 2015. Using polygenic risk scores to predict multiple phenotypes in a test sample is important for cross-validation of polygenic prediction, and is a strategy being quickly picked up by research groups with access to ample phenotyping within a sample. We suspect many more applications of this method will follow, to various types of clinical and non-clinical samples.
Cross-Disorder Group of the Psychiatric Genomics C, Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45(9):984–94. To examine shared genetic etiology, the Cross-Disorder Group of the PGC used genome-wide genotype data from case-control groups for SZ, BP, MDD, ASD, and ADHD. Using univariate and bivariate methods to examine overlap across disorders, SNPs explained 17–29% of the variance in liability.
Faraone SV, Biederman J, Wozniak J. Examining the comorbidity between attention deficit hyperactivity disorder and bipolar I disorder: a meta-analysis of family genetic studies. Am J Psychiatry. 2012;169(12):1256–66.
Van Snellenberg JX, de Candia T. Meta-analytic evidence for familial coaggregation of schizophrenia and bipolar disorder. Arch Gen Psychiatry. 2009;66(7):748–55.
Mortensen PB, Pedersen MG, Pedersen CB. Psychiatric family history and schizophrenia risk in Denmark: which mental disorders are relevant? Psychol Med. 2010;40(2):201–10.
Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C, Davidson M, et al. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch Gen Psychiatry. 2012;69(11):1099–103.
Rapoport J, Chavez A, Greenstein D, Addington A, Gogtay N. Autism spectrum disorders and childhood-onset schizophrenia: clinical and biological contributions to a relation revisited. J Am Acad Child Adolesc Psychiatry. 2009;48(1):10–8.
Lichtenstein P, Carlstrom E, Rastam M, Gillberg C, Anckarsater H. The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiatry. 2010;167(11):1357–63.
Ronald A, Simonoff E, Kuntsi J, Asherson P, Plomin R. Evidence for overlapping genetic influences on autistic and ADHD behaviours in a community twin sample. J Child Psychol Psychiatry. 2008;49(5):535–42.
Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, Rickaby J, et al. Rare copy number variation discovery and cross-disorder comparisons identify risk genes for ADHD. Sci Transl Med. 2011;3(95):95ra75.
Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11:126.
Kozak MJ, Cuthbert BN. The NIMH Research Domain Criteria initiative: background, issues, and pragmatics. Psychophysiology. 2016;53(3):286–97.
Cuthbert BN, Kozak MJ. Constructing constructs for psychopathology: the NIMH research domain criteria. J Abnorm Psychol. 2013;122(3):928–37.
Hettema JM. Psychophysiology of threat response, paradigm shifts in psychiatry, and RDoC: implications for genetic investigation of psychopathology. Psychophysiology. 2016;53(3):348–50.
Cross-Disorder Phenotype Group of the Psychiatric GC, Craddock N, Kendler K, Neale M, Nurnberger J, Purcell S, et al. Dissecting the phenotype in genome-wide association studies of psychiatric illness. Br J Psychiatry. 2009;195(2):97–9.
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.
Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316(5829):1336–41.
Edwards AC, Bigdeli TB, Docherty AR, Bacanu S, Lee D, de Candia TR, et al. Meta-analysis of positive and negative symptoms reveals schizophrenia modifier genes. Schizophr Bull. 2016;42(2):279–87.
Farmer AE, Jones I, Williams J, McGuffin P. Defining schizophrenia: Operational criteria. J Ment Health. 1993;2(3):209–22.
Docherty AR, Bigdeli TB, Edwards AC, Bacanu S, Lee D, Neale MC, et al. Genome-wide gene pathway analysis of psychotic illness symptom dimensions based on a new schizophrenia-specific model of the OPCRIT. Schizophr Res. 2015;164(1-3):181–6.
Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Schizophrenia Working Group of Psychiatric Genomics C, et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry. 2014;19(9):1017–24. Research has observed a significant correlation between polygenic risk score and clinical dimensions. Ruderfer and colleagues reported a SZ-versus-BP polygenic score that differentiated the two disorders in several independent samples. In addition, they conducted a cross-disorder case–control analysis and examined associations with symptom dimensions. Results indicated five regions reached genome-wide significance (CACNA1C, IFI44L, MHC, TRANK1 and MAD1L1) and a novel locus near PIK3C2A. These findings indicate that examining relationships between clinical symptom dimensions and polygenic signatures can provide informative results about the overlap of major disorders.
Buckley PF, Miller BJ, Lehrer DS, Castle DJ. Psychiatric comorbidities and schizophrenia. Schizophr Bull. 2009;35(2):383–402.
Simeone JC, Ward AJ, Rotella P, Collins J, Windisch R. An evaluation of variation in published estimates of schizophrenia prevalence from 1990 horizontal line 2013: a systematic literature review. BMC Psychiatry. 2015;15:193.
Murphy DL, Timpano KR, Wheaton MG, Greenberg BD, Miguel EC. Obsessive-compulsive disorder and its related disorders: a reappraisal of obsessive-compulsive spectrum concepts. Dialogues Clin Neurosci. 2010;12(2):131–48.
Bottas A, Cooke RG, Richter MA. Comorbidity and pathophysiology of obsessive-compulsive disorder in schizophrenia: is there evidence for a schizo-obsessive subtype of schizophrenia? J Psychiatry Neurosci. 2005;30(3):187–93.
Docherty AR, Coleman MJ, Tu X, Deutsch CK, Mendell NR, Levy DL. Comparison of putative intermediate phenotypes in schizophrenia patients with and without obsessive-compulsive disorder: examining evidence for the schizo-obsessive subtype. Schizophr Res. 2012;140(1-3):83–6.
Poyurovsky M, Zohar J, Glick I, Koran LM, Weizman R, Tandon R, et al. Obsessive-compulsive symptoms in schizophrenia: implications for future psychiatric classifications. Compr Psychiatry. 2012;53(5):480–3.
Schirmbeck F, Zink M. Comorbid obsessive-compulsive symptoms in schizophrenia: contributions of pharmacological and genetic factors. Front Pharmacol. 2013;4:99.
de Haan L, Dudek-Hodge C, Verhoeven Y, Denys D. Prevalence of psychotic disorders in patients with obsessive-compulsive disorder. CNS Spectr. 2009;14(8):415–7.
Larsson HJ, Eaton WW, Madsen KM, Vestergaard M, Olesen AV, Agerbo E, et al. Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol. 2005;161(10):916-25; discussion 26-8.
Daniels JL, Forssen U, Hultman CM, Cnattingius S, Savitz DA, Feychting M, et al. Parental psychiatric disorders associated with autism spectrum disorders in the offspring. Pediatrics. 2008;121(5):e1357–62.
Williams NM, Owen MJ. Genetic abnormalities of chromosome 22 and the development of psychosis. Curr Psychiatry Rep. 2004;6(3):176–82.
Docherty AR, Sponheim SR. Anhedonia as a phenotype for the Val158Met COMT polymorphism in relatives of patients with schizophrenia. J Abnorm Psychol. 2008;117(4):788–98.
Ceaser A, Csernansky JG, Barch DM. COMT influences on prefrontal and striatal blood oxygenation level-dependent responses during working memory among individuals with schizophrenia, their siblings, and healthy controls. Cogn Neuropsychiatry. 2013;18(4):257–83.
Bourgeron T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat Rev Neurosci. 2015;16(9):551–63.
Crespi B, Stead P, Elliot M. Evolution in health and medicine Sackler colloquium: comparative genomics of autism and schizophrenia. Proc Natl Acad Sci U S A. 2010;107 Suppl 1:1736–41.
Kendler KS, Neale MC. Endophenotype: a conceptual analysis. Mol Psychiatry. 2010;15(8):789–97.
Gonzalez-Mantilla AJ, Moreno-De-Luca A, Ledbetter DH, Martin CL. A cross-disorder method to identify novel candidate genes for developmental brain disorders. JAMA Psychiatry. 2016;73(3):275–83.
Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012;28(19):2540–2.
Stefansson H, Meyer-Lindenberg A, Steinberg S, Magnusdottir B, Morgen K, Arnarsdottir S, et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature. 2014;505(7483):361–6. This study used alternative methods to examine cross-disorder CNVs using a quantitative phenotypic framework: the group targeted cognitive deficits very broadly in an Icelandic sample, predicting specific deficits to be associated with overlapping SZ/AUT CNVs. In this approach, CNVs provided an entry point to investigations into the mechanisms of brain dysfunction.
Consortium TP, Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, et al. The PsychENCODE project. Nat Neurosci. 2015;18(12):1707–12. In the near future, the PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains. This research begins with a focus on ASD, BP, and SZ, and will examine the coheritability of these disorders. These highly anticipated analyses could further elucidate biological mechanisms underlying pleiotropy in psychiatric illness.
Lee SH, Goddard ME, Wray NR, Visscher PM. A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012;36(3):214–24.
Andreassen OA, Thompson WK, Dale AM. Boosting the power of schizophrenia genetics by leveraging new statistical tools. Schizophr Bull. 2014;40(1):13–7.
Bulik-Sullivan B, Loh P-R, Finucane H, Ripke S, Yang J, Psychiatric Genomics Consortium SWG, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. 2014 2014-01-01 00:00:00.
Lazzeroni LC, Lu Y, Belitskaya-Levy I. P-values in genomics: apparent precision masks high uncertainty. Mol Psychiatry. 2014;19(12):1336–40.
Wray NR, Lee SH, Kendler KS. Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur J Hum Genet. 2012;20(6):668–74.
Conflict of Interest
Dr. Anna R. Docherty, Dr. Arden A. Moscati, and Dr. Ayman H. Fanous declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
This article is part of the Topical Collection on Genetics and Neuroscience
About this article
Cite this article
Docherty, A.R., Moscati, A.A. & Fanous, A.H. Cross-Disorder Psychiatric Genomics. Curr Behav Neurosci Rep 3, 256–263 (2016). https://doi.org/10.1007/s40473-016-0084-3
- Psychiatric Genomics Consortium