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...  Read more

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

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...  Read more

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

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...  Read more

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

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

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...  Read more

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.

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...  Read more

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

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...  Read more

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

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   Read more

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.

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...  Read more

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

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...  Read more

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.

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...  Read more

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

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

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...  Read more

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

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...  Read more

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

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 (  Read more

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