Two gene co-expression modules differentiate psychotics and controls

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.

Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Genome-wide association study of obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common, debilitating neuropsychiatric illness with complex genetic etiology. The International OCD Foundation Genetics Collaborative (IOCDF-GC) is a multi-national collaboration established to discover the genetic variation predisposing to OCD. A set of individuals affected with DSM-IV OCD, a subset of their parents, and unselected controls, were genotyped with several different Illumina SNP microarrays. After extensive data cleaning, 1465 cases, 5557 ancestry-matched controls and 400 complete trios remained, with a common set of 469,410 autosomal and 9657 X-chromosome single nucleotide polymorphisms (SNPs). Ancestry-stratified case-control association analyses were conducted for three genetically-defined subpopulations and combined in two meta-analyses, with and without the trio-based analysis. In the case-control analysis, the lowest two P-values were located within DLGAP1 (P=2.49 × 10(-6) and P=3.44 × 10(-6)), a member of the neuronal postsynaptic density complex. In the trio analysis, rs6131295, near BTBD3, exceeded the genome-wide significance threshold with a P-value=3.84 × 10(-8). However, when trios were meta-analyzed with the case-control samples, the P-value for this variant was 3.62 × 10(-5), losing genome-wide significance. Although no SNPs were identified to be associated with OCD at a genome-wide significant level in the combined trio-case-control sample, a significant enrichment of methylation QTLs (P<0.001) and frontal lobe expression quantitative trait loci (eQTLs) (P=0.001) was observed within the top-ranked SNPs (P<0.01) from the trio-case-control analysis, suggesting these top signals may have a broad role in gene expression in the brain, and possibly in the etiology of OCD.

Genome-wide association study of Tourette's syndrome

Tourette's syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (P<5 × 10(-8)); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (P=1.85 × 10(-6)). A secondary analysis including an additional 211 cases and 285 controls from two closely related Latin American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (P=3.6 × 10(-7) for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder.

Large genomic region free of GWAS-based common variants contains fertility-related genes

DNA variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), are unevenly distributed across the human genome. Currently, dbSNP contains more than 6 million human SNPs, and whole-genome genotyping arrays can assay more than 4 million of them simultaneously. In our study, we first questioned whether published genome-wide association studies (GWASs) assays cover all regions well in the genome. Using dbSNP build 135 data, we identified 50 genomic regions longer than 100 Kb that do not contain any common SNPs, i.e., those with minor allele frequency (MAF)≥1%. Secondly, because conserved regions are generally of functional importance, we tested genes in those large genomic regions without common SNPs. We found 97 genes and were enriched for reproduction function. In addition, we further filtered out regions with CNVs listed in the Database of Genomic Variants (DGV), segmental duplications from Human Genome Project and common variants identified by personal genome sequencing (UCSC). No region survived after those filtering. Our analysis suggests that, while there may not be many large genomic regions free of common variants, there are still some “holes” in the current human genomic map for common SNPs. Because GWAS only focused on common SNPs, interpretation of GWAS results should take this limitation into account. Particularly, two recent GWAS of fertility may be incomplete due to the map deficit. Additional SNP discovery efforts should pay close attention to these regions.

Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants

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.

Genome-wide association study of irritable vs. elated mania suggests genetic differences between clinical subtypes of bipolar disorder

The use of clinical features to define subtypes of a disorder may aid in gene identification for complex diseases. In particular, clinical subtypes of mania may distinguish phenotypic subgroups of bipolar subjects that may also differ genetically. To assess this possibility, we performed a genome-wide association study using genotype data from the Bipolar Genome Study (BiGS) and subjects that were categorized as having either irritable or elated mania during their most severe episode. A bipolar case-only analysis in the GAIN bipolar sample identified several genomic regions that differed between irritable and elated subjects, the most significant of which was for 33 SNPs on chromosome 13q31 (peak p = 2×10(-7)). This broad peak is in a relative gene desert over an unknown EST and between the SLITRK1 and SLITRK6 genes. Evidence for association to this region came predominantly from subjects in the sample that were originally collected as part of a family-based bipolar linkage study, rather than those collected as bipolar singletons. We then genotyped an additional sample of bipolar singleton cases and controls, and the analysis of irritable vs. elated mania in this new sample did not replicate our previous findings. However, this lack of replication is likely due to the presence of significant differences in terms of clinical co-morbity that were identified between these singleton bipolar cases and those that were selected from families segregating the disorder. Despite these clinical differences, analysis of the combined sample provided continued support for 13q31 and other regions from our initial analysis. Though genome-wide significance was not achieved, our results suggest that irritable mania results from a distinct set of genes, including a region on chromosome 13q31.

Preliminary genetic imaging study of the association between estrogen receptor-α gene polymorphisms and harsh human maternal parenting

A failure of neural changes initiated by the estrogen surge in late pregnancy to reverse the valence of infant stimuli from aversive to rewarding is associated with dysfunctional maternal behavior in nonhuman mammals. Estrogen receptor-α plays the crucial role in mediating these neural effects of estrogen priming. This preliminary study examines associations between estrogen receptor-α gene polymorphisms and human maternal behavior. Two polymorphisms were associated with human negative maternal parenting. Furthermore, hemodynamic responses in functional magnetic resonance imaging to child stimuli in neural regions associated with social cognition fully mediated the association between genetic variation and negative parenting. This suggests testable hypotheses regarding a biological pathway between genetic variants and dysfunctional human maternal parenting.

Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing.

Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci

Background

Autism spectrum disorder is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for an association of common variants with autism susceptibility risk have met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum.

Methods

In this study, we utilized publically available genome-wide association study data from the Autism Genome Project and annotated the results (P <0.001) for expression quantitative trait loci present in the parietal lobe (GSE35977), cerebellum (GSE35974) and lymphoblastoid cell lines (GSE7761). We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency to generate an empirical P-value indicating statistically significant enrichment of expression quantitative trait loci in top results from the autism genome-wide association study.

Results

Our findings show a global enrichment of brain expression quantitative trait loci, but not lymphoblastoid cell line expression quantitative trait loci, among top single nucleotide polymorphisms from an autism genome-wide association study. Additionally, the data implicates individual genes SLC25A12, PANX1 and PANX2 as well as pathways previously implicated in autism.

Conclusions

These findings provide supportive rationale for the use of annotation-based approaches to genome-wide association studies.

Genome-wide association study of personality traits in bipolar patients

OBJECTIVE

Genome-wide association study was carried out on personality traits among bipolar patients as possible endophenotypes for gene discovery in bipolar disorder.

METHODS

The subscales of Cloninger's Temperament and Character Inventory (TCI) and the Zuckerman-Kuhlman Personality Questionnaire (ZKPQ) were used as quantitative phenotypes. The genotyping platform was the Affymetrix 6.0 SNP array. The sample consisted of 944 individuals for TCI and 1007 for ZKPQ, all of European ancestry, diagnosed with bipolar disorder by Diagnostic and Statistical Manual of Mental Disorders-IV criteria.

RESULTS

Genome-wide significant association was found for two subscales of the TCI, rs10479334 with the 'Social Acceptance versus Social Intolerance' subscale (Bonferroni P = 0.014) in an intergenic region, and rs9419788 with the 'Spiritual Acceptance versus Rational Materialism' subscale (Bonferroni P = 0.036) in PLCE1 gene. Although genome-wide significance was not reached for ZKPQ scales, lowest P values pinpointed to genes, RXRG for Sensation Seeking, GRM7 and ITK for Neuroticism Anxiety, and SPTLC3 gene for Aggression Hostility.

CONCLUSION

After correction for the 25 subscales in TCI and four scales plus two subscales in ZKPQ, phenotype-wide significance was not reached.

Database of genetic studies of bipolar disorder

This study describes the construction and preliminary analysis of a database of summary level genetic findings for bipolar disorder from the literature. The database is available for noncommercial use at http://bioprogramming.bsd.uchicago.edu/BDStudies/. This may be the first complete collection of published gene-specific linkage and association findings on bipolar disorder, including genome-wide association studies. Both the positive and negative findings have been incorporated so that the statistical and contextual significance of each finding may be compared semi-quantitatively and qualitatively across studies of mixed technologies. The database is appropriate for searching a literature populated by mainly underpowered studies, and if 'hits' are viewed as tentative knowledge for future hypothesis generation. It can serve as the basis for a mega-analysis of candidate genes. Herein, we discuss the most robust and best replicated gene findings to date in a contextual manner.

Genetic association of bipolar disorder with the β(3) nicotinic receptor subunit gene

OBJECTIVE

Owing to the clinical relationship between bipolar disorder and nicotine dependence, we investigated two research questions: (i) are genetic associations with nicotine dependence different in individuals with bipolar disorder as compared with individuals without bipolar disorder, and (ii) do loci earlier associated with nicotine dependence have pleiotropic effects on these two diseases.

METHOD

Our study consisted of 916 cases with bipolar disorder and 1028 controls. On the basis of known associations with nicotine dependence, we genotyped eight single-nucleotide polymorphisms (SNPs) on chromosome 8 (three bins) in the regions of CHRNB3 and CHRNA6, and six SNPs on chromosome 15 (three bins) in the regions of CHRNA5 and CHRNA3.

RESULTS

To determine whether the genetic associations with nicotine dependence are different in bipolar disorder than in the general population, we compared allele frequencies of candidate SNPs between individuals with nicotine dependence only and individuals with both nicotine dependence and bipolar disorder. There were no statistical differences between these frequencies, indicating that genetic association with nicotine dependence is similar in individuals with bipolar disorder as in the general population. In the investigation of pleiotropic effects of these SNPs on bipolar disorder, two highly correlated synonymous SNPs in CHRNB3, rs4952 and rs4953, were significantly associated with bipolar disorder (odds ratio 1.7, 95% confidence interval: 1.2-2.4, P=0.001). This association remained significant both after adjusting for a smoking covariate and analyzing the association in nonsmokers only.

CONCLUSION

Our results suggest that (i) bipolar disorder does not modify the association between nicotine dependence and nicotinic receptor subunit genes, and (ii) variants in CHRNB3/CHRNA6 are independently associated with bipolar disorder.

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.

Genome-wide association of bipolar disorder suggests an enrichment of replicable associations in regions near genes

Although a highly heritable and disabling disease, bipolar disorder's (BD) genetic variants have been challenging to identify. We present new genotype data for 1,190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2,191 cases and 1,434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication (P = 1.5×10⁻⁷). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons (±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies.

Bipolar disorder (BD) is a highly heritable disease that has been difficult to characterize genetically. We have genotyped 1,190 BD cases and 401 controls to find regions of the genome associated with BD. After combining these data with previously existing genotyped samples, we did not find any genome-wide significant associations. However, when we used an additional study to prioritize loci for replication and meta-analysis purposes, we found that we were more likely to see an association in our sample with variants for which we had the highest power. We quantified this effect using logistic regression and saw a strong association between power to detect an effect based on an initial study's results and replication P-value in a second study (P = 1.5×10⁻⁷), supporting the presence of shared genetic risk factors across the studies. Moreover, this association was stronger when we restricted analysis to SNPs near coding regions, and it was further enriched when SNPs had the same direction of effect in both studies. This result supports the presence of genetic factors underlying BD near exons whose collective effect results in a detectable signal and provides a framework for assessing the potential for replication when combining results from multiple studies.

After GWAS: searching for genetic risk for schizophrenia and bipolar disorder

Ten years ago it was widely expected that the genetic basis of common disease would be resolved by genome-wide association studies (GWAS), large-scale studies in which the entire genome is covered by genetic markers. However, the bulk of heritable variance remains unexplained. The authors consider several alternative research strategies. For instance, whereas it has been hypothesized that a common disease is associated primarily with common genetic variants, it is now plausible that multiple rare variants each have a potent effect on disease risk and that they could accumulate to become a substantial component of common disease risk. This idea has become more appealing since the discovery that copy number variants (CNVs) are a substantial source of human mutations and are associated with multiple common diseases. CNVs are structural genomic variants consisting of microinsertions, microdeletions, and transpositions in the human genome. It has been argued that numerous rare CNVs are plausible causes of a substantial proportion of common disease, and rare CNVs have been found to be potent risk factors in schizophrenia and autism. Another approach is to "parse the genome," i.e., reanalyze subsets of current GWAS data, since the noise inherent in genome-wide approaches may be hiding valid associations. Lastly, technological advances and declining costs may allow large-scale genome-wide sequencing that would comprehensively identify all genetic variants. Study groups even larger than the 10,000 subjects in current meta-analyses would be required, but the outcomes may lead to resolution of our current dilemma in common diseases: Where is the missing heritability?

Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

Accuracy of CNV Detection from GWAS Data

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites—Birdsuite, Partek, HelixTree, and PennCNV-Affy—in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two “gold standards,” the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a “gold standard” for detection of CNVs remains to be established.

An investigation of candidate regions for association with bipolar disorder

We performed a case-control study of 1,000 cases and 1,028 controls on 1,509 markers, 1,139 of which were located in a 8 Mb region on chromosome 6 (105-113 Mb). This region has shown evidence of involvement in bipolar disorder (BP) in a number of other studies. We find association between BP and two SNPs in the gene LACE1. SNP rs9486880 and rs11153113 (both have P-values of 2 × 10(-5)). Both P-values are in the top 5% of the distribution derived from null simulations (P = 0.02 and 0.01, respectively). LACE is a good candidate for BP; it is an ATPase. We genotyped 173 other markers in 17 other positional and/or functional loci but found no further evidence of association with BP.

Genetic control of individual differences in gene-specific methylation in human brain

We have observed extensive interindividual differences in DNA methylation of 8590 CpG sites of 6229 genes in 153 human adult cerebellum samples, enriched in CpG island "shores" and at further distances from CpG islands. To search for genetic factors that regulate this variation, we performed a genome-wide association study (GWAS) mapping of methylation quantitative trait loci (mQTLs) for the 8590 testable CpG sites. cis association refers to correlation of methylation with SNPs within 1 Mb of a CpG site. 736 CpG sites showed phenotype-wide significant cis association with 2878 SNPs (after permutation correction for all tested markers and methylation phenotypes). In trans analysis of methylation, which tests for distant regulation effects, associations of 12 CpG sites and 38 SNPs remained significant after phenotype-wide correction. To examine the functional effects of mQTLs, we analyzed 85 genes that were with genetically regulated methylation we observed and for which we had quality gene expression data. Ten genes showed SNP-methylation-expression three-way associations-the same SNP simultaneously showed significant association with both DNA methylation and gene expression, while DNA methylation was significantly correlated with gene expression. Thus, we demonstrated that DNA methylation is frequently a heritable continuous quantitatively variable trait in human brain. Unlike allele-specific methylation, genetic polymorphisms mark both cis- and trans-regulatory genetic sites at measurable distances from their CpG sites. Some of the genetically regulated DNA methylation is directly connected with genetically regulated gene expression variation.

Cerebellar telomere length and psychiatric disorders

We tested whether telomere length is altered in the brains of patients diagnosed with major depression (MD), bipolar disorder (BD) and schizophrenia (SZ) by measuring mean telomere length (mTL) with real-time PCR. The samples are cerebellar gray matter from 46 SZ, 46 BP, and 15 MD patients, and 48 healthy controls. We found no difference in mTL between SZ and controls, BD and controls, MD and controls, or all cases and controls; no correlation between mTL and age was observed, either. This suggests that brain gray matter is unlikely to be related to the telomere length shortening reported in blood of psychiatric patients. White matter deserves further investigation as it has been reported to have a different mTL dynamic from gray matter. Since mTL has been reported to be a heritable quantitative trait, we also carried out genome-wide mapping of genetic factors for mTL, treating mTL as a quantitative trait. No association survived correction of multiple testing for the number of SNPs studied. The previously reported rs2630578 (BICD1) association was not replicated. This suggests that telomere length of cerebellar gray matter is determined by multiple loci with "weak effects."

Genome-wide linkage and follow-up association study of postpartum mood symptoms

OBJECTIVE

Family studies have suggested that postpartum mood symptoms might have a partly genetic etiology. The authors used a genome-wide linkage analysis to search for chromosomal regions that harbor genetic variants conferring susceptibility for such symptoms. The authors then fine-mapped their best linkage regions, assessing single nucleotide polymorphisms (SNPs) for genetic association with postpartum symptoms.

METHOD

Subjects were ascertained from two studies: the NIMH Genetics Initiative Bipolar Disorder project and the Genetics of Recurrent Early-Onset Depression. Subjects included women with a history of pregnancy, any mood disorder, and information about postpartum symptoms. In the linkage study, 1,210 women met criteria (23% with postpartum symptoms), and 417 microsatellite markers were analyzed in multipoint allele sharing analyses. For the association study, 759 women met criteria (25% with postpartum symptoms), and 16,916 SNPs in the regions of the best linkage peaks were assessed for association with postpartum symptoms.

RESULTS

The maximum linkage peak for postpartum symptoms occurred on chromosome 1q21.3-q32.1, with a chromosome-wide significant likelihood ratio Z score (Z(LR)) of 2.93 (permutation p=0.02). This was a significant increase over the baseline Z(LR) of 0.32 observed at this locus among all women with a mood disorder (permutation p=0.004). Suggestive linkage was also found on 9p24.3-p22.3 (Z(LR)=2.91). In the fine-mapping study, the strongest implicated gene was HMCN1 (nominal p=0.00017), containing four estrogen receptor binding sites, although this was not region-wide significant.

CONCLUSIONS

This is the first study to examine the genetic etiology of postpartum mood symptoms using genome-wide data. The results suggest that genetic variations on chromosomes 1q21.3-q32.1 and 9p24.3-p22.3 may increase susceptibility to postpartum mood symptoms.