Increased Expression in Dorsolateral Prefrontal Cortex of CAPON in Schizophrenia and Bipolar Disorder

BACKGROUND: We have previously reported linkage of markers on chromosome 1q22 to schizophrenia, a finding supported by several independent studies. Within this linkage region, we have identified significant linkage disequilibrium between schizophrenia and markers within the gene for carboxyl-terminal PDZ ligand of neuronal nitric oxide synthase (CAPON). Prior sequencing of the ten exons of CAPON failed to reveal a coding mutation associated with illness. METHODS AND FINDINGS: We screened a human fetal brain cDNA library and identified a new isoform of CAPON that consists of the terminal two exons of the gene, and verified the expression of the predicted corresponding protein in human dorsolateral prefrontal cortex (DLPFC). We examined the expression levels of both the ten-exon CAPON transcript and this new isoform in postmortem brain samples from the Stanley Array Collection. Quantitative real-time PCR analysis of RNA from the DLPFC in 105 individuals (35 with schizophrenia, 35 with bipolar disorder, and 35 psychiatrically normal controls) revealed significantly (p < 0.005) increased expression of the new isoform in both schizophrenia and bipolar disorder. Furthermore, this increased expression was significantly associated (p < 0.05) with genotype at three single-nucleotide polymorphisms previously identified as being in linkage disequilibrium with schizophrenia. CONCLUSION: Based on the known interactions between CAPON, neuronal nitric oxide synthase (nNOS), and proteins associated with the N-methyl-D-aspartate receptor (NMDAR) complex, overexpression of either CAPON isoform would be expected to disrupt the association between nNOS and the NMDAR, leading to changes consistent with the NMDAR hypofunctioning hypothesis of schizophrenia. This study adds support to a role of CAPON in schizophrenia, produces new evidence implicating this gene in the etiology of bipolar disorder, and suggests a possible mechanism of action of CAPON in psychiatric illness

Validation of a Cost-Efficient Multi-Purpose SNP Panel for Disease Based Research

BACKGROUND: Here we present convergent methodologies using theoretical calculations, empirical assessment on in-house and publicly available datasets as well as in silico simulations, that validate a panel of SNPs for a variety of necessary tasks in human genetics disease research before resources are committed to larger-scale genotyping studies on those samples. While large-scale well-funded human genetic studies routinely have up to a million SNP genotypes, samples in a human genetics laboratory that are not yet part of such studies may be productively utilized in pilot projects or as part of targeted follow-up work though such smaller scale applications require at least some genome-wide genotype data for quality control purposes such as DNA "barcoding" to detect swaps or contamination issues, determining familial relationships between samples and correcting biases due to population effects such as population stratification in pilot studies. PRINCIPAL FINDINGS: Empirical performance in classification of relative types for any two given DNA samples (e.g., full siblings, parental, etc) indicated that for outbred populations the panel performs sufficiently to classify relationship in extended families and therefore also for smaller structures such as trios and for twin zygosity testing. Additionally, familial relationships do not significantly diminish the (mean match) probability of sharing SNP genotypes in pedigrees, further indicating the uniqueness of the "barcode." Simulation using these SNPs for an African American case-control disease association study demonstrated that population stratification, even in complex admixed samples, can be adequately corrected under a range of disease models using the SNP panel. CONCLUSION: The panel has been validated for use in a variety of human disease genetics research tasks including sample barcoding, relationship verification, population substructure detection and statistical correction. Given the ease of genotyping our specific assay contained herein, this panel represents a useful and economical panel for human geneticists

Genetic Linkage and Association Analysis for Loneliness in Dutch Twin and Sibling Pairs Points to a Region on Chromosome 12q23–24

We obtained evidence from a large study in Dutch twins (N = 8387) and siblings (N = 2295) that variation in loneliness has a genetic component. The heritability estimate for loneliness, which was assessed as an ordinal trait, was 40% and did not differ between males and females. There were 682 sibling pairs with genotypic (around 400 microsatellite markers) data. We combined phenotypic and genotypic data to carry out a genome scan to localize QTLs for loneliness. One region on chromosome 12q23.3-24.3, showed near suggestive linkage. Genetic association tests within this region revealed significant association (p-value 0.009) with one of the alleles of marker D12S79 and with one of the alleles of neighbouring marker D12S395 (p-value 0.043). We review evidence for linkage in this region for psychiatric disorders and discuss our findings within this context. © 2006 Springer Science+Business Media, Inc

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis

Combined linkage and linkage disequilibrium analysis of a motor speech phenotype within families ascertained for autism risk loci

Using behavioral and genetic information from the Autism Genetics Resource Exchange (AGRE) data set we developed phenotypes and investigated linkage and association for individuals with and without Autism Spectrum Disorders (ASD) who exhibit expressive language behaviors consistent with a motor speech disorder. Speech and language variables from Autism Diagnostic Interview-Revised (ADI-R) were used to develop a motor speech phenotype associated with non-verbal or unintelligible verbal behaviors (NVMSD:ALL) and a related phenotype restricted to individuals without significant comprehension difficulties (NVMSD:C). Using Affymetrix 5.0 data, the PPL framework was employed to assess the strength of evidence for or against trait-marker linkage and linkage disequilibrium (LD) across the genome. Ingenuity Pathway Analysis (IPA) was then utilized to identify potential genes for further investigation. We identified several linkage peaks based on two related language-speech phenotypes consistent with a potential motor speech disorder: chromosomes 1q24.2, 3q25.31, 4q22.3, 5p12, 5q33.1, 17p12, 17q11.2, and 17q22 for NVMSD:ALL and 4p15.2 and 21q22.2 for NVMSD:C. While no compelling evidence of association was obtained under those peaks, we identified several potential genes of interest using IPA. Conclusion: Several linkage peaks were identified based on two motor speech phenotypes. In the absence of evidence of association under these peaks, we suggest genes for further investigation based on their biological functions. Given that autism spectrum disorders are complex with a wide range of behaviors and a large number of underlying genes, these speech phenotypes may belong to a group of several that should be considered when developing narrow, well-defined, phenotypes in the attempt to reduce genetic heterogeneity

Behavioral and molecular genetics of reading-related AM and FM detection thresholds

Auditory detection thresholds for certain frequencies of both amplitude modulated (AM) and frequency modulated (FM) dynamic auditory stimuli are associated with reading in typically developing and dyslexic readers. We present the first behavioral and molecular genetic characterization of these two auditory traits. Two extant extended family datasets were given reading tasks and psychoacoustic tasks to determine FM 2 Hz and AM 20 Hz sensitivity thresholds. Univariate heritabilities were significant for both AM (h2 = 0.20) and FM (h2 = 0.29). Bayesian posterior probability of linkage (PPL) analysis found loci for AM (12q, PPL = 81 %) and FM (10p, PPL = 32 %; 20q, PPL = 65 %). Bivariate heritability analyses revealed that FM is genetically correlated with reading, while AM was not. Bivariate PPL analysis indicates that FM loci (10p, 20q) are not also associated with reading

Molecular Determinants of Survival Motor Neuron (SMN) Protein Cleavage by the Calcium-Activated Protease, Calpain

Spinal muscular atrophy (SMA) is a leading genetic cause of childhood mortality, caused by reduced levels of survival motor neuron (SMN) protein. SMN functions as part of a large complex in the biogenesis of small nuclear ribonucleoproteins (snRNPs). It is not clear if defects in snRNP biogenesis cause SMA or if loss of some tissue-specific function causes disease. We recently demonstrated that the SMN complex localizes to the Z-discs of skeletal and cardiac muscle sarcomeres, and that SMN is a proteolytic target of calpain. Calpains are implicated in muscle and neurodegenerative disorders, although their relationship to SMA is unclear. Using mass spectrometry, we identified two adjacent calpain cleavage sites in SMN, S192 and F193. Deletion of small motifs in the region surrounding these sites inhibited cleavage. Patient-derived SMA mutations within SMN reduced calpain cleavage. SMN(D44V), reported to impair Gemin2 binding and amino-terminal SMN association, drastically inhibited cleavage, suggesting a role for these interactions in regulating calpain cleavage. Deletion of A188, a residue mutated in SMA type I (A188S), abrogated calpain cleavage, highlighting the importance of this region. Conversely, SMA mutations that interfere with self-oligomerization of SMN, Y272C and SMNΔ7, had no effect on cleavage. Removal of the recently-identified SMN degron (Δ268-294) resulted in increased calpain sensitivity, suggesting that the C-terminus of SMN is important in dictating availability of the cleavage site. Investigation into the spatial determinants of SMN cleavage revealed that endogenous calpains can cleave cytosolic, but not nuclear, SMN. Collectively, the results provide insight into a novel aspect of the post-translation regulation of SMN

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era