Recent work has led to the identification of several susceptibility genes for autism spectrum disorder (ASD) and an increased appreciation of the importance of rare and de novo mutations. Some of the mutations may be very hard to detect using current strategies, especially if they are located in regulatory regions. We present a new approach to identify functional mutations that exploit the fact that many rare mutations disrupt the expression of genes from a single parental chromosome. The method incorporates measurement of the relative expression of the two copies of a gene across the genome using single nucleotide polymorphism arrays. Allelic expression has been successfully used to study common regulatory polymorphisms; however, it has not been implemented as a screening tool for rare mutation. We tested the potential of this approach by screening for monoallelic expression in lymphoblastoid cell lines derived from a small ASD cohort. After filtering regions shared across multiple samples, we identified genes showing monoallelic expression in specific ASD samples. Validation by quantitative sequencing demonstrated that the genes (or only part of them) are monoallelic expressed. The genes included both previously suspected risk factors for ASD and novel candidates. In one gene, named autism susceptibility candidate 2 (AUTS2), we identified a rare duplication that is likely to be the cause of monoallelic expression. Our results demonstrate the ability to identify rare regulatory mutations using genome-wide allelic expression screens, capabilities that could be expanded to other diseases, especially those with suspected involvement of rare dominantly acting mutations.

Genome-wide association studies (GWAS) with small sample size have had limited statistical power in identifying schizophrenia susceptibility genes. This is exemplified by the fact that one of the most convincing associations was detected only after meta-analyses of three different GWAS. Here we used meta-analysis to study the association of two single-nucleotide polymorphisms (SNPs) (rs7341475 and rs17746501) previously indicated to be associated with schizophrenia by a GWAS of Ashkenazi Jews (AJ). In the initial report, rs7341475 was associated only in women, while rs17746501 was associated in both men and women. We collected genotyping results of samples published in four GWAS for the two SNPs, additional to results from AJ. We used the Mantel-Haenszel method to combine the data of the different samples. For both SNPs, the results of the meta-analysis of all samples, including the initial report, did not reach a genome-wide significance level. However, the association between rs7341475 and schizophrenia in women, after excluding the data from AJ, was significant (P = 9.0 x 10(-3)), with a calculated odds ratio (OR) of 1.11, much smaller than the original result. Association between rs17746501 and schizophrenia was significant in four of the new samples, showing evidence for heterogeneity and very small effect when tested across all samples (P = 0.016, OR = 1.06). These findings suggest that the two SNPs might have a small effect on schizophrenia risk and suggest that meta-analyses of very large samples are needed to adequately study the contribution of common variants to schizophrenia susceptibility.

Human embryonic stem cells are derived from the inner cell mass of pre-implantation embryos. The cells have unlimited proliferation potential and capacity to differentiate into the cells of the three germ layers. Human embryonic stem cells are used to study human embryogenesis and disease modeling and may in the future serve as cells for cell therapy and drug screening. Human embryonic stem cells are usually isolated from surplus normal frozen embryos and were suggested to be isolated from diseased embryos detected by pre-implantation genetic diagnosis. Here we report the isolation of 12 human embryonic stem cell lines and their thorough characterization. The lines were derived from embryos detected to have aneuploidy by pre-implantation genetic screening. Karyotype analysis of these cell lines showed that they are euploid, having 46 chromosomes. Our interpretation is that the euploid cells originated from mosaic embryos, and in vitro selection favored the euploid cells. The undifferentiated cells exhibited long-term proliferation and expressed markers typical for embryonic stem cells such as OCT4, NANOG, and TRA-1-60. The cells manifested pluripotent differentiation both in vivo and in vitro. To further characterize the different lines, we have analyzed their ethnic origin and the family relatedness among them. The above results led us to conclude that the aneuploid mosaic embryos that are destined to be discarded can serve as source for normal euploid human embryonic stem cell lines. These lines represent various ethnic groups; more lines are needed to represent all populations.

We and others have previously reported linkage to schizophrenia on chromosome 10q25-q26 but, to date, a susceptibility gene in the region has not been identified. We examined data from 3606 single-nucleotide polymorphisms (SNPs) mapping to 10q25-q26 that had been typed in a genome-wide association study (GWAS) of schizophrenia (479 UK cases/2937 controls). SNPs with P<0.01 (n=40) were genotyped in an additional 163 UK cases and those markers that remained nominally significant at P<0.01 (n=22) were genotyped in replication samples from Ireland, Germany and Bulgaria consisting of a total of 1664 cases with schizophrenia and 3541 controls. Only one SNP, rs17101921, was nominally significant after meta-analyses across the replication samples and this was genotyped in an additional six samples from the United States/Australia, Germany, China, Japan, Israel and Sweden (n=5142 cases/6561 controls). Across all replication samples, the allele at rs17101921 that was associated in the GWAS showed evidence for association independent of the original data (OR 1.17 (95% CI 1.06-1.29), P=0.0009). The SNP maps 85 kb from the nearest gene encoding fibroblast growth factor receptor 2 (FGFR2) making this a potential susceptibility gene for schizophrenia.

A proportion of the genetic variants underlying complex phenotypes do so through their effects on gene expression, so an important challenge in complex trait analysis is to discover the genetic basis for the variation in transcript abundance. So far, the potential of mapping both quantitative trait loci (QTLs) and expression quantitative trait loci (eQTLs) in rodents has been limited by the low mapping resolution inherent in crosses between inbred strains. We provide a megabase resolution map of thousands of eQTLs in hippocampus, lung, and liver samples from heterogeneous stock (HS) mice in which 843 QTLs have also been mapped at megabase resolution. We exploit dense mouse SNP data to show that artifacts due to allele-specific hybridization occur in approximately 30% of the cis-acting eQTLs and, by comparison with exon expression data, we show that alternative splicing of the 3' end of the genes accounts for <1% of cis-acting eQTLs. Approximately one third of cis-acting eQTLs and one half of trans-acting eQTLs are tissue specific. We have created an important systems biology resource for the genetic analysis of complex traits in a key model organism.

We describe a multistage approach to identify single nucleotide polymorphisms (SNPs) associated with neuroticism, a personality trait that shares genetic determinants with major depression and anxiety disorders. Whole genome association with 452 574 SNPs was performed on DNA pools from approximately 2000 individuals selected on extremes of neuroticism scores from a cohort of 88 142 people from southwest England. The most significant SNPs were then genotyped on independent samples to replicate findings. We were able to replicate association of one SNP within the PDE4D gene in a second sample collected by our laboratory and in a family-based test in an independent sample; however, the SNP was not significantly associated with neuroticism in two other independent samples. We also observed an enrichment of low P-values in known regions of copy number variations. Simulation indicates that our study had approximately 80% power to identify neuroticism loci in the genome with odds ratio (OR)>2, and approximately 50% power to identify small effects (OR=1.5). Since we failed to find any loci accounting for more than 1% of the variance, the heritability of neuroticism probably arises from many loci each explaining much less than 1%. Our findings argue the need for much larger samples than anticipated in genetic association studies and that the biological basis of emotional disorders is extremely complex.

Sex differences in schizophrenia are well known, but their genetic basis has not been identified. We performed a genome-wide association scan for schizophrenia in an Ashkenazi Jewish population using DNA pooling. We found a female-specific association with rs7341475, a SNP in the fourth intron of the reelin (RELN) gene (p = 2.9 x 10(-5) in women), with a significant gene-sex effect (p = 1.8 x 10(-4)). We studied rs7341475 in four additional populations, totaling 2,274 cases and 4,401 controls. A significant effect was observed only in women, replicating the initial result (p = 2.1 x 10(-3) in women; p = 4.2 x 10(-3) for gene-sex interaction). Based on all populations the estimated relative risk of women carrying the common genotype is 1.58 (p = 8.8 x 10(-7); p = 1.6 x 10(-5) for gene-sex interaction). The female-specific association between RELN and schizophrenia is one of the few examples of a replicated sex-specific genetic association in any disease.

BACKGROUND: Exploiting synteny between mouse and human disease loci has been proposed as a cost-effective method for the identification of human susceptibility genes. Here we explore its utility in an analysis of a human personality trait, neuroticism, which can be modeled in mice by tests of emotionality. We investigated a mouse emotionality locus on chromosome 1 that contains no annotated genes but abuts four regulators of G protein signaling, one of which (rgs2) has been previously identified as a quantitative trait gene for emotionality. This locus is syntenic with a human region that has been consistently implicated in the genetic aetiology of neuroticism. METHODS: The functional candidacy of 29 murine sequence variants was tested by a combination of gel shift and transient transfection assays. Murine sequences that contained functional variants and exhibited significant cross-species conservation were prioritized for investigation in humans. Genetic association with neuroticism was tested in 1869 high and 2032 low unrelated individuals scored for neuroticism, selected from the extremes of 88,141 people from southwest England. RESULTS: Fifteen sequence variants contributed to variation in the expression of rgs18, the gene lying at the edge of the quantitative trait loci (QTL) interval. There was no evidence of association between neuroticism and single nucleotide polymorphisms (SNPs) lying in the human regions homologous to those of mouse functional variants. One SNP, rs6428058, in a region of sequence conservation 644 kb upstream of RGS18, showed significant association (p = .000631). CONCLUSIONS: It is unlikely that a single variant is responsible for the mouse emotionality locus on chromosome 1. This level of underlying genetic complexity means that although cross-species QTL concordance may be invaluable for the identification of human disease loci, it is unlikely to be as informative in the identification of human disease-causing variants.

Animal models of psychiatric diseases are useful tools for screening new drugs and for investigating the mechanisms of those disorders. Despite the difficulties inherent in modelling human psychiatric phenotypes in animals, there has been recent success identifying mutations in mice that give rise to some of the characteristic features of anxiety, depression, schizophrenia, autism, obsessive-compulsive disorder and bipolar disorder. In some cases these models have the additional strength that drugs used to treat the human condition alleviate the symptoms in mice. Robust genetic evidence of the involvement of multiple susceptibility genes in psychiatric disease will enable future studies to move from single-gene models to models with multiple modified loci, with the promise of better representing the complexity of the human diseases.

Major depression is one of the most common and most debilitating disorders in the world. A wealth of data indicate that additive genetic effects contribute to at least 30% of the variance in liability to major depression, yet attempts to identify the molecular basis of susceptibility using standard family based linkage and genetic association methodologies have had limited success. Alternative approaches have recently been advocated, such as the inclusion of gene by environment interactions and the use of endophenotypes. Our own data indicate that the genetic architecture of affective illness is more complex than expected. A whole genome association study of neuroticism, a personality trait that shares many of the same susceptibility loci as depression, reveals that the individual effect sizes are less than 1%. Larger sample sizes and more sophisticated analytical approaches will be needed than have hitherto been applied.

We carried out a genome-wide association study of schizophrenia (479 cases, 2,937 controls) and tested loci with P < 10(-5) in up to 16,726 additional subjects. Of 12 loci followed up, 3 had strong independent support (P < 5 x 10(-4)), and the overall pattern of replication was unlikely to occur by chance (P = 9 x 10(-8)). Meta-analysis provided strongest evidence for association around ZNF804A (P = 1.61 x 10(-7)) and this strengthened when the affected phenotype included bipolar disorder (P = 9.96 x 10(-9)).

Schizophrenia is a severe mental disorder striking mainly young adults and leading to life-long disability in a substantial portion of the sufferers. On the other hand, substantial knowledge about its etiology and pathogenesis is still lacking. Therefore the European Science Foundation (ESF) sponsored a meeting of a panel of European experts on schizophrenia research to discuss the state of art and future perspectives of key topics in this area. The fields covered genetics, epidemiology, animal models, molecular neuropathology and imaging. This was a first step to establish a network of European groups dedicated to Schizophrenia research. The coming calls of the frame work program will be used to strengthen this network in order to achieve substantial progress in understanding and treating this devastating illness.

Several lines of evidence have established the presence of an association between a 3-Mb deletion in chromosome 22q11 and schizophrenia. In this paper we present a complete high-density SNP scan of this segment using DNA pools, and demonstrate significant association between two distinct regions and schizophrenia in an Ashkenazi Jewish population. One of these regions contains the previously identified COMT gene. The pattern of association and linkage disequilibrium (LD) in the second region suggest that DGCR2, which encodes a putative adhesion receptor protein, is the susceptibility gene. We confirmed the association between DGCR2 and schizophrenia through individual genotyping of 1,400 subjects. In a gene expression analysis the risk allele of a coding SNP associated with schizophrenia was found to be associated with a reduced expression of DGCR2. Interestingly, the expression of DGCR2 was also found to be elevated in the dorsolateral prefrontal cortex of schizophrenic patients relative to matched controls. This increase is likely to be explained by exposure to antipsychotic drugs. To test that hypothesis, we looked at rats exposed to antipsychotic medication and found significantly elevated levels of DGCR2 transcripts. The genetic and functional evidences here reported suggest a possible role of the DGCR2 gene in the pathology of schizophrenia and also in the therapeutic effects of antipsychotic drugs.

High-resolution genetic maps are required for mapping complex traits and for the study of recombination. We report the highest density genetic map yet created for any organism, except humans. Using more than 10,000 single nucleotide polymorphisms evenly spaced across the mouse genome, we have constructed genetic maps for both outbred and inbred mice, and separately for males and females. Recombination rates are highly correlated in outbred and inbred mice, but show relatively low correlation between males and females. Differences between male and female recombination maps and the sequence features associated with recombination are strikingly similar to those observed in humans. Genetic maps are available from http://gscan.well.ox.ac.uk/#genetic_map and as supporting information to this publication.

Exposure to agricultural insecticides, together with yet incompletely understood predisposing genotype/phenotype elements, notably increase the risk of Parkinson's disease. Here, we report findings attributing the increased risk in an insecticide-exposed rural area in Israel to interacting debilitating polymorphisms in the ACHE/PON1 locus and corresponding expression variations. Polymorphisms that debilitate PON1 activity and cause impaired AChE overproduction under anticholinesterase exposure were strongly overrepresented in patients from agriculturally exposed areas, indicating that they confer risk of Parkinson's disease. Supporting this notion, serum AChE and PON1 activities were both selectively and significantly lower in patients than in healthy individuals and in carriers of the risky polymorphisms as compared with other Parkinsonian patients. Our findings suggest that inherited interactive weakness of AChE and PON1 expression increases the insecticide-induced occurrence of Parkinson's disease.

Association studies are now primarily being conducted with single nucleotide polymorphisms because they are present everywhere in the genome and can be genotyped in "high throughput" formats. Microsatellite markers have a higher degree of polymorphism than single nucleotide polymorphisms and have been widely used in both linkage and association studies of disease. Polymorphic microsatellite markers with several alleles can readily detect linkage disequilibrium but at any given locus there may be differences between single nucleotide polymorphisms and microsatellites in their power to detect linkage disequilibrium because of the evolutionary history of the locus, especially the rate at which both the single nucleotide polymorphisms and microsatellite polymorphisms have mutated and the number of disease mutations and their history. In the current study, we examined the efficiency of microsatellite markers in association analysis by looking at all existent microsatellite markers in the catechol-O-methyltransferase gene region and by genotyping these microsatellites in a large cohort of schizophrenia patients and healthy controls, a subset of a sample where catechol-O-methyltransferase and schizophrenia were found to be associated. We also estimated the levels of linkage disequilibrium between these microsatellites and the previously reported single nucleotide polymorphisms (within the catechol-O-methyltransferase gene) found to be associated with schizophrenia. A modest allelic association of P=0.041 was found between schizophrenia and the microsatellite marker D22S944, which was not significant, however, when corrected for all microsatellites tested. Nevertheless, significant linkage disequilibrium was found between this marker and the three single nucleotide polymorphisms within the catechol-O-methyltransferase gene that displayed association with the disease in the previously published research on this sample. Significant linkage disequilibrium was also observed between microsatellites up to approximately 300 kb distant from those single nucleotide polymorphisms. Although significant, the extent of linkage disequilibrium in terms of r2 was small (in the order of 0.01).

The shared ancestry of mouse inbred strains, together with the availability of sequence and phenotype information, is a resource that can be used to map quantitative trait loci (QTL). The difficulty in using only sequence information lies in the fact that in most instances the allelic state of the QTL cannot be unambiguously determined in a given strain. To overcome this difficulty, the performance of multiple crosses between various inbred strains has been proposed. Here we suggest and evaluate a general approach, which consists of crossing the two strains used initially to map the QTL and any new strain. We have termed these crosses "yin-yang," because they are complementary in nature as shown by the fact that the QTL will necessarily segregate in only one of the crosses. We used the publicly available SNP database of chromosome 16 to evaluate the mapping resolution achievable through this approach. Although on average the improvement of mapping resolution using only four inbred strains was relatively small (i.e., reduction of the QTL-containing interval by half at most), we found a great degree of variability among different regions of chromosome 16 with regard to mapping resolution. This suggests that with a large number of strains in hand, selecting a small number of strains may provide a significant contribution to the fine mapping of QTL.