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Genetics Roundup: The Rare, the Common…the Structural, and the Single-Nucleotide

16 August 2011. The recent rise of whole-exome methods, which allow rapid and fairly inexpensive “deep sequencing” of all protein-coding regions in search of previously undetected genetic variants, promises to powerfully broaden the scope of psychiatric genomics beyond the common single-nucleotide variants sought in genomewide association studies (GWAS), and the large and rare structural anomalies targeted in copy number variation (CNV) studies. Indeed, based on their newly published exome-sequencing study, Maria Karayiorgou and Joseph Gogos of Columbia University in New York City and colleagues make the remarkable claim that rare, de novo single-point mutations and small indels (insertions and deletions) of the sort identified in their work may “account for more than half of the cases of sporadic schizophrenia.”

Whether or not this will prove accurate, the “mixed economy” of schizophrenia genomics (see SRF related news story) continues to thrive, as reflected in the variety of approaches and theoretical perspectives of several other recent studies.

A common cause
GWAS and the search for common genetic variants—generally defined as present in at least 5 percent of a population—are alive and kicking, as evidenced by two European studies published from overlapping consortia.

Some shortcomings of GWAS done so far are that sample sizes have been too small to assign statistical significance to many identified single-nucleotide polymorphisms (SNPs), or that potentially interesting SNP signals may have been “washed out” by the ethnic or diagnostic heterogeneity of many samples studied so far.

In a study published in Molecular Psychiatry, Marcella Riestchel of the University of Heidelberg, Mannheim; Sven Cichon of the University of Bonn; and colleagues throughout Germany and elsewhere in Europe aimed to address the latter concern by conducting a GWAS of well-characterized schizophrenia patients exclusively from Germany and The Netherlands (464 and 705, respectively) and 3,714 ethnically matched controls from the same countries.

No SNP reached genomewide significance in the first GWAS, which the authors ascribe to “insufficient power.” The group analyzed the top 43 SNP results in a separate sample of 2,569 patients and 4,088 controls from Germany, Holland, and Denmark, and found “nominal significance” for nine. The strongest associations were found for four closely associated SNPs located on chromosome 11 in introns near AMBRA1 (activating molecule in beclin-1-regulated), an important gene in early neural development. Another intriguing gene nearby is CHRM4, which codes for the muscarinic acetylcholine receptor M4, an attractive potential drug target that modulates dopaminergic transmission. Other candidate genes in the region are DGKZ (diacylglycerol kinase zeta) and Midkine (MDK). One other SNP, on chromosome 18 between CCDC68 (coiled-coil domain containing 68) and http://www.szgene.org/geneoverview.asp?geneid=161 TCF4 (transcription factor 4), survived Bonferroni correction for multiple testing.

In the combined GWAS and replication samples, one chromosome 11 T/C SNP identified in the previous analyses—rs11819869—surpassed genomewide statistical significance (3.89 x 10-9), a finding that was confirmed for the T risk allele of the SNP in 15 other European samples.

The researchers then looked for evidence that this variant marks a genetic influence on brain function, using data from normal subjects performing tests of cognitive ability while undergoing functional MRI. In one test, they found that carriers of the T allele had significantly increased activation in a region of subgenual cingulate cortex that has been implicated in schizophrenia, while C carriers showed decreased activation in the same region. This, the authors say, constitutes, “evidence that the identified risk allele is functional in a neural system relevant to the disorder.”

Both CCDC68 and TCF4 also show their faces in the second GWAS, a meta-analysis published in Human Molecular Genetics that, according to the authors, “buttresses the notion that larger sample sizes will allow the identification of additional common variants.” Kari Stefansson of deCODE Genetics, Reykjavik, Iceland, and colleagues from many other institutions drew from a combined dataset of an unprecedented 18,206 schizophrenia cases and 42,536 controls.

The group had previously conducted studies filtered for all SNPs that had attained a genomewide p-value greater than 1 x 10-5 in their well-known “SGENE-plus-ISC-MGS” GWAS and meta-analysis (see SRF related news story).

Here, first author Stacy Steinberg of deCODE and colleagues extended that work by examining loci reaching 1 x 10-4 in that dataset in an additional sample of 4,704 cases and 7,478 controls from Europe and the United States. In all, 39 SNPs from a variety of genomic regions were tested, including many in the major histocompatibility complex region (MHC) implicated in the 2009 work. In a subsequent follow-up consisting of 1,014 cases and 1,144 controls recruited in Germany, eight achieved genomewide significance (p <5 x 10-8), including two novel SNPs.

The newly identified SNPs were at 2p15.1, near VRK2 (vaccinia-related kinase), a gene thought to be involved in maintaining neuronal structure and preventing cell death, and at 18q21.2, smack between CCDC8 and TCF4. Though this SNP could influence either gene, or both, the growing evidence for a link between TCF4 and schizophrenia and other mental disorders (see Blake et al., 2010 for a discussion) suggests to the authors that the SNP acts through that gene.

Of the six replicated SNPs, four were found in two MHC subregions, one is located near NRGN, and one is in an intron of TCF4.

Divide and conquer
Another approach to solving the sample-size problem in genetics is to study subgroups of patients who share distinctive clinical phenotypes, a method chosen for a recent study of NRG3 (neuregulin-3) by an Australian group led by Assen Jablensky of the University of Western Australia in Perth. This growth factor gene, at 10q22-23, has been associated with schizophrenia in linkage studies of Ashkenazi, Scottish, and Han Chinese families (Fallin et al., 2003; Benzel et al., 2007; Faraone et al., 2006). An analysis of Ashkenazi case-control and familial datasets by a Johns Hopkins group (Chen et al., 2009) had found no association of the 10q22-23 region with schizophrenia per se, but a factor analysis of the data revealed an association between two SNPs (rs6584400 and rs10883866) in an intron of NRG3 with schizophrenia cases characterized by a “delusion factor.”

First author Bharti Morar and colleagues set out to replicate these findings in a group of 411 patients and 223 controls—the team aimed for rough ethnic homogeneity in their sample by recruiting only “Anglo-Irish” subjects—who were assigned to either a “pervasive cognitive deficit” or a “relatively spared cognition” group based on performance in a range of neuropsychological tests.

The researchers found a nominally significant association in the patient sample as a whole between one SNP studied by the Hopkins team, and a weak association for the other (odds ratios of 1.45 and 1.36, respectively). However, factor analysis revealed that these overall associations were entirely attributable to stronger associations (odds ratios of 1.67 and 1.49) in the subgroup with spared cognition.

Morar and colleagues take these findings as evidence that “the schizophrenia phenotype comprises heterogenous components influenced by multiple gene loci.”

Structural problems
Another recent paper bemoans the current state of research on CNVs in schizophrenia. Rolf Ophoff of the University of California, Los Angeles, and coauthors from a number of different institutions point out that informative meta-analyses such as those commonly performed on candidate gene data are difficult to conduct in the world of CNVs, where few complete datasets and not many raw data have been made publicly available. As a result, say the authors, the field’s findings so far have identified rare but recurrent deletions affecting multiple genes (e.g., at 1q21.1, 15q13.3, and 15q11.2) or genomic one-hit wonders, with little overlap between studies (see SRF related news story).

First author Jacobine E. Buizer-Voskamp of the University of Utrecht, The Netherlands, and colleagues thus hope to set an example by releasing the raw data from their own whole-genome study of 834 cases and 672 controls from The Netherlands, which targeted all CNVs 50 kb and larger. The group identified 2,437 CNVs in the subject pool overall, and confirmed results from other studies showing that deletions are more common in cases than controls, a pattern that becomes more pronounced as CNVs increase in size. The work corroborates previous linkages of the 1q42 region (the site of DISC1), 2p25, 15q13, and 22q11.2 with schizophrenia, findings given additional support in the authors’ comprehensive review of the literature on cytogenic and chromosomal anomalies in the disorder. Noting a clustering of reported cytogenic abnormalities at 5q35.1 in their literature review, the group also highlights a CNV they discovered there, in a region harboring candidate genes SLIT3, GABRP, and FGF18.

Another recent, but much larger, CNV study, from the Genetic Risk and Outcome in Psychosis (GROUP) consortium, focused on the 15q11.2-13.3 region, an imprinted area in which gene expression depends on parental origin. Involvement of this region in Prader-Willi syndrome and Angelman syndrome, caused respectively by deletions of paternal or maternal origin, is well documented, as is a more recently recognized syndrome characterized by autism that is associated with duplications of maternal origin (see, e.g., Cook et al., 1997). After turning up a microduplication at 15q11.2-13.1 in one patient with early-onset schizophrenia, the team searched more comprehensively in a European sample of 7,582 patients with schizophrenia or schizoaffective disorder and 41,370 controls.

As reported in the American Journal of Psychiatry, first author Andrés Ingason and corresponding author Thomas Werge of Copenhagen University Hospital, Denmark, and colleagues found 11 carriers of duplications in 15q11-13, five of whom had diagnoses of schizophrenia or schizoaffective disorder; all carried duplications of maternal origin. Two other patients carrying such duplications had received psychiatric diagnoses—of bipolar disorder and autism—and one control subject with a maternally derived duplication had been diagnosed with Alzheimer’s disease. By contrast, the only paternally derived duplications were observed in two control subjects, neither of whom had a psychiatric diagnosis. The authors highlight UBE3A as a gene of interest in this region because it is only expressed on the maternal chromosome, and it is related to synapse development and glutamate signaling.

A large target
In the exome-sequencing study from Karayiorgou and Gogos's group, first author Bin Xu of Columbia University and colleagues isolated sporadic schizophrenia cases by studying trios comprising subjects, 53 of whom were patients and 22 controls, and their unaffected parents, obtaining blood samples from all participants (for an account of the first published exome-sequencing effort in the field, see SRF related news story).

The researchers identified 40 de novo events affecting 40 genes in 27 (~51 percent) of the cases, including 35 point mutations, one dinucleotide substitution, and four indels; 10 cases carried more than one of these mutations. Of the 35 point mutations, 32 were non-synonymous, and were predicted to affect protein function, as were the four indels. Some others were determined to potentially disrupt splicing. In contrast, only seven controls carried these newly identified de novo mutations.

Of particular interest to the authors was a point mutation in DGCR2 on 22q11.1 in an otherwise structurally sound chromosome, which might contribute to the schizophrenia risk associated with 22q11.1 microdeletions.

The research group concludes that the “large mutational target” of 40 affected genes cited in the study supports a role for de novo events in schizophrenia and offers an explanation for the persistence of the disorder, despite the decrease in reproductive rate associated with it.

In the diverse research landscape of schizophrenia genetics (see SRF related news story), theories, methods, and data can all be contentious, but data appear to be emerging from all fronts, and perhaps all this controversy will move the field forward. Steinberg and colleagues propose that an ecumenical approach embracing GWAS, CNV research, and exome sequencing makes the most sense, in the hope that “eventually, a collection of variants—rare and common, structural and single-nucleotide—may account for a substantial portion of schizophrenia heritability, as has been shown for other common diseases such as type 2 diabetes.”—Pete Farley.

References:
Buizer-Voskamp JE, Muntjewerff JW; Genetic Risk and Outcome in Psychosis (GROUP) Consortium, Strengman E, Sabatti C, Stefansson H, Vorstman JA, Ophoff RA. Genome-wide Analysis Shows Increased Frequency of Copy Number Variation Deletions in Dutch Schizophrenia Patients. Biol Psychiatry. 2011 Apr 12. Abstract

Ingason A, Kirov G, Giegling I, Hansen T, Isles AR, Jakobsen KD, Kristinsson KT, le Roux L, Gustafsson O, Craddock N, Möller HJ, McQuillin A, Muglia P, Cichon S, Rietschel M, Ophoff RA, Djurovic S, Andreassen OA, Pietiläinen OP, Peltonen L, Dempster E, Collier DA, St Clair D, Rasmussen HB, Glenthøj BY, Kiemeney LA, Franke B, Tosato S, Bonetto C, Saemundsen E, Hreidarsson SJ; GROUP Investigators, Nöthen MM, Gurling H, O'Donovan MC, Owen MJ, Sigurdsson E, Petursson H, Stefansson H, Rujescu D, Stefansson K, Werge T. Maternally derived microduplications at 15q11-q13: implication of imprinted genes in psychotic illness. Am J Psychiatry. 2011 Apr;168(4):408-17. Abstract

Morar B, Dragović M, Waters FA, Chandler D, Kalaydjieva L, Jablensky A. Neuregulin 3 (NRG3) as a susceptibility gene in a schizophrenia subtype with florid delusions and relatively spared cognition. Mol Psychiatry. 2011 Aug;16(8):860-6. Abstract

Rietschel M, Mattheisen M, Degenhardt F; GROUP Investigators; Genetic Risk and Outcome in Psychosis (GROUP Investigators), Kahn RS, Linszen DH, Os JV, Wiersma D, Bruggeman R, Cahn W, de Haan L, Krabbendam L, Myin-Germeys I, Mühleisen TW, Kirsch P, Esslinger C, Herms S, Demontis D, Steffens M, Strohmaier J, Haenisch B,Breuer R, Czerski PM, Giegling I, Strengman E, Schmael C, Mors O, Mortensen PB, Hougaard DM, Orntoft T, Kapelski P, Priebe L, Basmanav FB, Forstner AJ, Hoffmann P, Meier S, Nikitopoulos J, Moebus S, Alexander M, Mössner R, Wichmann HE, Schreiber S, Rivandeneira F, Hofman A, Uitterlinden AG, Wienker TF, Schumacher J, Hauser J, Maier W, Cantor RM, Erk S, Schulze TG; SGENE-plus Consortium; (Only those persons responsible for the samples of Replication 2 are listed), Stefansson H, Steinberg S, Gustafsson O, Sigurdsson E, Petursson H, Kong A, Stefansson K, Pietiläinen OP, Tuulio-Henriksson A, Paunio T, Lonnqvist J, Suvisaari J, Peltonen L, Ruggeri M, Tosato S, Walshe M, Murray R, Collier DA, Clair DS, Hansen T, Ingason A, Jakobsen KD, Duong L, Werge T, Melle I, Andreassen OA, Djurovic S, Bitter I, Réthelyi JM, Abramova L, Kaleda V, Golimbet V, Jönsson EG, Terenius L, Agartz I, Winkel RV, Kenis G, Hert MD, Veldink J, Wiuf C, Didriksen M, Craddock N, Owen MJ, O'Donovan MC, Børglum AD, Rujescu D, Walter H, Meyer-Lindenberg A, Nöthen MM, Ophoff RA, Cichon S. Association between genetic variation in a region on chromosome 11 and schizophrenia in large samples from Europe. Mol Psychiatry. 2011 Jul 12. Abstract

Steinberg S, de Jong S; Irish Schizophrenia Genomics Consortium, Andreassen OA, Werge T, Børglum AD, Mors O, Mortensen PB, Gustafsson O, Costas J, Pietiläinen OP, Demontis D, Papiol S, Huttenlocher J, Mattheisen M, Breuer R, Vassos E, Giegling I, Fraser G, Walker N, Tuulio-Henriksson A, Suvisaari J, Lönnqvist J, Paunio T, Agartz I, Melle I, Djurovic S, Strengman E; GROUP, Jürgens G, Glenthøj B, Terenius L, Hougaard DM, Orntoft T, Wiuf C, Didriksen M, Hollegaard MV, Nordentoft M, van Winkel R, Kenis G, Abramova L, Kaleda V, Arrojo M, Sanjuán J, Arango C, Sperling S, Rossner M, Ribolsi M, Magni V, Siracusano A, Christiansen C, Kiemeney LA, Veldink J, van den Berg L, Ingason A, Muglia P, Murray R, Nöthen MM, Sigurdsson E, Petursson H, Thorsteinsdottir U, Kong A, Rubino IA, De Hert M, Réthelyi JM, Bitter I, Jönsson EG, Golimbet V, Carracedo A, Ehrenreich H, Craddock N, Owen MJ, O'Donovan MC; Wellcome Trust Case Control Consortium, Ruggeri M, Tosato S, Peltonen L, Ophoff RA, Collier DA, St Clair D, Rietschel M, Cichon S, Stefansson H, Rujescu D, Stefansson K. Common Variants at VRK2 and TCF4 Conferring Risk of Schizophrenia. Hum Mol Genet. 2011 Jul 26. Abstract

Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet. 2011 Aug 7. Abstract

Comments on News and Primary Papers


Primary Papers: Exome sequencing supports a de novo mutational paradigm for schizophrenia.

Comment by:  Bryan Roth, SRF Advisor
Submitted 15 August 2011
Posted 16 August 2011
  I recommend this paper

This is the first report of exome sequencing of sporadic cases of schizophrenia and for that reason is interesting. There are likely to be many more of these sorts of papers appearing over the next few months, and it will be informative to compare and tabulate the results once they are available.

Of most interest to me was that none of the reported de novo mutations was replicated among any of the individuals sequenced, indicating that if these de novo mutations in the exome are pathogenic for schizophrenia, the genetic landscape is vastly more complex than previously imagined.

View all comments by Bryan Roth

Primary Papers: Exome sequencing supports a de novo mutational paradigm for schizophrenia.

Comment by:  Patrick Sullivan, SRF AdvisorJin Szatkiewicz
Submitted 21 August 2011
Posted 23 August 2011

Xu et al. (2011) tested the hypothesis that de-novo exon mutations play a major role in schizophrenia by sequencing the exomes of 53 sporadic case trios and 22 unaffected control trios. The experimental procedures for mutation identification were well done technically. However, a number of issues deserve closer consideration.

First, a major study design in human genetics is the evaluation of pedigrees densely affected with a disease under the assumption that etiological variants are more likely to segregate in these so-called multiplex pedigrees. Xu et al. took a very different approach by studying schizophrenia cases with no history of schizophrenia or schizoaffective disorder in their first- or second-degree relatives. Their assumption is that a deterministic exonic mutation occurred that was necessary and sufficient for the development of schizophrenia. In effect, the assumption is that these cases represent different Mendelian forms of schizophrenia. Moreover, as cases are heterozygous for the de-novo mutations, the authors make the fairly strong assumption that the de-novo variants identified act in a dominant mode.

Although not discussed in the paper, there are other models by which schizophrenia can occur sporadically. Indeed, sporadic cases could be more likely to have schizophrenia due to environmental etiologies (e.g., head trauma, CNS infection, obstetric trauma, cannabis use, psychotogenic drug use, etc.). Some of these alternative etiologies can be very difficult to uncover clinically, and it would have been helpful to read more about the authors’ efforts to exclude environmental etiological factors.

As another example, there is now fairly strong evidence that schizophrenia is highly polygenic with a large number of common genetic variants of subtle, probabilistic effect (the Psychiatric GWAS Consortium’s schizophrenia mega-analysis is in press in Nature Genetics). It is possible that some of these sporadic cases resulted from matings between two unaffected parents with moderate to high numbers of schizophrenia risk loci.

Second, in regard to the results, the proportion of de-novo mutations was somewhat higher in cases than in controls, although the difference was not statistically significant. This is not really in line with the main hypothesis, and there could have been more discussion of this result.

The authors then used computational algorithms to predict the proportion of deleterious de-novo mutations, and found a significantly higher proportion in schizophrenia cases than controls. No functional data were presented. Thus, the trustworthiness of this observation is dependent upon the robustness of the algorithms that predict the functional consequences of a mutation. Are these in-silico predictions accurate? These algorithms are definitely imperfect (it is not uncommon for three different algorithms to give different answers). Without additional evidence and functional data, the statement that these deleterious mutations “have a high likelihood of causation with respect to schizophrenia” is assumption-laden and has not been proven.

Third, the author concluded that “de-novo mutations account for more than half of the sporadic cases of schizophrenia.” This claim is unquestionably premature, and not supported by the data.

The single most important lesson learned in human genetics in the past generation is the need for replication. The authors have made an intriguing observation, but, without replication, it is difficult to know if this is secure knowledge or merely another high-profile, false-positive finding resulting from some cryptic bias. Furthermore, the study samples were Afrikaners, a group with an unusual population history, and it is an open question whether the result would generalize to samples elsewhere in the world. Finally, the sample sizes are small and could be subject to any number of sampling biases.

View all comments by Patrick Sullivan
View all comments by Jin Szatkiewicz

Comments on Related Papers


Related Paper: Statistical epistasis and progressive brain change in schizophrenia: an approach for examining the relationships between multiple genes.

Comment by:  Karoly Mirnics, SRF Advisor
Submitted 2 September 2011
Posted 7 September 2011
  I recommend this paper

This approach is noteworthy—in the fashionable world of CNVs, we must not forget our previous SNP findings. Modeling epistasis between genes should be a priority, yet there are very few approaches and publications in this arena (compared to the number of publication on single gene effects). It is also nice to see that data replicate, and that we see the usual suspects, including PDE4B, RELN, ERBB4, DISC1, and NRG1.

View all comments by Karoly Mirnics

Comments on Related News


Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Todd LenczAnil Malhotra (SRF Advisor)
Submitted 3 July 2009
Posted 3 July 2009

The three companion papers published in Nature provide important new evidence for a role of the MHC complex and common variation across the genome in risk for schizophrenia. These studies have exploited the availability of comprehensive genotyping technologies, coupled with large cohorts of cases and controls, to identify candidate loci for disease susceptibility.

A notable feature of these papers is the clear willingness of each of the groups to share its data, and to provide overlapping presentations of each others’ results. The combination of datasets permitted the statistical significance of the MHC findings to emerge, thereby increasing confidence in results. The implication that immune processes may interact with genetic risk to influence schizophrenia risk is consistent with several lines of evidence, including our own small GWAS study (Lencz et al., 2007) implicating cytokine receptors in schizophrenia susceptibility.

Perhaps most intriguing is the finding from the International Schizophrenia Consortium demonstrating that a “score” test—combining information from many thousands of common variants—can reliably differentiate patients and controls across multiple psychiatric cohorts. These results indicate that hundreds, if not thousands, of genes of small effect may contribute to schizophrenia risk. Moreover, these same genes were shown to contribute to bipolar risk (but not risk for non-psychiatric disorders such as diabetes).

Much more work remains to be done in psychiatric genetics. While the score test accounted for about 3 percent of the observed case-control variance, statistical modeling suggested that common variation could explain as much as one-third or more of the total risk. Nevertheless, there remains a substantial proportion of genetic “dark matter” (unexplained variance), given the high heritability of a disorder such as schizophrenia. Complementary approaches are needed to further parse the source of the common genetic variance, as well as to identify rare yet highly penetrant mutations. Additional techniques, such as pharmacogenetic studies and endophenotypic research, will help to explicate the functionality and clinical significance of observed risk alleles.

View all comments by Todd Lencz
View all comments by Anil Malhotra

Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Daniel Weinberger, SRF Advisor
Submitted 3 July 2009
Posted 3 July 2009

The three Nature papers reporting GWAS results in a large sample of cases of schizophrenia and controls from around Western Europe and the U.S. are decidedly disappointing to those expecting this strategy to yield conclusive evidence of common variants predicting risk for schizophrenia. Why has this extensive and very costly effort not produced more impressive results? There are likely to be many explanations for this, involving the usual refrains about clinical and genetic heterogeneity, diagnostic imprecision, and technical limitations in the SNP chips. But the likely, more fundamental problem in psychiatric genetics involves the biologic complexity of the conditions themselves, which renders them especially poorly suited to the standard GWAS strategy. The GWA analytic model assumes fixed, predictable relationships between genetic risk and illness, but simple relationships between genetic risk and complex pathophysiological mechanisms are unlikely. Many biologic functions show non-linear relationships, and depending on the biologic context, more of a potential pathogenic factor, can make things worse or it can make them better. Studies of complex phenotypes in model systems illustrate that individual gene effects depend upon non-linear interactions with other genes (Toma et al., 2002; Shaoa et al, 2008). Similar observations are beginning to emerge in human disorders, e.g., in risk for cancer (Lo et al., 2008) and depression (Pezawas et al., 2008).

The GWA approach also assumes that diagnosis represents a unitary biological entity, but most clinical diagnoses are syndromal and biologically heterogeneous, and this is especially true in psychiatric disorders. Type 2 diabetes is the clinical expression of changes in multiple physiologic processes, including in pancreatic function, in adipose cell function, as well as in eating behavior. Likewise, hypertension results from abnormalities in many biologic processes (e.g., vascular reactivity, kidney function, CNS control of blood pressure, metabolic factors, sodium regulation), and even a large effect on any specific process within a subset of individuals will seem small when measured in large unrelated samples (Newton-Cheh et al., 2009). In the case of the cognitive and emotional problems associated with psychiatric disorders, the biologic pathways to clinical manifestations are probably much more heterogeneous. While the results of GWAS in disorders like type 2 diabetes and hypertension have been more informative than in the schizophrenia results so far, they, too, have been disappointing, considering all the fanfare about their expectations. But given the pathophysiologic realities of diabetes, hypertension, or psychiatric disorders, how could the effect of any common genetic variant acting on only one of the diverse pathophysiological mechanisms implicated in these disorders be anything other than small when measured in large pathophysiologically heterogeneous populations? Other approaches, e.g., family studies, studies of smaller but much better characterized samples, and studies of genetic interactions in these samples, will be necessary to understand the variable genetic architectures of such biologically complex and heterogeneous disorders.

References:

Toma DP, White KP, Hirsch J and Greenspan RJ: Identification of genes involved in Drosophila melanogaster geotaxis, a complex behavioral trait. Nature Genetics 2002; 31: 349-353. Abstract

Shaoa H, Burragea LC, Sinasac DS et al : Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis. PNAS 2008 105: 19910–19914. Abstract

Lo S-W, Chernoff H, Cong L, Ding Y, and Zheng T: Discovering interactions among BRCA1 and other candidate genes associated with sporadic breast cancer. PNAS 2008; 105: 12387–12392. Abstract

Pezawas L, Meyer-Lindenberg A, Goldman AL, et al.: Biologic epistasis between BDNF and SLC6A4 and implications for depression. Mol Psychiatry 2008;13:709-716. Abstract

Newton-Cheh C, Larson MG, Vasan RS: Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure. Nat Gen 2009; 41: 348-353. Abstract

View all comments by Daniel Weinberger

Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Irving Gottesman, SRF Advisor
Submitted 3 July 2009
Posted 3 July 2009
  I recommend the Primary Papers

The synthesis and extraction of the essence of the 3 Nature papers by Heimer and Farley represents science reporting at its best. Completion of the task while the ink was still wet shows that SRF is indeed in good hands. Congratulations on being concise, even-handed, non-judgmental, and challenging under the pressure of time.

View all comments by Irving Gottesman

Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Christopher RossRussell L. Margolis
Submitted 6 July 2009
Posted 6 July 2009

Schizophrenia Genetics: Glass Half Full?
While it may be disappointing that the GWAS described above did not identify more genes, they nevertheless represent a landmark in psychiatric genetics and suggest a dual approach for the future: continued large-scale genetic association studies along with alternative genetic approaches leading to the discovery of new genetic etiologies, and more functional investigations to identify pathways of pathogenesis—which may themselves suggest new etiologies.

The consistent identification of an association with the MHC locus reinforces (without proving, as pointed out in the SRF news story) long-standing interest in the involvement of infectious or immune factors in schizophrenia pathogenesis (Yolken and Torrey, 2008). Epidemiologic and neuropathological studies that include patients selected for the presence or absence of immunologic genetic risk variants could potentially clarify etiology; cell and mouse model studies could clarify pathogenesis (Ayhan et al., 2009). It is striking that a major genetic finding in schizophrenia serves to reinforce the concept of environmental risk factors.

The two specific genes identified by the SGENE consortium, NRGN and TCF4, offer intriguing new leads into schizophrenia. This should foster a number of further genetic and neurobiological studies. Deep resequencing (and CNV analysis) can detect rare causative mutations, as exemplified by TCF4 mutations leading to Pitt-Hopkins syndrome. Neurogranin already has clear connections to interesting signaling pathways related to glutamate transmission. A hope is that further studies of both gene products and their interactions will identify pathogenic pathways.

The ISC used common genetic variants “en masse” to generate a “polygene score” from discovery samples of patients; that score was able to predict case status in test populations. The success of this approach provides very strong evidence that a portion of schizophrenia risk status is attributable to common genetic variants acting in concert and that schizophrenia shares genetic factors with bipolar disorder, but not with other diseases. This analysis has multiple practical implications for the direction of research. First, since polygenic factors explain only a portion of the genetic risk, the search for other genetic factors—rare mutations of major effect detectable by deep sequencing, CNVs, variations in tandem repeats (Bruce et al., 2009, in press), and other genomic lesions—takes on new importance. Second, a meaningful integration of polygenic factors in a way that facilitates understanding of schizophrenia pathogenesis and the discovery of therapeutic targets will require identification of relevant pathways. Examination of patient-derived material—such as neurons differentiated from induced pluripotent stem cells taken from well-characterized, patient populations—may be of great value.

The remarkable overlap between the genetic factors of schizophrenia and bipolar disorder suggests the need for further and more inclusive clinical studies—not just of “endophenotypes,” but also of the phenotypes themselves, together, rather than in isolation (Potash and Bienvenu, 2009). For instance, it is only within the past few years that the importance of cognitive dysfunction in schizophrenia has been appreciated. Cognition in bipolar disorder is even less well studied.

How much is really known about the longitudinal course of both disorders? Do genetic factors predict disease outcome? It is only recently that studies have focused intensively on the early course of schizophrenia and its prodrome. Much more is still to be learned, and even less is known about bipolar disorder. In conjunction with this greater understanding of clinical phenotype, it will clearly be necessary to refine the approach to phenotype by establishing the biological framework for these diseases and by establishing biomarkers, such as disruption in white matter (Karlsgodt et al., 2009) or abnormalities in functional networks (Demirci et al., 2009), that cut across current nosological categories. In turn, longitudinal study of clinical, imaging, and functional outcomes of schizophrenia and bipolar disorders should facilitate both focused candidate genetic studies and GWAS of large populations.

References:

Yolken RH, Torrey EF. Are some cases of psychosis caused by microbial agents? A review of the evidence. Mol Psychiatry. 2008 May;13(5):470-9. Abstract

Ayhan Y, Sawa A, Ross CA, Pletnikov MV. Animal models of gene-environment interactions in schizophrenia. Behav Brain Res. 2009 Apr 18. Abstract

Potash JB, Bienvenu OJ. Neuropsychiatric disorders: Shared genetics of bipolar disorder and schizophrenia. Nat Rev Neurol. 2009 Jun;5(6):299-300. Abstract

Karlsgodt KH, Niendam TA, Bearden CE, Cannon TD. White matter integrity and prediction of social and role functioning in subjects at ultra-high risk for psychosis. Biol Psychiatry. 2009 May 6. Epub ahead of print. Abstract

Demirci O, Stevens MC, Andreasen NC, Michael A, Liu J, White T, Pearlson GD, Clark VP, Calhoun VD. Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls. Neuroimage. 2009 Jun;46(2):419-31. Abstract

Bruce HA, Sachs NA, Rudnicki DD, Lin SG, Willour VL, Cowell JK, Conroy J, McQuaid D, Rossi M, Gaile DP, Nowak NJ, Holmes SE, Sklar P, Ross CA, DeLisi LE, Margolis RL. Long tandem repeats as a form of genomic copy number variation: structure and length polymorphism of a chromosome 5p repeat in control and schizophrenia populations. Psychiatric Genetics, in press.

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Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  David Collier
Submitted 6 July 2009
Posted 6 July 2009
  I recommend the Primary Papers

This report is unnecessarily negative, from my point of view. The three studies show not only that GWAS can identify susceptibility alleles for schizophrenia, but that the majority of risk comes from common variants of small effect. These can be found, but as in other complex traits and diseases, such as obesity and height, considerable power is needed, because effect sizes are small, meaning greater samples sizes. This approach works: there are now almost 60 variants influencing height (Hirschhorn et al., 2009; Soranzo et al., 2009; Sovio et al., 2009). Furthermore, the genes identified so far from both traditional mapping, CNV analysis and GWAS, point to two biological pathways, the integrity of the synapse (neurexin 1, neurogranin, etc.) and the wnt/GSK3β signaling pathway (DISC1, TCF4, etc.), which is involved in functions such as neurogenesis in the brain. The identification of disease pathways for schizophrenia has major implications and should not be underestimated. It would be daft to lose nerve now and turn away from GWAS just as they are bearing fruit.

I would like to correct/expand on my comments to Peter Farley, to say that while statistical significance for some markers may be reached sooner, significance for many of the hundreds if not thousands of common schizophrenia susceptibility alleles of small effect might not emerge until samples of 100,000 cases and more than 100,000 controls have been collected. Another point is that organizations such the Wellcome Trust are already assembling case samples for schizophrenia as well as control samples.

Also, I would like to clarify that I believe the remainder of genetic variation, after common variation has been taken into account, will come from some combination of rare CNVs, other rare variants such as SNPs and other types of genetic marker such as variable number of tandem repeats (VNTRs) and of course the much neglected contribution from gene-environment interactions, in which main genetic effects may be obscured.

References:

Hirschhorn JN, Lettre G. Progress in genome-wide association studies of human height. Horm Res. 2009 Apr 1 ; 71 Suppl 2():5-13. Abstract

Soranzo N, Rivadeneira F, Chinappen-Horsley U, Malkina I, Richards JB, Hammond N, Stolk L, Nica A, Inouye M, Hofman A, Stephens J, Wheeler E, Arp P, Gwilliam R, Jhamai PM, Potter S, Chaney A, Ghori MJ, Ravindrarajah R, Ermakov S, Estrada K, Pols HA, Williams FM, McArdle WL, van Meurs JB, Loos RJ, Dermitzakis ET, Ahmadi KR, Hart DJ, Ouwehand WH, Wareham NJ, Barroso I, Sandhu MS, Strachan DP, Livshits G, Spector TD, Uitterlinden AG, Deloukas P. Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame size. PLoS Genet. 2009 Apr 1 ; 5(4):e1000445. Abstract

Sovio U, Bennett AJ, Millwood IY, Molitor J, O'Reilly PF, Timpson NJ, Kaakinen M, Laitinen J, Haukka J, Pillas D, Tzoulaki I, Molitor J, Hoggart C, Coin LJ, Whittaker J, Pouta A, Hartikainen AL, Freimer NB, Widen E, Peltonen L, Elliott P, McCarthy MI, Jarvelin MR. Genetic determinants of height growth assessed longitudinally from infancy to adulthood in the northern Finland birth cohort 1966. PLoS Genet. 2009 Mar 1 ; 5(3):e1000409. Abstract

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Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Michael O'Donovan, SRF AdvisorNick CraddockMichael Owen (SRF Advisor)
Submitted 9 July 2009
Posted 9 July 2009

Some commentators in their reflections take a rather negative view on what has been achieved through the application of GWAS technology to schizophrenia and psychiatric disorders more generally. We strongly disagree with this position. Below, we give examples of a number of statements that can be made about the aetiology of schizophrenia and bipolar disorder that could not be made at high levels of confidence even two years ago that are based upon evidence deriving from the application of GWAS.

1. We know with confidence that the role of rare copy number variants in schizophrenia is not limited to 22q11DS (VCFS) (reviewed recently in O’Donovan et al., 2009). We do not yet know how much of a contribution, but we know the identity of an increasing number of these. Most span multiple genes so it may prove problematic as it has in 22q11DS to identify the relevant molecular mechanisms. However, for one locus, the CNVs are limited to a single gene: Neurexin1 (Kirov et al., 2008; Rujescu et al., 2009). Genetic findings are merely the start of the journey to a deeper biological understanding, but no doubt many neurobiological researchers have already embarked on that journey in respect of neurexin1.

2. Although we have argued in this forum that some of the major pre-GWAS findings in schizophrenia very likely reflect true susceptibility genes (DTNBP1, NRG1, etc), we now have at least 4 novel loci where the evidence is more definitive (ZNF804A, MHC, NRGN, TCF4), (O’Donovan et al., 2008a; ISC, 2009; Shi et al., 2009; Stefansson et al., 2009) and two novel loci (Ferreira et al., 2008) in bipolar disorder (ANK3 and CACNA1C), at least one of which (CACNA1C) additionally confers risk of schizophrenia (Green et al., 2009). This is obviously a small part of the picture, but it is certainly better than no picture at all. These findings also offer a much more secure foundation than the earlier findings upon which to build follow up studies, for example brain imaging, and cognitive phenotypes (Esslinger et al., 2009), and even candidate gene studies. We would not regard the first convincing evidence that altered calcium channel function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.

3. We can say with confidence that common alleles of small effect are abundant in schizophrenia, and that they contribute to a substantial part of the population risk (ISC, 2009). Identifying any one of these at stringent levels of statistical significance may be challenging in terms of sample sizes. As we have pointed out before, merging multiple datasets may indeed obscure some true associations because of sometimes unpredictable relationships between risk alleles and those assayed indirectly in GWAS studies (Moskvina and O’Donovan, 2007). Nevertheless, that many of the same alleles are overrepresented in multiple independent GWAS datasets from different countries (ISC, 2009) means that larger samples offer the prospect of identifying many more of these. This is not to say that large samples are the only approach; genetic heterogeneity may well underpin some aspects of clinical heterogeneity (Craddock et al., 2009a). However, with the exception of individual large pedigrees, it is not yet evident which type of clinical sample one should base a small scale study on. It should also be self-evident that the analysis of multiple samples, each with a different phenotypic structure, will pose major problems in respect of multiple testing and subsequent replication. Moreover, ascertaining special samples that represent putative subtypes of the clinical (and endophenotypic) spectrum of psychosis will first require large samples to be carefully assessed and the relevant subjects extracted. Subsequently, downstream, evaluation of specific genotype-phenotype relationships will require the remainder of the clinical population to be genotyped in a suitably powered way to show that those effects are specific to some clinical features of the disorder. Increasingly, it is ascertainment and assessment that dominate the cost of GWAS studies so it is not clear this approach will achieve any economies. We must also remember that after a GWAS study, there remains the opportunity to look in a controlled manner for relatively specific associations in the context of the heterogeneous clinical picture. For example we are aware of a number of papers in development that will exploit the sorts of multi-locus tests reported by the ISC to refine diagnostics, and no doubt many other applications of this will emerge in the next year or so.

Critics should bear in mind that the GWAS data are not just there for the ‘headline’ genome-wide findings, but that the data will be available to mine for years to come. The findings reported to date are based on only the simplest analyses.

4. If it were the case that the thousands of SNPs of small effect were randomly distributed across biological systems, none being of more relevance to pathophysiology than another, identifying them would probably be a pointless endeavour. However, there is no reason to believe this will be the case. We have recently shown that in bipolar disorder, the GWAS signals are enriched in particular biological pathways (Holmans et al., 2009) and we also published strong evidence for a relatively selective involvement of the GABAergic system in schizoaffective disorder (Craddock et al., 2009b). We are aware of an as-yet unpublished independent sample with similar findings. We would not regard the first convincing evidence that altered GABA function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.

Incidentally it is a common misconception that the identification of risk alleles of small effect necessarily confers no useful insights into pathogenesis and possible drug targets. For example, common alleles in PPARG and KCNJ11 have been robustly shown to confer risk to Type 2 diabetes (T2D) but with odds ratios in the region of only 1.14 (of similar magnitude to those revealed by GWAS of schizophrenia). PPARG encodes the target for the thiazolidinedione class of drugs used to treat T2D. KCNJ11 encodes part of the target for another class of diabetes drug, the sulphonylureas (Prokopenko et al., 2008). Moreover, studies of novel associated variants identified in T2D GWAS in healthy, non-diabetic, populations have demonstrated that for most, the primary effect on T2D susceptibility is mediated through deleterious effects on insulin secretion, rather than insulin action (Prokopenko et al., 2008). Further examples of insights into the biology of common diseases coming from the identification of loci of small effect are the implication of the complement system in age-related macular degeneration and autophagy in Crohn’s disease (Hirschhorn, 2009). Already, efforts are under way to translate the new recognition of the role of autophagy in Crohn’s disease into new therapeutic leads (Hirschhorn, 2009). Of course many of the loci identified in GWAS implicate genes whose functions are as yet largely or completely unknown, and determining those functions is a prerequisite of translating those findings. Nevertheless, we believe that the greatest benefits that will accrue from the continued discovery of risk loci through GWAS will come from the assembly of that information into novel disease pathways leading to novel therapeutic targets.

5. We can say with confidence that bipolar disorder and schizophrenia substantially overlap, at least in terms of polygenic risk (ISC, 2009). As clinicians, we do not regard that knowledge as a trivial achievement.

6. We can say with confidence from studies of CNVs that schizophrenia and autism share at least some risk factors in common (O’Donovan et al., 2009). We believe that is also an important insight.

The above are major achievements in what we expect to be a long but accelerating process of picking apart the origins of schizophrenia and other psychotic disorders. We do not think that any other research discipline in psychiatry has done more to advance that knowledge in the past 100 years.

Like that other common familial diseases, the genetics of schizophrenia and bipolar disorder is a “mixed economy” of common alleles of small effect and rare alleles of large and small effects, including CNVs. Those who are concerned at the cost of collecting large samples for GWAS studies must bear in mind that the robust identification of both types of mutation will require similarly large samples; we will just have to get used to that fact if we want to make progress. Collecting samples like this may be expensive, but as clinicians, we know those costs are trivial compared with the human and economic costs of psychotic disorders.

The question of phenotype definition is one which we have repeatedly addressed (Craddock et al., 2009a). Unquestionably, if we knew the true pathophysiological basis of these disorders, we could do better. The fact is that we don’t. Given that, it must be extremely encouraging that despite the problems, risk loci can be robustly identified by GWAS using samples defined by current diagnostic criteria. Moreover, armed with GWAS data in these heterogeneous populations, additional risk genes can be identified through strategies aimed at refining the phenotype that are not constrained by the current dichotomous view of the functional psychoses. We have shown at least one way in which this might be achieved without imposing a further burden of multiple testing (Craddock et al., 2009b), and have little doubt that others will emerge. We agree that approaches to phenotyping that more directly index underlying pathophysiology are highly appealing, and will ultimately be necessary for understanding the mechanistic relationships between gene and disorder. However, the two cardinal assumptions upon which the use of endophenotypes is predicated for gene discovery are questionable. First, there is little good evidence that putative endophenotypes are substantially simpler genetically than “exophenotypes” (Flint and Munafo, 2007). Second, there is rarely good evidence that the current crop of popular putative endophenotypes lie on the disease pathway, indeed there seems to be substantial pleiotropy in the genetics of complex traits, psychosis included (Prokopenko et al., 2008; O’Donovan et al., 2008b).

Finally, we reiterate that while only small parts of the heritability of any complex disorder have been accounted for, large-scale genetic approaches have been extremely successful in studies of non-psychiatric diseases (Manolio et al., 2008) and have led to substantial advances in our understanding of pathogenesis, even for diseases like Crohn’s disease where there was already prior knowledge of pathogenesis from other research methods (Mathew, 2008).

Psychiatry starts from a situation in which there is no robust prior knowledge of pathogenesis for the major phenotypes. Recent findings suggest that mental illness may be the medical field that will actually benefit most over the coming years from application of these powerful molecular genetic technologies.

References:
Craddock N, O'Donovan MC, Owen MJ. (2009a) Psychosis Genetics: Modeling the Relationship between Schizophrenia, Bipolar Disorder, and Mixed (or "Schizoaffective") Psychoses. Schizophrenia Bulletin 35(3):482-490. Abstract

Craddock N, Jones L, Jones IR, Kirov G, Green EK, Grozeva D, Moskvina V, Nikolov I, Hamshere ML, Vukcevic D, Caesar S, Gordon-Smith K, Fraser C, Russell E, Norton N, Breen G, St Clair D, Collier DA, Young AH, Ferrier IN, Farmer A, McGuffin P, Holmans PA, Wellcome Trust Case Control Consortium (WTCCC), Donnelly P, Owen MJ, O’Donovan MC. Strong genetic evidence for a selective influence of GABAA receptors on a component of the bipolar disorder phenotype. Molecular Psychiatry advanced online publication 1 July 2008; doi:10.1038/mp.2008.66. (b) Abstract

Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Arnold C, Haddad L, Mier D, Opitz von Boberfeld C, Raab K, Witt SH, Rietschel M, Cichon S, Meyer-Lindenberg A. (2009) Neural mechanisms of a genome-wide supported psychosis variant. Science 324(5927):605. Abstract

Ferreira MAR, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, Kirov G, Perlis RH, Green EK, Smoller JW, Grozeva D, Stone J, Nikolov I, Chambert K, Hamshere ML, Nimgaonkar V, Moskvina V, Thase ME, Caesar S, Sachs GS, Franklin J, Gordon-Smith K, Ardlie KG, Gabriel SB, Fraser C, Blumenstiel B, Defelice M, Breen G, Gill M, Morris DW, Elkin A, Muir WJ, McGhee KA, Williamson R, MacIntyre DJ, McLean A, St Clair D, VanBeck M, Pereira A, Kandaswamy R, McQuillin A, Collier DA, Bass NJ, Young AH, Lawrence J, Ferrier IN, Anjorin A, Farmer A, Curtis D, Scolnick EM, McGuffin P, Daly MJ, Corvin AP, Holmans PA, Blackwood DH, Wellcome Trust Case Control Consortium (WTCCC), Gurling HM, Owen MJ, Purcell SM, Sklar P and Craddock NJ. (2008) Collaborative genome-wide association analysis of 10,596 individuals supports a role for Ankyrin-G (ANK3) and the alpha-1C subunit of the L-type voltage-gated calcium channel (CACNA1C) in bipolar disorder. Nature Genetics 40:1056-1058. Abstract

Flint J, Munafò MR. (2007) The endophenotype concept in psychiatric genetics. Psychological Medicine 37(2):163-180. Abstract

Green EK, Grozeva D, Jones I, Jones L, Kirov G, Caesar S, Gordon-Smith K, Fraser C, Forty L, Russell E, Hamshere ML, Moskvina V, Nikolov I, Farmer A, McGuffin P, Wellcome Trust Case Consortium, Holmans PA, Owen MJ, O’Donovan MC and Craddock N. (2009) Bipolar disorder risk allele at CACNA1C also confers risk to recurrent major depression and to schizophrenia. Molecular Psychiatry (in press).

Hirschhorn JN. (2009) Genomewide association studies--illuminating biologic pathways. New England Journal of Medicine 360(17):1699-1701. Abstract

Holmans P, Green E, Pahwa J, Ferreira M, Purcell S, Sklar P, Owen M, O’Donovan M, Craddock N. Gene ontology analysis of GWAS datasets provide insights into the biology of bipolar disorder. The American Journal of Human Genetics 2009 Jun 17 [Epub ahead of print]. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009 Jul 1 [Epub ahead of print]. Abstract

Kirov G, Gumus D, Chen W, Norton N, Georgieva L, Sari M, O'Donovan MC, Erdogan F, Owen MJ, Ropers HH, Ullmann R. (2008) Comparative genome hybridization suggests a role for NRXN1 and APBA2 in schizophrenia. Human Molecular Genetics 17(3):458-465. Abstract

Manolio TA, Brooks LD, Collins FS. (2008) A HapMap harvest of insights into the genetics of common disease. Journal of Clinical Investigation 118(5):1590-1605. Abstract

Mathew CG. (2008) New links to the pathogenesis of Crohn disease provided by genome-wide association scans. Nature Review Genetics 9(1):9-14. Abstract

Moskvina V and O'Donovan MC. (2007) Detailed analysis of the relative power of direct and indirect association studies and the implications for their interpretation. Human Heredity 64(1):63-73. Abstract

O’Donovan MC, Kirov G, Owen MJ. (2008a) Phenotypic variations on the theme of CNVs. Nature Genetics 40(12):1392-1393. Abstract

O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, Nikolov I, Hamshere M, Carroll L, Georgieva L, Dwyer S, Holmans P, Marchini JL, Spencer C, Howie B, Leung H-T, Hartmann AM, Möller H-J, Morris DW, Shi Y, Feng G, Hoffmann P, Propping P, Vasilescu C, Maier W, Rietschel M, Zammit S, Schumacher J, Quinn EM, Schulze TG, Williams NM, Giegling I, Iwata N, Ikeda M, Darvasi A, Shifman S, He L, Duan J, Sanders AR, Levinson DF, Gejman P, Molecular Genetics of Schizophrenia Collaboration , Cichon S, Nöthen MM, Gill M, Corvin A, Rujescu D, Kirov G, Owen MJ. (2008b) Identification of novel schizophrenia loci by genome-wide association and follow-up. Nature Genetics 40:1053-1055. Abstract

O’Donovan MC, Craddock N, Owen MJ. Genetics of psychosis; Insights from views across the genome. Human Genetics 2009 Jun 12 [Epub ahead of print]. Abstract

Prokopenko I, McCarthy MI, Lindgren CM. (2008) Type 2 diabetes: new genes, new understanding. Trends in Genetics 24(12):613-621. Abstract

Rujescu D, Ingason A, Cichon S, Pietiläinen OP, Barnes MR, Toulopoulou T, Picchioni M, Vassos E, Ettinger U, Bramon E, Murray R, Ruggeri M, Tosato S, Bonetto C, Steinberg S, Sigurdsson E, Sigmundsson T, Petursson H, Gylfason A, Olason PI, Hardarsson G, Jonsdottir GA, Gustafsson O, Fossdal R, Giegling I, Möller HJ, Hartmann AM, Hoffmann P, Crombie C, Fraser G, Walker N, Lonnqvist J, Suvisaari J, Tuulio-Henriksson A, Djurovic S, Melle I, Andreassen OA, Hansen T, Werge T, Kiemeney LA, Franke B, Veltman J, Buizer-Voskamp JE; GROUP Investigators, Sabatti C, Ophoff RA, Rietschel M, Nöthen MM, Stefansson K, Peltonen L, St Clair D, Stefansson H, Collier DA. (2009) Disruption of the neurexin 1 gene is associated with schizophrenia. Human Molecular Genetics 18(5):988-996. Abstract

Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe'er I, Dudbridge F, Holmans PA, Whittemore AS, Mowry BJ, Olincy A, Amin F, Cloninger CR, Silverman JM, Buccola NG, Byerley WF, Black DW, Crowe RR, Oksenberg JR, Mirel DB, Kendler KS, Freedman R & Gejman PV. (2009) Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature doi:10.1038/nature08192. Abstract

Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, Werge T, Pietiläinen OPH, Mors O, Mortensen PB, Sigurdsson E, Gustafsson O, Nyegaard M, Tuulio-Henriksson A, Ingason A, Hansen T, Suvisaari J, Lonnqvist J, Paunio T, Børglum AD, Hartmann A, Fink-Jensen A, Nordentoft M, Hougaard D, Norgaard-Pedersen B, Böttcher Y, Olesen J, Breuer R, Möller H-J, Giegling I, Rasmussen HB, Timm S, Mattheisen M, Bitter I, Réthelyi JM, Magnusdottir BB, Sigmundsson T, Olason P, Masson G, Gulcher JR, Haraldsson M, Fossdal R, Thorgeirsson TE, Thorsteinsdottir U, Ruggeri M, Tosato S, Franke B, Strengman E, Kiemeney LA, GROUP†, Melle I, Djurovic S, Abramova L, Kaleda V, Sanjuan J, de Frutos R, Bramon E, Vassos E, Fraser G, Ettinger U, Picchioni M, Walker N, Toulopoulou T, Need AC, Ge D, Yoon JL, Shianna KV, Freimer NB, Cantor RM, Murray R, Kong A, Golimbet V, Carracedo A, Arango C, Costas J, Jönsson EG, Terenius L, Agartz I, Petursson H, Nöthen MM, Rietschel M, Matthews PM, Muglia P, Peltonen L, St Clair D, Goldstein DB, Stefansson K, Collier DA & Genetic Risk and Outcome in Psychosis (GROUP). (2009) Common variants conferring risk of schizophrenia. Nature doi:10.1038/nature08186. Abstract

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Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Kevin J. Mitchell
Submitted 9 July 2009
Posted 9 July 2009

GWAS Results: Is the Glass Half Full or 95 Percent Empty?
The publication of the latest schizophrenia GWAS papers represents the culmination of a tremendous amount of work and unprecedented cooperation among a large number of researchers, for which they should be applauded. In addition to the hope of finding new “schizophrenia genes,” GWAS have been described by some of the researchers involved as, more fundamentally, a stern test of the common variants hypothesis. Based on the meagre haul of common variants dredged up by these three studies and their forerunners, this hypothesis should clearly now be resoundingly rejected—at least in the form that suggests that there is a large, but not enormous, number of such variants, which individually have modest, but not minuscule, effects. There are no common variants of even modest effect.

However, Purcell and colleagues now argue for a model involving vast numbers of variants, each of almost negligible effect alone. The authors show that an aggregate score derived from the top 10-50 percent of a set of 74,000 SNPs from the association results in a discovery sample can predict up to 3 percent of the variance in a target group. Simply put, a set of putative “risk alleles” can be defined in one sample and shown, collectively, to be very slightly (though highly significantly in a statistical sense) enriched in the test sample, compared to controls. This is consistent across several different schizophrenia samples and even in two bipolar disorder samples. The authors go on to perform a set of control analyses that suggest that these results are not due to obvious population stratification or genotype rate effects (although effects at this level are obviously prone to cryptic artifacts).

If taken at face value, what do these results mean? They imply some kind of polygenic effect on risk, but of what magnitude? The answer to that depends on the interpretation of the additional simulations performed by the authors. They argue that the risk allele set inevitably contains very many false positives, which dilute the predictive power of the real positives hidden among them. Based on this logic, if we only knew which were the real variants to look at, then the variance explained in the target group would be much greater.

To try and estimate the magnitude of the effect of the polygenic load of “true risk” alleles, the authors conducted a series of simulations, varying parameters such as allele frequencies, genotype relative risks, and linkage disequilibrium with genotyped markers. They claim that these analyses converge on a set of models that recapitulate the observed data and that all converge on a true level of variance explained of around 34 percent, demonstrating a large polygenic component to the genetic architecture of schizophrenia.

These simulations adopt a level of statistical abstraction that should induce a healthy level of skepticism or at least reserved judgment on their findings. Most fundamentally, they rely explicitly for their calculations of the true variance on a liability-threshold model of the genetic architecture of schizophrenia. In effect, the “test” of the model incorporates the assumption that the model is correct.

The liability-threshold model is an elegant statistical abstraction that allows the application of the powerful statistics of normal distributions. Unfortunately, it suffers from the fact that it has no support whatsoever and makes no biological sense. First, there is no justification for assuming a normal distribution of “underlying liability,” whatever that term is taken to mean. Second, as usual when it is invoked, the nature of this putative threshold is not explained, though it surreptitiously implies some form of very strong epistasis (to explain the difference in risk between someone with x liability alleles and someone else with x+1 alleles). If this model is not correct, then these simulations are fatally flawed.

Even if the model were correct, the calculations are far from convincing. From a starting set of 560 models, the authors arrive at seven that are consistent with the observed degree of prediction in the target samples. According to the authors, the fact that these seven models converge on a small range of values for the underlying variance explained by the markers is evidence that this value (around 34 percent) represents the true situation. What is not highlighted is the fact that the values for the actual additive genetic variance (taking into account incomplete linkage disequilibrium between the markers and the assumed causal variants) across these models ranges from 34 percent to 98 percent and that the number of SNPs assumed to be having an effect ranges from 4,625 to 74,062. This extreme variation in the derived models hardly inspires confidence in the authors’ claim that their data “strongly support a polygenic basis to schizophrenia that (1) involves common SNPs, [and] (2) explains at least one-third of the total variation in liability.” (italics added)

From a more theoretical perspective, it should be noted that a polygenic model involving thousands of common variants of tiny effect cannot explain and will not contribute to the observed heightened familial relative risks. Such risk can only be explained by a variant of large effect or by an oligogenic model involving at most two to three loci (Bodmer and Bonilla, 2008; Hemminki et al., 2008; Mitchell and Porteous, in preparation). It seems much more likely that the observed predictive power in the target samples represents a modest “genetic background” effect, which could influence the penetrance and expressivity of rare, causal mutations. However, if the point of GWAS is to find genetic variants that are predictive of risk or that shed light on the pathogenic mechanisms of the disease, then clearly, even if such variants can be found by massively increasing sample sizes, their identification alone would not achieve or even appreciably contribute to either of these goals.

References:

Hemminki K, Försti A, Bermejo JL. The “common disease-common variant” hypothesis and familial risks. PLoS ONE. 2008 Jun 18;3(6):e2504. Abstract

Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008 Jun;40(6):695-701. Abstract

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Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  David J. Porteous, SRF Advisor
Submitted 9 July 2009
Posted 10 July 2009
  I recommend the Primary Papers

Thumbs up or down on schizophrenia GWAS?
The triumvirate of schizophrenia GWAS studies just published in Nature gives cause for thought, and bears close scrutiny and reflection. To my reading, these three studies individually and collectively lead to an unambiguous conclusion—there is a lot of genetic heterogeneity and not one individual variant of common ancient origin accounts for a significant fraction of the genetic liability. To put it another way, there is no ApoE equivalent for schizophrenia. Strong past claims for ZNF804A and others look to have fallen by the statistical wayside. Putting the results of all three studies together does appear to provide support for a long known, pre-GWAS association with HLA, but otherwise it is hard to give a strong "thumbs up" to any specific result, not least because of the lack of replication between studies. The results are nevertheless important because the common disease, common variant model, on which GWAS are based and the associated cost justified, is strongly rejected as the main contributor to the genetic variance.

The ISC proposes a highly polygenic model with thousands of variants having an additive effect on both schizophrenia and bipolar disorder. I find no fault with their evidence, but its meaning and interpretation remains speculative. Simply consider the fact that SNPs carefully selected to tag half the genome account for about a third of the variance. It follows that the lion's share has gone undetected and will, by design and limitation, remain impervious to the GWAS strategy.

Part of the GWAS appeal is that the genotyping is technically facile and it is easier to collect lots of cases than it is families, but for as long as a diagnosis of schizophrenia or BP depends upon DSM-IV or ICD-10 classification, then diagnostic uncertainty will have a major effect on true power and validity of statistical association, both positive or negative. Indeed, the longstanding evidence from variable psychopathology amongst related individuals, the recent epidemiology evidence for shared genetic risk for schizophrenia and BP, and the further evidence supporting this from the ISC GWAS, all suggest that we should be returning more to family-based studies as a strategy to reduce genetic heterogeneity and find explanatory genetic variants. Plainly, adding ever more uncertainty through ever larger sample sizes is neither smart nor efficient.

I would certainly give the thumbs up to the full and unencumbered release of the primary data to the community as a whole, as this could usefully recoup some of the GWAS investment. It would facilitate a range of statistical and bioinformatics analyses and, who knows, there might be hidden nuggets of statistical support for independent genetic and biological studies.

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Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Sagiv Shifman
Submitted 11 July 2009
Posted 11 July 2009

The main question that arises from the three large genomewide association studies published in Nature is, What should we do next?

One important way forward would be to follow up the association findings in the MHC region. We need to understand the biological mechanism underlying this association. If the association signal is indeed related to infectious diseases, this line of inquiry may lead to the highly desired development of a treatment that might prevent the diseases in some cases.

One possible explanation for the association between schizophrenia and the MHC region (6p22.1) is that infection during pregnancy leads to disturbances of fetal brain development and increases the risk of schizophrenia later in life. A possible test for the theory of infectious diseases as risk factors for schizophrenia would be to study the associated SNPs in 6p22.1 in fathers and mothers of subjects with schizophrenia relative to parents of control subjects. If the 6p22.11 region is related to the tendency of mothers to be infected by viruses during pregnancy, we would expect the SNPs in 6p22.1 to be most strongly associated with being a mother to a subject with schizophrenia.

Another broader and more complicated part of the question is: What would be the best strategy for continued study of the genetic causes of schizophrenia? There shouldn’t be only one way to proceed. Testing samples that are 10 times larger seems likely to lead to the identification of more genes, but with much smaller effect size. Testing the association of common variants with schizophrenia is unlikely to lead to the development of genetic diagnostic tools in the near future. If we want to understand the biology of the disease, it might be easier to concentrate our efforts on the identification of rare inherited and non-inherited variants with large effect on the phenotype. Such rare variants are easier to model in animals (relative to common variants with very small functional effect) and might even account for a larger proportion of cases.

View all comments by Sagiv Shifman

Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Alan BrownPaul Patterson
Submitted 17 July 2009
Posted 17 July 2009

The three companion papers in this week’s issue of Nature, in our view, support the case for investigating interaction between susceptibility genes and infectious exposures in schizophrenia. We and others have argued previously that genetic studies conducted in isolation from environmental factors, and studies of environmental influences in the absence of genetic data, are necessarily limited. Maternal influenza, rubella, toxoplasmosis, herpes simplex virus, and other infections have each been associated with an increased risk of schizophrenia, with effect sizes ranging from twofold to over fivefold. While these epidemiologic findings clearly require replication in independent cohorts, two new developments provide further support for the hypothesis. First, a growing number of animal studies of maternal immune activation have documented behavioral and brain phenotypes in offspring that are analogous to findings from clinical research in schizophrenia, and these findings are mediated in large part by specific cytokines (Meyer et al., 2009; Patterson, 2008). Second, recent evidence indicates that maternal infection is also related to deficits in executive and other cognitive functions and neuropathology thought to arise from disruptions in brain development (Brown et al., 2009a; Brown et al., 2009b).

While the MHC region contains genes not involved in the immune system, in light of the epidemiologic findings on maternal infection, it is intriguing to see that this region is once more implicated in genetic studies of schizophrenia as the importance of this region in the response to infectious insults cannot be ignored. Although it is heartening to see that the potential implications of these findings for infectious etiologies were raised in the article from the SGENE plus group, an analysis of the frequency of SNPs by season of birth falls well short of the type of research that will yield definitive findings on the relationships between susceptibility genes and infectious insults. Hence, we advocate a strategy aimed at large scale genetic analyses of schizophrenia cases using birth cohorts with infectious exposures documented from prospectively collected biological samples from the prenatal period. If the schizophrenia-related pathogenic mechanisms by which MHC-related genetic variants operate involve interactions with prenatal infection, we would expect that studies of gene-infection interaction will yield larger effect sizes than those found in these new papers. The evidence from these papers and the epidemiologic literature should also facilitate narrowing of the number of candidate genes to be tested for interactions with infectious insults, thereby ameliorating the potential for type I error due to multiple comparisons.

References:

Meyer U, Feldon J, Fatemi SH. In-vivo rodent models for the experimental investigation of prenatal immune activation effects in neurodevelopmental brain disorders. Neurosci Biobehav Rev . 2009 Jul 1; 33(7):1061-79. Abstract

Patterson PH. Immune involvement in schizophrenia and autism: Etiology, pathology and animal models. Behav Brain Res. 2008 Dec 24; Abstract

Brown AS, Vinogradov S, Kremen WS, Poole JH, Deicken RF, Penner JD, McKeague IW, Kochetkova A, Kern D, Schaefer CA. Prenatal exposure to maternal infection and executive dysfunction in adult schizophrenia. Am J Psychiatry . 2009a Jun 1 ; 166(6):683-90. Abstract

Brown AS, Deicken RF, Vinogradov S, Kremen WS, Poole JH, Penner JD, Kochetkova A, Kern D, Schaefer CA. Prenatal infection and cavum septum pellucidum in adult schizophrenia. Schizophr Res . 2009b Mar 1 ; 108(1-3):285-7. Abstract

View all comments by Alan Brown
View all comments by Paul Patterson

Related News: Largest GWAS Analysis to Date Offers Only Two New Candidate Genes

Comment by:  Javier Costas
Submitted 17 July 2009
Posted 17 July 2009
  I recommend the Primary Papers

Two hundred years after Darwin’s birth and 150 years after the publication of On the Origin of Species, these three papers in Nature show the important role of natural selection in shaping the genetic architecture of schizophrenia susceptibility. If we compare the GWAS results for schizophrenia with those obtained for other diseases, it seems that there are less common risk alleles and/or lower effect sizes in schizophrenia than in many other complex diseases (see, for instance, the online catalog of published GWAS at NHGRI). This fact strongly suggests that negative selection limits the spread of susceptibility alleles, as expected due to the decreased fertility of schizophrenic patients.

Interestingly, the MHC region may be an exception. This region represents a classical example of balancing selection, i.e., the presence of several variants at a locus maintained in a population by positive natural selection (Hughes and Nei, 1988). In the case of the MHC, this balancing selection seems to be related to pathogen resistance or MHC-dependent mating choice. Therefore, the presence of common schizophrenia susceptibility alleles at this locus might be explained by antagonistic pleiotropic effects of alleles maintained by natural selection.

If negative selection limits the spread of schizophrenia risk alleles, most of the genetic susceptibility to schizophrenia is likely due to rare variants. Resequencing technologies will allow the identification of many of these variants in the near future. In the meantime, it would be interesting to focus our attention on non-synonymous SNPs at low frequency. Based on human-chimpanzee comparisons and human sequencing data, Kryukov et al. (2008) have shown that a large fraction of de novo missense mutations are mildly deleterious (i.e., they are subject to weak negative selection) and therefore they can still reach detectable frequencies. Assuming that most of these mildly deleterious alleles may be detrimental (i.e., they confer risk for disease) the authors conclude that numerous rare functional SNPs may be major contributors to susceptibility to common diseases Kryukov et al., 2008. Similar conclusions were obtained by the analysis of the relative frequency distribution of non-synonymous SNPs depending on their probability to alter protein function (Barreiro et al., 2008; Gorlov et al., 2008). As shown by Evans et al. (2008), genomewide scans of non-synonymous SNPs might complement GWAS, being able to identify rare non-synonymous variants of intermediate penetrance not detectable by current GWAS panels.

References:

Barreiro LB, Laval G, Quach H, Patin E, Quintana-Murci L (2008) Natural selection has driven population differentiation in modern humans. Nat Genet 40: 340-5. Abstract

Evans DM, Barrett JC, Cardon LR (2008) To what extent do scans of non-synonymous SNPs complement denser genome-wide association studies? Eur J Hum Genet 16: 718-23. Abstract

Gorlov IP, Gorlova OY, Sunyaev SR, Spitz MR, Amos CI (2008) Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet 82: 100-12. Abstract

Hughes AL, Nei M (1988) Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature 335: 167-70. Abstract

Kryukov GV, Pennacchio LA, Sunyaev SR (2007) Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am J Hum Genet 80: 727-39. Abstract

View all comments by Javier Costas

Related News: Autism Exome: Lessons for Schizophrenia?

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 20 April 2012
Posted 23 April 2012
  I recommend the Primary Papers

Fascinating papers that likely presage work in the pipeline from multiple groups for schizophrenia. Truly groundbreaking work by some of the best groups in the business. Required reading for those interested in psychiatric genomics.

The identified loci provide important new windows into the neurobiology of ASD.

The results also pertain to the longstanding debate about the nature of ASD: does it result from many individually rare, Mendelian-like variants (potentially a different one in each person) and/or from the summation of the effects of many different common variants of subtle effects?

The multiple rare variant model now seems unlikely for ASD as, contrary to the expectations of some, ASD did not readily resolve into a handful of Mendelian-like diseases. (This comment is of course qualified by the limits of the technologies - which have, however, identified causal mutations for many monogenetic disorders.)

Readers might also want to read Ben Neale's comments on these papers at the Genomes Unzipped website.

View all comments by Patrick Sullivan

Related News: New Mutations Mount as Fathers Age

Comment by:  Dolores Malaspina
Submitted 27 August 2012
Posted 27 August 2012

The new report by Kong et al. (2012) demonstrates that paternal age is likely to be an important source of mutations that are relevant for schizophrenia, as we earlier hypothesized (Malaspina, 2001). Kong et al. demonstrated that the diversity in human mutation rates for offspring is dominated by the paternal age at conception. Following our initial observation that advancing paternal age was substantially associated with an increasing risk for schizophrenia, explaining a quarter of the population's attributable risk for schizophrenia (Malaspina et al., 2001), many scientists found it difficult to accept that the father’s age could be a risk pathway for schizophrenia. By contrast, the hypothesis that paternal age explained the risk for achondroplastic dwarfism achieved far greater immediate acceptance over 20 years ago (i.e., Thompson et al., 1986). While these new findings will surely advance our understanding of many de novo neuropsychiatric conditions, they also substantiate biological versus psychosocial causation theories for severe neuropsychiatric conditions.

References:

Malaspina D. Paternal factors and schizophrenia risk: de novo mutations and imprinting. Schizophr Bull . 2001 ; 27(3):379-93. Abstract

Malaspina D, Harlap S, Fennig S, Heiman D, Nahon D, Feldman D, Susser ES. Advancing paternal age and the risk of schizophrenia. Arch Gen Psychiatry . 2001 Apr ; 58(4):361-7. Abstract

Thompson JN Jr, Schaefer GB, Conley MC, Mascie-Taylor CG. Achondroplasia and parental age. N Engl J Med. 1986 Feb 20;314(8):521-2. Abstract

View all comments by Dolores Malaspina

Related News: New Mutations Mount as Fathers Age

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 27 August 2012
Posted 27 August 2012

Kong et al. sequenced 78 pedigree clusters (mostly parent-offspring trios) to around 30x coverage. After careful quality control, they identified an average of 63 new mutations per trio. These mutations were “de novo” in that they were absent in the parents but present in an offspring and assumed to have occurred during gametogenesis.

Intriguingly, more of these mutations occurred in older parents. The authors present several lines of evidence to implicate fathers rather than mothers, and estimated that there were about two extra de novo mutations per year of increase in paternal age. This conclusion is consistent with several of the exome sequencing papers published in Nature a few months ago.

Increased paternal age is an epidemiological risk factor for schizophrenia and autism, with relative risks on the order of two and five, respectively. This paper suggests a potential mechanism for the paternal age effect that might eventually prove to be relevant for some fraction of cases.

It is important to note that advanced paternal age is a risk factor, not a determining feature. Risk is increased, but not in a deterministic manner.

View all comments by Patrick Sullivan

Related News: New Mutations Mount as Fathers Age

Comment by:  John McGrath, SRF Advisor
Submitted 28 August 2012
Posted 28 August 2012
  I recommend the Primary Papers

In 2001, Dolores Malaspina alerted the research community to the link between advanced paternal age and increased risk of schizophrenia—she suggested that this may be due to de novo mutations in the male germ line (Malaspina et al., 2001). The study BY Kong et al. provides compelling evidence in support of this hypothesis (Kong et al., 2012). A related paper in Nature Genetics also demonstrates an association between paternal age and changes in microsatellite properties across generations (Sun et al., 2012).

While the hypothesis that de novo mutations accumulate due to copy error mutations in the production of germ cells in older males is compelling, it is still possible (albeit unlikely) that this association may be due to unmeasured confounding. For example, older men might be exposed to more environmental toxins that accumulate over time and subsequently cause mutations in the offspring of older dads as a byproduct of the greater exposure. There is also the evidence from Denmark indicating that, when adjusted for age of first child, the association between paternal age and risk of schizophrenia fades out (Petersen et al., 2011). This finding suggests that selective factors may also operate (e.g., perhaps related to personality of schizotypal men, etc.).

However, animal experiments can provide useful clues to this puzzle (Foldi et al., 2011). Mouse models of advanced paternal age indicate that the offspring of older sires differ from control animals on behavior and brain structure (Smith et al., 2009; Foldi et al., 2010). Of particular relevance for the study by Kong et al., a mouse experiment found that the offspring of older sires were significantly more likely to have de novo copy number variants (Flatscher-Bader et al., 2011).

We now have convergent evidence from risk factor epidemiology, animal experiments, and genetic studies. The evidence supports an increased risk of schizophrenia in the offspring of older fathers, and points to age-related mutagenesis in the male germ cell. It is still not clear why these age-related events seem to differentially impact on neurodevelopmental disorders (e.g., autism is also linked to paternal age). Perhaps neocortical development is less well "buffered" (compared to more phylogenetically ancient organs); thus, de novo mutations can more readily "decanalize" certain features of brain development (McGrath et al., 2011). From an evolutionary developmental biology perspective (evo-devo), the dictum goes “Last in, first to break.”

It is rare that different fields of research converge in such an obedient fashion. It is time that we pause and reflect on this important milestone—and also offer a rousing “three cheers for Dolores Malapsina!”

References:

Flatscher-Bader T, Foldi CJ, Chong S, Whitelaw E, Moser RJ, Burne TH, Eyles DW, McGrath JJ. Increased de novo copy number variants in the offspring of older males. Transl Psychiatry. 2011 Aug 30;1:e34. Abstract

Foldi CJ, Eyles DW, McGrath JJ, Burne TH. Advanced paternal age is associated with alterations in discrete behavioural domains and cortical neuroanatomy of C57BL/6J mice. Eur J Neurosci. 2010 Feb;31(3):556-64. Abstract Foldi CJ, Eyles DW, Flatscher-Bader T, McGrath JJ, Burne TH. New perspectives on rodent models of advanced paternal age: relevance to autism. Front Behav Neurosci . 2011 ; 5():32. Abstract

Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G, Gudjonsson SA, Sigurdsson A, Jonasdottir A, Jonasdottir A, Wong WS, Sigurdsson G, Walters GB, Steinberg S, Helgason H, Thorleifsson G, Gudbjartsson DF, Helgason A, Magnusson OT, Thorsteinsdottir U, Stefansson K. Rate of de novo mutations and the importance of father's age to disease risk. Nature. 2012 Aug 23;488(7412):471-5. Abstract

Malaspina D, Harlap S, Fennig S, Heiman D, Nahon D, Feldman D, Susser ES. Advancing paternal age and the risk of schizophrenia. Arch Gen Psychiatry. 2001 Apr ; 58(4):361-7. Abstract

McGrath JJ, Hannan AJ, Gibson G. Decanalization, brain development and risk of schizophrenia. Transl Psychiatry. Abstract

Petersen L, Mortensen PB, Pedersen CB. Paternal age at birth of first child and risk of schizophrenia. Am J Psychiatry. 2011 Jan;168(1):82-8. Abstract

Smith RG, Kember RL, Mill J, Fernandes C, Schalkwyk LC, Buxbaum JD, Reichenberg A. Advancing paternal age is associated with deficits in social and exploratory behaviors in the offspring: a mouse model. PLoS One. 2009 Dec 30;4(12):e8456. Abstract

Sun JX, Helgason A, Masson G, Ebenesersdóttir SS, Li H, Mallick S, Gnerre S, Patterson N, Kong A, Reich D, Stefansson K. A direct characterization of human mutation based on microsatellites. Nat Genet. 2012 Aug 23. Abstract

View all comments by John McGrath

Related News: New Mutations Mount as Fathers Age

Comment by:  Georg Winterer (Disclosure)
Submitted 28 August 2012
Posted 28 August 2012
  I recommend the Primary Papers

Just a few thoughts:

One question is whether it is just age per se that produces de novo mutations or an accumulation of environmental effects like drug abuse, alcohol, or other potentially harmful toxic environments, etc. What I also would like to know is whether it is the number of sperm cycles; in that case, men who are sexually more active should have a greater risk to produce more de novo mutations.

View all comments by Georg Winterer

Related News: New Mutations Mount as Fathers Age

Comment by:  Michael O'Donovan, SRF AdvisorGeorge Kirov
Submitted 31 August 2012
Posted 31 August 2012

In a genomic sequencing study of 78 parent-proband trios (21 probands with schizophrenia, 44 with autism spectrum disorder [ASD]), Kong and colleagues (2012) identify almost 5,000 DNA single base changes that occurred as a result of new mutations. For five of the trios, the proband had a child who was also sequenced, and in this subset with three generations of data, Kong and colleagues were able to determine if the mutations had arisen on the paternal or maternal chromosomes. Although this subsample was small, paternal chromosomes showed much greater variance in the number of mutations than maternal chromosomes, suggesting that paternal variables are more relevant to variance in the overall de novo mutation rate than maternal variables. In the larger sample as a whole, although the parental origin of the mutations could not be determined, the number of new mutations carried by an individual could be almost completely explained by a combination of random variation and paternal age. Models of linear and of exponential increases in the number of mutations by paternal age both described the data well, the ability to distinguish between the two being constrained by a lack of fathers at the higher age. Children of fathers aged 40 had approximately twice the number of mutations as those aged 20. After accounting for random variation and paternal age, in this sample, there was very little residual variation to be explained by other factors, including maternal age and within-population environmental exposures. A possible impact of cross-population environmental exposures was not addressed, since all the subjects came from Iceland.

Overall, the findings from what is yet another impressive paper from the deCODE group support the proposition that paternal age is an important factor in determining the probability that a child might inherit a new mutation (see Goriely and Wilkie, 2012, for a wider discussion of earlier data on paternal age and mutation rates, particularly in sperm) and additionally quantify this effect in the context of other possible unexplained variables.

This is clearly an important paper for understanding factors dictating the rate by which new mutations occur, and is therefore a paper that will have wide relevance to diseases to which such mutations make a substantial contribution. But from the perspective of most readers of this Forum, it is more important to note what the study is not about.

There is good evidence that risk of schizophrenia increases with paternal age (Malaspina et al., 2001; Zammit et al., 2003; Frans et al., 2011). This is certainly compatible with the involvement of new mutations of the sort described in the paper by Kong and colleagues, but there are several alternative explanations. For example, fathers with high trait liability for schizophrenia might have subclinical characteristics making them less effective at reproduction (e.g., they may find it more difficult to find a partner) and, as a result, elderly fathers might be enriched for transmissible schizophrenia alleles. Consistent with this (and other explanations not dependent on new mutations), one large Danish study found that the paternal age effect was best explained by age at which fathers first reproduce, not the age (which is more relevant to new mutations) when the affected offspring was conceived (Petersen et al., 2011). Of general importance as it is, the study by Kong and colleagues makes no contribution to resolving to what extent the paternal age effect observed in schizophrenia (and autism) is explained by new mutations, or indeed to what extent new mutations are involved in these disorders at all. Indeed, as the authors point out, the fact that they have studied probands, the majority of whom are affected by schizophrenia or ASD, is an irrelevance; essentially identical findings would be expected if they had studied other types of families. This is because the average proband carries over 60 de novo mutations, of which, even under an extreme model in which all schizophrenia is caused by de novo mutations, at most, one or two (if any) might be schizophrenia or ASD relevant. Consequently, de novo mutations related to the phenotype of the proband cannot substantially contribute to the overall pattern of results.

Overall, this study provides empirical evidence for a mechanism by which some of the paternal age effects might be explained by de novo point mutations, but it is worth stressing that the fact that the authors have studied schizophrenia and ASD is incidental, and this study does not address the extent by which, if at all, mutations of this type make any contribution to schizophrenia (or autism). Finally, since the results of this paper have been widely reported (at least in the UK), we think it is important to note for the general reader that, while the paternal age effect of risk of schizophrenia (and autism) seems to be real, the vast majority of people with schizophrenia are not born to elderly fathers. More importantly, since the causal direction of the paternal age effect on schizophrenia risk is unknown, there is currently no strong reason to urge potential fathers to consider earlier reproduction as a strategy for reducing risk of this particular disorder.

References:

Zammit S, Allebeck P, Dalman C, Lundberg I, Hemmingson T, Owen MJ, Lewis G. Paternal age and risk for schizophrenia. Br J Psychiatry. 2003 Nov;183:405-8. Abstract

Malaspina D, Harlap S, Fennig S, Heiman D, Nahon D, Feldman D, Susser ES. Advancing paternal age and the risk of schizophrenia. Arch Gen Psychiatry. 2001 Apr;58(4):361-7. Abstract

Goriely A, Wilkie AO. Paternal age effect mutations and selfish spermatogonial selection: causes and consequences for human disease. Am J Hum Genet. 2012 Feb 10;90(2):175-200. Review. Abstract

Frans EM, McGrath JJ, Sandin S, Lichtenstein P, Reichenberg A, Långström N, Hultman CM. Advanced paternal and grandpaternal age and schizophrenia: a three-generation perspective. Schizophr Res. 2011 Dec;133(1-3):120-4. Epub 2011 Oct 14. Abstract

Petersen L, Mortensen PB, Pedersen CB. Paternal age at birth of first child and risk of schizophrenia. Am J Psychiatry. 2011 Jan;168(1):82-8. Epub 2010 Oct 15. Abstract

View all comments by Michael O'Donovan
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Related News: New Mutations Mount as Fathers Age

Comment by:  Bernard Crespi
Submitted 3 September 2012
Posted 5 September 2012
  I recommend the Primary Papers

Kong et al. (2012) is an outstanding paper that provides the first detailed quantification of how human de novo mutations in sperm and eggs vary with parental age. The paper and its aftermath provide a number of important lessons for researchers studying neurodevelopmental disorders and parental age:

1. The work demonstrates directly that CpG dinucleotides contribute the lion's share of new mutations. CpG sites are of particular interest in understanding effects of de novo mutations because they differentially create new transcription factor binding sites (Zemojtel et al., 2011), as well as mediate the effects of methylation and genomic imprinting. Such findings might help to focus efforts at interpreting the functional importance of the myriad de novo variants that pepper each genome.

2. The work generates an apparent paradox: if, as the authors claim, paternal age so strongly predominates over maternal age in its de novo mutational effects, why do so many parental-age studies of autism and schizophrenia show clear effects of maternal age as well (e.g., Lopez-Castroman et al., 2010; Parner et al., 2012; Rahbar et al., 2012; Sandin et al., 2012)? Might maternal-age effects be mediated by different processes?

3. The X chromosome was not included in the analysis, despite its expected contribution to de novo mutational effects being much stronger than for autosomes, due to its hemizygosity (as found, e.g., in intellectual disability). A recent study also strikingly implicates the X chromosome in psychosis risk, perhaps involving epigenetic mechanisms (Goldstein et al., 2011).

4. It is important to avoid neurodevelopmental tunnel vision with regard to parental age effects. Advanced maternal age, for example, has been documented as a risk factor for a suite of other conditions, including hypertension, diabetes, cancer, and Alzheimer's (for a review, see Myrskylä and Fenelon, 2012), as expected if it exerts effects on all polygenic conditions.

5. As anyone following popular media accounts will have noticed, the paper has been fundamentally misinterpreted in translation from the scientific to popular literature. Contrary to almost all reports in the popular press (including, e.g., The New York Times), the paper clearly does not show that higher paternal age is associated with mutations that increase the risk of autism or schizophrenia. As noted by other commentators, to do so would require that the authors link paternal age with the number of new mutations that are actually known to contribute to autism or schizophrenia. This muddle should caution authors to be as clear in explaining what their findings do not show as they are in explaining what they actually demonstrate. If subsequent work shows that age-dependent point mutations themselves do not mediate increased autism or schizophrenia risk, scientific credibility will unjustifiably suffer.

6. Finally, the press has jumped on advanced parental age as an important possible factor in the increased diagnoses of autism over the past 30 or so years. But if increased mutation load has increased rates of autism, why haven't rates of schizophrenia increased in lockstep, albeit with a 20-year delay?

Parental age has been suspected as an important factor in genetically based, de novo conditions since Weinberg (of Hardy-Weinberg fame) noticed in 1912 that children with achondroplasia (a form of dwarfism) were later-born in sibships. One hundred years later, we are one large step closer to understanding why. Let us help to ensure that this step is free of de novo errors of interpretation and implication, and move forward with speed.

References:

Goldstein JM, Cherkerzian S, Seidman LJ, Petryshen TL, Fitzmaurice G, Tsuang MT, Buka SL. Sex-specific rates of transmission of psychosis in the New England high-risk family study. Schizophr Res. 2011 May;128(1-3):150-5. Abstract

Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G, Gudjonsson SA, Sigurdsson A, Jonasdottir A, Jonasdottir A, Wong WS, Sigurdsson G, Walters GB, Steinberg S, Helgason H, Thorleifsson G, Gudbjartsson DF, Helgason A, Magnusson OT, Thorsteinsdottir U, Stefansson K. Rate of de novo mutations and the importance of father's age to disease risk. Nature. 2012 Aug 22; 488: 471-5. Abstract

Lopez-Castroman J, Gómez DD, Belloso JJ, Fernandez-Navarro P, Perez-Rodriguez MM, Villamor IB, Navarrete FF, Ginestar CM, Currier D, Torres MR, Navio-Acosta M, Saiz-Ruiz J, Jimenez-Arriero MA, Baca-Garcia E. Differences in maternal and paternal age between schizophrenia and other psychiatric disorders. Schizophr Res. 2010 Feb;116(2-3):184-90. Abstract

Myrskylä M, Fenelon A. Maternal Age and Offspring Adult Health: Evidence From the Health and Retirement Study. Demography . 2012 Aug 28. Abstract

Parner ET, Baron-Cohen S, Lauritsen MB, Jørgensen M, Schieve LA, Yeargin-Allsopp M, Obel C. Parental age and autism spectrum disorders. Ann Epidemiol. 2012 Mar;22(3):143-50. Abstract

Rahbar MH, Samms-Vaughan M, Loveland KA, Pearson DA, Bressler J, Chen Z, Ardjomand-Hessabi M, Shakespeare-Pellington S, Grove ML, Beecher C, Bloom K, Boerwinkle E. Maternal and Paternal Age are Jointly Associated with Childhood Autism in Jamaica. J Autism Dev Disord. 2012 Sep;42(9):1928-38. Abstract

Sandin S, Hultman CM, Kolevzon A, Gross R, MacCabe JH, Reichenberg A. Advancing maternal age is associated with increasing risk for autism: a review and meta-analysis. J Am Acad Child Adolesc Psychiatry. 2012 May;51(5):477-486.e1. Abstract

Zemojtel T, Kielbasa SM, Arndt PF, Behrens S, Bourque G, Vingron M. CpG deamination creates transcription factor-binding sites with high efficiency. Genome Biol Evol. 2011;3:1304-11. Abstract

View all comments by Bernard Crespi

Related News: Exome Sequencing Hints at Prenatal Genes in Schizophrenia

Comment by:  Sven CichonMarcella RietschelMarkus M. Nöthen
Submitted 5 October 2012
Posted 5 October 2012

The new exome sequencing study by Xu et al. confirms previous results by the same research group (Xu et al., 2011) and by an independent group (Girard et al., 2011) that a significantly higher frequency of protein-altering de novo single nucleotide variants (SNVs) and in/dels is found in sporadic patients with schizophrenia. It is certainly reassuring that this observation has now been confirmed in an independent and considerably larger sample (134 patient-parent trios and 34 control-parent trios).

A closer look also reveals differences between this study and the study by Girard et al.: Xu et al. do not find a significantly higher overall de novo mutation rate per base per generation when comparing schizophrenia and control trios (1.73 x 10-08 vs. 1.28 x 10-08). In contrast, the Girard study found 2.59 x 10-08 de novo mutations in schizophrenia trios as opposed to the 1.1 x 10-08 events reported in the general population by the 1000 Genomes Project. The larger sample size in the new study by Xu et al., however, suggests that their estimation of the de novo mutation rates may be more precise now.

What eventually seems to count is the quality of the de novo mutations in the sporadic schizophrenia patients. The function of the genes hit by the non-synonymous/deleterious (as defined by in-silico scores) mutations is diverse and shows similarity with functions reported for common risk genes for schizophrenia identified by GWAS. Interestingly, there is an overrepresentation of genes that are predominantly expressed during embryogenesis, strongly highlighting a possible effect of neurodevelopmental disturbances in the etiology of schizophrenia (and nicely supporting what has already been concluded from GWAS).

It would probably be very interesting to estimate the penetrance of such de novo mutations to get a feeling for their individual impact on the development of the disease. In the absence of a reasonable number of individuals with the same mutation, however, this will be a difficult task.

Another aspect that is missing in the current paper, but is accessible to investigation, is the frequency/quality of de novo mutations in trios with a family history of schizophrenia and comparison to the figures seen in the sporadic trios. That might (or might not) support the authors’ conclusion that de novo events play a strong role in sporadic cases (and not in familial cases).

References:

Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet . 2011 Sep ; 43(9):864-8. Abstract

Girard SL, Gauthier J, Noreau A, Xiong L, Zhou S, Jouan L, Dionne-Laporte A, Spiegelman D, Henrion E, Diallo O, Thibodeau P, Bachand I, Bao JY, Tong AH, Lin CH, Millet B, Jaafari N, Joober R, Dion PA, Lok S, Krebs MO, Rouleau GA. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet . 2011 Sep ; 43(9):860-3. Abstract

View all comments by Sven Cichon
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View all comments by Markus M. Nöthen

Related News: Exome Sequencing Hints at Prenatal Genes in Schizophrenia

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 5 October 2012
Posted 5 October 2012

This paper by the productive group at Columbia increases our knowledge of the role of rare exon mutations in schizophrenia. The authors applied exome sequencing—a newish high-throughput sequencing technology—to trios consisting of both parents plus an offspring with schizophrenia. The authors focused on a subset of the genome (the “exome,” genetic regions believed to code for protein) on a subset of genetic variants (SNPs and insertion/deletion variants) of predicted functional significance, and on one type of inheritance (“de novo“ mutations, those absent in both parents and present in the offspring with schizophrenia).

The sample sizes are the largest yet reported for schizophrenia—231 affected trios and 34 controls. About 28 percent of these samples were reported in 2011 (Xu et al., 2011). A recent schizophrenia sequencing study (N = 166) from the Duke group was unrevealing (Need et al., 2012). The numbers in the Xu, 2012 paper are small compared to the three Nature trio studies for autism (see SRF related news story), an approximately threefold larger trio study for schizophrenia (in preparation), a case-control exome sequencing study for schizophrenia (total N ~5,000, in preparation), and a case-control exome chip study for schizophrenia (total N ~11,000, in preparation).

The authors reported:

more mutations with older fathers, as has been reported before (see SRF related news story). Note that advanced paternal age is an established risk factor for schizophrenia.

more de novo/predicted functional/exonic mutations in schizophrenia than in controls. However, the difference was slight, one-sided P = 0.03. One can quibble with the use of a one-tailed test (should never be used, in my opinion), but it is difficult to interpret this result unless paternal age is included as a covariate in this critical test.

an impressive set of bioinformatic and integrative analyses—see the paper for the large amount of work they did.

as might be predicted given the small sample size and the rarity of these sorts of mutations, there was no statistically significant pile-up of variants in specific genes. Hence, to my reading, the authors do not compellingly implicate any specific genes in the pathophysiology of schizophrenia. This conclusion is consistent with Need et al., 2012, and I note that the autism work implicated only a few genes (e.g., CHD8 and KATNAL2).

Note that the authors would disagree with the above, as they chose to focus on a set of genes that they thought stood out (reporting an aggregate P of 0.002), and the last third of the paper focuses on these genes. However, the human genetics community now insists on two critical points for implicating specific genes in associations with a disorder. The first is statistical significance, and the critical P value for an exome sequencing study is on the order of 1E-6. The second is replication. In my view, neither of these standards are achieved. However, their observations are intriguing, and may well eventually move us forward.

The key observation in this paper is the increased rate of de novo variation in schizophrenia cases. Is the increased rate indeed part of an etiological process? In other words, older fathers have an increased chance of exonic mutations, and these, in turn, increase risk for schizophrenia? Or are these merely hitch-hikers of no particularly biological import?

A major issue with exome studies is that there are so many predicted functional variants in apparently normal people. We all carry on the order of 100 exonic variants of predicted functional consequences with on the order of 20 genes that are probable knockouts. If part of the risk for schizophrenia indeed resides in the exome, very large studies will be required to identify such loci confidently. Moreover, published work on autism and unpublished work for type 2 diabetes, coronary artery disease, and schizophrenia suggest that this will require very large sample sizes, on the order of 100 times more than reported here. And, it is possible that the exome is not all that important for schizophrenia.

References:

Xu B, Roos JL, Dexheimer P, Boone B, Plummer B, Levy S, Gogos JA, Karayiorgou M. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat Genet . 2011 Sep ; 43(9):864-8. Abstract

Need AC, McEvoy JP, Gennarelli M, Heinzen EL, Ge D, Maia JM, Shianna KV, He M, Cirulli ET, Gumbs CE, Zhao Q, Campbell CR, Hong L, Rosenquist P, Putkonen A, Hallikainen T, Repo-Tiihonen E, Tiihonen J, Levy DL, Meltzer HY, Goldstein DB. Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia. Am J Hum Genet . 2012 Aug 10 ; 91(2):303-12. Abstract

View all comments by Patrick Sullivan

Related News: New Exome Evidence Points to Old Suspect in Schizophrenia

Comment by:  Francis McMahon, SRF Advisor
Submitted 23 January 2014
Posted 28 January 2014

I think these studies do represent real progress. Finding genetic support for particular pathways provides unique evidence for a causative role of these pathways in disease. Why didn't the case-control study point to individual genes? Disorders such as schizophrenia may be more like a plane crash than a typical inherited disease: Since many things can go wrong, each crash is different, but damage to key systems is very likely to lead to a bad outcome. The finding in Fromer et al. that there are 18 genes with recurrent deleterious de novo events should allow scientists to focus on these genes as especially important. The overlaps with autism and intellectual disability are interesting, though not entirely unexpected. Will we also see gene overlaps with illnesses such as bipolar disorder? It wouldn't surprise me if some of the same genes are involved, but with fewer, less deleterious hits.

View all comments by Francis McMahon