h"p://dx.doi.org/10.1101/048991

h"p://dx.doi.org/10.1101/048991

One Sentence Summary: Comprehensive heritability analysis of brain phenotypes demonstrates aclear role for common genetic variation across neurological and psychiatric disorders, withsubstantial overlap within the latter.

Linkagedisequilibriumscoreregression(LDSC)toquan9fytheextentofsharedgene9ccontribu9onsacross23braindisorders(n=842,820),11quan9ta9veandfourdichotomoustraitsofinterest(n=722,125)basedongenome-wideassocia9onmeta-analyses.Psychiatricdisordersshowsubstan9alsharingofcommonvariantrisk,whilemanyneurologicaldisordersappearmoredis9nctfromoneanother,sugges9ngsubstan9vedifferencesinthespecificityofthegene9ce9ologyofthesedisorders.Thehighdegreeofgene9ccorrela9onamongthepsychiatrictraitssuggeststhatgene9criskfactorsforpsychiatricdisordersdonotrespectclinicaldiagnos9cboundaries,congruentwiththeclinicalcontroversiesinclassifica9on.Thebroadandcon9nuousnatureofpsychiatricdisorderspectrahavebeenclinicallyrecognizedforalong9me(36-38),andtheseresultssuggestthatsharedbiologicalmechanismssubstan2allycontributeacrosspsychiatricdiagnoses.Gene9callyinformedanalysesmayprovidethebasisforsomedegreeofrestructuringofpsychiatricnosology(consistentwiththehistoricalimpactoftwin-andfamily-basedresults).Furtherelucida9onofthegene9coverlap,especiallyasdis9nctlocimapontoasubsetofdisorders,mayformthebasisforeitherdefiningnewclinicalphenotypesorsupportamovetoamorecon9nuousviewofpsychiatricphenotypes.h"p://dx.doi.org/10.1101/048991

36. R. Kendell, A. Jablensky, Distinguishing between the validity and utility of psychiatric diagnoses.Am J Psychiatry 160, 4 (Jan, 2003).

37. A. S. Cristino et al., Neurodevelopmental and neuropsychiatric disorders represent aninterconnected molecular system. Mol Psychiatry 19, 294 (Mar, 2014).

38. D. A. Regier et al., Limitations of diagnostic criteria and assessment instruments for mentaldisorders. Implications for research and policy. Arch Gen Psychiatry 55, 109 (Feb, 1998).

h"p://dx.doi.org/10.1101/048991

h"p://dx.doi.org/10.1101/048991

 Genetic correlation matrix across neurological phenotypes, after hierarchical clustering

Colorofeachboxindicatesthemagnitudeofthecorrela9on,whilesizeoftheboxesindicatesitssignificance,withsignificantcorrela2onsfillingeachboxcompletely.Asterisksindicategene9ccorrela9onswhicharesignificantaVerBonferronicorrec9on.Smallblackboxesindicatetestswherestudieshavecompleteoverlap.Asterisksindicatesignificantgene9ccorrela9onaVermul9pletes9ngcorrec9on.ICH–intracerebralhemorrhage.h"p://dx.doi.org/10.1101/048991

Genetic correlation matrix across neurological and psychiatric phenotypes

Colorofeachboxindicatesthemagnitudeofthecorrela9on,whilesizeoftheboxesindicatesitssignificance,withsignificantcorrela9onsfillingeachboxcompletely.Asterisksindicategene9ccorrela9onswhicharesignificantaVerBonferronicorrec9on.ADHD–a[en9ondeficithyperac9vitydisorder;ASD–au9smspectrumdisorder;ICH–intracerebralhemorrhage;MDD–majordepressivedisorder;OCD––obsessive-compulsivedisorder.h"p://dx.doi.org/10.1101/048991

 Genetic correlations across brain disorders and traits of interestColorofeachboxindicatesthemagnitudeofthecorrela9on,whilesizeoftheboxesindicatesitssignificance,withsignificantcorrela9onsfillingeachboxcompletely.Asterisksindicategene9ccorrela9onswhicharesignificantaVerBonferronicorrec9on.ADHD–a[en9ondeficithyperac9vitydisorder;ASD–au9smspectrumdisorder;ICH–intracerebralhemorrhage;MDD–majordepressivedisorder;OCD–obsessive-compulsivedisorder;BMI–body-massindexh"p://dx.doi.org/10.1101/048991

O’Donovan,M.,&Owen,M.(2016).TheimplicaFonsofthesharedgeneFcsofpsychiatricdisorders.Naturemedicine,22(11),1214–1219.

Genetic correlation between schizophrenia and selected psychiatric disorders. Psychiatric disorders showing significant evidence (P ≤ 0.001) for overlaps between common variant contributions to schizophrenia and other psychiatric disorders. Overlaps are expressed as correlation in heritability (rg) captured by SNPs. Data are from ref. 39. ADHD, attention-deficit hyperactivity disorder; OCD, obsessive-compulsive disorder. 

39.AnFlla,V.etal.Analysisofsharedheritabilityincommondisordersofthebrain.PreprintatbioRxivh"p://dx.doi.org/10.1101/048991(2016).O’Donovan,M.,&Owen,M.(2016).TheimplicaFonsofthesharedgeneFcsofpsychiatricdisorders.Naturemedicine,22(11),1214–1219.

We refer to genic pleiotropy when the altered function of a gene influences multiple traits (note that the term trait includes phenotypes that are not necessarily abnormal or symptoms of disorders). Allelic pleiotropy, a subtype of genic pleiotropy, occurs when the same gene variant influences multiple traits. This is exemplified by phenylketonuria (PKU), in which causative mutations are pleiotropic for intellectual disability, lack of pigmentation and various metabolic changes that can be measured in the blood. 

O’Donovan, M., & Owen, M. (2016). The implicaFons of the shared geneFcs ofpsychiatricdisorders.Naturemedicine,22(11),1214–1219.

Types of pleiotropy. Adjacent genes containing functional variants (FV; yellow circles) that together directly influence four distinct phenotypes (green shapes). The three phenotypes directly influenced by FV1 and FV2 are examples of genic pleiotropy. The circle and pentagon phenotypes influenced by FV1 are examples of allelic pleiotropy in which the same variant rather than just the same gene influences multiple phenotypes. FV1 influences the red triangle phenotype, but this is indirect and occurs only through the direct effects of FV1 on the circle phenotype. This is mediated pleiotropy. Alleles at FV2 and FV3 are correlated (through linkage disequilibrium; LD) with the same single-nucleotide polymorphism (SNP). Accordingly, the SNP will be associated with both phenotypes that are caused by those functional variants. The SNP is depicted midpoint between the genes, but could be positioned anywhere in the region of LD, including in one of the genes. This is pseudo-pleiotropy resulting from co-localization. This region is also prone to a deletion CNV, which results in complete loss of function of both genes, by virtue of which it is associated with all five phenotypes. In a literal sense, all of the green phenotypes in this instance are now examples of allelic pleiotropy (directly caused by the same CNV allele at a single locus).

O’Donovan,M.,&Owen,M.(2016).TheimplicaFonsofthesharedgeneFcsofpsychiatricdisorders.Naturemedicine,22(11),1214–1219.

Relative CNV frequencies. Relative frequencies for schizophrenia (SZ)-associated CNVs. Frequency is expressed as the fold increase in each disorder relative to the estimated population frequency. Data are taken from ref. 21 and based on loci reported as associated with schizophrenia (12,21). CNVs are described by cytogenetic position, the named syndrome most strongly affiliated with the locus, or in the case of NRXN1, gene name. The lifetime population risk for SZ is approximately 1%, and for ID and ASD combined (ASD/ID), 4% (ref. 20). WBS, Williams–Beuren syndrome; PWS/AS, Prader–Willi syndrome/Angelman syndrome; VCFS, velo-cardio-facial syndrome; CNV, copy-number variant; del, deletion; dup, duplication. 

12.Rees,E.etal.Analysisofcopynumbervaria9onsat15schizophrenia-associatedloci.Br.J.Psychiatry204,108–114(2014).20.Kirov,G.etal.Thepenetranceofcopynumbervaria9onsforschizophreniaanddevelopmentaldelay.Biol.Psychiatry75,378–385(2014).21.Rees,E.etal.Analysisofintellectualdisabilitycopynumbervariantsforassocia9onwithschizophrenia.JAMAPsychiatry73,963–969(2016).O’Donovan,M.,&Owen,M.(2016).TheimplicaFonsofthesharedgeneFcsofpsychiatricdisorders.Naturemedicine,22(11),1214–1219.

Hypothesized Relationship between Current Diagnostic Categories, Extent of Neurodevelopmental Impairment and Associated Cognitive Dysfunction, Symptoms, and Various Risk FactorsThis simple model integrates data from a number of sources (Pasamanick et al., 1956; Craddock and Owen, 2010; Owen et al., 2011; Kirov et al., 2012; Girirajan et al., 2011; Fromer et al., 2014; Grozeva et al., 2010; Girirajan et al, 2011)to propose that psychiatric syndromes as currently classified occupy a gradient with the syndromes ordered by decreasing relative contribution of genetically and/or environmentally induced neurodevelopmental impairment. This indexes the number of structures and circuits that are affected, which in turn is manifest by the extent and degree of associated cognitive impairment. This approach accepts that current diagnostic approaches have some utility in defining groups of cases that are more closely related than chance. A key feature is that it regards current categorical diagnoses as arbitrary divisions of what is essentially a continuous landscape. This model makes predictions about the relative extent of brain dysfunction (number of structures and circuits affected) in the various clinical syndromes and the relationships and likely similarities between disorders. In theinterests of clarity, this two-dimensional representation does not show the severity of individual syndromes. These are conceived as being orthogonal and as reflecting the severity, rather than the extent, of damage to structures and circuits.

Owen,M.(2014).NewApproachestoPsychiatricDiagnosFcClassificaFon.Neuron,84(3).doi:10.1016/j.neuron.2014.10.028

Possible applications of the RDoC framework RDoC has been conceptualized as a matrix with four dimensions (Cuthbert andInsel 2013; Cuthbert 2014): (1) domains of functioning, which are further subdivided into dimensional constructs; (2) units of analysis; (3) developmental aspects, and (4) environmental aspects. The domains of functioning and the dimensional constructs contained within them have been selected based on current understanding of neural circuitry. The figure shows two possible scenarios within the framework. In exemplar 1, patients are chosen for study on the basis that they have delusions and no constraints placed on their DSM diagnosis. The study might then focus on the relationship of reward prediction error to strength ofdelusional symptoms in an fMRI paradigm. In exemplar 2, patients are selected because they have a particular pathogenic CNV. The study then focuses upon delineating the range of responses to a set of cognitive paradigms as well as clinical symptoms.

Owen,M.(2014).NewApproachestoPsychiatricDiagnosFcClassificaFon.Neuron,84(3).doi:10.1016/j.neuron.2014.10.028Cuthbert,B.N.(2014).TheRDoCframework:facilita2ngtransi2onfromICD/DSMtodimensionalapproachesthatintegrateneuroscienceandpsychopathology.WorldPsychiatry13,28–35.Cuthbert,B.N.,andInsel,T.R.(2013).Towardthefutureofpsychiatricdiagnosis:thesevenpillarsofRDoC.BMCMed.11,126.

Craddock, N., and Owen, M.J. (2005). The beginning of the end for theKraepelinian dichotomy. Br. J. Psychiatry 186, 364–366.Owen, M.J., O’Donovan, M.C., Thapar, A., and Craddock, N. (2011). Neurodevelopmentalhypothesis of schizophrenia. Br. J. Psychiatry 198, 173–175.

Kirov, G., Pocklington, A.J., Holmans, P., Ivanov, D., Ikeda, M., Ruderfer, D.,Moran, J., Chambert, K., Toncheva, D., Georgieva, L., et al. (2012). De novoCNV analysis implicates specific abnormalities of postsynaptic signalling complexesin the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153.

Girirajan, S., Brkanac, Z., Coe, B.P., Baker, C., Vives, L., Vu, T.H., Shafer, N.,Bernier, R., Ferrero, G.B., Silengo, M., et al. (2011). Relative burden of largeCNVs on a range of neurodevelopmental phenotypes. PLoS Genet. 7,e1002334, http://dx.doi.org/10.1371/journal.pgen.1002334.

Fromer, M., Pocklington, A.J., Kavanagh, D.H., Williams, H.J., Dwyer, S.,Gormley, P., Georgieva, L., Rees, E., Palta, P., Ruderfer, D.M., et al. (2014).De novo mutations in schizophrenia implicate synaptic networks. Nature506, 179–184.

Grozeva,D.,Kirov,G.,Ivanov,D.,Jones,I.R.,Jones,L.,Green,E.K.,StClair,D.M.,Young,A.H.,Ferrier,N.,Farmer,A.E.,etal.;WellcomeTrustCaseControlConsorFum(2010).Rarecopynumbervariants:apointofrarityingeneFcriskforbipolardisorderandschizophrenia.Arch.Gen.Psychiatry67,318–327.

Gandal,M.;Leppa,V.;Won,H.;Parikshak,N.;Geschwind,D.TheRoadtoPrecisionPsychiatry:TranslaFngGeneFcsintoDiseaseMechanisms.NatNeurosci2016,19,1397–1407.

polygenic risk scoring (PRS) quantifies within an individual the aggregate effect of common variants for a given trait, typically calculated as the sum of trait-associated alleles across the genome, weighted by effect size. (PRScanalsobeapowerfultoolforiden9fyingpa9entsubgroups.Forexample,polygenicriskforbipolar disorder predicts manic symptoms in schizophrenia, but not other clinical symptoms,sugges9ngadis9nctmechanis9cunderpinningforthissymptomdomain(65))

Gene targets can also be inferred sta9s9cally, relying on expressionquan9ta9ve trait loci (eQTL), which iden9fies variants that are associatedwithchangesingeneexpressioninagivencelltypeor9ssue.

Genetic and environmental contribution to liability for neuropsychiatric disease. (a) ACE model liability estimates are compiled for various neuropsychiatric disorders derived from large-scale twin and/or population-based studies. (b) Genetic contributions can be further partitioned by variant classes, including common, rare inherited, and rare de novo mutations. The contribution of de novo variants to disease liability is lower than their overall frequency in cases due to incomplete penetrance. 

ACE:Heritability(h2)referstothepropor9onofphenotypicvarianceduetogene9cfactorsandinthenarrowsenseisalsoreferredtoasaddi9vegene9cvariance(A).Environmentalfactorscanbepar99onedintothecommon,sharedenvironment(C)andtheresidual,nonsharedenvironmentalvariance(E).

Gandal,M.;Leppa,V.;Won,H.;Parikshak,N.;Geschwind,D.TheRoadtoPrecisionPsychiatry:TranslaFngGeneFcsintoDiseaseMechanisms.NatNeurosci2016,19,1397–1407.

Neurobiological framework for interpretation of individual disease-associated variants. (a) When considering a neurobiological framework for interpretation of disease-associated genetic variation, it is most important to begin with variants that meet genome-wide significance thresholds (20). (b) Independent replication is also critical, which can be supported by prior reported associations in a clinical genetic database and by an appropriate observed frequency in large population reference databases. (c) Functional annotation differs for coding and noncoding variants, although some general principles apply to both (for example, inheritance, evolutionary conservation). For coding variants, the target gene is known and annotation is initially based on impact to the amino acid sequence. Synonymous mutations, often interpreted as neutral, can contribute to human disease risk by changing transcription factor or microRNA binding or by altering mRNA stability or secondary structure. Nonsense, frameshift, and canonical splice site mutations are generally placed in the most deleterious, likely gene disrupting category, although their disease association must still be statistically supported. Interpretation of missense mutations is more difficult, relying typically on evolutionary constraint or by inferred disruption of protein structure or biochemical function. Functional annotation of noncoding variants is a rapidly evolving area, but can be broadly conceptualized as (top) predicting a regulatory effect and (bottom) identifying target gene(s). Computational methods can predict the likelihood that noncoding regions act as enhancers, repressors, or insulators within a given tissue or cell line on the basis of epigenetic annotations. Gene targets can be inferred through statistical frameworks such as eQTL or by mapping intrachromosomal physical binding interactions through chromosome conformation capture methods. 

Gandal,M.;Leppa,V.;Won,H.;Parikshak,N.;Geschwind,D.TheRoadtoPrecisionPsychiatry:TranslaFngGeneFcsintoDiseaseMechanisms.NatNeurosci2016,19,1397–1407.

Functional annotation differs for coding and noncoding variants, although some general principles apply to both (for example, inheritance, evolutionary conservation). For coding variants, the target gene is known and annotation is initially based on impact to the amino acid sequence. Synonymous mutations, often interpreted as neutral, can contribute to human disease risk by changing transcription factor or microRNA binding or by altering mRNA stability or secondary structure. Nonsense, frameshift, and canonical splice site mutations are generally placed in the most deleterious, likely gene disrupting category, although their disease association must still be statistically supported. Interpretation of missense mutations is more difficult, relying typically on evolutionary constraint or by inferred disruption of protein structure or biochemical function. Functional annotation of noncoding variants is a rapidly evolving area, but can be broadly conceptualized as (top) predicting a regulatory effect and (bottom) identifying target gene(s). Computational methods can predict the likelihood that noncoding regions act as enhancers, repressors, or insulators within a given tissue or cell line on the basis of epigenetic annotations. Gene targets can be inferred through statistical frameworks such as eQTL or by mapping intrachromosomal physical binding interactions through chromosome conformation capture methods. 

(d) Predictions of the potential impact of a variant on the target gene should be experimentally validated. Gene-level disruption can be confirmed in a cell-based experimental system, as long as genomic and epigenetic context are considered. Model organisms with construct validity may also be useful. (e) Once the proximal biological effect of a disease-associated variant is determined, disease mechanisms can begin to be inferred through follow up investigation in preclinical or clinical settings. Performing comprehensive clinical and medical phenotyping of individuals harboring specific, known disease-associated variants will be especially important for mechanistic insight as well as future ‘genotype-first’ precision medicine approaches. NHGRI, National Human Genome Research Institute; EBI, European Bioinformatics Institute; ATAC-seq, assay for transposase-accessible chromatin with sequencing; DHS, DNase I hypersensitivity sites; ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; TSS, transcription start site; SIFT, sorting intolerant from tolerant; MAPP, multivariate analysis of protein polymorphism.

Gandal,M.;Leppa,V.;Won,H.;Parikshak,N.;Geschwind,D.TheRoadtoPrecisionPsychiatry:TranslaFngGeneFcsintoDiseaseMechanisms.NatNeurosci2016,19,1397–1407.

An exemplary recent example of this type of approach was the investigation of the top genome-wide significant locus in schizophrenia, spanning the highly complex MHC region   This work combined fine mapping of this locus inschizophrenia with a newly generated reference of structural haplotypes topredict that disease-associated variants func9on by increasing expression ofthe complement component 4A gene (C4A) in brain. The role of C4A wasverifiedusinggeneexpressionprofilinginschizophreniabrainsamples,andtheC4 protein was shown to regulate synap9c pruning in a rodent model,iden9fying one of the causal neurobiological mechanisms contribu9ng todisease risk (40). Integra9ve approaches have also been undertaken tocharacterize other GWAS loci in schizophrenia—for example, iden9fying riskvariants that func9on as eQTL andmap to enhancer regions encoding the L-typecalciumchannelCACNA1C(95).40.Sekar,A.etal.SchizophreniariskfromcomplexvariaFonofcomplementcomponent4.Nature530,177–183(2016).95. Roussos, P. et al. A role for noncoding varia9on in schizophrenia. CellReports9,1417–1429(2014).

Fromeretal.GeneexpressionelucidatesfuncFonalimpactofpolygenicriskforschizophreniaNeuroscience19:1442-1453(2016)

 CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ~20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases. 

Fromeretal.GeneexpressionelucidatesfuncFonalimpactofpolygenicriskforschizophreniaNeuroscience19:1442-1453(2016)

Brain gene expression in schizophrenia. (a) RNA sequencing data from more than 500 brains is used for three sets of analyses. Red or pink shading in brains denotes samples from people with schizophrenia vs. healthy control subjects. (b) Differential expression (DE) between brains from patients with schizophrenia and controls pinpoints genes that are misregulated in disease. (c) Network analyses reveal molecular pathways with altered disease connectivity. (d) Genetic analysis of gene expression levels identifies regulatory genetic variation. These expression associations are compared to genetic associations with schizophrenia. In the examples, expression levels of gene 1 are influenced by genetic variation and are the most similar to the schizophrenia associations, making it a candidate for contributing to the disease.

Albert,F.(2016).Brains,genesandpower.NatureNeuroscience,19(11),1428–1430.

18. C. Schizophrenia Working Group of the Psychiatric Genomics, Biological insights from 108schizophrenia-associated genetic loci. Nature 511, 421 (Jul 24, 2014).

19. J. C. Lambert et al., Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci forAlzheimer's disease. Nat Genet 45, 1452 (Dec, 2013).

20. M. A. Nalls et al., Large-scale meta-analysis of genome-wide association data identifies six newrisk loci for Parkinson's disease. Nat Genet 46, 989 (Sep, 2014).

21. V. Anttila et al., Genome-wide meta-analysis identifies new susceptibility loci for migraine. NatGenet 45, 912 (Aug, 2013).

R.A.Power,M.Pluess,Heritabilityes9matesoftheBigFivepersonalitytraitsbasedoncommongene9cvariants.Transla9onalpsychiatry5,e604(2015).