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Cognitive Deficits Found in Controls Carrying Neuropsychiatric Risk CNVs

December 18, 2013. Rare copy number variants (CNVs) associated with schizophrenia or autism affect cognition and brain structure even in carriers who do not have the illnesses, according to new research published online in Nature on December 18, 2013. Led by Kari Stefansson, deCODE genetics/Amgen, Reykjavík, Iceland, and Andreas Meyer-Lindenberg, University of Heidelberg, Mannheim, Germany, the study finds that these carrier controls show cognitive functioning that is intermediate between subjects with schizophrenia and population controls. The paper also reports that deletion or duplication of the same region on the short arm of chromosome 15 produces opposite changes in gray and white matter structures, which the authors say provides the first evidence of an allele dose effect on human brain structure.

Findings that some CNVs—small deletions and duplications of chromosomal regions—increase the risk for neuropsychiatric illnesses such as schizophrenia (see SRF overview and SRF related news story) and autism (see SRF related news story) have been accumulating over the past several years. The CNVs are present only in a small minority of patients, but for that group, they may be a major force behind the illnesses. In addition, these DNA troublemakers are not fully penetrant—not everyone who has a particular CNV will develop schizophrenia or autism. Just how (and if) the CNVs affect these control carriers is largely unknown, but they afford researchers a unique chance to study the effects of CNVs without many of the complicating factors such as medication and chronicity that go hand in hand with neuropsychiatric illnesses.

In the current study, first authors Hreinn Stefansson and Meyer-Lindenberg examined the effect of 26 rare CNVs associated with schizophrenia or autism (chosen based on a literature search) on cognition and brain structure in an Icelandic cohort. Altogether, the CNVs were present in 1,178 (1.16 percent) of 101,655 subjects. Carriers between 18 and 65 years of age, including both controls and those with neuropsychiatric illnesses, were analyzed to determine the effects of the CNVs on reproductive outcome (fecundity). Three hits were found: Individuals with a 16p11.2 deletion or 22q11.21 duplication had fewer children by age 45 than controls did, and the effects were significantly greater in males than in females. Subjects with a 16p12.1 deletion, on the other hand, displayed increased fecundity. Schizophrenia subjects also exhibited reduced fecundity, similar to previous reports (Haukka et al., 2003), and the effect was also greater in males than females.

P’s, Q’s, and cognition
To examine the effect of the CNVs on cognition, the researchers administered a neuropsychological battery to 167 control carriers (no diagnosis of a psychotic illness, autism, intellectual disability, or developmental delay), 465 controls carrying other CNVs not associated with psychiatric illnesses, and 475 population controls lacking any large CNVs. In addition, 161 subjects with schizophrenia who were not carriers of the neuropsychiatric CNVs were also examined. The tests measured cognitive domains such as attention, working memory, and processing speed that are altered in schizophrenia.

Consistent with the literature, schizophrenia subjects performed worse than controls on all tests. Neuropsychiatric CNV carriers showed milder deficits, performing at a level in between the schizophrenia subjects and population controls, while controls carrying CNVs not associated with neuropsychiatric illnesses showed no such difficulties. Accounting for IQ diminished the effects, a finding not surprising given that cognitive tests measure abilities that contribute to IQ, write the authors.

Neuropsychiatric CNV control carriers displayed significantly lower scores on the General Assessment of Function scale (GAF), as well as more depression and suicidal ideation than population controls. They also appeared to have a greater history of reading and mathematical learning disabilities than population controls, while carriers of other CNVs did not show this effect.

CNVs singled out
Eleven of the 26 CNVs were present in enough study participants to permit an analysis of their individual effects on cognition. Six CNVs, including the 16p11.2 and 16p12.1 deletions associated with fecundity changes as well as a 16p11.2 duplication, were significantly associated with large verbal and/or performance IQ deficits in carrier controls. Several CNVs were also associated with performance on individual cognitive tests, a history of learning difficulties, and GAF score. Similar to the findings when the neuropsychiatric CNVs were lumped together, accounting for IQ also diminished these effects.

Four CNVs were associated with milder cognitive deficits in carrier controls. For example, a deletion at 15q11.2 between breakpoints 1 and 2 (15q11.2[BP1-BP2]; n = 47) that has previously been linked to schizophrenia and developmental delays was associated with only small deficits in the neuropsychological battery and a slightly lower IQ, although control carriers had significantly lower GAF scores and significant histories of reading and math learning difficulties. Carriers of the reciprocal duplication were not different from controls on any of the measures.

MRI anomalies
Concentrating on brain areas implicated in a meta-analysis of first-episode psychosis (Radua et al., 2012), Stefansson, Meyer-Lindenberg, and colleagues found that 15q11.2(BP1-BP2) deletion (n = 15) and duplication (n = 55) control carriers exhibited several structural MRI abnormalities compared to population controls (n = 201). Deletion carriers exhibited lower perigenual anterior cingulate cortex gray matter volume, as well as reduced temporal lobe and increased corpus callosum white matter volumes. The authors noted that these changes partially overlap with those observed during the onset of psychosis. Importantly, opposite changes in the same gray and white matter areas were observed in the duplication carriers, “the first demonstration of allele-dose-dependent effects of CNVs on the structure of the human brain,” they wrote.

However, Stefansson, Meyer-Lindenberg, and colleagues point out that although the 15q11.2(BP1-BP2) deletion appeared to have a negative impact on reading and math learning in control carriers, those with the duplication performed as well as controls, suggesting that the reciprocal changes in brain volume may not extend to learning.

The current findings that cognitive and structural brain abnormalities of schizophrenia are also found in control subjects carrying high-risk CNVs bolster “the idea that cognitive abnormalities are fundamental defects in schizophrenia,” conclude the authors.—Allison A. Curley.

Reference:
Stefansson H, Meyer-Lindenberg A, Steinberg S, Magnusdottir B, Morgen K, Arnarsdottir S, Bjornsdottir G, Walters GB, Jonsdottir G, Doyle OM, Tost H, Grimm O, Kristjansdottir S, Snorrason H, Davidsdottir SR, Gudmundsson LJ, Jonsson GF, Stefansdottir B, Helgadottir H, Haraldsson M, Jonsdottir B, Thygesen JH, Schwarz AJ, Didriksen M, Stensbřl TB, Brammer M, Kapur S, Halldorsson JG, Hreidarsson S, Saemundsen E, Sigurdsson E, Stefansson K. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature. 2013 Dec 18. Abstract

Comments on News and Primary Papers
Comment by:  Daniel Weinberger, SRF Advisor
Submitted 19 December 2013
Posted 19 December 2013

The latest important result from the Icelandic population genetic study confirms from a new vantage point what has been clear from over two decades of research: that genetic risk for schizophrenia is associated with cognitive deficits independent of the presence of illness. The earlier work to identify this association included studies of discordant monozygotic twins (Goldberg et al., 1990; Cannon et al., 2000) and more studies of healthy siblings (Egan et al., 2001). These results are consistent with the view that susceptibility genes for developmental neuropsychiatric disorders are genes that influence brain development and function. Cognitive assays are proxies for integrative neural functions that reflect these effects.

The interpretation of the imaging data is less clear, however. While reduced measures of gray matter volume in cingulate and insula have been found in some studies of first-episode psychosis, these findings are not typical in patients with schizophrenia diagnoses and are generally not found in healthy relatives (Honea et al., 2008; Owens et al., 2012), suggesting that they are not associated with genetic risk for schizophrenia in the general population. Indeed, data linking increased genetic risk for schizophrenia with measurements made on structural MRI scans have been unconvincing, even in much larger samples.

References:

Goldberg TE, Ragland JD, Torrey EF, Gold J, Bigelow LB and Weinberger DR. Neuropsychological assessment of monozygotic twins discordant for schizophrenia. Arch Gen Psychiatry. 1990;47:1066-1072. Abstract

Cannon TD, Huttunen MO, Lonnqvist J, Tuulio-Henriksson A, Pirkola T, Glahn D, Finkelstein J, Hietanen M, Kaprio J, Koskenvuo M. The inheritance of neuropsychological dysfunction in twins discordant for schizophrenia Am J Hum Genet. 2000;67:369–382. Abstract

Egan MF, Goldberg TE, Gscheidle T, Weirich M, Rawlings R, Hyde TM, Bigelow L and Weinberger DR. Relative risk for cognitive impairments in siblings of patients with schizophrenia. Biol Psychiatry. 2001;50:98-107. Abstract

Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, Verchinski B, Passingham RE, Weinberger DR and Callicott JH. Is gray matter volume an intermediate phenotype for schizophrenia? A VBM study of patients with schizophrenia and their healthy siblings. Biol Psychiatry. 2008;63:465-474. Abstract

Owens SF, Picchioni MM, Ettinger U, McDonald C, Walshe M, Schmechtig A, Murray RM, Rijsdijk F, Toulopoulou T. Prefrontal deviations in function but not volume are putative endophenotypes for schizophrenia. Brain. 2012;135:2231–2244. Abstract

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Related News: Autism Genes: A Handful, or More?

Comment by:  Daniel Weinberger, SRF Advisor
Submitted 19 March 2007
Posted 19 March 2007

Sense and Nonsense: General Lessons from Genetic Studies of Autism
The capability to characterize genetic variation across the entire genome in one fell swoop has generated considerable enthusiasm and expectation that the important genes for mental illness will “finally” be found. Whole genome association (WGA) is being touted as the path to genetic success in psychiatry. Is this sensible? Before considering the likely successes and limitations of this new capability, it is worth reminding ourselves of how we got here.

With respect to schizophrenia, over 50 years of studies of twin samples and of infants adopted away at birth have demonstrated that the lion’s share of risk for schizophrenia is determined by genes, to the tune of over 70 percent of the variance in liability (“heritability”). Family segregation studies have shown that the pattern of relative risk across relationships is most consistent with at minimum oligogenic inheritance, and more likely polygenic inheritance (Gottesman, I. I., Schizophrenia Genesis: The Origin of Madness, New York: W.H. Freeman.1991). After over a decade of linkage studies, it is clear that across diverse family samples, schizophrenia is not related to a common genetic locus, and no locus accounts for more than a fraction of risk for illness. Because we know that schizophrenia is highly heritable, the failure of linkage to reveal a chromosomal locus providing a highly significant LOD score in most samples is not because there are no genetic variations accounting for the heritability, but because, among other reasons, there is just too much locus heterogeneity across samples.

If we accept that schizophrenia is polygenic and genetically heterogeneous, meaning that in any sample under study, some cases will be ill because they have risk genes W, X, Y, and Z, while other cases will be ill because they have risk genes C, D, E, and F, then any common linkage signals will be diluted by this genetic heterogeneity if these genes are spread throughout the genome. In light of this situation, why, then, have some recent linkage studies of schizophrenia revealed significant and replicable linkage regions? Notwithstanding improvement in ascertainment methods and the informativeness of DNA marker sets, it is likely that linkage has worked in some regions of the genome because some of the genetic heterogeneity is concentrated in these areas, meaning that heterogeneity across families does not necessarily dilute the linkage signal at these loci. For example, in the 8p linkage peak, there are at least five genes that have been found to show association with schizophrenia in various samples: NRG1, PCM1, PPP3CC, DRP2, and FZD3, so if 10 percent of the families have risk alleles in NRG1 that contribute to their risk profile, and even if 10 percent have no NRG1 risk alleles but PCM1 alleles, and the same for PPP3CC and so on, this genetic heterogeneity will not dilute the linkage signal and the 8p locus containing these five genes will be positive in these families. Of course, in a subsequent association study, samples will be positive or negative for any one of these individual genes depending on which alleles happen to be enriched in that sample. This is how heterogeneity affects the prospects for positive linkage and association. Many observers of psychiatric genetics who argue against the validity of linkage and association in psychiatry like to talk about multifactorial medical illnesses such as heart disease and schizophrenia being genetically heterogeneous, but they do not like the walk when it comes to acknowledging the implications for finding association, positive or negative.

Heterogeneity has obvious implications for studies that attempt to survey variation in the entire genome and compare allele frequencies across ill and well samples. Heterogeneity in such studies dilutes the statistical effect of any single DNA polymorphism in the entire sample. Because literally hundreds of thousands of variations may be typed at one time, many of which have no prior probability of being related to the phenotype of interest, it is critical to employ some approach to statistical correction for the possibility of random positive associations. If one were to correct for 500,000 tests, the likelihood that any SNP related to a condition like schizophrenia will survive this level of correction, at least to the extent that the illness is polygenic and heterogeneous, is very small. Based on the strength of the existing data, none of the well-supported candidate susceptibility genes for schizophrenia that have been identified to date (e.g., DTNPB1, NRG1, DISC1, etc.) would survive such correction. It has been argued that the solution to this conundrum is the collection of very large datasets. This may increase power and generate impressive p values for a few genes, but the effect size of the association does not change with sample size, only the p value. It is also important to remember that the larger the sample size, the greater the potential for heterogeneity, because the collection of very large samples often requires multiple collection centers, each with their own ascertainment quirks. Thus, this approach runs the risk of a paradoxical reduction in the strength of linkage and association (see Brzustowicz, 2007).

These considerations have implications for studies of the genetic origins of other neuropsychiatric disorders, such as depression, bipolar disorder, anxiety disorders, and autism. Two recent important papers related to autism illustrate each of these points and offer important lessons for WGA studies that will be emerging soon related to schizophrenia and other psychiatric disorders.

The paper by the Autism Genome Project Consortium (AGPC) reports the largest linkage study of families (over 1,490 families) with children having the autism spectrum syndrome and the most informative set of linkage markers yet reported. This study illustrates in dramatic detail the complications alluded to above. Many areas of the genome show evidence of linkage, i.e., locus heterogeneity, but the individual signals are statistically weak. Indeed, using strict criteria for statistical analysis, no region would have been considered positive, and the region that was closest (11p12-13) was not identified as a promising region in earlier linkage studies.

In a series of exploratory post-hoc reanalyses of the data, trying to create more theoretically homogeneous clinical samples (e.g., gender specific, narrower diagnosis), several linkage signals became slightly more positive, but also involving regions of the genome not highlighted in earlier linkage studies. Does this failure to find an impressive statistical result in such an impressively large sample mean that this study is negative? Not if we expect autism to be genetically complex in the ways enumerated above. The results are exactly what would be predicted. Indeed, similar results have been reported before (Risch et al., 1999). The AGPC study also discovered regions where evidence of genomic structural changes, so-called sequence copy number variations (CNVs), might be associated with clinical diagnosis. Their data suggest that as many as 253 CNVs were discovered in 196 cases. The CNVs were found in many chromosomal regions (i.e., locus heterogeneity); involved duplications more often than deletions; varied considerably from one family to another; were spontaneous in most cases but inherited in some; and were most often found only in one individual, though recurrences occurred across ill subjects in some instances. It is very difficult to determine from these data how much of the genetic contribution to autism in this sample is explained by these copy number variations. In a few families, where multiple affected individuals had the same deletion, the data look convincing. However, it appears that CNVs were just as frequent, just as large (average 3.4 Mb) and just as likely to be duplications or deletions in the unaffected siblings of the children with autism.

The paper by Sebat and colleagues surveys the genome exclusively for evidence of structural changes related to variable copy numbers of DNA sequences and uses a putatively more sensitive method. They discovered submicroscopic deletions of 17 chromosomal regions in 14 children with autism spectrum disorder (7 percent of their ill sample). By design, all of the deletions described in this report were de novo, or spontaneous, meaning they were not found in the parents of the affected offspring and were thus not inherited. In other words, these deletions do not explain the very substantial heritability of autism, nor did they map to the regions of the genome that have shown up in linkage studies, which look specifically for loci that contribute to heritable risk (including the regions in the AGPC), nor did they highlight genes that have emerged from linkage studies as likely candidates accounting for the heritability of autism. Moreover, with one exception, all of the deletions were private, meaning they occurred in only one individual. As Sebat and colleagues point out, however, the infrequency of these copy number variations does not preclude them from pointing to more generalizable insights about genetic risk factors that operate in other cases. The genes affected by these infrequent structural variations may in other cases show common variations (e.g., SNPs) that contribute more widely to genetic liability. It is not clear how much overlap there is between the findings of these two studies, but clearly there are major differences.

The bottom line here is that genetic heterogeneity appears to be the rule in autism. While most cases are related to a complex set of inherited risk factors, some may be related to spontaneous genetic lesions, with many different lesions producing a similar clinical phenotype. None of this should surprise us, as diverse congenital encephalopathies can manifest the autism syndrome, e.g., fragile X syndrome, Rett syndrome, tuberous sclerosis. From a genetic point of view, autism is a syndrome that can be reached from many directions, along many paths. It is not likely that autism is any more of a discrete disease entity than, say, blindness or mental retardation.

So where does this leave us with respect to the goal of fully defining the genetic origins of mental disorders such as schizophrenia? The current list of promising candidate genes for schizophrenia is growing rapidly, and some already are leading to insights about potential pathophysiologic mechanisms and potential treatment targets (Straub and Weinberger, 2006). Genome variation scans will hopefully uncover many more novel genes that contribute to the risk for schizophrenia, and regardless of their outcome, these types of studies will be very important. It is likely that within the next 5 years we will have a good sense of all the common genetic variants that contribute to schizophrenia across many world samples. It is also likely that some cases will be related to structural variations (e.g., the 22q11 deletion associated with the velocardiofacial syndrome [VCFS]), both spontaneous and inherited. But, a phoenix rising from this newest chapter of investigation is not likely. Rather, as the recent autism studies illustrate, many genetic loci and many genes, again each accounting for only a relatively small percentage of ill subjects, will likely be the legacy of these studies. It is the legacy of all the work up to this point, and it is not likely to be different now that we can do many more of the same SNP assays all at one time. I doubt that genes that are discovered via WGA or related approaches will show greater effect sizes than the current top candidates, but there certainly will be more of them. Schizophrenia, like autism, is almost certainly a disorder that can be reached from many directions, along many paths. This being said, is it likely that a few genes with “highly significant” p values will be observed in a few of the multitude of WGA studies that will hit the press over the next year or two? Of course it is. Will these be the most important genes? Not necessarily. The challenge for our using these new data will be to make strategic choices about which of the various signals to pursue further and how to pursue them. The most important genes will be the ones that can be translated into meaningful information about disease mechanisms, therapeutic target identification, and clinical prediction.

View all comments by Daniel Weinberger

Related News: Autism Genes: A Handful, or More?

Comment by:  Paul Patterson
Submitted 21 March 2007
Posted 22 March 2007

Regarding the very high "heritability" of schizophrenia and autism: these values are usually based on twin studies, and there is good reason to be skeptical about these numbers.

For instance, the frequency of schizophrenia in dizygotic twins is twice as high as for siblings, suggesting a role for the fetal environment. Second, the concordance for monozygotic twins is 60 percent if they share a placenta, but only 11 percent if they have separate placentas, again highlighting the importance of the fetal environment. (Two-thirds of monozygotic twins share a placenta.) It is also relevant that roughly two-thirds of schizophrenia subjects do not have a primary or secondary relative with the disorder.

No one questions that genes play a role in the risk for schizophrenia and autism, but twins share a fetal environment as well as genes. The importance of the fetal environment is very well illustrated by the work of Brown and colleagues in their studies of the risk factor, maternal respiratory infection.

References:

Phelps J, Davis J, Schartz K. Nature, Nurture, and Twin Research Strategies. Curr. Directions in Pyschol. Sci. 1997;6:117-120.

Brown AS. Prenatal infection as a risk factor for schizophrenia. Schizophr Bull. 2006 Apr;32(2):200-2. Epub 2006 Feb 9. Abstract

Brown AS, Susser ES. In utero infection and adult schizophrenia. Ment Retard Dev Disabil Res Rev. 2002;8(1):51-7. Review.

Ryan B, Vandenbergh J. Intrauterine position effects. Neuroscience and Biobehavioral Reviews. 2002;26:665–678. Abstract

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Related News: Autism Genes: A Handful, or More?

Comment by:  Ben Pickard
Submitted 24 March 2007
Posted 24 March 2007

The Curious Incident of the Gap in the Chromosome
Our bodies are accustomed to a double dose of genes. The cellular ecosystem has been evolutionarily fine-tuned to this baseline of gene expression. Even the exceptions to the rule such as the sex-specific imbalance of X/Y chromosomes or the set of imprinted genes serve to highlight the compensatory mechanisms that have allowed the cell to adapt. Therefore, it is not surprising that chromosomal dosage changes are associated with disease states.

An ever-increasing appreciation of the link between disease and gene copy number has followed closely behind advances in techniques that have enabled the measurement of copy number variation at ever-greater resolution and sensitivity. Starting with Giemsa-stained chromosomes in classical cytogenetics, which identified visible aneuploidies such as trisomy 21, the field has progressed through fluorescence in situ hybridization (FISH) studies which pinpointed finer abnormalities, including those discovered through comparative genomic hybridization and sub-telomeric analysis, to today’s chip-based approaches, which can survey the whole genome at once. (In fact, as an aside, the sensitivity of the current state-of-the-art techniques is only likely to be truly improved upon with the advent of whole-genome sequencing—realistically, that is not likely for a decade or so.)

Despite this progress, the one-off nature and scarcity of many chromosome abnormalities have often led to their dismissal as genetic quirks and not relevant to disease biology at the population level. Perhaps the tide is now turning in their favor as recent studies of sub-microscopic gene copy number changes have yielded intriguing and provocative discoveries. The two papers summarized on this site asked whether a proportion of autism spectrum disorders are caused by CNVs. The same question could, and doubtless will, be asked of schizophrenia, bipolar disorder, and other psychiatric conditions and so is worthy of discussion in this forum. The answer for autism seems to be a resounding “yes,” and this is likely to precipitate a sea change in autism research, both at the genetic and biological levels. Sebat et al. (Science, 15 March, 2007) and The Autism Genome Project Consortium (“AGPC,” Nature Genetics, 18 February, 2007) used slightly different variations on the chip theme in their studies: the former had the advantage of a more discrete output for copy number compared to the continuous distribution from the latter approach. This had consequences for the setting of statistical detection thresholds, but both groups were quite thorough in the confirmation of many of their findings using secondary detection approaches.

Understanding the Consequences of Experimental Design: Choice of Samples and Assessment
The samples chosen for analysis by both research groups focused on nominally family-based collections rather than sporadic cases. Thus, the mutations represented are highly likely to be of higher penetrance and relatively rare. In my opinion, the high level of locus heterogeneity that accompanies such a sample set makes the multiple-family linkage approach unlikely to yield practical dividends—indeed, the linkage component from the AGPC group is the least impressive aspect of their paper. The main linkage peak at 11p12-p13 was not a replication of the typical autism linkage findings (e.g., chromosome 7q, etc.; for review see Klauck, 2006). Additionally, above-threshold LOD scores were not significantly improved when diagnostic boundaries were changed or CNV carriers removed from the data. In fact, one of the most impressive features of the Sebat paper was the enlightened subdivision of the samples based not on phenotype, but rather by the nature of the inheritance patterns of autistic spectrum disorders within the families (the same may be true for the AGPC data, but the information is not explicitly categorized). This stratification into “simplex” (single case within the family) and “multiplex” (more than one affected individual) must be telling us something about the genetic architecture of complex genetic disorders. The results indicate that de novo CNVs were four times more common in the simplex families than multiplex. Let’s examine a hypothetical explanation for this finding. First, the simplex families may not be, or rather may not go on to be, true “families” in the genetic sense—their mutations are of the lower penetrance, “susceptibility altering” class. Such CNV mutations would not produce the densely affected families that are so attractive to gene mappers and so will never be collected and categorized as “multiplex.” The fact that three CNV regions (2q37.3, 3p14.2, and 20p13) are independently present twice in the Sebat simplex group adds weight to these CNVs being “common” risk variants—perhaps they are ripe candidates for a case-control association study in a larger simplex/sporadic cohort? The type of CNVs present in the multiplex families are, by definition, of sufficient penetrance for the multiplex classification to become possible: this class of mutations will probably be rarer. One supportive observation for the distinction between the two CNV types rests on the fact that there is no overlap between identified multiplex and simplex CNV regions—will that remain the case as further studies are carried out? Another, from the AGPC paper, is that many of their familial CNVs lie over previously identified linkage hotspots or known balanced chromosomal rearrangements (breakpoints, see below).

However, two mysteries remain: the predominance of CNV deletions in the Sebat paper compared to the stated overrepresentation of duplications in the AGPC paper. Whether this is a technical or family sample choice issue remains to be elucidated. Secondly, and perhaps more vague a problem, is the seldom addressed nature of the mutations identified in neuropsychiatric disorders. The archetypal mutations we learn about in undergraduate lectures, primarily in the context of neoplasms, include gain-of-function (oncogenes), loss-of-function (tumor suppressors), dominant negative and so on. Chromosome abnormalities in general, and CNVs in particular, seem to suggest that autism spectrum disorder (ASD), schizophrenia, and bipolar disorder are diseases in which gene dosage changes are the only pathological mechanism. Is this a real biological phenomenon or merely a methodological ascertainment bias? If the latter, how might we better adapt our gene hunting strategies to target other forms of mutation?

A Gene in the Hand Is Worth 50 Under a Linkage Peak
In the warm afterglow of an experimental tour-de-force, the biological ramifications can sometimes be sidelined. What genes have these CNVs affected and what does this tell us about the biology of autism spectrum disorder, we can ask, not forgetting that this work should be considered in the context of the history of other genetic and biological studies on ASD.

The first, and perhaps most impressive, finding is that of a CNV covering the Neurexin 1 (NRXN1) gene. The protein encoded by this gene interacts with a family of receptors called Neuroligins. Interestingly, Neuroligin 3 (NLGN3) and Neuroligin 4 (NLGN4) have been linked to ASD through chromosome abnormalities and mutations detected in rare cases. Moreover, SHANK3 has recently been identified as an ASD candidate through the study of cytogenetic abnormalities and several point mutations. SHANK3 protein has also been demonstrated to bind to neuroligins. This amazing convergence is reminiscent of another recent celebrity pairing in the schizophrenia field: the discovery of DISC1 and PDE4B through independent chromosome abnormalities followed by the discovery that their proteins functionally interact. The identification of these four ASD candidate genes is likely to stimulate much research into this nascent signaling pathway, particularly in the context of its supposed role in synaptogenesis.

Many of the CNVs affect gene clusters, and only by analyzing multiple overlapping deletions or systematically examining the gene candidates individually will the causative ASD genes be identified. This seems to be the case for the genes ZFP42 and PACRG, which have been found both in large CNVs with multiple genes affected and singly in smaller CNVs. Several additional CNVs were identified which were small enough, or within large enough genes (large size seems to be a anecdotally reported feature of genes identified through a variety of cytogenetic approaches) to implicate just that gene. These include SLC4A10, FLJ16237, A2BP1, NFIA, GAB2, PCDH7, PCDH9, CDH8, C18orf58, FHOD3, C2orf10, MAN2A1, CSMD1, and TRPM3 as a conservative selection. Two aspects of biology immediately spring to mind when viewing these genes. Firstly, the three members of the cadherin family identified fall into the same biological role as the neuroligins, namely cell adhesion. A related gene, FAT, has also been implicated in familial bipolar disorder. Secondly, the identification of MAN2A1 encoding a component enzyme in the pathway which post-translationally modifies proteins through glycosylation adds another gene from this process to a list including ALG9/DIBD1 and MGAT5 , both of which have been implicated in psychiatric illness. Together with the list of genes identified through CNV analysis, one can add USP6, NBEA, ST7, AUTS2, SSBP1, GRPR, and SHANK3, discovered in previous studies of autism spectrum disorder chromosome abnormalities. These candidates (and those identified in the psychoses) provide a wealth of resources for future functional and genetic studies. However, on the journey to a more rigorous biological definition of ASD, it may be a mistake to attempt to squeeze the functions of these genes into one unifying but unhelpfully vague cellular grouping, e.g., “signal transduction” or “metabolism.” Rather, biological investigations might benefit from trying to place these disparate genes in the context of their roles in the functioning of the brain regions or subsystems in which they are expressed. A hard task undoubtedly, but an endeavor that is likely to provide us with a more holistic understanding of the conditions.

View all comments by Ben Pickard