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Timothy Syndrome (TS) is caused by very rare exonic mutations of the CACNA1C gene that produce delayed inactivation of Cav1.2 voltage-gated calcium channels during cellular action potentials, with greatly increased influx of calcium into the activated cells. The major clinical feature of this syndrome is a long QT interval that results in cardiac arrhythmias. However, TS also includes cognitive impairment, autism and major developmental delays in many of the patients. We observed the appearance of bipolar disorder (BD) in a patient with a previously reported case of TS, who is one of the very few patients to survive childhood. This is most interesting because the common single-nucleotide polymorphism (SNP) most highly associated with BD is rs1006737, which we show here is a cis -expression quantitative trait locus for CACNA1C in human cerebellum, and the risk allele (A) is associated with decreased expression. To combine the CACNA1C perturbations in the presence of BD in this patient and in patients with the common CACNA1C SNP risk allele, we would propose that either increase or decrease in calcium influx in excitable cells can be associated with BD. In treatment of BD with calcium channel blocking drugs, we would predict better response in patients without the risk allele, because they have increased CACNA1C expression.

Badner and Gershon (2001) presented a technique of meta-analysis of linkage data that could be applied to published genome scans. It combines the reported P-values of individual studies, after correcting each value for the size of the region containing a minimum P-value. Simulations demonstrated that the type I error rate was at least as low as that for a single genome scan and thus genome-wide significance criteria may be applied. Power to detect linkage was at least as high as the power of pooling the data from all the studies. We applied this method to all the published genome scans for bipolar disorder and schizophrenia. We found the strongest evidence for susceptibility loci on 13q (P < 6 x 10(-6)) and 22q (P < 1 x 10(-5)) for bipolar disorder, and on 8p (P < 2 x 10(-4)), 13q (P < 7 x 10(-5)), and 22q (P < 9 x 10(-5)) for schizophrenia.

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.

We conducted a systematic study of top susceptibility variants from a genome-wide association (GWA) study of bipolar disorder to gain insight into the functional consequences of genetic variation influencing disease risk. We report here the results of experiments to explore the effects of these susceptibility variants on DNA methylation and mRNA expression in human cerebellum samples. Among the top susceptibility variants, we identified an enrichment of cis regulatory loci on mRNA expression (eQTLs), and a significant excess of quantitative trait loci for DNA CpG methylation, hereafter referred to as methylation quantitative trait loci (mQTLs). Bipolar disorder susceptibility variants that cis regulate both cerebellar expression and methylation of the same gene are a very small proportion of bipolar disorder susceptibility variants. This finding suggests that mQTLs and eQTLs provide orthogonal ways of functionally annotating genetic variation within the context of studies of pathophysiology in brain. No lymphocyte mQTL enrichment was found, suggesting that mQTL enrichment was specific to the cerebellum, in contrast to eQTLs. Separately, we found that using mQTL information to restrict the number of single-nucleotide polymorphisms studied enhances our ability to detect a significant association. With this restriction a priori informed by the observed functional enrichment, we identified a significant association ( rs12618769 , P bonferroni <0.05) from two other GWA studies (TGen+GAIN; 2191 cases and 1434 controls) of bipolar disorder, which we replicated in an independent GWA study (WTCCC). Collectively, our findings highlight the importance of integrating functional annotation of genetic variants for gene expression and DNA methylation to advance the biological understanding of bipolar disorder.

Findings from family and twin studies support a genetic contribution to the development of sexual orientation in men. However, previous studies have yielded conflicting evidence for linkage to chromosome Xq28. We conducted a genome-wide linkage scan on 409 independent pairs of homosexual brothers (908 analyzed individuals in 384 families), by far the largest study of its kind to date. We identified two regions of linkage: the pericentromeric region on chromosome 8 (maximum two-point LOD = 4.08, maximum multipoint LOD = 2.59), which overlaps with the second strongest region from a previous separate linkage scan of 155 brother pairs; and Xq28 (maximum two-point LOD = 2.99, maximum multipoint LOD = 2.76), which was also implicated in prior research. Results, especially in the context of past studies, support the existence of genes on pericentromeric chromosome 8 and chromosome Xq28 influencing development of male sexual orientation.

Although the concept of meta-analysis of multiple linkage scans of a genetic trait is not new, it can be difficult to apply to published data given the lack of consistency in the presentation of linkage results. In complex inheritance common diseases, there are many instances where one or two studies meet genome-wide criteria for significant or suggestive linkage but several other studies do not show even nominally significant results with the same region. One possibility for resolving differences between study results would be to combine an available result parameter of several studies. We describe here a method of regional meta-analysis, the multiple-scan probability (MSP), which can be used on published results. It combines the reported P-values of individual studies, after correcting each value for the size of the region containing a minimum P-value. Analyses of the power of MSP and of its type I error rates are presented. The type I error rate is at least as low as that for a single genome scan and thus genome-wide significance criteria may be applied. We also demonstrate appropriate criteria for this type of meta-analysis when the most significant study is included, and when that study is used to define a region of interest and then excluded. In our simulations, meta-analysis is at least as powerful as pooling data. Finally, we apply this method of meta-analysis to the evidence for linkage of autism susceptibility loci and demonstrate evidence for a susceptibility locus at 7q.

Despite compelling evidence that genetic factors contribute to bipolar disorder (BP), attempts to identify susceptibility genes have met with limited success. This may be due to the genetic heterogeneity of the disorder. We sought to identify susceptibility loci for BP in a genome-wide linkage scan with and without clinical covariates that might reflect the underlying heterogeneity of the disorder. We genotyped 428 subjects in 98 BP families at the Center for Inherited Disease Research with 402 microsatellite markers. We first carried out a non-parametric linkage analysis with MERLIN, and then reanalyzed the data with LODPAL to incorporate clinical covariates for age at onset (AAO), psychosis and comorbid anxiety. We sought to further examine the top findings in the covariate analysis in an independent sample of 64 previously collected BP families. In the non-parametric linkage analysis, three loci were nominally significant under a narrow diagnostic model and seven other loci were nominally significant under a broader model. The top findings were on chromosomes 2q24 and 3q28. The covariate analyses yielded additional evidence for linkage on 3q28 with AAO in the primary and independent samples. Although none of the linked loci were genome-wide significant, their congruence with prior results and, for the covariate analyses, their identification in two separate samples increases the likelihood that they are true positives and deserve further investigation. These findings further demonstrate the value of considering clinical features that may reflect the underlying heterogeneity of disease in order to facilitate gene mapping.

Although the concept of meta-analysis of multiple linkage scans of a genetic trait is not new, it can be difficult to apply to published data given the lack of consistency in the presentation of linkage results. In complex inheritance common diseases, there are many instances where one or two studies meet genome-wide criteria for significant or suggestive linkage but several other studies do not show even nominally significant results with the same region. One possibility for resolving differences between study results would be to combine an available result parameter of several studies. We describe here a method of regional meta-analysis, the multiple-scan probability (MSP), which can be used on published results. It combines the reported P-values of individual studies, after correcting each value for the size of the region containing a minimum P-value. Analyses of the power of MSP and of its type I error rates are presented. The type I error rate is at least as low as that for a single genome scan and thus genome-wide significance criteria may be applied. We also demonstrate appropriate criteria for this type of meta-analysis when the most significant study is included, and when that study is used to define a region of interest and then excluded. In our simulations, meta-analysis is at least as powerful as pooling data. Finally, we apply this method of meta-analysis to the evidence for linkage of autism susceptibility loci and demonstrate evidence for a susceptibility locus at 7q.

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n =1001 cases; n =1033 controls), and one involving a sample of individuals of African ancestry (AA; n =345 cases; n =670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 ( P =1.6 × 10 −6 ) and rs10193871 in NAP5 at 2q21.2 ( P =9.8 × 10 −6 ). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 ( P =1.5 × 10 −6 ) and rs2769605 in NTRK2 at 9q21.33 ( P =4.5 × 10 −5 ). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53 ; other evidence implicates BD candidate genes such as SLITRK2 . We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 ( P =1.4 × 10 −6 ), rs4657247 in RGS5 at 1q23.3 ( P =4.1 × 10 −6 ), and rs7078071 in BTBD16 at 10q26.13 ( P =4.5 × 10 −6 ). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.