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Charting Genetic Diversity—First Haplotype Map Appears

1 November 2005. Early navigators who ventured into the vast unknown were sometimes rewarded with landfall on warm, tropical islands. Modern-day explorers charting diversity in the genetic code have found a few hot spots of their own. In the October 27 Nature, an international crew of investigators called the International HapMap Consortium published their first draft of the human haplotype chart. This navigational aid promises to help researchers plumb the depths of the human genome for hazards, that is, variations that confer susceptibility to a myriad of diseases.

The HapMap project, led by David Altshuler at the Broad Institute of Harvard and MIT and Peter Donnelly at the University of Oxford in England, began in October 2002 with the goal of drafting a haplotype map within 3 years. Almost 3 years to the day later, the consortium, with research input from Canada, China, Japan, Nigeria, the UK, and the US, released a phase I map based on 269 sequenced genomes. The DNA samples were obtained from volunteers in Tokyo, Japan; Ibadan, Nigeria; Beijing, China; and Utah, USA.

Haplotypes are a means of cataloging genetic variance. Though 99.9 percent of the 3 billion or so nucleotides that make up the human genetic code are identical among the world’s 6.5 billion people, it is the 0.1 percent difference that ensures we don’t all look, sound, or think alike. And while that variety may add spice to life, it is also a large part of the reason for why some of us succumb to cancer, diabetes, or a disease of the central nervous system. Though some diseases can be blamed on a single letter change in the genetic code—the single nucleotide polymorphism (SNP)—more complex diseases are thought to result from a number of such changes. This represents an enormous challenge when trying to identify what specific combinations of mutations confer susceptibility to disease, or resistance to a drug. If you thought searching for a needle in a haystack was hard, consider trying to find five or ten needles among the 23 haystacks that are the human chromosomes.

This is where the haplotype comes in. A haplotype is a section of DNA that contains many single nucleotide polymorphisms. Because SNPs come and go infrequently, haplotypes are relatively stable. They also tend to be inherited as a whole block because genetic recombination, which could potentially rearrange the haplotype, is also somewhat rare. So if you inherit one haplotype SNP, you most likely inherit all the other associated SNPs, too.

For this reason, haplotype analysis has the potential to reduce significantly the amount of searching researchers need to do to find SNPs that are associated with a disease or other phenotype, such as drug resistance or sensitivity. In short, by finding one needle in the haystack, you can pull out many others along with it. In their paper, The HapMap Consortium reports how this redundancy may be even more extensive than previously thought. Using a pair-wise comparison method to analyze all the SNPs genotyped, they found that identifying a “tag” SNP every 5-10 kilobases of DNA is sufficient to reveal all the common variants in genome samples obtained from the Utah, Chinese, and Japanese volunteers. A slightly more dense array of SNPs (one every two to five kilobases) would achieve the same result for the Nigerian samples.

In practice, this means that in order to identify, with reasonable accuracy, which of the 10 million or so SNPs a person carries, researchers would only have to test about 250,000 tag SNPs. To be 100 percent accurate the number jumps up to about 450,000 tag SNPs (600,000 in the case of the Nigerian population), still less than 10 percent of the total. Hence, identifying haplotypes should not only be faster, but also cheaper than expected. In fact, consortium member Yusuke Nakamura, University of Tokyo, estimates that haplotype mapping could reduce the cost of searching for inherited genetic factors by 10- to 20-fold.

The point is illustrated by David Goldstein and Gianpiero Cavalleri from Duke University in an accompanying News & Views. Four years ago, these authors launched a project to identify which polymorphisms in the gene SCN1A are responsible for dictating a given patient’s response to an epileptic drug. It took the research team two years to identify the common SNPs and appropriate tags. “Today, the same job can be accomplished with simple computer algorithms, in minutes, using the HapMap data,” they write.

The value of the HapMap project is illustrated in an accompanying Nature paper from Vivian Cheung and colleagues at the University of Pennsylvania and The Children’s Hospital, both in Philadelphia. These researchers used the HapMap data to identify SNPs that influence gene expression. For 15 of 27 different genes previously identified as being heavily influenced by genetic variation, the authors found that their HapMap-based study agreed with previous findings—the HapMap analysis pointed to exactly the same cis-regulatory regions in the DNA. For one gene, chitinase 3-like 2 (CHI3L2), Cheung and colleagues were able to identify the exact SNP that regulated expression—a G to T mutation that leads to stronger binding of RNA polymerase II, which makes messenger RNA. “Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases,” write the authors.

Though haplotype analysis may increase efficiency, there are concerns that it may do so at a cost—the studies may be weak in terms of statistical power. The second phase of the HapMap project, which is designed to uncover considerably more SNPs, may help in this regard, and in the meantime, methods exist that can be employed to increase the power of the studies. So conclude Altshuler and colleagues in a related Nature Genetics paper published online October 23. Joint first authors Paul de Bakker, Roman Yelensky and colleagues report that there are numerous ways of carrying out the analysis to preserve statistical power. They found, for example, that analyzing all haplotypes for association, not just those that have been linked to known SNPs, can increase the chances of detecting rare polymorphisms that cause disease.—Tom Fagan.

Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P; International HapMap Consortium. A haplotype map of the human genome. Nature. 2005 Oct 27;437(7063):1299-320. Abstract

Goldstein DB, Cavalleri GL. Genomics: understanding human diversity. Nature. 2005 Oct 27;437(7063):1241-2. No abstract available. Abstract

Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT. Mapping determinants of human gene expression by regional and genome-wide association. Nature. October 27, 2005;437:1365-1369. Abstract

De Bakker PIW, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet. 2005 Nov;37(11):1217-1223. Epub 2005 Oct 23. Abstract

Comments on News and Primary Papers
Comment by:  John Hardy
Submitted 1 November 2005
Posted 1 November 2005

With the completion of the HapMap and its commercialization by Illumina and Affymetrix, it should be possible for researchers to find susceptibility alleles which have an odds ratio of >2 for any disorder, including Alzheimer disease, over the next couple of years. The expense will be high: Sample sizes of about 500 cases and 500 controls will be needed, and the cost per sample is on the order of $900. But if there are anymore genes with the effect size of ApoE out there, for AD or other diseases, we should now be able to find them.

View all comments by John HardyComment by:  Lars Bertram
Submitted 4 November 2005
Posted 4 November 2005

Q&A with Lars Bertram, who is developing the SchizophreniaGene database.

Q: Does the map provide enough resolution?
A: On average, the haplotype map has investigated about 1 SNP every 5,000 bases (i.e., 5 kb). For most applications this density should be sufficient to allow linkage disequilibrium mapping of common variants with at least moderate effects in genetically complex diseases. However, a phase 2 of the HapMap is planned which will probably more than quadruple this resolution.

Q: Will the HapMap help in complex diseases, where several variants on different chromosomes must interact, for example?
A: While the HapMap has many valuable uses in designing and interpreting future genetic association in AD and other diseases, it will unfortunately not help to better understand interactions between different genetic loci or non-genetic factors, because such interactions likely vary from phenotype to phenotype.

Q: Will the HapMap help in diseases where gene silencing, mRNA splicing, and other post-transcriptional and post-translational modifications are key to the pathophysiology?
A: If these pathophysiological changes are actually caused by common genetic variants in the genome, HapMap will definitely help us find them. It will still require a good number of experiments, though, to actually prove the causal relationship between associated SNPs on the one hand, and differences in mRNA splicing (for instance) on the other hand.

Q: Is the principle of tagging haplotypes scientifically sound, or does it run the risk of missing out on haplotypes that are low in frequency but high in consequence?
A: The principle of tagging haplotypes to cover untyped common genetic variants is certainly sound, and—with the data provided by the current HapMap release—has just become a whole lot easier. As everything in science, it does have limitations (such as finding very low-frequency polymorphisms or haplotypes). However, this is a rapidly evolving field and the planned phase 2 release of the HapMap, together with novel analytic strategies, should facilitate even the search for such uncommon variants in the near future.

View all comments by Lars BertramComment by:  Stephen J. Glatt
Submitted 13 November 2005
Posted 13 November 2005

The completion of the HapMap is a major advance for science, and one which will particularly benefit the field of psychiatry. Schizophrenia research has been hampered by a failure to replicate genetic linkage and association studies, and this may in part owe to population differences in allele frequency, haplotype structure, and the inability to select the proper genes and polymorphisms for analysis. The HapMap reduces the "search space" for genetic markers that will show associations with complex diseases, like schizophrenia, across samples, and will thus facilitate the causal polymorphisms that may be shared across these populations. The completion of the first phase of the HapMap is not just important as a milestone marking progress in mapping the human genome, but also it is important for the enhanced level of scientific inquiry it can enable.

View all comments by Stephen J. Glatt

Comments on Related News

Related News: DISC1 Delivers—Genetic, Molecular Studies Link Protein to Axonal Transport

Comment by:  Akira Sawa, SRF Advisor
Submitted 12 January 2007
Posted 12 January 2007

Although DISC1 is multifunctional, its role for neurite outgrowth has been substantially characterized for the past couple of years (Ozeki et al., 2003; Miyoshi et al., 2003; Kamiya et al., 2006). These studies indicated that DISC1 is involved in neurite outgrowth by more than one mechanism, such as interactions with NUDEL/NDEL1 and FEZ1.

These two papers from Kaibuchi’s lab provide further understanding of how DISC1 is involved in neuronal outgrowth. Kaibuchi’s group identified kinesin heavy chain of kinesin-1 as a novel interactor of DISC1. In their papers, a novel role for DISC1, to link kinesin-1 (microtubule-dependent and plus-end directed motor) to several cellular molecules, including NUDEL, LIS1, 14-3-3, and Grb2, is reported. DISC1 and kinesin-1 are, therefore, responsible to sort Grb2 to the distal part of axons where Grb2 functions as an adaptor and plays a role in NT-3-induced phosphorylation of ERK1/2. This mechanism well explains our previous work, led by Ryota Hashimoto, reporting that knockdown of DISC1 expression results in decreased levels of phosphorylation of ERK1/2 and Akt in primary cortical neurons (Hashimoto et al., 2006).

The interaction of DISC1 and kinesin-1 may also be interesting from the perspective of psychiatric genetics. First, the mechanism proposed in one of the papers (Taya et al., 2007) supports the notion that the C-terminal truncated DISC1 fragment—that occurs due to the balanced translocation in an extended Scottish family—functions as a dominant-negative. Second, the domain of DISC1 responsible for kinesin-1 is overlapped with the haplotype block region(s) that are positive in more than one association study of DISC1 and major mental illnesses.

View all comments by Akira Sawa

Related News: DISC1 Delivers—Genetic, Molecular Studies Link Protein to Axonal Transport

Comment by:  Luiz Miguel Camargo (Disclosure)
Submitted 13 January 2007
Posted 13 January 2007

Two recent back-to-back papers, published this month in Journal of Neuroscience, highlight the value of protein-protein interactions in determining the biological role of a key schizophrenia risk factor, DISC1, in processes that are important for the proper development of neurons.

Key questions need to be addressed once having established a set of interactors for a given protein. First, where do these proteins interact on the target molecule? Second, do these interactions take place at the same time (i.e., do they form a complex)? Third, in what context do these interactions occur (temporal, tissue/cell compartment, signaling), and, fourth, are the biological processes of the interacting molecules affected/regulated by the protein of interest? The Kaibuchi lab, as exemplified in the works by Taya et al. and Shinoda et al., elegantly address some of these questions in the context of DISC1 interactions with Grb2, Nudel (NDEL1), 14-3-3ε, and kinesin-1. The key findings of these papers are as follows:

1. Identification of the interaction sites, or more importantly, which part of DISC1 is involved in particular processes, for example, that axon elongation is dependent on the N-terminal, but not the C-terminal portion of DISC1. This suggests that the DISC1 role in axon elongation is mediated by interactions with the N-terminal portion of DISC1 that could be competed for by the truncated protein in a dominant negative fashion (Camargo et al., 2007).

2. Although a protein may have many interacting partners, such as DISC1, these interactions may not occur at the same time. For example, DISC1 is able to form a ternary complex with kinesin-1 and NDEL1 or with kinesin-1 and Grb2. However, a ternary complex of DISC1-Grb2-NDEL1 is not possible as Grb2 and NDEL1 may be competing for the same interaction site on DISC1.

3. Protein interactions may occur in certain cellular compartments, in the case of DISC1, the cell body and the distal part of axons.

4. Neurotrophin-induced axon elongation requires DISC1.

These papers confirm some of the hypotheses raised by the interactions that we have recently derived for DISC1 and some of its interacting partners (see Camargo et al., 2007). From the DISC1 interactome, we concluded that DISC1 may affect key intracellular transport mechanisms, such as those regulated by kinesins, and that DISC1 may be downstream of neurotrophin receptors, via its interaction with SH3BP5, an adaptor protein, which we found to interact with SOS1, a guanine exchange factor that binds Grb2 and responds to signaling of neurotrophin receptors. These observations have been validated by Taya et al. and Shinoda et al. and demonstrate the value of the DISC1 interactome in understanding the role of DISC1, and as a valuable resource to the wider community.

The molecular function of DISC1, as defined by its structure, still remains elusive, requiring a more dedicated effort on this front. The good news is that, via its protein-protein interactions, significant progress on the role of DISC1 in key biological processes has been achieved, as illustrated by the work of different labs (Brandon et. al., 2004; Millar et al., 2005; Kamiya et al., 2005; and now by Shinoda et al. and Taya et al.).

View all comments by Luiz Miguel Camargo

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?


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).


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