De Novo CNVs Linked to Bipolar Disorder
28 December 2011. Bipolar disorder has joined the ranks of brain disorders associated with spontaneously occurring mutations, according to a study published in Neuron on December 22. Led by Jonathan Sebat of the University of California in San Diego, the study finds a higher frequency of non-inherited de-novo copy number variants (CNVs), the wholesale loss or gain of chromosomal segments, in bipolar disorder and in schizophrenia compared to controls. Though the CNV locations were not specific to bipolar disorder, these de novo events seem to contribute to the risk for this disease.
The study also bolsters the role of de novo CNVs in increasing risk for schizophrenia, a story that continues to gain momentum (see SRF related news story). The new findings implicate some familiar loci in schizophrenia, including genes involved in synapses and neurodevelopment.
“The study highlights the importance of a genetic model involving rare and disruptive variants to further our understanding of complex neuropsychiatric traits,” write Santhosh Girirajan and Evan Eichler of the University of Washington in Seattle in an accompanying perspective piece.
Since rare CNVs arrived on the scene as players in the genetic etiology of autism and schizophrenia, bipolar disorder has watched from the sidelines, with mixed evidence for CNV involvement (Priebe et al., 2011; and see SRF related news story). As SRF reported earlier this year from the World Congress on Psychiatric Genetics (see SRF related news story), Sebat and colleagues decided to try the de novo route in bipolar disorder because de novo CNVs had delivered some of the original evidence of CNV involvement in autism and schizophrenia, giving greater effect sizes than those obtained from inherited CNVs (Sebat et al., 2007; Xu et al., 2008). Similarly, in the new study Sebat’s group found that de novo CNVs increased the risk for bipolar disorder with an odds ratio of about 4—several times that reported for inherited CNVs (~1.3). This supports the idea that de novo CNVs are more damaging than those passed from generation to generation, and can contribute to the risk for bipolar disorder.
First author Dheeraj Malhotra and colleagues screened the genomes of 788 subject-mother-father trios, with 185 subjects diagnosed with bipolar disorder, 177 with schizophrenia, and 426 healthy controls. Using a comparative genomic hybridization (CGH) array with higher-than-usual density of 2.1 million probes that can detect CNVs down to 10 kb, the researchers turned up 23 de novo CNVs—14 deletions and nine duplications—in the subjects but not in their parents. Size-wise, these ranged from 15.1 kb to 7,178 kb, with a median of 112 kb, and contained a median of two genes.
These de novo CNVs occurred more frequently in bipolar disorder and schizophrenia compared to controls. Specifically, 10 of the CNVs were found in eight bipolar subjects (i.e., 4.3 percent of bipolar subjects had de novo CNVs; p = 0.009 compared to controls), nine of the CNVs were found in eight schizophrenia subjects (4.5 percent; p = 0.007 compared to controls), and four were found in four controls (0.9 percent).
The frequency of de novo CNVs was also associated with age of illness onset in bipolar disorder, but not in schizophrenia. Subjects with early-onset bipolar disorder, becoming ill at 18 years old or younger, had more de novo CNVs (odds ratio = 6.3) than those who became ill later (odds ratio = 2.9). In contrast, family history of mental illness, defined as a first-degree relative diagnosed with bipolar disorder, major depression, schizophrenia, schizoaffective disorder, autism, or intellectual disability, did not influence de novo CNV frequency. Individuals with bipolar disorder or schizophrenia without a family history of mental illness did not have a higher frequency of de novo CNVs compared to those with a positive family history. This contrasts with previous findings of a higher proportion of de novo CNVs in sporadic cases of schizophrenia (see SRF related news story) and autism (see SRF related news story), and the discrepancies may lie in how family history is determined by different studies.
The researchers also looked at inherited CNVs to see if they contributed any risk for bipolar disorder or schizophrenia. Among the gene-hitting CNVs larger than 100 kb, they found an enrichment for inherited duplications in cases of familial bipolar disorder (OR = 1.77; p = 0.03). All other permutations of CNV type and disorder did not find evidence of larger inherited CNV burdens in familial or sporadic cases.
Location, location, location
Returning to the de novo CNVs, the researchers did not find that the chromosomal locations of those found in bipolar disorder were specific to the disorder. For example, three CNVs landed in regions already implicated in other disorders (3q29, 9p23, 16p11.2), and a follow-up analysis of the 23 de novo CNV locations in genome data from the Bipolar Genome Study (BiGS) did not find an association among these regions and bipolar disorder. In contrast, three regions were significantly associated with schizophrenia in a follow-up analysis of data from the Molecular Genetics of Schizophrenia Study (MGS): 3q29, 7q36.3, and 16p11.2, which have all been previously implicated in schizophrenia.
Pathway enrichment analysis helped to glean some insight into the functions potentially disrupted by these CNVs. The functional categories impacted by the de novo CNVs found in bipolar disorder included cell proliferation, cell shape, and phospholipid metabolism, with the authors noting that the biological relevance of these categories is “far from obvious.” In contrast, the functional categories impacted by the schizophrenia de novo CNVs fit with the familiar themes of neural development and synapses.
But what to make of the significant increase of de novo CNVs in bipolar disorder that don’t seem all that specific to the disease? Girirajan and Eichler attempt an explanation in their perspective article, in which they suggest that the ultimate phenotype of a CNV associated with different conditions depends on the genomic context in which the CNV finds itself. When a CNV occurs alongside other genetic “hits”—be they inherited or de novo variants—this results in a more severe phenotype than when occurring alone, and the authors have turned up evidence for such an additive effect (Girirajan et al., 2011). This view puts bipolar disorder on the less severe end of a neuropsychiatric phenotype continuum, which includes schizophrenia, autism, and, at the most severe end, intellectual disability. The frequency of duplication CNVs, as opposed to more drastic deletions, found in bipolar disorder in this study may fit with this.
If true, then finding the full complement of rare variants either through arrays that can detect ever-smaller CNVs or sequencing to find point mutations (see SRF related news story) will be important in understanding how genetic variants combine to influence risk for neuropsychiatric disorders.—Michele Solis.
Malhotra D, McCarthy S, Michaelson JJ, Vacic V, Burdick KE, Yoon S, Cichon S, Corvin A, Gary S, Gershon ES, Gill M, Karayiorgou M, Kelsoe JR, Krastoshevsky O, Krause V, Leibenluft E, Levy DL, Makarov V, Bhandari A, Malhotra AK, McMahon FJ, Nöthen MM, Potash JB, Rietschel M, Schulze TG, Sebat J. High Frequencies of De Novo CNVs in Bipolar Disorder and Schizophrenia. Neuron. 2011 Dec 22; 72: 951-963. Abstract
Girirajan S, Eichler EE. De Novo CNVs in Bipolar Disorder: Recurrent Themes or New Directions? Neuron. 2011 Dec 22; 72: 885-887. 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.
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
View all comments by Paul Patterson
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
Related News: More Evidence for CNVs in Schizophrenia Etiology—Jury Still Out on Practical Implications
Comment by: Christopher Ross, Russell L. Margolis
Submitted 1 August 2008
Posted 1 August 2008
The two recent papers in Nature, from the Icelandic group (Stefansson et al., 2008), and the International Schizophrenia Consortium (2008) led by Pamela Sklar, represent a landmark in psychiatric genetics. For the first time two large studies have yielded highly significant consistent results using multiple population samples. Furthermore, they arrived at these results using quite different methods. The Icelandic group used transmission screening and focused on de novo events, using the Illumina platform in both a discovery population and a replication population. By contrast, the ISC study was a large population-based case-control study using the Affymetrix platform, which did not specifically search for de novo events.
Both identified the same two regions on chromosome 1 and chromosome 15, as well as replicating the previously well studied VCFS region on chromosome 22. Thus, we now have three copy number variants which are replicated and consistent across studies. This provides data on rare highly penetrant variants complementary to the family based study of DISC1 (Porteous et al., 2006), in which the chromosomal translocation clearly segregates with disease, but in only one family. In addition, they are in general congruent with three other studies (Walsh et al., 2008; Kirov et al., 2008; Xu et al., 2008) which also demonstrate a role for copy number variation in schizophrenia. These studies together should put to rest many of the arguments about the value of genetics in psychiatry, so that future studies can now begin from a firmer base.
However, these studies also raise at least as many questions as they answer. One is the role of copy number variation in schizophrenia in the general population. The number of cases accounted for by the deletions on chromosome 1 and 15 in the ISC and Icelandic studies is extremely small--on the order of 1% or less. The extent to which copy number variation, including very rare or even private de novo variants, will account for the genetic risk for schizophrenia in the general population is still unknown. The ISC study indicated that there is a higher overall load of copy number variations in schizophrenia, broadly consistent with Walsh et al and Xu et al but backed up by a much larger sample size, allowing the results to achieve high statistical significance. The implications of these findings are still undeveloped,
Another issue is the relationship to the phenotype of schizophrenia in the general population. Many more genotype-phenotype studies will need to be done. It will be important to determine whether there is a higher rate of mental retardation in the schizophrenia in these studies than in other populations.
Another question is the relationship between these copy number variations (and other rare events) and the more common variants accounting for smaller increases in risk, as in the recent O’Donovan et al. (2008) association study in Nature Genetics. It is far too early to know, but there may well be some combination of rare mutations plus risk alleles that account for cases in the general population. This would then be highly reminiscent of Alzheimer’s disease, Parkinson’s disease, and other diseases which have been studied for a longer period of time.
For instance, in Alzheimer’s disease there are rare mutations in APP and presenilin, as well as copy number variation in APP, with duplications causing the accelerated Alzheimer’s disease seen in Down syndrome. These appear to interact with the risk allele in APOE, and possibly other risk alleles, and are part of a pathogenic pathway (Tanzi and Bertram, 2005). Similarly in Parkinson’s disease, rare mutations in α-synuclein, LRRK2 and other genes can be causative of PD, though notably the G2019S mutation in LRRK2 has incomplete penetrance. In addition, duplications or triplications of α-synuclein can cause familial PD, and altered expression due to promoter variants may contribute to risk. By contrast, deletions in Parkin cause an early onset Parkinsonian syndrome (Hardy et al., 2006). Finally, much of PD may be due to genetic risk factors or environmental causes that have not yet been identified. Further studies will likely lead to the elucidation of pathogenic pathways. These diseases can provide a paradigm for the study of schizophrenia and other psychiatric diseases. One difference is that the copy number variations in the neurodegenerative diseases are often increases in copies (as in APP and α-synuclein), consistent with gain of function mechanisms, while the schizophrenia associations were predominantly with deletions, suggesting loss of function mechanisms. The hope is that as genes are identified, they can be linked together in pathways, leading to understanding of the neurobiology of schizophrenia (Ross et al., 2006).
The key unanswered questions, of course, are what genes or other functional domains are deleted at the chromosome 1, 15, and 22 loci, whether the deletions at these loci are sufficient in themselves to cause schizophrenia, and, if sufficient, the extent to which the deletions are penetrant. Both of the current studies identified deletions large enough to include several genes. The hope is that at least a subset of copy number variations (unlike SNP associations identified in schizophrenia to date) may be causative, making the identification of the relevant genes or other functional domains—at least in principle—more feasible.
Another tantalizing observation is that the copy number variations associated with schizophrenia were defined by flanking repeat regions. This raises the question of the extent to which undetected smaller insertions, deletions or other copy number variations related to other repetitive motifs, such as long tandem repeats, may also be associated with schizophrenia. Identification and testing of these loci may prove a fruitful approach to finding additional genetic risk factors for schizophrenia.
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Related News: More Evidence for CNVs in Schizophrenia Etiology—Jury Still Out on Practical Implications
Comment by: Daniel Weinberger, SRF Advisor
Submitted 3 August 2008
Posted 3 August 2008
Several recent reports have suggested that rare CNVs may be highly penetrant genetic factors in the pathogenesis of schizophrenia, perhaps even singular etiologic events in those cases of schizophrenia who have them. This is potentially of enormous importance, as the definitive identification of such a “causative” factor may be a major step in unraveling the biologic mystery of the condition. I would stress several issues that need to be considered in putting these recent findings into a broader perspective.
It is very difficult to attribute illness to a private CNV, i.e., one found only in a single individual. This point has been potently illustrated by a study of clinically discordant MZ twins who share CNVs (Bruder et al., AJHG, 2008). Inherited CNVs, such as those that made up almost all of the CNVs described in the childhood onset cases of the study by Walsh et al. (Science, 2008), are by definition not highly penetrant (since they are inherited from unaffected parents). The finding by Xu et al. (Nat Gen, 2008) that de novo (i.e., non-inherited) CNVs are much more likely to be associated with cases lacking a family history is provocative but difficult to interpret as no data are given about the size of the families having a family history and those not having such a history. Unless these family samples are of comparable size and obtained by a comparable ascertainment strategy, it is hard to know how conclusive the finding is. Indeed, in the study of Walsh et al., rare CNVs were just as likely to be found in patients with a positive family history. Finally, in contrast to private CNVs, recurrent (but still rare) CNVs, such as those identified on 1q and 15q in the studies of the International Schizophrenia Consortium (Nature, 2008) and Stefansson et al. (Nature, 2008), are strongly implicated as being associated with the diagnosis of schizophrenia and therefore likely involved in the causation of the illnesses in the cases having these CNVs. In all, these new CNV regions, combined with the VCFS region on 22q, suggest that approximately five to 10 patients out of 1,000 who carry the diagnosis of schizophrenia may have a well-defined genetic lesion (i.e., a substantial deletion or duplication).
The overarching question now is how relevant these findings are to the other 99 percent of individuals with this diagnosis who do not have these recurrent CNVs. Before we had the capability to perform high-density DNA hybridization and SNP array analyses, chromosomal anomalies associated with the diagnosis of schizophrenia were identified using cytogenetic techniques. Indeed, VCFS, XXX, XXY (Kleinfelter’s syndrome), and XO (Turner syndrome) have been found with similarly increased frequency in cases with this diagnosis in a number of studies. Now that we have greater resolution to identify smaller structural anomalies, the list of congenital syndromes that increase the possibility that people will manifest symptoms that earn them this diagnosis appears to be growing rapidly. Are we finding causes for the form of schizophrenia that most psychiatrists see in their offices, or are we instead carving out a new set of rare congenital syndromes that share some clinical characteristics, as syphilis was carved out from the diagnosis of schizophrenia at the turn of the twentieth century? Is schizophrenia a primary expression of these anomalies or a secondary manifestation? VCFS is associated with schizophrenia-like phenomena but even more often with mild mental retardation, autism spectrum, and other psychiatric manifestations. The same is true of the aneuploidies that increase the probability of manifesting schizophrenia symptoms. The two new papers in Nature allude to the possibility that epilepsy and intellectual limitations may also be associated with these CNVs. The diagnostic potential of any of these new findings cannot be determined until the full spectrum of their clinical manifestations is clarified.
One of the important insights that might emerge from identification of these new CNV syndromes is the identification of candidate genes that may show association with schizophrenia based on SNPs in these regions. VCFS has been an important source of promising candidate genes with broader clinical relevance (e.g., PRODH, COMT). Stefansson et al. report, however, that none of the 319 SNPs in the CNV regions showed significant association with schizophrenia in quite a large sample of individuals not having deletions in these regions. The Consortium report also presumably has the results of SNP association testing in these regions in their large sample but did not report them. It is very important to explore in greater genetic detail these regions of the genome showing association with the diagnosis of schizophrenia in samples lacking these lesions and to fully characterize the clinical picture of individuals who have them. It is hoped that insights into the pathogenesis of symptoms related to this diagnosis will emerge from these additional studies.
Anyone who has worked in a public state hospital or chronic schizophrenia care facility (where I spent over 20 years) is not surprised to find an occasional patient with a rare congenital or acquired syndrome who expresses symptoms similar to those individuals also diagnosed with schizophrenia who do not have such rare syndromes. Our diagnostic procedures are not precise, and the symptoms that earn someone this diagnosis are not specific. Schizophrenia is not something someone has; it is a diagnosis someone is given. In an earlier comment for SRF on structural variations in the genome related to autism, I suggested that, “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.” These new CNV syndromes manifesting schizophrenia phenomena are probably a reminder that the same is true of what we call schizophrenia.
View all comments by Daniel Weinberger
Related News: WCPG 2011—A Capital Day for CNVs in Schizophrenia
Comment by: John McGrath, SRF Advisor
Submitted 17 September 2011
Posted 20 September 2011
De novo CNVs are associated with advanced paternal age in a mouse model
While the association between advanced paternal age and an increased risk of various neuropsychiatric disorders such as schizophrenia and autism is now well established, the mechanism underpinning this finding remains unclear. Putative mechanisms include de-novo mutations and/or epigenetic mechanisms. In light of the growing body of evidence linking copy number variants (CNVs) with these same disorders, we used a mouse model to explore the hypothesis that the offspring of older males have an increased risk of de-novo CNVs. C57BL/6J sires that were three- and 12-16 months old were mated with three-month-old dams to create control offspring and offspring of old sires, respectively. Applying genomewide microarray screening technology, seven distinct CNVs were identified in a set of 12 offspring and their parents.
Competitive quantitative PCR confirmed these CNVs in the original set and also established their frequency in an independent set of 77 offspring and their parents. On the basis of the combined samples, six de-novo CNVs were detected in the offspring of older sires, whereas none were detected in the control group. Two of the CNVs were associated with behavioral and/or neuroanatomical phenotypic features. One of the de-novo CNVs involved Auts2 (autism susceptibility candidate 2), and other CNVs included genes linked to schizophrenia, autism, and brain development.
Our results support the hypothesis that the offspring of older fathers have an increased risk of neurodevelopmental disorders such as schizophrenia and autism by generation of de-novo CNVs in the male germline.
T Flatscher-Bader, CJ Foldi, S Chong, E Whitelaw, RJ Moser, THJ Burne, DW Eyles, JJ McGrath. (2011) Increased de novo copy number variants in the offspring of older males. Translational Psychiatry. View the free full-text article.
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