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WCPG 2007—Schizophrenia, Bipolar GWA Results Prompt Calls for Bigger Samples

22 October 2007. There has been great anticipation in the world of psychiatric research over the past year, with the community awaiting the results of a number of genome-wide association studies (GWASs). The preliminary results of some of the schizophrenia and bipolar studies were presented in oral sessions at the recent World Congress on Psychiatric Genetics, 7-11 October, in New York City. Similar pictures emerged for both disorders—no strong (p <10-6) replications across studies, no candidates with strong effects on disease risk (i.e., no odds ratios approaching 2.0), and no clear replications of genes implicated by candidate gene studies. However, the field can now investigate the hypothesis that this large crop of candidates contains small-effect susceptibility genes for these disorders.

Speakers did agree on two points: that it is necessary to practice “aggressive data-sharing” to increase sample sizes, and to apply ruthless quality-control techniques on initial results to weed out false positives.

Schizophrenia GWA studies
The schizophrenia GWA session was held on 11 October and chaired by Ridha Joober of Douglas Hospital in Montreal. Among the six talks were new data from the recently published schizophrenia GWAS led by Todd Lencz of Zucker Hillside Hospital in Glen Oaks, New York (see SRF related news story), as well as unpublished GWA studies presented by Jennifer Stone of the Broad Institute in Cambridge, Massachusetts, Mick O'Donovan of Cardiff University in the United Kingdom, Lin He of Shanghai Jiao Tong University in China, Patrick Sullivan of the University of North Carolina, Chapel Hill, and Sven Cichon of Bonn University in Germany. While the researchers did not find markers associated with schizophrenia at experiment-wide significance values, their "hits"—markers with p values in the 10-7 to 10-5 range—do offer new candidate genes for further examination.

The Cichon study used the Illumina HumanHap550 platform and the Stone, O'Donovan, He, Lencz, and Sullivan studies used the Affymetrix 500K platform (as well as the Affymetrix 1 million chip in the case of Stone). In addition to a number of markers outside coding regions, three groups mentioned genes by name that they deemed to have evidence for association with schizophrenia: HLA-DRB1, HLA-DRB5, SETBP1, CRLS1, ZNF323, and C20orfg26 (Stone); ZNF804A, RPGRIP1L, and NOS1 (O'Donovan); CUTL1 (Cichon).

While Sullivan noted that none of the markers in the CATIE sample showed genome-wide significant association with schizophrenia, his group's subsequent analysis showed preliminary support for NRG1 and DISC1, and indeed, for DISC1 the findings cluster at the breakpoint identified in the original reports by the Porteous group.

There was one departure from the many genes of small-effect paradigm: Lencz described a new approach by the Malhotra group wherein they probed their dataset for recessive genes that might contribute larger genetic effects in schizophrenia. Specifically they looked for regions with improbably long stretches ("runs") of homozygosity—statistically improbable sequences in which the same nucleotide sequence was inherited from both parents. While this is apparently a common feature of the genome even in healthy subjects, this phenomenon was observed to occur with significantly greater frequency in patients with schizophrenia. They identified a greater number of runs of homozygosity among patients than among controls in nine distinct genomic regions, four of which contain genes previously suggested to be susceptibility genes for schizophrenia (CAPON [aka NOS1AP], NSF. ATF2, and PIK3C3).

Conference organizer Lynn Delisi of New York University raised the question of why there was no replication of popular candidates such as NRG1 or DTNPB1. It was noted by several speakers that the current GWA technologies, despite covering 500,000 SNPs, are still sampling just a small fraction of the genome, and were not designed for specific coverage of these gene candidates, which consequently are not well covered. Sullivan suggested it would take in the range of two million SNP chips to densely cover the top current candidates.

One audience member cautioned against using a "sliding scale" for picking out interesting candidates, noting that gene markers with significance figures in the range of 10-4 may contain a small percentage of true positives. Daniel Weinberger of NIMH commented that, similar to the candidate gene approach, it would make sense to look more closely at "hits" with lower than genome-wide significance but prior evidence from other lines of research.

Bipolar GWA studies
The bipolar GWASs presented on 8 October were a mixture of published and unpublished work. The Wellcome Trust study presented by Nick Craddock of Cardiff University, and the NIMH study, presented by Amber Baum of NIMH in Bethesda, Maryland, were published in the past half year (see SRF related news story). These authors presented new data from their groups' efforts to verify their results in each other's publicly available datasets. Unpublished bipolar GWASs were presented by Pamela Sklar of the Broad Institute, Laura Scott of the University of Michigan in Ann Arbor, and Sven Cichon of Bonn University. The Baum, Scott, and Cichon studies used the Illumina HumanHap550 platform and the Craddock and Sklar studies used the Affymetrix 500K platform.

A number of genes, all of small effect at best, were highlighted by the different authors as ranking at the top of their respective lists of significant associations: PALB2, GABRB1 (and other GABA receptor subunit genes), GRM7, and SYN3 (Craddock); CACNAIC, MYO5B, and EGRF (Sklar), MAN2A, PRKCE, and DAGLA (Scott); DGKH, ZIP3, JAM3 (Baum).

Session chair Steve Faraone complimented the speakers both on their willingness to show SNP numbers and on the level of current and promised data-sharing. Craddock said that the experience with GWA studies in type 2 diabetes show you need big sample sizes, but warned that the p values and odds ratios will not be impressive, meaning that some true risk genes may not show up even in larger samples.

Several groups highlighted the importance of quality control. For example, Craddock noted that all of their initial top "hits" were thrown out during quality control steps. This seems to be a particular problem with the Affymetrix chips, for which researchers said that up to 25 percent of results have to be discarded.—Hakon Heimer.

Comments on News and Primary Papers
Comment by:  William Carpenter, SRF Advisor (Disclosure)
Submitted 7 November 2007
Posted 8 November 2007

Terrific update and summary for those of us not attending the meeting.

View all comments by William Carpenter

Comments on Related News


Related News: Genetic Homozygosity Runs in Schizophrenia Families

Comment by:  Ben Pickard
Submitted 7 December 2007
Posted 7 December 2007

Schizophrenia as genetic pelmanism
If you take a brand new pack of cards and start shuffling, it is not hard to appreciate that the longer you continue, the less likely it will be that you will find a series of cards in the same order as in the beginning. The European and Asian genomes are like a pack of cards that effectively started shuffling as humans first walked “Out of Africa” some 100,000 years ago. Meiotic recombination is the shuffling process and the result is a decreasing ability to predict at the gross level what combinations of marker alleles will be found together on a chromosome. African populations, with a longer “shuffling” time and without population bottlenecks (which effectively reorder the cards) show the least predictability (“linkage disequilibrium,” LD) across their genomes.

There are two counteracting forces to halt or even reverse this entropic breakdown. Firstly, if a particular region becomes strongly selected for, then its frequency increase in the population will, in the medium-term, outrun the shuffling effect such that the region flanking the selected genetic variant will maintain its order (Gibson et al., 2006; Li et al., 2006). This is known as a “selective sweep,” and numerous post-HapMap studies have successfully fished out regions of our genomes under this selective pressure (e.g., the lactose tolerance variant in populations where milk became a part of the staple prehistoric diet (Tishkoff et al., 2007). Secondly, and rather more obscurely, there can be physical restraints to recombination shuffling. These usually involve the physical reordering of sequence on our chromosomes, for example, in the case of paracentric inversions. The physical alignment of normal and inverted DNA sequences during meiosis is thus prevented and so recombination is suppressed, leading to greater LD.

Now imagine the situation where reasonably common stretches of less-shuffled chromosomes exist in the population. These are more likely to be found as matching pairs in any given individual compared to other parts of the genome. This appears to the researcher as a long stretch or tract of homozygous DNA. Such tracts have been studied elsewhere, particularly in the context of mapping and identifying recessive disease genes in remote, consanguineous (inbred) populations where the recessive mutations in genomic DNA of reduced allelic complexity are not only more likely to be exposed but occur within prominent tracts which co-segregate with the diagnosis. A newly published paper by Lencz et al. takes all of these ideas and combines them into a single strategy to hunt for schizophrenia-causing genes. They took raw data from their recently published genomewide association study of schizophrenia (178 cases of schizophrenia and 144 healthy controls: Lencz et al., 2007) and reassessed it for the presence of long “runs of homozygosity” (ROH) restricted to the case group. Their hypothesis was that if these regions existed, they would contain recessive mutations contributing to the disease.

Three hundred thirty-nine common ROHs were identified in the study, making up 12-13 percent of the total genome. The largest of these were predominantly found spanning the chromosome centromeres. This is perhaps not surprising since recombination rates have long been known to be reduced (through repression rather than selective sweep) at centromeres (see Kong et al., 2002). Nine of the commonest ROHs neatly overlap with previously described regions from selective sweep studies, as would be predicted. The key finding, however, was that when ROHs were compared between cases and controls, nine were found significantly more frequently in schizophrenia. Within these tracts, numerous genes were identified and, of these, there is pre-existing evidence in support of a few of them as potential candidates including NOS1AP, ATF2, NSF, MAPT, PIK3C3, and SNTG1.

One caveat to these findings is that a region of homozygosity, a loss of heterozygosity, copy number variation (CNV), and a deletion can, in some instances, all refer to the same genomic lesion and are not simple to distinguish by chip-based genotyping. The authors are careful to spell out technical and biological reasons for believing that their findings are a reflection of true homozygosity, but further independent verification would be reassuring, particularly in the context of how CNVs/genomic rearrangements might complicate recombination rates.

The significance of these findings is that we now have the potential to explore a brand new mutation class in a complex genetic disorder. Until now, the major research techniques such as linkage, association, and cytogenetics have only identified (and perhaps can only identify) dominantly behaving variants, albeit mostly with reduced penetrance. These are presumed to act through gain-of-function or, more likely, loss-of-function/haploinsufficiency mechanisms. The ROH regions described here are predicted to house reasonably common recessive risk variants: such properties meaning that they are not likely to be present in ascertained families with high densities of affected individuals but rather sporadic cases of illness where these alleles have, by chance, been inherited from both parents. It is not entirely clear why some of the more common ROHs didn’t feature in the original association study based on this data, particularly in genotype frequency rather than allele frequency analyses.

Nevertheless, the authors also make an additional, intriguing claim that these ROHs are not only overrepresented in the schizophrenia cohort because they are causative but because they have also been subject to positive selection. They cite the discovery of these ROHs in previous selective sweep scans, their more recent derivation from ancestral haplotypes, the presence of genes within which show selection pressure through alternative analyses, and their restriction to Caucasian populations as good evidence for such a claim. This effect may be due to some form of “heterozygote advantage” (also known as “overdominance”) which maintains or promotes the deleterious allele in the population. Examples where this phenomenon has been observed include recessive mutations giving rise to sickle-cell anemia, cystic fibrosis, and triose phosphate isomerase deficiency. Others have previously hypothesized that selection for the greater cognitive abilities in Homo sapiens compared to earlier hominins might have been at the cost of the emergence of schizophrenia, although the timescales of this kind of selection and the kind resulting in selective sweep are likely to be vastly different. An alternative explanation discussed in the paper is that rare recessive mutations could have “hitchhiked” their way to prominence within the selective sweep driven by a favorable variant in a closely linked gene. This latter idea seems more reasonable, given the difficulty in trying to imagine what cognitive or neurodevelopmental features would have been exclusively beneficial for the Caucasian population. It might also tally with some of the phenotypic epiphenomena that may coexist with schizophrenia (e.g., altered risk of rheumatoid arthritis, etc).

Finally, as an aside, this represents the third method of analysis, after the principal case-control studies and prediction of copy number variants, which can be applied to the large genomewide genotyping datasets being produced in numerous labs. Are there other aces waiting to be found in the hand?

References:

Gibson J, Morton NE, Collins A. Extended tracts of homozygosity in outbred human populations. Hum Mol Genet. 2006 Mar 1;15(5):789-95. Abstract

Kong A, Gudbjartsson DF, Sainz J, Jonsdottir GM, Gudjonsson SA, Richardsson B, Sigurdardottir S, Barnard J, Hallbeck B, Masson G, Shlien A, Palsson ST, Frigge ML, Thorgeirsson TE, Gulcher JR, Stefansson K. A high-resolution recombination map of the human genome. Nat Genet. 2002 Jul 1;31(3):241-7. Abstract

Lencz T, Morgan TV, Athanasiou M, Dain B, Reed CR, Kane JM, Kucherlapati R, Malhotra AK. Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia. Mol Psychiatry. 2007 Jun 1;12(6):572-80. Abstract

Li LH, Ho SF, Chen CH, Wei CY, Wong WC, Li LY, Hung SI, Chung WH, Pan WH, Lee MT, Tsai FJ, Chang CF, Wu JY, Chen YT. Long contiguous stretches of homozygosity in the human genome. Hum Mutat. 2006 Nov 1;27(11):1115-21. Abstract

Tishkoff SA, Reed FA, Ranciaro A, Voight BF, Babbitt CC, Silverman JS, Powell K, Mortensen HM, Hirbo JB, Osman M, Ibrahim M, Omar SA, Lema G, Nyambo TB, Ghori J, Bumpstead S, Pritchard JK, Wray GA, Deloukas P. Convergent adaptation of human lactase persistence in Africa and Europe. Nat Genet. 2007 Jan 1;39(1):31-40. Abstract

Lencz T, Lambert C, DeRosse P, Burdick KE, Morgan TV, Kane JM, Kucherlapati R, Malhotra AK. (2007) Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. PNAS.

View all comments by Ben Pickard

Related News: Genetic Homozygosity Runs in Schizophrenia Families

Comment by:  Chris Carter
Submitted 20 December 2007
Posted 21 December 2007

This is a remarkable paper, not only for the genes described but also for its original and inventive design. As already stated by the authors, two genes identified in these regions (PIK3C3 and NOS1AP) have already been implicated in schizophrenia. A number of others are convincing candidates and can be related to genes and processes relevant to the disease. For example, Chimaerin 1 (CHN1) (found in roh52) binds to the NMDA receptor subunit GRIN2A and regulates the morphology and density of dendritic spines (Van de Ven et al., 2005; Buttery et al., 2006). Dendritic spine density is reduced in the frontal cortex in schizophrenia (Glantz and Lewis, 2000). ATF6 (found in roh15) is a key player in the endoplasmic reticulum stress pathway and regulates the expression of another gene implicated in schizophrenia, XBP1 (Hirota et al., 2006).

Perhaps even more interesting is EIF2S1 (found in roh291). This is an eif2α subunit phosphorylated by four stress-responsive eif2α kinases that are themselves activated by viruses (pkr/EIF2AK2), starvation (gcn2/EIF2AK4), oxidative stress (hri/EIF2AK1), and endoplasmic reticulum stress (perk/EIF2AK3) (cf ATF6 and XBP1). Phosphorylated eif2α turns off protein synthesis by inhibiting the actions of the translation initiation factor eif2b, and also activates the transcription factor ATF4, that turns on a series of programs designed to counter the effects of these stressors, including genes controlling glutathione homoeostasis (Carter, 2007). ATF4 is a binding partner of DISC1 (Morris et al., 2003), while mutations in eif2b are responsible for a disease that selectively attacks oligodendrocytes, vanishing white matter disease (van der Knaap et al., 2006). Famine (Susser et al., 1996) and viral infections, for example, prenatal influenza (Sham et al., 1992), are risk factors for schizophrenia, and oxidative stress (Gysin et al., 2007) and endoplasmic reticulum stress (XBP1, ATF6) also play a role in its pathology. Oligodendrocyte cell loss is also prevalent in schizophrenia (Uranova et al., 2007).

EIF2S1 is thus at the hub of a network activated by environmental risk factors implicated in schizophrenia. The outputs of this network (eif2b and ATF4) regulate oligodendrocyte function and glutathione homoeostasis (inter alia). As a recent clinical trial has reported some benefit with the glutathione precursor N-acetyl cysteine, in schizophrenic patients (Lavoie et al., 2007), this network and the genes therein may be extremely pertinent.

The genes and risk factors implicated in schizophrenia are annotated at Polygenic Pathways. This site is fairly regularly updated and now contains links to GeneCards from the Weizman Institute of Science and a selected set of Kegg pathways from the Kanehisa Laboratories (see also SchizophreniaGene).

References:

Van de Ven TJ, VanDongen HM, VanDongen AM. The nonkinase phorbol ester receptor alpha 1-chimerin binds the NMDA receptor NR2A subunit and regulates dendritic spine density. J Neurosci. 2005 Oct 12;25(41):9488-96. Abstract

Buttery P, Beg AA, Chih B, Broder A, Mason CA, Scheiffele P. The diacylglycerol-binding protein alpha1-chimaerin regulates dendritic morphology. Proc Natl Acad Sci U S A. 2006 Feb 7;103(6):1924-9. Abstract

Glantz LA, Lewis DA. Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia. Arch Gen Psychiatry. 2000 Jan;57(1):65-73. Abstract

Hirota M, Kitagaki M, Itagaki H, Aiba S. Quantitative measurement of spliced XBP1 mRNA as an indicator of endoplasmic reticulum stress. J Toxicol Sci. 2006 May;31(2):149-56. Abstract

Carter CJ. eIF2B and oligodendrocyte survival: where nature and nurture meet in bipolar disorder and schizophrenia? Schizophr Bull. 2007 Nov;33(6):1343-53. Epub 2007 Feb 27. Abstract

Morris JA, Kandpal G, Ma L, Austin CP. DISC1 (Disrupted-In-Schizophrenia 1) is a centrosome-associated protein that interacts with MAP1A, MIPT3, ATF4/5 and NUDEL: regulation and loss of interaction with mutation. Hum Mol Genet. 2003 Jul 1;12(13):1591-608. Abstract

van der Knaap MS, Pronk JC, Scheper GC. Vanishing white matter disease. Lancet Neurol. 2006 May 1;5(5):413-23. Abstract

Susser E, Neugebauer R, Hoek HW, Brown AS, Lin S, Labovitz D, Gorman JM. Schizophrenia after prenatal famine. Further evidence. Arch Gen Psychiatry. 1996 Jan 1;53(1):25-31. Abstract

Sham PC, O'Callaghan E, Takei N, Murray GK, Hare EH, Murray RM. Schizophrenia following pre-natal exposure to influenza epidemics between 1939 and 1960. Br J Psychiatry. 1992 Apr 1;160():461-6. Abstract

Gysin R, Kraftsik R, Sandell J, Bovet P, Chappuis C, Conus P, Deppen P, Preisig M, Ruiz V, Steullet P, Tosic M, Werge T, Cuénod M, Do KQ. Impaired glutathione synthesis in schizophrenia: convergent genetic and functional evidence. Proc Natl Acad Sci U S A. 2007 Oct 16;104(42):16621-6. Abstract

Uranova NA, Vostrikov VM, Vikhreva OV, Zimina IS, Kolomeets NS, Orlovskaya DD. The role of oligodendrocyte pathology in schizophrenia. Int J Neuropsychopharmacol. 2007 Aug;10(4):537-45. Epub 2007 Feb 21. Abstract

Lavoie S, Murray MM, Deppen P, Knyazeva MG, Berk M, Boulat O, Bovet P, Bush AI, Conus P, Copolov D, Fornari E, Meuli R, Solida A, Vianin P, Cuénod M, Buclin T, Do KQ. Glutathione Precursor, N-Acetyl-Cysteine, Improves Mismatch Negativity in Schizophrenia Patients. Neuropsychopharmacology. 2007 Nov 14; [Epub ahead of print] Abstract

View all comments by Chris Carter

Related News: Sweeping SchizophreniaGene Study Applies New Criteria to Finger Suspects

Comment by:  Stephen J. Glatt
Submitted 17 July 2008
Posted 21 July 2008
  I recommend the Primary Papers

The paper by Allen et al. is a tremendously useful addition to the fields of schizophrenia research, psychiatric genetics, and medical genetics. By efficiently summarizing a tremendous amount of work, Allen et al. have endeavored to provide a "state-of-the-art" summary that most of us, as individuals, struggle to accomplish; they have largely succeeded in their attempt. This manuscript, and the continual availability of the SZGene database, should long serve as invaluable resources for the increasingly complex task of building polygenic models of risk for schizophrenia. Furthermore, these methods, which were initially implemented in the AlzGene database, have clearly generalized quite successfully to SZGene and thus, should be easy enough to scale up to cover many other psychiatric disorders as well. In this way, the contribution to psychiatric genetics, and possibly other disorders outside of psychiatry, is crystalline.

Aside from the database, the contribution of the recent manuscript to the field of schizophrenia research is also tremendous. As pointed out by the authors, several of the significantly associated genes identified by their meta-analyses were never before studied in this manner, so a whole new set of top candidate genes was identified. This work also served to confirm the results of prior meta-analyses from my group and others, which is always reassuring. Application of the HuGENet criteria to grading the detected associations is useful as a heuristic, but it must be kept in mind that that while these criteria reflect a consensus, they also reflect a moving target. One difficulty in implementing grades (especially the "overall" grade) is analogous to difficulties often encountered in meta-analyses when rating the quality of studies, and that is the ambiguity of ratings. Thus, on a seven-point quality scale (or a three-letter-grade scale), a score can be arrived at by a variety of combinations of flaws or strengths, but similar scores may not (often do not) reflect identical strengths and weaknesses of the graded studies. For example, I, for one, am not certain that having a relatively low number of minor alleles reflected in a meta-analytic result (especially if it is a rare variant) is as big a decrement as the pooled OR dropping from significance when the initial study is omitted.

Nevertheless, I reiterate that the use of this heuristic grading system is helpful, but should be taken with a grain of salt. Overall, the paper and its conclusions are a great contribution to this field and warrant mass attention. The ultimate question, not yet addressed here but apparently on the horizon, is how well the emerging GWASs detect these "positive control" associations, or we might say how well these hypothesis-driven results stack up against new candidates to emerge from the high-throughput generation of novel hypotheses....

View all comments by Stephen J. Glatt

Related News: Channeling Mental Illness: GWAS Links Ion Channels, Bipolar Disorder

Comment by:  Melvin G. McInnis
Submitted 19 August 2008
Posted 19 August 2008

The work by Ferreira et al. exemplifies the growing enthusiasm for collaborative work among investigators and marks the new era of collaborative genetic research in complex disorders. The LD data found in the extant HapMap SNPs allow investigators to use sophisticated computational approaches to impute genotypes based on these HapMap data sets and the data generated from the experimental sample, thereby maximizing the utility of the actual genotyping itself. Nothing short of brilliant. Correlates between imputed and true genotypes were estimated to be 0.987, which is quite good. The significance estimates of the combined data analyses of the three data sets identifies two genes (ANK3 and CACNA1C) in the genomewide significance range with a p value of 10-8, which is most reassuring and even more so considering that the CACNA1C gene was identified previously. The humbling fact in the mix is that the odds ratios are modest, ranging from 1.2 to 1.4, which is nonetheless in a similar arena as other complex genetic disorders such as diabetes. It is further humbling (and consistent with the modest ORs) to consider that the frequency of the risk allele for the CACNA1C gene is 7.5 percent in the BP cases and 5.6 percent in the unaffected control individuals. Finally, there was no effect of the sub-diagnostic categories, age of onset, presence of psychosis, or sex. The highly encouraging point is that these genes appear to be in pathways that are affected by lithium, the gold standard of care for BP disorder. The anchorage of a genetic finding within a mechanism of an established treatment for BP disorder (lithium) lends substantial credibility to overall results. The next questions of research will relate to the efficacy of lithium relative to genotypes of these genes and others within their pathways. These findings raise several clinical questions, and integration of clinical outcome patterns with genetic data can be expected to shed further light on the etiology of the disease and the genetics of treatment response. Long live lithium.

View all comments by Melvin G. McInnis

Related News: Channeling Mental Illness: GWAS Links Ion Channels, Bipolar Disorder

Comment by:  John I. Nurnberger, Jr.
Submitted 19 August 2008
Posted 19 August 2008

Ferreira et al. propose two specific genes to be related to bipolar disorder, ANK3, which is indirectly related to sodium channels, and CACNA1C, which is a calcium channel subunit. They hypothesize that bipolar disorder is, at least in part, a channelopathy. This hypothesis is consistent with a number of physiological observations made over the past several decades, as reviewed elsewhere.

The genetic data these authors present is certainly suggestive. They have analyzed three independent data sets, STEP-UCL (Sklar et al., 2008), Wellcome Trust (Wellcome Trust Case Control Consortium, 2007), and a third set called ED-DUB-STEP2 (not yet published). Their total sample exceeds 4,000 cases and 6,000 controls. They have direct genotype data on >300,000 SNPs and have imputed nearly 1.5 million additional. Their highest significance values (10-7 to 10-9) include a combination of genotyped and imputed SNPs. For each of these, the combined p value is a product of modest but consistent associations in the three independent data sets.

ANK3 features rs10994336 at 9x10e-9 and rs1938526 at 1x10e-8. By my reading, these two polymorphisms are both slightly distal to the gene but the second is within 10-20 kB. The first of these is imputed, and thus the p value should probably be judged as more imprecise. Both of these polymorphisms are associated with an odds ratio of ~1.4 and a minor allele frequency of ~5 percent in controls.

The CACNA1C data is based on more common polymorphisms (~30 percent in controls) and an OR~1.2. Again two SNPs are featured (rs1006737 at 7x10e-8, genotyped, and rs1024582 at 2x10-7, imputed). A third region near an uncharacterized gene (on 15q14) is also featured.

Examination of available published data from STEP-UCL and WTCCC on ANK3 and CACNA1C does not show obvious evidence of association among SNPs across each of the named genes, but reasonably consistent signals of modest significance, which is what one might expect, and this does suggest that the featured SNPs are not completely anomalous, but may represent a pattern of genotype deviation across the two genes.

Needless to say, our investigators in the GAIN (Genetic Analysis Information Network) bipolar group are extremely interested in this report and are avidly following the lead provided by Ferreira to attempt to confirm these signals in our own data. I am also pleased to say that GAIN has provided the stimulus for an international consortium that includes representatives from the Ferreira group as well as many other investigators, dedicated to assembling yet larger samples of bipolar cases and controls to elucidate the genetics of this condition through genomewide methods.

This is an important report, and it may represent a breakthrough in bipolar genetic studies. The signals for ANK3 and CACNA1C appear very promising, and we hope that they prove to be consistently observed in other data sets as well. We anticipate that additional confirmed single genes will emerge soon as well, and that the genetic structure of these disorders will be elucidated using similar methods in large data sets in the coming years.

View all comments by John I. Nurnberger, Jr.

Related News: Channeling Mental Illness: GWAS Links Ion Channels, Bipolar Disorder

Comment by:  Peter P. Zandi
Submitted 21 August 2008
Posted 21 August 2008

Are we there yet? Have we in the field of bipolar genetics finally been delivered to the promised land by GWAS? For the past year or so since GWAS burst on the scene, we have had to watch with envy as an impressive list of genes were convincingly implicated in a range of other complex diseases like type 2 diabetes, the apparent poster child for GWAS. Now, is it our turn?

The first attempts at individual-level GWAS of bipolar disorder by WTCCC and STEP-UCL were exciting because of their novelty, but the results were not particularly overwhelming. None of the findings withstood correction for the massive multiple testing inherent in GWAS, and those at the top were of ambiguous relevance to bipolar disorder. Confronted with such uninspiring findings, one could not be faulted for experiencing pangs of doubt that maybe for psychiatric disorders, GWAS would prove no better than its dusty old predecessor, the genomewide linkage study, in illuminating the underlying genetic architecture.

Nevertheless, encouraged by the lessons learned from GWAS of type 2 diabetes that the road to the promised land is not paved in individual glory but in collaborations and consortiums, the investigators of WTCCC and STEP-UCL combined samples with a third previously unstudied collection (dubbed ED-DUB-STEP2) to assemble one of the largest samples in bipolar disorder yet to be analyzed by GWAS. The combined sample included 4,387 cases and 6,209 controls genotyped at 325,690 overlapping SNPs, which after imputation yielded data on 1.8 million variants. The results from this effort were recently reported in a manuscript by Ferreira and colleagues published in the latest edition of Nature Genetics.

Despite potential concerns about the genetic and/or clinical heterogeneity of the combined sample (e.g., the genomic inflation factor was estimated to be 1.11, even after controlling for two quantitative indices of population ancestry, which might suggest residual stratification or other unaccounted biases), the results from this effort are encouraging and provide some hope to those who may have been losing their faith. The most notable findings were in ANK3 on chromosome 10q21 and CACNA1C on chromosome 12p13. Multiple SNPs were associated across a 195-kb region of ANK3, and the top SNPs had p-values <5 x 10-8 which is often invoked as an appropriate threshold for genomewide significance in GWAS. Multiple nearby SNPs were also reassuringly associated in CACNA1C, although the top SNP just missed the threshold for genomewide significance. In both ANK3 and CACNA1C the top SNPs were consistently associated in the same direction across all three individual samples, lending credence to the claim that these associations are real. Lending further credence is the fact that ANK3 and CACNA1C are biologically plausible candidates for bipolar disorder, and indeed highlight the possibility that this disorder is an ion channelopathy. Interestingly, the associations with ANK3 and CACNA1C in each of the individual samples were relatively modest and became remarkable only in the combined sample, thus providing support for the rationale to combine samples in order to increase the power to detect those more modest signals that are presumably real but buried amidst the noise of a single study.

Although the evidence is promising, more samples will be needed to confirm the findings before we can say with confidence that we have in hand our first real bipolar susceptibility genes. Fortunately, several such samples have been or will soon be GWASed, including one from GAIN, and it will be of great interest to see whether the current findings are sustained in these new samples. Moreover, there are plans to combine all existing GWAS of bipolar disorder, as part of the initiative referred to as the Psychiatric GWAS Consortium, which should provide an even more definitive picture of the role of ANK3 and CACNA1C, as well as reveal other genes with more modest relative risks whose identities up until now have been obscured.

So, are we there yet? Maybe not just yet, but we are headed in the right direction and I think I can see the promised land.

View all comments by Peter P. Zandi

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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