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WCPG 2012—Schizophrenia GWAS Make Waves

1 November 2012. The search for the common variants that contribute to psychiatric disorders using genomewide association studies (GWAS) continues (see SRF genetics review), with researchers also refining their hypotheses to ask whether variants found in one population apply to other ethnicities, or whether some variants might modify individual features of disease. Although the combined effects of validated common variants are small so far (the so-called “missing heritability” problem), researchers seemed optimistic that there were more variants to be found, and that an ensemble of variants—rare and common alike—would combine to influence risk for psychiatric disorders.

Wednesday’s plenary speakers at the 20th World Congress of Psychiatric Genetics held in Hamburg, Germany, 14-18 October, emphasized this polygenic nature, with Peter Visscher of the University of Queensland in Brisbane, Australia, by arguing that liability for a disorder like schizophrenia would stem from a combination of rare and common variants, de novo variants, and environment, and that different cases would carry different “portfolios” of risk factors. Although rare and common variants contribute to schizophrenia risk, Visscher remarked on a strange absence of the “low frequency” variants in between (with minor allele frequency between 0.5 and 5 percent) that may be revealed through larger sample sizes and/or sequencing efforts (see SRF related news story). By reviewing how heritability is estimated in the first place, he suggested that a recent estimate for schizophrenia heritability based on national registry data may be more representative of the samples used in GWAS (Wray et al., 2012); though lower than other estimates (e.g., 0.67 compared to 0.81 from Sullivan et al., 2003), this is still a substantial amount of heritability, which he argued could not be accounted for by de novo events, gene-gene interactions, or epigenetics.

To commemorate his receipt of the Snow and Ming Tsuang Lifetime Achievement Award, Raymond Crowe of the University of Iowa, Iowa City, recounted highlights from his career, which began at a time when American psychiatrists were not convinced of the heritability of mental illness. Describing his first forays into the genetic basis of panic disorder, he said that even these early family studies pointed to a polygenic, complex trait. As new findings confirm this with an unimaginable diversity of variants, he quipped, “This is not your mom and dad’s polygenic model.”

Polygenicity extreme
Supporters of GWAS insist that larger sample sizes are required to get common variants for psychiatric disorders up and over the very high bar set for genomewide significance (p <5 x 10-8), and this has been the case for other complex traits like diabetes or height. Now, schizophrenia fulfills this prediction, too, according to Stephan Ripke of the Broad Institute, Cambridge, Massachusetts, who presented results from the largest yet GWAS for schizophrenia organized by the Psychiatric Genomics Consortium (no longer called the "Psychiatric GWAS Consortium," but retaining the acronym "PGC"). Since their previous study, which found seven signals (see SRF related news story), the consortium has more than doubled the size of their discovery set, reaching over 50,000 samples. This “second wave” produced 62 genomewide significant hits, and 80 percent of these variants have the same effect in a replication dataset. Though the effect sizes of the 62 signals were small, together accounting for only 3 percent of risk for schizophrenia, Ripke said that the polygenic score used to estimate the collective contributions of all promising variants—even those not reaching genomewide significance—put their contribution even higher. He declined to mention specific genes, but instead emphasized the collaboration that made the detection of these signals possible, with some researchers even contributing data before publishing on it themselves. “The best parties are when everyone brings something to share,” he said.

Beyond Europe
A key question is whether these GWAS-detected signals, which are largely derived from samples of European descent, generalize to other ethnicities. Do disruptions to the same sets of genes increase risk for schizophrenia among diverse populations? Several posters at the Congress presented efforts to replicate GWAS and candidate gene results in far-flung, though small, samples, with mixed results in Indonesia, Japan, and Bosnia.

In a talk on Monday, Teresa de Candia of the University of Colorado, Boulder, advised some caution about generalizing GWAS results, based on her estimates of the genetic risk for schizophrenia shared between European-Americans and African-Americans. Using data from the Molecular Genetics of Schizophrenia (MGS) sample, she reasoned that if the same SNPs predict schizophrenia in both populations, the genetic similarity among those with schizophrenia would be greater than the genetic similarity between cases and controls, regardless of ethnicity. This comparison gave a middling answer, with evidence for both unique and shared risk factors, with the shared portion decreasing as African ancestry increased (the African-American genotypes indicate mixing with populations of European descent). The lack of shared variance may reflect unique causal variants in the two populations, or it may be due to the different genome structures of different ethnicities, which here means that an SNP marking a causal variant in European-Americans may not be associated with that causal variant (i.e., not be in linkage disequilibrium) in African-Americans.

Dimensions of disease
Despite their diagnostic categories, different mental illnesses resemble each other somewhat, and studies find that they run together in families (see SRF related news story; SRF news story). Noting phenotypic similarities between schizophrenia and bipolar disorder, and the emerging genetic overlaps between them, Ripke of the Broad Institute presented results in a talk on Tuesday from the PGC cross disorder group, which aims to identify shared genetic loci across distinct psychiatric disorders. A GWAS on combined schizophrenia and bipolar disorder samples (n = 19,800) compared to controls (n = 19,400) revealed several genomewide significant hits, flagging known genes (e.g., CACNA1C) and new ones (e.g., PIK3C2A, a kinase within an intracellular signaling pathway). To identify disease-specific signals, the researchers compared the two disorders directly, but no genomewide significant hits emerged, possibly because of the limited sample size. Ripke also explored whether the combined effects of variants fingered in bipolar disorder GWAS (quantified with a polygenic score) could explain symptoms in schizophrenia, like mania, depression, positive symptoms, and negative symptoms. Only one correlation emerged, with polygenic scores of bipolar-detected risk alleles measured in people with schizophrenia increasing with mania severity.

This suggests that common variants can describe more than just risk for a particular disorder—they might also modulate severity of symptoms, even across disease categories. In the poster aisles, interest for this dimensional picture of common variant action also seemed to be building. For example, a poster from Ayman Fanous of Georgetown University in Washington, DC, described a search for genetic markers in schizophrenia cases that varied with age at onset, positive symptoms, negative symptoms, mania, and depression. No genomewide significant hits came out of the subset of the PGC sample he used, but several promising ones (p <10-5) highlighted nervous system genes that have not turned up before in GWAS focused on risk alone. This suggests these genes may work independently from risk genes to modify aspects of the disorder.

GWAS meets cytomegalovirus
In a talk on Tuesday, Anders Børglum of Aarhus University in Denmark presented data on gene-environment interactions that may contribute to schizophrenia, based on a unique Danish resource that banks bloodspots of every person born in Denmark since 1981. From the blood, researchers not only gather SNP data about the people, but can also evaluate their mothers' infection status at the time of birth by measuring antibody titers to certain pathogens. This allowed researchers to directly test a hypothesis about maternal infection during pregnancy, specifically infection by cytomegalovirus, contributing to schizophrenia risk (see SRF related news story). Though no genomewide significant signals were found in a GWAS of 915 cases and 915 controls, Børglum did report a significant interaction between a SNP flagging CTNNA3, a gene involved in cell adhesion, and maternal cytomegalovirus infection, which increased risk fivefold. Børglum suggested that cytomegalovirus may disconnect complexes involved in cell adhesion, which could include alterations to synaptic function in schizophrenia.—Michele Solis.

Comments on Related News


Related News: Large Family Study Links Genetics of Schizophrenia, Bipolar Disorder

Comment by:  Alastair Cardno
Submitted 7 April 2009
Posted 7 April 2009
  I recommend the Primary Papers

The results of the family/adoption study by Lichtenstein et al. (2009) and our twin study (Cardno et al., 2002) are remarkably similar. Using a non-hierarchical diagnostic approach, the genetic correlation between schizophrenia and bipolar/mania was 0.60 in the family/twin study and 0.68 in the twin study. The heritability estimates were somewhat lower in the family/adoption (~60 percent) than twin study (~80 percent), but can still be said to be substantial and similar for both disorders.

When we adopted a hierarchical approach, with schizophrenia above mania, we found no monozygotic twin pairs where one twin had schizophrenia and the other had bipolar/mania, but with their considerably larger sample, Lichtenstein et al. (2009) were able to confirm a significantly elevated risk for bipolar disorder in siblings of probands with schizophrenia (RR = 2.7), even when individuals with co-occurrence of both disorders were excluded.

I think there is a potentially interesting link between the family/adoption and twin studies focusing mainly on non-hierarchical diagnoses: Owen and Craddock’s (2009) commentary on the family/adoption study, where they advocate a dimensional approach, and Will Carpenter’s SRF comment regarding the value of domains of psychopathology. The non-hierarchical approach (where individuals can have a diagnosis of both schizophrenia and bipolar disorder during their lifetime) could be viewed as a form of dimensional/domains of psychopathology approach, with schizophrenia and bipolar disorder each having a dimension of liability, and there is now evidence from family, twin, and adoption analyses that these dimensions are correlated, i.e., that there is some overlap in etiological influences.

If schizophrenia and bipolar disorder share some causal factors in common, what might be the implications for the unresolved status of schizoaffective disorder? Our twin study suggested that the genetic (but not environmental) liability to schizoaffective disorder is entirely shared with schizophrenia and mania, defined non-hierarchically (Cardno et al., 2002). If so, and if schizophrenia and bipolar disorder share some genetic susceptibility loci in common, while other loci are not shared, then risk of schizoaffective disorder (or perhaps the bipolar subtype) could be elevated either by the coincidental co-occurrence of non-shared susceptibility loci, or by the occurrence of loci that are common to both disorders.

In this case, any loci that influence risk of schizoaffective disorder (bipolar subtype?) should also increase risk of schizophrenia and/or bipolar disorder, and this model would be refuted if any relatively specific susceptibility loci for schizoaffective disorder were confidently identified.

Some further outstanding issues:



References:

Cardno AG, Rijsdijk FV, Sham PC, Murray RM, McGuffin P. A twin study of genetic relationships between psychotic symptoms. American Journal of Psychiatry 2002;159:539-545. Abstract

Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 2009;373:234-9. Abstract

Owen MJ, Craddock N. Diagnosis of functional psychoses: time to face the future. Lancet 2009;373:190-191. Abstract

View all comments by Alastair Cardno

Related News: Schizophrenia Genetics 2: The Rise of GWAS

Comment by:  Chris Carter
Submitted 7 April 2010
Posted 8 April 2010

I wonder whether the relative lack of success in schizophrenia GWAS may be because the origin of schizophrenia may lie not so much in the genetic make-up of people with schizophrenia themselves, but in their prenatal experience, and possibly with the genes of the mother rather than with those of the offspring. Famine, rubella, influenza, herpes (HSV1 and HSV2), and poliovirus infection as well as high fever during pregnancy have all been listed as risk factors for the offspring developing schizophrenia in later life, as have maternal preeclampsia and obstetric complications. (See page at Polygenic Pathways for the many references.)

Maternal resistance to these effects is likely to be gene-dependent. Is it worth considering GWAS in the mothers rather than in the offspring?

View all comments by Chris Carter

Related News: GWAS Goes Bigger: Large Sample Sizes Uncover New Risk Loci, Additional Overlap in Schizophrenia and Bipolar Disorder

Comment by:  David J. Porteous, SRF Advisor
Submitted 21 September 2011
Posted 21 September 2011

Consorting with GWAS for schizophrenia and bipolar disorder: same message, (some) different genes
On 18 September 2011, Nature Genetics published the results from the Psychiatric Genetics Consortium of two separate, large-scale GWAS analyses, for schizophrenia (Ripke et al., 2011) and for bipolar disorder (Sklar et al., 2011), and a joint analysis of both. By combining forces across several consortia who have previously published separately, we should now have some clarity and definitive answers.

For schizophrenia, the Stage 1 GWAS discovery data came from 9,394 cases and 12,462 controls from 17 studies, imputing 1,252,901 SNPs. The Stage 2 replication sample comprised 8,442 cases and 21,397 controls. Of the 136 SNPs which reached genomewide significance in Stage 1, 129 (95 percent) mapped to the MHC locus, long known to be associated with risk of schizophrenia. Of the remaining seven SNPs, five mapped to previously identified loci. In total, just 10 loci met or exceeded the criteria of genomewide significance of p <5 x 10-8 at Stage 1 and/or Stage 2. The 10 "best" SNPs identified eight loci: MIR137, TRIM26, CSM1, CNNM2, NT5C2 and TCF4 were tagged by intragenic SNPs, while the remaining two were at some distance from a known gene (343 kb from PCGEM1 and 126 kb from CCDC68). More important than the absolute significance levels, the overall odds ratios (with 95 percent confidence intervals) ranged from 1.08 (0.96-1.20) to 1.40 (1.28-1.52). These fractional increases contrast with the ~10-fold increase in risk to the first-degree relative of someone with schizophrenia (Gottesman et al., 2010).

Six of these eight loci have been reported previously, but ZNF804A, a past favorite, was noticeably absent from the "top 10" list. The main attention now will surely be on MIR137, a newly discovered locus which encodes a microRNA, mir137, known to regulate neuronal development. The authors remark that 17 predicted MIR137 targets had a SNP with a p <10-4, more than twice as many as for the control gene set (p <0.01), though this relaxed significance cutoff seems somewhat arbitrary and warrants further examination. The result for MIR137 immediately begs the questions, Does the "risk" SNP affect MIR137 function directly or indirectly, and if so, does it affect the expression of any of the putative targets identified here? These are fairly straightforward questions: positive answers are vital to the biological validation of these statistical associations. As has been the case for follow-up studies of ZNF804A, however (reviewed by Donohoe et al., 2010), unequivocal answers from GWAS "hits" can be hard to come by, not least because of the very modest relative risks that they confer. Let us hope that this is not the case for MIR137, but it is of passing note that for two of the eight replication cohorts, the direction of effect for MIR137 was in the opposite direction from the Stage 1 finding. Taken together with the odds ratios reported in the range of 1.11-1.22, the effect size for the end phenotype of schizophrenia may be challenging to validate functionally. Perhaps a relevant intermediate phenotype more proximal to the gene will prove tractable.

For bipolar disorder, Stage 1 comprised 7,481 cases versus 9,250 controls, and identified 34 promising SNPs. These were replicated in Stage 2 in an independent set of 4,496 cases and a whopping 42,422 controls: 18 of the 34 SNPs survived at p <0.05. Taking Stage 1 and 2 together confirmed the previous "hot" finding for CACNA1C (Odds ratio = 1.14) and introduced a new candidate in ODZ4 (Odds ratio = 0.88, i.e., the minor allele is presumably "protective" or under some form of selection). Previous candidates ANK3 and SYNE1 looked promising at Stage 1, but did not replicate at Stage 2.

Finally, in a combined analysis of schizophrenia plus bipolar disorder versus controls, three of the respective "top 10" loci, CACNA1C, ANK3, and the ITIH3-ITIH4 region, came out as significant overall. This is consistent with the earlier evidence from the ISC for an overlap between the polygenic index for schizophrenia and bipolar disorder (Purcell et al., 2009). It is also consistent with the epidemiological evidence for shared genetic risk between schizophrenia and bipolar disorder (Lichtenstein et al., 2009; Gottesman et al., 2010).

What can we take from these studies? The authorship lists alone speak to the size of the collaborative effort involved and the sheer organizational task, depending on your point of view, that most of the positive findings were reported on previously could be seen as valuable "replication," or unnecessary duplication of cost and effort. Whichever way you look at it, though, just two new loci for schizophrenia and one for bipolar looks like a modest return for such a gargantuan investment. It begs the question as to whether the GWAS approach is gaining the hoped-for traction on major mental illness. Indeed, the evidence suggests that the technology tide is rapidly turning away from allelic association methods and towards rare mutation detection by copy number variation, exome, and/or whole-genome sequencing (Vacic et al., 2011; Xu et al., 2011).

Family studies are, as ever and always, of critical importance in genetics, and to distinguish between inherited and de-novo mutations. While the emphasis of GWAS has been on the impact of common, ancient allelic variation, it has become ever more obvious from both past linkage studies and from contemporary GWAS and CNV studies just how heterogeneous these conditions are, and how little note individual cases and families take of conventional DSM diagnostic boundaries. Improved genetic and other tools through which to stratify risk, define phenotypes, and predict outcomes are clearly needed. Whether such tools can be derived for GWAS data remains to be seen. It is important to remind ourselves of two things. First, case/association studies tell us something about the average impact (odds ratio, with confidence interval) of a given allele in the population studied. In these very large GWAS, this measure of impact will be approximating to the European population average. The odds ratios tell us that the impact per allele is modest. More importantly in some ways, the allele frequencies also tell us that the vast majority of allele carriers are not affected. Likewise, a high proportion of cases are not carriers. In the main, they are subtle risk modifiers rather than causal variants. That said, follow-up studies may define rare, functional genetic variants in MIR137 or CACNA1C or ANK3 that are tagged by the risk allele and that have sufficiently strong effects in a subset of cases for a causal link to be made. With this new GWAS data in hand, these sorts of questions can now be addressed.

It should also be said that there is clearly a wealth of potentially valuable information lying below the surface of the most statistically significant findings, but how to sort the true from the false associations? Should the MIR137 finding, and the targets of MIR137, be substantiated by biological analysis, then that would certainly be something well worth knowing and following up on. Network analysis by gene ontology and protein-protein interaction may yield more, but these approaches need to be approached with caution when not securely anchored from a biologically validated start point. Epistasis and pleiotropy are most likely playing a role, but even in these large sample sets, the power to determine statistical (as opposed to biological) evidence is challenging. All told, one is left thinking that more incisive findings have and will in the future come from family-based approaches, through structural studies (CNVs and chromosome translocations), and, in the near future, whole-genome sequencing of cases and relatives.

References:

Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, Scolnick E, Cichon S, St Clair D, Corvin A, Gurling H, Werge T, Rujescu D, Blackwood DH, Pato CN, Malhotra AK, Purcell S, Dudbridge F, Neale BM, Rossin L, Visscher PM, Posthuma D, Ruderfer DM, Fanous A, Stefansson H, Steinberg S, Mowry BJ, Golimbet V, de Hert M, Jönsson EG, Bitter I, Pietiläinen OP, Collier DA, Tosato S, Agartz I, Albus M, Alexander M, Amdur RL, Amin F, Bass N, Bergen SE, Black DW, Børglum AD, Brown MA, Bruggeman R, Buccola NG, Byerley WF, Cahn W, Cantor RM, Carr VJ, Catts SV, Choudhury K, Cloninger CR, Cormican P, Craddock N, Danoy PA, Datta S, de Haan L, Demontis D, Dikeos D, Djurovic S, Donnelly P, Donohoe G, Duong L, Dwyer S, Fink-Jensen A, Freedman R, Freimer NB, Friedl M, Georgieva L, Giegling I, Gill M, Glenthøj B, Godard S, Hamshere M, Hansen M, Hansen T, Hartmann AM, Henskens FA, Hougaard DM, Hultman CM, Ingason A, Jablensky AV, Jakobsen KD, Jay M, Jürgens G, Kahn RS, Keller MC, Kenis G, Kenny E, Kim Y, Kirov GK, Konnerth H, Konte B, Krabbendam L, Krasucki R, Lasseter VK, Laurent C, Lawrence J, Lencz T, Lerer FB, Liang KY, Lichtenstein P, Lieberman JA, Linszen DH, Lönnqvist J, Loughland CM, Maclean AW, Maher BS, Maier W, Mallet J, Malloy P, Mattheisen M, Mattingsdal M, McGhee KA, McGrath JJ, McIntosh A, McLean DE, McQuillin A, Melle I, Michie PT, Milanova V, Morris DW, Mors O, Mortensen PB, Moskvina V, Muglia P, Myin-Germeys I, Nertney DA, Nestadt G, Nielsen J, Nikolov I, Nordentoft M, Norton N, Nöthen MM, O'Dushlaine CT, Olincy A, Olsen L, O'Neill FA, Orntoft TF, Owen MJ, Pantelis C, Papadimitriou G, Pato MT, Peltonen L, Petursson H, Pickard B, Pimm J, Pulver AE, Puri V, Quested D, Quinn EM, Rasmussen HB, Réthelyi JM, Ribble R, Rietschel M, Riley BP, Ruggeri M, Schall U, Schulze TG, Schwab SG, Scott RJ, Shi J, Sigurdsson E, Silverman JM, Spencer CC, Stefansson K, Strange A, Strengman E, Stroup TS, Suvisaari J, Terenius L, Thirumalai S, Thygesen JH, Timm S, Toncheva D, van den Oord E, van Os J, van Winkel R, Veldink J, Walsh D, Wang AG, Wiersma D, Wildenauer DB, Williams HJ, Williams NM, Wormley B, Zammit S, Sullivan PF, O'Donovan MC, Daly MJ, Gejman PV. Genome-wide association study identifies five new schizophrenia loci. Nat Genet . 2011 Sep 18. Abstract

Psychiatric GWAS Consortium Bipolar Disorder Working Group, Sklar P, Ripke S, Scott LJ, Andreassen OA, Cichon S, Craddock N, Edenberg HJ, Nurnberger JI Jr, Rietschel M, Blackwood D, Corvin A, Flickinger M, Guan W, Mattingsdal M, McQuillin A, Kwan P, Wienker TF, Daly M, Dudbridge F, Holmans PA, Lin D, Burmeister M, Greenwood TA, Hamshere ML, Muglia P, Smith EN, Zandi PP, Nievergelt CM, McKinney R, Shilling PD, Schork NJ, Bloss CS, Foroud T, Koller DL, Gershon ES, Liu C, Badner JA, Scheftner WA, Lawson WB, Nwulia EA, Hipolito M, Coryell W, Rice J, Byerley W, McMahon FJ, Schulze TG, Berrettini W, Lohoff FW, Potash JB, Mahon PB, McInnis MG, Zöllner S, Zhang P, Craig DW, Szelinger S, Barrett TB, Breuer R, Meier S, Strohmaier J, Witt SH, Tozzi F, Farmer A, McGuffin P, Strauss J, Xu W, Kennedy JL, Vincent JB, Matthews K, Day R, Ferreira MA, O'Dushlaine C, Perlis R, Raychaudhuri S, Ruderfer D, Hyoun PL, Smoller JW, Li J, Absher D, Thompson RC, Meng FG, Schatzberg AF, Bunney WE, Barchas JD, Jones EG, Watson SJ, Myers RM, Akil H, Boehnke M, Chambert K, Moran J, Scolnick E, Djurovic S, Melle I, Morken G, Gill M, Morris D, Quinn E, Mühleisen TW, Degenhardt FA, Mattheisen M, Schumacher J, Maier W, Steffens M, Propping P, Nöthen MM, Anjorin A, Bass N, Gurling H, Kandaswamy R, Lawrence J, McGhee K, McIntosh A, McLean AW, Muir WJ, Pickard BS, Breen G, St Clair D, Caesar S, Gordon-Smith K, Jones L, Fraser C, Green EK, Grozeva D, Jones IR, Kirov G, Moskvina V, Nikolov I, O'Donovan MC, Owen MJ, Collier DA, Elkin A, Williamson R, Young AH, Ferrier IN, Stefansson K, Stefansson H, Thornorgeirsson T, Steinberg S, Gustafsson O, Bergen SE, Nimgaonkar V, Hultman C, Landén M, Lichtenstein P, Sullivan P, Schalling M, Osby U, Backlund L, Frisén L, Langstrom N, Jamain S, Leboyer M, Etain B, Bellivier F, Petursson H, Sigur Sson E, Müller-Mysok B, Lucae S, Schwarz M, Schofield PR, Martin N, Montgomery GW, Lathrop M, Oskarsson H, Bauer M, Wright A, Mitchell PB, Hautzinger M, Reif A, Kelsoe JR, Purcell SM. Large-scale genome-wide association analysis of bipolar disorder reveals a new susceptibility locus near ODZ4. Nat Genet. 2011 Sep 18. Abstract

Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet . 2009 Jan 17 ; 373(9659):234-9. Abstract

Gottesman II, Laursen TM, Bertelsen A, Mortensen PB. Severe mental disorders in offspring with 2 psychiatrically ill parents. Arch Gen Psychiatry . 2010 Mar 1 ; 67(3):252-7. Abstract

Donohoe G, Morris DW, Corvin A. The psychosis susceptibility gene ZNF804A: associations, functions, and phenotypes. Schizophr Bull . 2010 Sep 1 ; 36(5):904-9. Abstract

Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature . 2009 Aug 6 ; 460(7256):748-52. Abstract

Vacic V, McCarthy S, Malhotra D, Murray F, Chou HH, Peoples A, Makarov V, Yoon S, Bhandari A, Corominas R, Iakoucheva LM, Krastoshevsky O, Krause V, Larach-Walters V, Welsh DK, Craig D, Kelsoe JR, Gershon ES, Leal SM, Dell Aquila M, Morris DW, Gill M, Corvin A, Insel PA, McClellan J, King MC, Karayiorgou M, Levy DL, DeLisi LE, Sebat J. Duplications of the neuropeptide receptor gene VIPR2 confer significant risk for schizophrenia. Nature . 2011 Mar 24 ; 471(7339):499-503. 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 Jan 1 ; 43(9):864-8. Abstract

View all comments by David J. Porteous

Related News: GWAS Goes Bigger: Large Sample Sizes Uncover New Risk Loci, Additional Overlap in Schizophrenia and Bipolar Disorder

Comment by:  Patrick Sullivan, SRF Advisor
Submitted 26 September 2011
Posted 26 September 2011
  I recommend the Primary Papers

The two papers appearing online in Nature Genetics last Sunday are truly important additions to our increasing knowledge base for these disorders. The core analyses have been presented multiple times at international meetings in the past two years.

Since then, the available sample sizes for both schizophrenia and bipolar disorder have grown considerably. If the recently published data are any guide, the next round of analyses should be particularly revealing.

The PGC results and almost all of the data that were used in these reports are available by application to the controlled-access repository.

Please see the references for views of this area that contrast with those of Professor Porteous.

References:

Sullivan P. Don't give up on GWAS. Molecular Psychiatry. 2011 Aug 9. Abstract

Kim Y, Zerwas S, Trace SE, Sullivan PF. Schizophrenia genetics: where next? Schizophr Bull. 2011;37:456-63. Abstract

View all comments by Patrick Sullivan

Related News: GWAS Goes Bigger: Large Sample Sizes Uncover New Risk Loci, Additional Overlap in Schizophrenia and Bipolar Disorder

Comment by:  Edward Scolnick
Submitted 28 September 2011
Posted 29 September 2011
  I recommend the Primary Papers

It is clear in human genetics that common variants and rare variants have frequently been detected in the same genes. Numerous examples exist in many diseases. The bashing of GWAS in schizophrenia and bipolar illness indicates, by those who make such comments, a lack of understanding of human genetics and where the field is. When these studies were initiated five years ago, next-generation sequencing was not available. Large samples of populations or trios or quartets did not exist. The international consortia have worked to collect such samples that are available for GWAS now, as well as for detailed sequencing studies. Before these studies began there was virtually nothing known about the etiology of schizophrenia and bipolar illness. The DISC1 gene translocation in the famous family was an important observation in that family. But almost a decade later there is still no convincing data that variants in Disc1 or many of its interacting proteins are involved in the pathogenesis of human schizophrenia or major mental illness.

Sequencing studies touted to be the Occam's razor for the field are beginning, and already, as in the past in this field, preemptive papers are appearing inadequately powered to draw any conclusions with certainty. Samples collected by the consortia will be critical to clarify the role of rare variants. This will take time and care so as not to set the field back into the morass it used to be. GWAS are basically modern public health epidemiology providing important clues to disease etiology. Much work is clearly needed once hits are found, just as it has been in traditional epidemiology. But in many fields, GWAS has already led to important biological insights, and it is certain it will do so in this field as well because the underlying principles of human genetics apply to this field, also. The primary problem in the field is totally inadequate funding by government organizations that consistently look for shortcuts to gain insights and new treatments, and forget how genetics has transformed cancer, immunology, autoimmune and inflammatory diseases, and led to better diagnostics and treatments. The field will never understand the pathogenesis of these illnesses until the genetic architecture is deciphered. The first enzyme discovered in E. coli DNA biochemistry was a repair enzyme—not the enzyme that replicated DNA—and this was discovered through genetics. The progress in this field has been dramatic in the past five years. All doing this work realize that this is only a beginning and that there is a long hard road to full understanding. But to denigrate the beginning, which is clearly solid, makes no sense and indicates a provincialism unbecoming to a true scientist.

View all comments by Edward Scolnick

Related News: GWAS Goes Bigger: Large Sample Sizes Uncover New Risk Loci, Additional Overlap in Schizophrenia and Bipolar Disorder

Comment by:  Nick CraddockMichael O'Donovan (SRF Advisor)
Submitted 11 October 2011
Posted 11 October 2011

At the start of the millennium, only two molecular genetic findings could be said with a fair amount of confidence to be etiologically relevant to schizophrenia and bipolar disorder. The first of these was that deletions of chromosome 22q11 that are known to cause velo-cardio-facial syndrome also confer a substantial increase in risk of psychosis. The second was the discovery by David St Clair, Douglas Blackwood, and colleagues (St Clair et al., 1990) of a balanced translocation involving chromosomes 1 and 11 that co-segregates with a range of psychiatric phenotypes in a single large family, was clearly relevant to the etiology of illness in that family (Blackwood et al., 2001). The latter finding has led to the conjecture, based upon a translocation breakpoint analysis reported by Kirsty Millar, David Porteous, and colleagues (Millar et al., 2000), that elevated risk in that family is conferred by altered function of a gene eponymously named DISC1. Just over a decade later, what can we now say with similar degrees of confidence? The relevance of deletions of 22q11 has stood the test of time—indeed, has strengthened—through further investigation (Levinson et al., 2011, being only one example), while the relevance of DISC1 remains conjecture. That the evidence implicating this gene is no stronger than it was all those years ago provides a clear illustration of the difficulties inherent in drawing etiological inferences from extremely rare mutations regardless of their effect size.

However, with the publication of several GWAS and CNV papers, culminating in the two mega-analyses reported by the PGC that are the subject of this commentary, one on schizophrenia, one on bipolar disorder, together reporting a total of six novel loci, very strong evidence has accumulated for approximately 20 new loci in psychosis. The majority of these are defined by SNPs, the remainder by copy number variants, and virtually all (including the rare, relatively high-penetrance CNVs) have emerged through the application of GWAS technology to large case-control samples, not through the study of linkage or families. Have GWAS approaches proven their worth? Clearly, the genetic findings represent the tip of a very deeply submerged iceberg, and it is possible that not all will stand the test of time and additional data, although the current levels of statistical support suggest the majority will do so. Nevertheless, the findings of SNP and CNV associations (including 22q11 deletions) seem to us to provide the first real signs of progress in uncovering strongly supported findings of primary etiological relevance to these disorders. Although SNP effects are small, the experience from other complex phenotypes is that statistically robust genetic associations, even those of very small effect, can highlight biological pathways of etiological (height; Lango Allen et al., 2010) and of possible therapeutic relevance (Alzheimer's disease; Jones et al., 2010). Moreover, it would seem intuitively likely that even if capturing the total heritable component of a disorder is presently a distant goal, the greater the number of associations captured, the better will be the snapshot of the sorts of processes that contribute to a disorder, and that might therefore be manipulated in its treatment. Thus, there is evidence that building even a very incomplete picture of the sort of genes that influence risk is an excellent method of informing understanding of pathogenesis of a highly complex disorder (or set of disorders).

As in previous GWAS and CNV endeavors, the PGC studies have required a significant degree of altruism from the hundreds of investigators and clinicians who have shared their data with little hope of significant academic credit. Moreover, where ethical approval permitted, the datasets have been made virtually open source for other investigators who are not part of the study. Sadly, this generosity of spirit is not matched in the rather curmudgeonly commentary provided by David Porteous. Rather than challenging the science or conduct of the study, it appears to us that the commentary takes the easier route of damnation by faint praise, distortion, and even innuendo.

The strongest finding, that being of association to the extended MHC region, is dismissed as "long known to be associated with risk of schizophrenia." How that knowledge was acquired a long time ago is unclear, but it cannot have been based upon data. It is true that weak and inconsistent associations at the MHC locus have been reported, even predating the molecular genetic era (McGuffin et al., 1978), but not until the landmark studies of the International Schizophrenia Consortium (2009), the Molecular Genetics of Schizophrenia Consortium (AbstractShi et al., 2009), and the SGENE+ Consortium (Stefansson et al., 2009) have the findings been strong enough to be described as knowledge. Porteous’ dismissive tone continues with the phrase "just 10 loci met….," the word "just" being a qualifier that seems designed to denigrate rather than challenge the results. Given the paucity of etiological clues, others might consider this a good yield. The observation in which the effect sizes at the detected loci are contrasted "with the ~10-fold increase in risk to the first-degree relative of someone with schizophrenia" is so fatuous it is difficult to believe its function is anything other than to insinuate in the mind of the reader the impression of failure. Yet no one remotely aware of the expectations behind GWAS would expect that the effect sizes of any common risk allele would bear any resemblance to that of family history, the latter reflecting the combined effects of many risk alleles.

Among the most important findings of the PGC schizophrenia group were those of strong evidence for association between a variant in the vicinity of a gene encoding regulatory RNA MIR137, and the subsequent finding that schizophrenia association signals were significantly enriched (P <0.01) among predicted targets of this regulatory RNA. Of course, like the other findings, there is room for the already very strong data to be further strengthened, but that finding alone opens up a whole new window in potential pathogenic mechanisms. Yet Porteous casually throws four handfuls of mud, dismissing the enrichment p <0.01 as a "relaxed significance cutoff," which "seems somewhat arbitrary," and that "warrants further examination," and commenting that "it is of passing note that for two of the eight replication cohorts, the direction of effect for MIR137 was in the opposite direction from the Stage 1 finding." If Porteous feels he has the expertise to pronounce on this analysis, it would behoove him well to choose his words more carefully. Since when is a P value of <0.01 "relaxed" when applied to a test of a single hypothesis? Can he really be unaware of the longstanding convention of regarding P <0.05 as significant in specific hypothesis testing? If he is not unaware of this, why is it generally applicable but "somewhat arbitrary" in the context of the PGC study? As for "further examination being warranted," this is true of any scientific finding, but what does he specifically mean in the context of his commentary? And why is it of "passing note" that not all samples show trends in the same direction? In the context of the well-known issues in GWAS concerning individual small samples and power, what is surprising about that? There may be simple answers to these questions, but we find it difficult to draw any other conclusion than that the choice of language is anything other than another attempt to sow seeds of doubt through innuendo rather than analysis.

The remark that "ZNF804A, a past favourite, was noticeably absent" falls well short of the standard one might expect of serious discourse. The choice of language suggests a desire to denigrate rather than analyse, and to insinuate without specific evidence that any interest in this gene should now be over. In fact, the largest study of this gene to date is that of Williams et al. (2010), which actually includes at least two-thirds of the PGC discovery dataset and is based on over 57,000 subjects, a sample almost three times as large as the mega-analysis sample of the PGC.

Porteous’ overall conclusion from the two studies is "whichever way you look at it, though, just two new loci for schizophrenia and one for bipolar looks like a modest return for such a gargantuan investment." This appraisal is misleading. The PGC studies were actually relatively small investments, being based on a synthesis of pre-existing data. Since the studies use existing data, there is naturally an expectation that some of the loci identified will have been previously reported as either significant or have otherwise been flagged up as of interest, while some will be new. Overall, the return on the GWAS investment is not just the six novel loci (rather than three); it is the totality of the findings, which, as noted above, currently number about 20 loci. The schizophrenia research community should also be made aware, if they are not already, that the return on these investments is not "one off"; it is cumulative. In the coming years, the component datasets will continue to generate a return in new gene discoveries (including CNVs yet to be reported by the PGC) as they are added (at essentially no cost) to other emerging GWAS datasets being generated largely through charitable support. With the returns in the bank already, one could (and we do) argue that the investment is negligible, particularly given the cost in human and economic terms of continued ignorance about these illnesses that blight so many lives.

It is true that with so little being known compared with what is yet to be known, the biological insights that can be made from the existing data are limited. This is equally true of the common and rare variants identified so far, and we are not aware of any of the "incisive findings" that Porteous claims have already come from alternative approaches, although the emergence of strong evidence for deletions at NRXN1 as a susceptibility variant for schizophrenia through meta-analysis of case-control GWAS data (one of the extra returns on the GWAS data we referred to above) deserves that description (Kirov et al., 2009). But this is not a cause for despair; in contrast to the future promises made on behalf of other as yet unproven designs, for eyes and minds that are open enough to see, the recent papers provide unambiguous evidence for a straightforward route to identifying more genes and pathways involved in the disorder. Even Porteous has partial sight of this, since he notes that "there is clearly a wealth of potentially valuable information lying below the surface of the most statistically significant findings." What he appears unable to see is "how to sort the true from the false associations?" The answer for a large number of loci is simple. Better-powered studies based upon larger sample sizes.

We would like to add a note of caution for those who too readily denigrate case-control approaches in favor of hyping other approaches, none of which are yet so well proven routes to success. We are not against those approaches; indeed, we are actively involved in them. But we are concerned that the hype surrounding sequencing, and the generation of what we think are unrealistic expectations, will make those designs vulnerable to attack from those who seem only too keen to make premature and inaccurate pronouncements of failure, who seem desperate to derive straw from nuggets of gold. If, as we believe is likely, it turns out to be quite a few years more before sequencing studies become sufficiently powered to provide large numbers of robust findings, as for GWAS, the consequence could be withdrawal of substantial government funding before those designs have had a chance to live up to their potential. That such an outcome has already largely been achieved for GWAS in some countries might be a source of rejoicing in some quarters, but it should also send out a warning to all who broadly hold the view that understanding the genetics of these disorders is central to understanding their origins, and to improving their future management.

The recent PGC papers represent an impressive, international collaboration based upon methodologies that have a proven track record in delivering important biological insights into other complex disorders, and now in psychiatry. Given the complexity of psychiatric phenotypes, we believe it is likely that a variety of approaches, paradigms, and ideas will be essential for success, including the approaches espoused by those who believe the evidence is compatible with essentially Mendelian inheritance. Inevitably, there will be sincerely held differences of opinion concerning the best way forward, and, of course, in any area of science, reasoned arguments based upon a fair assessment of the evidence are essential. Nevertheless, given there are sufficient uncertainties about what can be realistically delivered in the short term by the newer technologies, we suggest that the cause of bringing benefit to patients will most likely be better served by humility, realism, and a constructive discussion in which there is no place for belittling real achievements, for arrogance, or for dogmatic posturing.

References

Blackwood DH, Fordyce A, Walker MT, St Clair DM, Porteous DJ, Muir WJ. Schizophrenia and affective disorders--cosegregation with a translocation at chromosome 1q42 that directly disrupts brain-expressed genes: clinical and P300 findings in a family. Am J Hum Genet. 2001 Aug;69(2):428-33. Abstract

International Schizophrenia Consortium Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009 Aug 6;460(7256):748-52. Abstract

Jones L, Holmans PA, Hamshere ML, Harold D, Moskvina V, Ivanov D, et al. Genetic evidence implicates the immune system and cholesterol metabolism in the etiology of Alzheimer's disease. PLoS One. 2010 Nov 15;5(11):e13950. Erratum in: PLoS One. 2011;6(2). Abstract

Kirov G, Rujescu D, Ingason A, Collier DA, O'Donovan MC, Owen MJ. Neurexin 1 (NRXN1) deletions in schizophrenia. Schizophr Bull. 2009 Sep;35(5):851-4. Epub 2009 Aug 12. Review. Abstract

Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 Oct 14;467(7317):832-8. Abstract

Levinson DF, Duan J, Oh S, Wang K, Sanders AR, Shi J, et al. Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. Am J Psychiatry. 2011 Mar;168(3):302-16. Abstract

McGuffin P, Farmer AE, Rajah SM. Histocompatability antigens and schizophrenia. Br J Psychiatry. 1978 Feb;132:149-51. Abstract

Millar JK, Wilson-Annan JC, Anderson S, Christie S, Taylor MS, Semple CA, et al. Disruption of two novel genes by a translocation co-segregating with schizophrenia. Hum Mol Genet. 2000 May 22;9(9):1415-23. Abstract

Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe'er I, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009 Aug 6;460(7256):753-7. Abstract

St Clair D, Blackwood D, Muir W, Carothers A, Walker M, Spowart G, et al. Association within a family of a balanced autosomal translocation with major mental illness. Lancet. 1990 Jul 7;336(8706):13-6. Abstract

Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al Common variants conferring risk of schizophrenia. Nature. 2009 Aug 6;460(7256):744-7. Abstract

The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011 Sep 18;43(10):969-976. Abstract

Williams HJ, Norton N, Dwyer S, Moskvina V, Nikolov I, Carroll L, et al. Fine mapping of ZNF804A and genome-wide significant evidence for its involvement in schizophrenia and bipolar disorder. Mol Psychiatry. 2011 Apr;16(4):429-41. Abstract

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Related News: GWAS Goes Bigger: Large Sample Sizes Uncover New Risk Loci, Additional Overlap in Schizophrenia and Bipolar Disorder

Comment by:  Todd LenczAnil Malhotra (SRF Advisor)
Submitted 11 October 2011
Posted 11 October 2011

It is worth re-emphasizing that efforts such as the Psychiatric GWAS Consortium do not rule out potentially important discoveries from alternative strategies such as endophenotypic approaches or examination of rare variants. Indeed, such strategies will be necessary to understand the functional mechanisms implicated by GWAS hits.

Moreover, we note that the two recently published PGC papers were not designed to exclude a role for previously identified candidate loci such as DISC1 (Hodgkinson et al., 2004), or prior GWAS findings such as rs1344706 at ZNF804A (Williams et al., 2011). For both these loci, and many others that have been proposed, meta-analysis of available samples suggest very small effect sizes (OR ~1.1), as might be expected for common variants. As noted in Supplementary Table S12 of the schizophrenia PGC paper (Ripke et al., 2011), the currently available sample size (~9,000 cases/~12,000 controls) of the discovery cohort was still underpowered to detect variants with odds ratios of 1.1, especially if they have a minor allele frequency of 20 percent or below.

An instructive example arises from the field of diabetes genetics. An association of a missense variant (rs1801282, Pro12Ala) in PPARG to type 2 diabetes was first reported in a sample of n = 91 Japanese-American patients (Deeb et al., 1998). Many subsequent studies failed to replicate the effect, and the initial large GWAS meta-analysis (involving >14,000 cases and ~18,000 controls; Zeggini et al., 2007) only detected the association at a p-value that would be considered non-significant by today’s standard (p =1.7*10-6). Interestingly, the authors deemed the association to be “confirmed,” and the result was widely accepted within that field. Subsequent meta-analysis, involving twice as many subjects (total n = 67,000), finally obtained conventional genomewide levels of significance (p <5*10-8; Gouda et al., 2010).

References:

Deeb SS, Fajas L, Nemoto M, Pihlajamäki J, Mykkänen L, Kuusisto J, Laakso M, Fujimoto W, Auwerx J. A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 1998 Nov;20(3):284-7. Abstract

Gouda HN, Sagoo GS, Harding AH, Yates J, Sandhu MS, Higgins JP. The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol. 2010 Mar 15;171(6):645-55. Abstract

Hodgkinson CA, Goldman D, Jaeger J, Persaud S, Kane JM, Lipsky RH, Malhotra AK. Disrupted in schizophrenia 1 (DISC1): association with schizophrenia, schizoaffective disorder, and bipolar disorder. Am J Hum Genet. 2004 Nov;75(5):862-72. Abstract

Williams HJ, Norton N, Dwyer S, Moskvina V, Nikolov I, Carroll L, Georgieva L, Williams NM, Morris DW, Quinn EM, Giegling I, Ikeda M, Wood J, Lencz T, Hultman C, Lichtenstein P, Thiselton D, Maher BS; Molecular Genetics of Schizophrenia Collaboration (MGS) International Schizophrenia Consortium (ISC), SGENE-plus, GROUP, Malhotra AK, Riley B, Kendler KS, Gill M, Sullivan P, Sklar P, Purcell S, Nimgaonkar VL, Kirov G, Holmans P, Corvin A, Rujescu D, Craddock N, Owen MJ, O'Donovan MC. Fine mapping of ZNF804A and genome-wide significant evidence for its involvement in schizophrenia and bipolar disorder. Mol Psychiatry. 2011 Apr;16(4):429-41. Abstract

Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS; Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI, Hattersley AT. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007 Jun 1;316(5829):1336-41. Abstract

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Related News: Family Roots for Autism, Schizophrenia, Bipolar Disorder

Comment by:  Bernard Crespi
Submitted 30 July 2012
Posted 30 July 2012

In a new paper in Archives of General Psychiatry that has received considerable media attention, Sullivan et al. (Sullivan et al., 2012) use register data from Sweden and Israel to show higher rates of ASDs among individuals with family histories of schizophrenia and bipolar disorder. The authors interpret these results as indicating that ASD, schizophrenia, and bipolar disorder share etiology. This is a very interesting hypothesis that, if supported, would have important implications for our understanding of the genetic underpinnings of schizophrenia in relation to other conditions. However, two alternative hypotheses not involving shared causation may, at least in part, help to explain their results.

First, a recent set of studies demonstrates that drug treatments for schizophrenia and bipolar disorder increase the incidence of ASDs, or their biologically based phenotypic correlates, in offspring. Croen et al. (Croen et al., 2011) reported that prenatal exposure to antidepressants (SSRIs) was associated with a twofold increase in risk of ASD. It is also notable that hyperserotoninemia has also been found in about one-third of autism cases (Burgess et al., 2006). Fetal exposure to the mood stabilizer valproate has been associated with a sevenfold increase in ASD risk (Bromley et al., 2008), and also serves as a model system for autism in animal studies. Use of clozapine and olanzapine during pregnancy has been associated with increased offspring head circumference (Bodén et al., 2012), which represents another well-validated correlate of autism (Courchesne et al., 2011). Moreover, environmental exposure to three psychoactive drugs (fluoxetine, venlafaxine, and carbamazepine) has been demonstrated to cause gene-expression changes that resemble those seen only in autism (Thomas and Klaper, 2012).

These results may help to explain mother-offspring and sib-sib associations of schizophrenia and bipolar disorder with ASDs. Such effects might be expected to be higher than those seen for fathers, but data were not presented in the report by Sullivan et al. on such parental sex differences. Effects of pharmacological treatment of fathers on ASD risk in offspring apparently have yet to be investigated, although paternal effects on offspring psychopathology and epigenetic profiles have been reported with regard to such factors as age (Hultman et al., 2011), and stress (Essex et al., 2011).

Second, the authors' data may also be attributable in part to false-positive diagnoses of premorbidity to schizophrenia (or bipolar disorder) as ASD in children, and conflation of schizotypal personality disorder (SPD) with high-functioning autism and Asperger's syndrome. Premorbidity to schizophrenia occurs in a notable proportion of cases, and most usually involves "negative symptoms" such as deficits in social interaction and language (discussed in Crespi, 2011). The clearest apparent evidence regarding this hypothesis comes from Sullivan et al. themselves, who noted that in their Study 1, 2,147 individuals had received a diagnosis of both ASD and (at discharge) schizophrenia or bipolar disorder. The authors excluded these cases as involving "diagnostic uncertainty." However, such uncertainties in the retained data may still influence the analyses. Thus, to the extent that individuals with diagnoses of ASD are under the age of onset for schizophrenia or bipolar disorder, they may exhibit false-positive diagnoses of premorbidity to schizophrenia or bipolar disorder as ASDs. Similar considerations apply to sibs differing in age.

Schizophrenia exhibits well-established genetic, symptomatic, and epidemiological overlap with both schizotypal personality disorder (SPD) and bipolar disorder (Carpenter et al., 2009). Additionally, first-order relatives of individuals with schizophrenia or affective psychosis show elevated rates of SPD (Schürhoff et al., 2005). These results indicate that SPD may show conflation in epidemiological data with high-functioning autism or Asperger's, due to the presence in both SPD and high-functioning forms of ASD of general social deficits and abnormalities. The possibility of such conflation is supported by: 1) the authors' finding that their familial association "was principally in cases without clinical indication of mental retardation," and 2) studies showing behavioral overlap of SPD with ASDs (based predominantly on questionnaires) (Barneveld et al., 2011), but a striking lack of data on overlap for developmental, physiological, or neurological phenotypes. Such conflation would falsely connect ASDs (which are actually SPD) with schizophrenia or bipolar disorder. It would appear more useful and realistic to consider the possibility and expected effects of diagnostic uncertainties than to presume that they do not exist.

This second set of considerations also applies to studies that would use GWAS data to evaluate hypotheses of how autism and schizophrenia are related to one another; even a rather small degree of false-positive conflation of premorbidity to schizophrenia with ASD could result in incorrect conclusions regarding the genetic etiologies of these sets of conditions. Such potential problems might be minimized by subsetting ASD cases into autism “sensu stricto,” given that PDD-NOS is the diagnostic category most likely to be conflated with schizophrenia premorbidity.

References:

Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C, Davidson M, Fruchter E, Hultman CM, Lundberg M, Långström N, Weiser M, Svensson AC, Lichtenstein P. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch Gen Psychiatry. 2012 Jul 2:1-5. Abstract

Croen LA, Grether JK, Yoshida CK, Odouli R, Hendrick V. Antidepressant use during pregnancy and childhood autism spectrum disorders. Arch Gen Psychiatry. 2011:68(11):1104-1112. Abstract

Burgess NK, Sweeten TL, McMahon WM, Fujinami RS. Hyperserotoninemia and altered immunity in autism. J Autism Dev Disord. 2006:36(5):697-704. Abstract

Bromley RL, Mawer G, Clayton-Smith J, Baker GA; Liverpool and Manchester Neurodevelopment Group. Autism spectrum disorders following in utero exposure to antiepileptic drugs. Neurology. 2008:71(23):1923-4. Abstract

Bodén R, Lundgren M, Brandt L, Reutfors J, Kieler H. Antipsychotics during pregnancy: relation to fetal and maternal metabolic effects. Arch Gen Psychiatry. 2012:69(7):715-21. Abstract

Courchesne E, Mouton PR, Calhoun ME, Semendeferi K, Ahrens-Barbeau C, Hallet MJ, Barnes CC, Pierce K. Neuron number and size in prefrontal cortex of children with autism. JAMA. 2011:306(18):2001-10. Abstract

Thomas MA, Klaper RD. Psychoactive pharmaceuticals induce fish gene expression profiles associated with human idiopathic autism. PLoS One. 2012;7(6):e32917. Abstract

Hultman CM, Sandin S, Levine SZ, Lichtenstein P, Reichenberg A. Advancing paternal age and risk of autism: new evidence from a population-based study and a meta-analysis of epidemiological studies. Mol Psychiatry. 2011:16(12):1203-12. Abstract

Essex MJ, Thomas Boyce W, Hertzman C, Lam LL, Armstrong JM, Neumann SM, Kobor MS. Epigenetic vestiges of early developmental adversity: childhood stress exposure and DNA methylation in adolescence. Child Dev. 2011 Sep 2. Abstract

Crespi B. One hundred years of insanity: genomic, psychological, and evolutionary models of autism in relation to schizophrenia. In: Ritsner M, ed. Handbook of Schizophrenia Spectrum Disorders, Volume I. New York, NY: Springer; 2011:163-185.

Carpenter WT, Bustillo JR, Thaker GK, van Os J, Krueger RF, Green MJ. The psychoses: cluster 3 of the proposed meta-structure for DSM-V and ICD-11. Psychol Med. 2009: 39(12):2025-42. Abstract

Schürhoff F, Laguerre A, Szöke A, Méary A, Leboyer M. Schizotypal dimensions: continuity between schizophrenia and bipolar disorders. Schizophr Res. 2005:80(2-3):235-42. Abstract

Barneveld PS, Pieterse J, de Sonneville L, van Rijn S, Lahuis B, van Engeland H, Swaab H. Overlap of autistic and schizotypal traits in adolescents with Autism Spectrum Disorders. Schizophr Res. 2011:126(1-3):231-6. Abstract

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Related News: Family Roots for Autism, Schizophrenia, Bipolar Disorder

Comment by:  William Carpenter, SRF Advisor (Disclosure)
Submitted 30 July 2012
Posted 30 July 2012

Shared risk for ASDs in bipolar and schizophrenia families is important, and the apparent gradient in risk with schizophrenia being greater than bipolar may be informative. From the view that schizophrenia and bipolar disorder are heterogeneous syndromes, the following is surmised:



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Related News: Family Roots for Autism, Schizophrenia, Bipolar Disorder

Comment by:  John McGrath, SRF Advisor
Submitted 30 July 2012
Posted 30 July 2012
  I recommend the Primary Papers

This impressive study adds to the growing body of evidence demonstrating that heritable factors are shared among autism, schizophrenia, and bipolar disorder. The authors suggest that genetic factors could underlie the findings, but also remind the reader that environmental factors could play a role. They note that twin-based studies of heritability in schizophrenia and autism have demonstrated appreciable contributions for environmental factors that were shared between the affected individuals—usually referred to as common environmental effects. It should be noted that in this context, the word “common” does not equate with “prevalent.” With respect to shared genetic factors, the growing body of evidence regarding structural variation such as copy number variants is impressive. With respect to non-genetic factors, more work is needed—prenatal infection (which could trigger maternal immune activation) and nutrition (e.g., low vitamin D) might be candidate domains. If there are shared environmental risk factors contributing to schizophrenia, bipolar disorder, and autism, and if these were potentially modifiable, then this would be a very attractive proposition from a public health perspective.

The study is also an excellent demonstration of collaborative epidemiology—three datasets from two nations were used to examine the same research questions. This is an efficient way to do science.

View all comments by John McGrath

Related News: Deciphering Themes for Schizophrenia’s Genetic Variation

Comment by:  Patrick Sullivan, SRF AdvisorDanielle Posthuma
Submitted 16 November 2012
Posted 16 November 2012

Gilman et al. pose exceptionally important and salient questions: given that increasingly detailed genomic data have established that many genes are now strongly implicated in the etiology of schizophrenia, how do we understand this? How can these different components of the “parts list” for schizophrenia be pieced together to derive a cogent etiological hypothesis for further testing?

The authors use a new computational approach to address these questions, and derive lists related to axon guidance, neuronal cell mobility, synaptic function, and chromosomal remodeling. Additional analyses suggest the coherence of their lists. These are good clues that deserve further evaluation.

It was intriguing that the authors included multiple types of genetic variation—rare but potent copy number variants (e.g., Kirov et al., 2012), rare exonic mutations (Xu et al., 2012), and common variations from genomewide association studies (Ripke et al., 2011)—as most authors have tended to conduct these analyses separately.

In sum, a nice contribution to the literature and initial steps towards tackling a tough problem in human genetics. But, there are four issues for readers to bear in mind in evaluating the results.

First, we hope that the authors make their program freely available. This is the standard in the field. Many of us are interested in evaluating the capacities of their program. To our knowledge, it is not now available, although it has been used in multiple published papers. We could find no link in the paper or on the senior author’s lab page.

Second, readers need to remember that this was an in-silico analysis. It produces hypotheses but does not (and cannot) provide proof. The methods are subject to multiple biases, and it was not clear how well these were controlled (see point 4 as well). We wondered whether known biases like gene size and LD patterns were well controlled.

Third, we would have liked to see greater scholarship. There is an unfortunate trend for computational biologists to produce tools without benchmarking them against existing tools or rigorously determining power and error rates. The lack of finding significant clusters in control sets is insufficient in showing the validity of their program. Are the authors’ claims that their new tool represents superiority truly justified?

Moreover, there are a lot of tools for performing analyses of these sorts (e.g., INRICH, FORGE, MAGENTA, Ingenuity, ALIGATOR, among many others). Indeed, these sorts of analyses are in the toolkits of most psychiatric genetics groups and are routinely applied. Given that there are many papers reporting results, a scholarly treatment of how their results compare to those of others and what the added value of their program is would have been useful.

Fourth, and most importantly, pathway analysis is completely dependent on the input—the genetic findings and the pathways. The findings that the authors used had issues. The CNV list is likely to change soon as the PGC CNV group completes its integrated analyses of tens of thousands of subjects. The exome list was based on a small and atypical sample, and much larger studies are in preparation (see SRF comment). The authors did not seem to confront the issue that all humans contain a lot of deleterious exonic variation. And (spoiler alert), the GWAS list is soon to increase markedly. More and more precise findings are sure to alter the results.

The pathways used were pretty standard—GO, KEGG, protein-protein interaction databases. Unfortunately, although widely used, these pathways have multiple issues. The content of many GO annotations and KEGG pathways have not been constructed by experts in the area. As one salient example, synaptic gene lists in standard pathway databases were quite imperfectly related to lists created by experts (Ruano et al., 2010). The authors also relied somewhat uncritically on the PPI databases. These have multiple issues, and some (unpublished) data suggest substantial error (i.e., large fractions of the predicted interactions are not, in fact, real or biologically meaningful). The fraction of the proteome screened adequately by these methods is small. Some interactions in these databases are non-specific, or occur between molecules that are never in the same place at the same time.

Indeed, the genes overrepresented in PPI databases were selected due to disease relevance or biological importance (e.g., there is a lot of work on P53). In general, the more a gene is investigated, the more interactions are found.

Still, this is a key paper, albeit a snapshot based on imperfect input data, and we look forward to seeing whether additional analyses confirm a role in schizophrenia of the networks identified currently with their program.

References:

Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, Grozeva D, Fjodorova M, Wollerton R, Rees E, Nikolov I, van de Lagemaat LN, Bayés A, Fernandez E, Olason PI, Böttcher Y, Komiyama NH, Collins MO, Choudhary J, Stefansson K, Stefansson H, Grant SG, Purcell S, Sklar P, O'Donovan MC, Owen MJ. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry. 2012 Feb; 17(2):142-53. Abstract

Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, Levy S, Gogos JA, Karayiorgou M. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012 Oct 3. Abstract

Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA, Scolnick E, Cichon S, St Clair D, Corvin A, Gurling H, Werge T, Rujescu D, Blackwood DH, Pato CN, Malhotra AK, Purcell S, Dudbridge F, Neale BM, Rossin L, Visscher PM, Posthuma D, Ruderfer DM, Fanous A, Stefansson H, Steinberg S, Mowry BJ, Golimbet V, de Hert M, Jönsson EG, Bitter I, Pietiläinen OP, Collier DA, Tosato S, Agartz I, Albus M, Alexander M, Amdur RL, Amin F, Bass N, Bergen SE, Black DW, Børglum AD, Brown MA, Bruggeman R, Buccola NG, Byerley WF, Cahn W, Cantor RM, Carr VJ, Catts SV, Choudhury K, Cloninger CR, Cormican P, Craddock N, Danoy PA, Datta S, de Haan L, Demontis D, Dikeos D, Djurovic S, Donnelly P, Donohoe G, Duong L, Dwyer S, Fink-Jensen A, Freedman R, Freimer NB, Friedl M, Georgieva L, Giegling I, Gill M, Glenthøj B, Godard S, Hamshere M, Hansen M, Hansen T, Hartmann AM, Henskens FA, Hougaard DM, Hultman CM, Ingason A, Jablensky AV, Jakobsen KD, Jay M, Jürgens G, Kahn RS, Keller MC, Kenis G, Kenny E, Kim Y, Kirov GK, Konnerth H, Konte B, Krabbendam L, Krasucki R, Lasseter VK, Laurent C, Lawrence J, Lencz T, Lerer FB, Liang KY, Lichtenstein P, Lieberman JA, Linszen DH, Lönnqvist J, Loughland CM, Maclean AW, Maher BS, Maier W, Mallet J, Malloy P, Mattheisen M, Mattingsdal M, McGhee KA, McGrath JJ, McIntosh A, McLean DE, McQuillin A, Melle I, Michie PT, Milanova V, Morris DW, Mors O, Mortensen PB, Moskvina V, Muglia P, Myin-Germeys I, Nertney DA, Nestadt G, Nielsen J, Nikolov I, Nordentoft M, Norton N, Nöthen MM, O'Dushlaine CT, Olincy A, Olsen L, O'Neill FA, Orntoft TF, Owen MJ, Pantelis C, Papadimitriou G, Pato MT, Peltonen L, Petursson H, Pickard B, Pimm J, Pulver AE, Puri V, Quested D, Quinn EM, Rasmussen HB, Réthelyi JM, Ribble R, Rietschel M, Riley BP, Ruggeri M, Schall U, Schulze TG, Schwab SG, Scott RJ, Shi J, Sigurdsson E, Silverman JM, Spencer CC, Stefansson K, Strange A, Strengman E, Stroup TS, Suvisaari J, Terenius L, Thirumalai S, Thygesen JH, Timm S, Toncheva D, van den Oord E, van Os J, van Winkel R, Veldink J, Walsh D, Wang AG, Wiersma D, Wildenauer DB, Williams HJ, Williams NM, Wormley B, Zammit S, Sullivan PF, O'Donovan MC, Daly MJ, Gejman PV. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011 Oct ; 43(10):969-76. Abstract

Ruano D, Abecasis GR, Glaser B, Lips ES, Cornelisse LN, de Jong AP, Evans DM, Davey Smith G, Timpson NJ, Smit AB, Heutink P, Verhage M, Posthuma D. Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. Am J Hum Genet. 2010 Feb 12;86(2):113-25. Abstract

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