Genetic association of CTNNA3 with late-onset Alzheimer's disease in females

Pathogenetic Insights into Developmental Coordination Disorder Reveal Substantial Overlap with Movement Disorders

Developmental Coordination Disorder (DCD) is a neurodevelopmental condition characterized by non-progressive central motor impairments. Mild movement disorder features have been observed in DCD. Until now, the etiology of DCD has been unclear. Recent studies suggested a genetic substrate in some patients with DCD, but comprehensive knowledge about associated genes and underlying pathogenetic mechanisms is still lacking. In this study, we first identified genes described in the literature in patients with a diagnosis of DCD according to the official diagnostic criteria. Second, we exposed the underlying pathogenetic mechanisms of DCD, by investigating tissue- and temporal gene expression patterns and brain-specific biological mechanisms. Third, we explored putative shared pathogenetic mechanisms between DCD and frequent movement disorders with a known genetic component, including ataxia, chorea, dystonia, and myoclonus. We identified 12 genes associated with DCD in the literature, which are ubiquitously expressed in the central nervous system throughout brain development. These genes are involved in cellular processes, neural signaling, and nervous system development. There was a remarkable overlap (62%) in pathogenetic mechanisms between DCD-associated genes and genes linked with movement disorders. Our findings suggest that some patients might have a genetic etiology of DCD, which could be considered part of a pathogenetic movement disorder spectrum.

Alpha-T-catenin is expressed in peripheral nerves as a constituent of Schwann cell adherens junctions

The adherens junction component, alpha-T-catenin (αTcat) is an established contributor to cardiomyocyte junction structure and function, but recent genomic studies link CTNNA3 polymorphisms to diseases with no clear cardiac underpinning, including asthma, autism and multiple sclerosis, suggesting causal contributions from a different cell-type. We show Ctnna3 mRNA is highly expressed in peripheral nerves (e.g. vagus and sciatic), where αTcat protein enriches at paranodes and myelin incisure adherens junctions of Schwann cells. We validate αTcat immunodetection specificity using a new Ctnna3-knock-out fluorescence reporter mouse line yet find no obvious Schwann cell loss-of-function morphology at the light microscopic level. CTNNA3/Ctnna3 mRNA is also abundantly detected in oligodendrocytes of the central nervous system via public databases, supporting a general role for αTcat in these unique cell-cell junctions. These data suggest that the wide range of diseases linked to CTNNA3 may be through its role in maintaining neuroglial functions of central and peripheral nervous systems. This article has a corresponding First Person interview with the co-first authors of the paper.

Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding

SUMMARY

Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes.

AVAILABILITY AND IMPLEMENTATION

Available at: https://insilico.github.io/npdr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Essential tremor: genetic update]

Essential tremor (ET) is a neurological movement disorder characterised by bilateral limb kinetic/postural tremor, with or without tremor in other body parts including head, voice and lower limbs. Since no causative genes for ET have been identified, it is likely that the disorder occurs as a result of complex genetic factors interacting with various cellular and environmental factors that can result in abnormal function of circuitry involving the cerebello-thalamo-cortical pathway. Genetic analyses have uncovered at least 14 loci and 11 genes that are related to ET, as well as various risk or protective genetic factors. Limitations in ET genetic analyses include inconsistent disease definition, small sample size, varied ethnic backgrounds and many other factors that may contribute to paucity of relevant genetic data in ET. Genetic analyses, coupled with functional and animal studies, have led to better insights into possible pathogenic mechanisms underlying ET. These genetic studies may guide the future development of genetic testing and counselling, and specific, pathogenesis-targeted, therapeutic strategies.

A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits

MOTIVATION

Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups.

RESULTS

We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data.

AVAILABILITY AND IMPLEMENTATION

The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus).

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review

OBJECTIVES

To systematically review findings of GWAS in schizophrenia (SZ) and in bipolar disorder (BD); and to interpret findings, with a focus on identifying independent replications.

METHOD

PubMed search, selection and review of all independent GWAS in SZ or BD, published since March 2011, i.e. studies using non-overlapping samples within each article, between articles, and with those of the previous review (Li et al., 2012).

RESULTS

From the 22 GWAS included in this review, the genetic associations surviving standard GWAS-significance were for genetic markers in the regions of ACSL3/KCNE4, ADCY2, AMBRA1, ANK3, BRP44, DTL, FBLN1, HHAT, INTS7, LOC392301, LOC645434/NMBR, LOC729457, LRRFIP1, LSM1, MDM1, MHC, MIR2113/POU3F2, NDST3, NKAPL, ODZ4, PGBD1, RENBP, TRANK1, TSPAN18, TWIST2, UGT1A1/HJURP, WHSC1L1/FGFR1 and ZKSCAN4. All genes implicated across both reviews are discussed in terms of their function and implication in neuropsychiatry.

CONCLUSION

Taking all GWAS to date into account, AMBRA1, ANK3, ARNTL, CDH13, EFHD1 (albeit with different alleles), MHC, PLXNA2 and UGT1A1 have been implicated in either disorder in at least two reportedly non-overlapping samples. Additionally, evidence for a SZ/BD common genetic basis is most strongly supported by the implication of ANK3, NDST3, and PLXNA2.

αT-catenin: A developmentally dispensable, disease-linked member of the α-catenin family

α-Catenins are actin-filament binding proteins and critical subunits of the cadherin-catenin cell-cell adhesive complex. They are found in nominally-defined epithelial (E), neural (N), and testis (T) forms transcribed from three distinct genes. While most of α-catenin research has focused on the developmentally essential founding member, αE-catenin, this review discusses recent studies on αT-catenin (CTNNA3), a developmentally dispensable isoform that is emerging as relevant to cardiac, allergic and neurological diseases.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection.

Assessment of Three New Loci from Genome-wide Association Study in Essential Tremor in Chinese population

Essential tremor (ET) is the most common tremor disorder. Evidences indicated that genetics plays an essential role in the researches of etiology. A new genome-wide association study (GWAS) from European population identified three novel loci in ET, which were rs10937625 in STK32B, rs17590046 in PPARGC1A, and rs12764057, rs10822974 and rs7903491 in CTNNA3. Due to the different genetic background in different population, we performed a case-control study to investigate these variants in a cohort of 533 subjects in Chinese population. We found a significant difference in the distributions of genotypes and alleles frequencies between ET and control groups of rs10937625 (genotype p = 0.037, OR = 0.69[0.48–0.98]; allele p = 0.033, OR = 0.82[0.69–0.99]) and rs7903491 (genotype p = 0.030, OR = 1.34[1.03–1.74]; allele p = 0.029, OR = 1.16[1.02–1.32]) after adjusted for age and gender. And no associations were detected between rs17590046 (genotype p = 0.794; allele p = 0.791), rs12764057 (genotype p = 0.337; allele p = 0.337), rs10822974 (genotype p = 0.102; allele p = 0.100) and ET in Chinese population individually. Our research supports that C allele of rs10937625 in STK32B is a protective factor and G allele of rs7903491 in CTNNA3 is a risk factor for ET in Chinese population.

Unraveling the genes implicated in Alzheimer's disease

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disorder and it is the most common form of dementia in the elderly. Early onset AD is caused by mutations in three genes: Amyloid-β precursor protein, presenilin 1 (PSEN1) and PSEN2. Late onset AD (LOAD) is complex and apolipoprotein E is the only unanimously accepted genetic risk factor for its development. Various genes implicated in AD have been identified using advanced genetic technologies, however, there are many additional genes that remain unidentified. The present review highlights the genetics of early and LOAD and summarizes the genes involved in different signaling pathways. This may provide insight into neurodegenerative disease research and will facilitate the development of effective strategies to combat AD.

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Genome-wide association study in essential tremor identifies three new loci

We conducted a genome-wide association study of essential tremor, a common movement disorder characterized mainly by a postural and kinetic tremor of the upper extremities. Twin and family history studies show a high heritability for essential tremor. The molecular genetic determinants of essential tremor are unknown. We included 2807 patients and 6441 controls of European descent in our two-stage genome-wide association study. The 59 most significantly disease-associated markers of the discovery stage were genotyped in the replication stage. After Bonferroni correction two markers, one (rs10937625) located in the serine/threonine kinase STK32B and one (rs17590046) in the transcriptional coactivator PPARGC1A were associated with essential tremor. Three markers (rs12764057, rs10822974, rs7903491) in the cell-adhesion molecule CTNNA3 were significant in the combined analysis of both stages. The expression of STK32B was increased in the cerebellar cortex of patients and expression quantitative trait loci database mining showed association between the protective minor allele of rs10937625 and reduced expression in cerebellar cortex. We found no expression differences related to disease status or marker genotype for the other two genes. Replication of two lead single nucleotide polymorphisms of previous small genome-wide association studies (rs3794087 in SLC1A2, rs9652490 in LINGO1) did not confirm the association with essential tremor.

Molecular Analysis of Hybrid Neurofibroma/Schwannoma Identifies Common Monosomy 22 and α-T-Catenin/CTNNA3 as a Novel Candidate Tumor Suppressor

Neurofibromas and schwannomas are benign Schwann cell-derived peripheral nerve sheath tumors arising sporadically and within neurofibromatoses. Multiple tumors are a hallmark of neurofibromatosis type 1 (NF1) and type 2 (NF2) and schwannomatosis. Neurofibromas in NF1 and schwannomas in NF2 or schwannomatosis are defined by distinctive molecular hits. Among these, multiple hybrid neurofibromas/schwannomas may also appear, not yet being defined by a molecular background. We therefore performed molecular analysis of 22 hybrid neurofibromas/schwannomas using array comparative genomic hybridization, immunohistochemistry, quantitative RT-PCR, and functional analyses of cultured Schwann cells. Furthermore, we analyzed SMARCB1 by fluorescence in situ hybridization and multiplex ligation-dependent probe. Monosomy 22 was identified in 44% of tumors of tested patients with hybrid neurofibromas/schwannomas. In addition, in a single case, we detected focal deletion of the α-T-catenin/CTNNA3 gene (10q21.3). To further characterize this candidate, transient knockdown of α-T-catenin in Schwann cells was performed. CTNNA3 depleted cells showed cytoskeletal abnormalities and reduced E-cadherin expression, indicating epithelial-mesenchymal transition-like abnormalities. To conclude, we uncovered loss of chromosome 22 in almost half of all cases with hybrid neurofibromas/schwannomas of patients with multiple peripheral nerve sheath tumors. We tagged α-T-catenin/CTNNA3 as a novel candidate gene. Our functional investigations might indicate involvement of α-T-catenin/CTNNA3 in the biology of peripheral nerve sheath tumors.

αT-catenin in restricted brain cell types and its potential connection to autism

BACKGROUND

Recent genetic association studies have linked the cadherin-based adherens junction protein alpha-T-catenin (αT-cat, CTNNA3) with the development of autism. Where αT-cat is expressed in the brain, and how its loss could contribute to this disorder, are entirely unknown.

METHODS

We used the αT-cat knockout mouse to examine the localization of αT-cat in the brain, and we used histology and immunofluorescence analysis to examine the neurobiological consequences of its loss.

RESULTS

We found that αT-cat comprises the ependymal cell junctions of the ventricles of the brain, and its loss led to compensatory upregulation of αE-cat expression. Notably, αT-cat was not detected within the choroid plexus, which relies on cell junction components common to typical epithelial cells. While αT-cat was not detected in neurons of the cerebral cortex, it was abundantly detected within neuronal structures of the molecular layer of the cerebellum. Although αT-cat loss led to no overt differences in cerebral or cerebellar structure, RNA-sequencing analysis from wild type versus knockout cerebella identified a number of disease-relevant signaling pathways associated with αT-cat loss, such as GABA-A receptor activation.

CONCLUSIONS

These findings raise the possibility that the genetic associations between αT-cat and autism may be due to ependymal and cerebellar defects, and highlight the potential importance of a seemingly redundant adherens junction component to a neurological disorder.

Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network

Background

The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.

Results

For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D.

Conclusions

Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.

Maternal Modifiers and Parent-of-Origin Bias of the Autism-Associated 16p11.2 CNV

Recurrent deletions and duplications at chromosomal region 16p11.2 are a major genetic contributor to autism but also associate with a wider range of pediatric diagnoses, including intellectual disability, coordination disorder, and language disorder. In order to investigate the potential genetic basis for phenotype variability, we assessed the parent of origin of the 16p11.2 copy-number variant (CNV) and the presence of additional CNVs in 126 families for which detailed phenotype data were available. Among de novo cases, we found a strong maternal bias for the origin of deletions (59/66, 89.4% of cases, p = 2.38 × 10(-11)), the strongest such effect so far observed for a CNV associated with a microdeletion syndrome. In contrast to de novo events, we observed no transmission bias for inherited 16p11.2 CNVs, consistent with a female meiotic hotspot of unequal crossover driving this maternal bias. We analyzed this 16p11.2 CNV cohort for the presence of secondary CNVs and found a significant maternal transmission bias for secondary deletions (32 maternal versus 14 paternal, p = 1.14 × 10(-2)). Of the secondary deletions that disrupted a gene, 82% were either maternally inherited or de novo (p = 4.3 × 10(-3)). Nine probands carry secondary CNVs that disrupt genes associated with autism and/or intellectual disability risk variants. Our findings demonstrate a strong bias toward maternal origin of 16p11.2 de novo deletions as well as a maternal transmission bias for secondary deletions that contribute to the clinical outcome on a background sensitized by the 16p11.2 CNV.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data

More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~10(6)) in the brain from thousands of subjects (n~10(3)). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC+NV)n(2)) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.

New functions for alpha-catenins in health and disease: from cancer to heart regeneration

Strong cell-cell adhesion mediated by adherens junctions is dependent on anchoring the transmembrane cadherin molecule to the underlying actin cytoskeleton. To do this, the cadherin cytoplasmic domain interacts with catenin proteins, which include α-catenin that binds directly to filamentous actin. Originally thought to be a static structure, the connection between the cadherin/catenin adhesion complex and the actin cytoskeleton is now considered to be dynamic and responsive to both intercellular and intracellular signals. Alpha-catenins are mechanosensing proteins that undergo conformational change in response to cytoskeletal tension thus modifying the linkage between the cadherin and the actin cytoskeleton. There are three α-catenin isoforms expressed in mouse and human: αE-catenin (CTNNA1), αN-catenin (CTNNA2) and αT-catenin (CTNNA3). This review summarizes recent progress in understanding the in vivo function(s) of α-catenins in tissue morphogenesis, homeostasis and disease. The role of α-catenin in the regulation of cellular proliferation will be discussed in the context of cancer and regeneration.

Whole exome sequencing in females with autism implicates novel and candidate genes

Classical autism or autistic disorder belongs to a group of genetically heterogeneous conditions known as Autism Spectrum Disorders (ASD). Heritability is estimated as high as 90% for ASD with a recently reported compilation of 629 clinically relevant candidate and known genes. We chose to undertake a descriptive next generation whole exome sequencing case study of 30 well-characterized Caucasian females with autism (average age, 7.7 ± 2.6 years; age range, 5 to 16 years) from multiplex families. Genomic DNA was used for whole exome sequencing via paired-end next generation sequencing approach and X chromosome inactivation status. The list of putative disease causing genes was developed from primary selection criteria using machine learning-derived classification score and other predictive parameters (GERP2, PolyPhen2, and SIFT). We narrowed the variant list to 10 to 20 genes and screened for biological significance including neural development, function and known neurological disorders. Seventy-eight genes identified met selection criteria ranging from 1 to 9 filtered variants per female. Five females presented with functional variants of X-linked genes (IL1RAPL1, PIR, GABRQ, GPRASP2, SYTL4) with cadherin, protocadherin and ankyrin repeat gene families most commonly altered (e.g., CDH6, FAT2, PCDH8, CTNNA3, ANKRD11). Other genes related to neurogenesis and neuronal migration (e.g., SEMA3F, MIDN), were also identified.

A genome-wide association meta-analysis of plasma Aβ peptides concentrations in the elderly

Amyloid beta (Aβ) peptides are the major components of senile plaques, one of the main pathological hallmarks of Alzheimer disease (AD). However, Aβ peptides' functions are not fully understood and seem to be highly pleiotropic. We hypothesized that plasma Aβ peptides concentrations could be a suitable endophenotype for a genome-wide association study (GWAS) designed to (i) identify novel genetic factors involved in amyloid precursor protein metabolism and (ii) highlight relevant Aβ-related physiological and pathophysiological processes. Hence, we performed a genome-wide association meta-analysis of four studies totaling 3 528 healthy individuals of European descent and for whom plasma Aβ1-40 and Aβ1-42 peptides levels had been quantified. Although we did not observe any genome-wide significant locus, we identified 18 suggestive loci (P<1 × 10(-)(5)). Enrichment-pathway analyses revealed canonical pathways mainly involved in neuronal functions, for example, axonal guidance signaling. We also assessed the biological impact of the gene most strongly associated with plasma Aβ1-42 levels (cortexin 3, CTXN3) on APP metabolism in vitro and found that the gene protein was able to modulate Aβ1-42 secretion. In conclusion, our study results suggest that plasma Aβ peptides levels are valid endophenotypes in GWASs and can be used to characterize the metabolism and functions of APP and its metabolites.

Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction

In this paper, we explore the effects of integrating multi-dimensional imaging genomics data for Alzheimer's disease (AD) prediction using machine learning approaches. Precisely, we compare our three recent proposed feature selection methods [i.e., multiple kernel learning (MKL), high-order graph matching based feature selection (HGM-FS), sparse multimodal learning (SMML)] using four widely-used modalities [i.e., magnetic resonance imaging (MRI), positron emission tomography (PET), cerebrospinal fluid (CSF), and genetic modality single-nucleotide polymorphism (SNP)]. This study demonstrates the performance of each method using these modalities individually or integratively, and may be valuable to clinical tests in practice. Our experimental results suggest that for AD prediction, in general, (1) in terms of accuracy, PET is the best modality; (2) Even though the discriminant power of genetic SNP features is weak, adding this modality to other modalities does help improve the classification accuracy; (3) HGM-FS works best among the three feature selection methods; (4) Some of the selected features are shared by all the feature selection methods, which may have high correlation with the disease. Using all the modalities on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the best accuracies, described as (mean ± standard deviation)%, among the three methods are (76.2 ± 11.3)% for AD vs. MCI, (94.8 ± 7.3)% for AD vs. HC, (76.5 ± 11.1)% for MCI vs. HC, and (71.0 ± 8.4)% for AD vs. MCI vs. HC, respectively.

The genetics of Alzheimer's disease

Alzheimer’s disease (AD) is a complex and heterogeneous neurodegenerative disorder, classified as either early onset (under 65 years of age), or late onset (over 65 years of age). Three main genes are involved in early onset AD: amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2). The apolipoprotein E (APOE) E4 allele has been found to be a main risk factor for late-onset Alzheimer’s disease. Additionally, genome-wide association studies (GWASs) have identified several genes that might be potential risk factors for AD, including clusterin (CLU), complement receptor 1 (CR1), phosphatidylinositol binding clathrin assembly protein (PICALM), and sortilin-related receptor (SORL1). Recent studies have discovered additional novel genes that might be involved in late-onset AD, such as triggering receptor expressed on myeloid cells 2 (TREM2) and cluster of differentiation 33 (CD33). Identification of new AD-related genes is important for better understanding of the pathomechanisms leading to neurodegeneration. Since the differential diagnoses of neurodegenerative disorders are difficult, especially in the early stages, genetic testing is essential for diagnostic processes. Next-generation sequencing studies have been successfully used for detecting mutations, monitoring the epigenetic changes, and analyzing transcriptomes. These studies may be a promising approach toward understanding the complete genetic mechanisms of diverse genetic disorders such as AD.

Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci

Genetic and environmental components as well as their interaction contribute to the risk of schizophrenia, making it highly relevant to include environmental factors in genetic studies of schizophrenia. This study comprises genome-wide association (GWA) and follow-up analyses of all individuals born in Denmark since 1981 and diagnosed with schizophrenia as well as controls from the same birth cohort. Furthermore, we present the first genome-wide interaction survey of single nucleotide polymorphisms (SNPs) and maternal cytomegalovirus (CMV) infection. The GWA analysis included 888 cases and 882 controls, and the follow-up investigation of the top GWA results was performed in independent Danish (1396 cases and 1803 controls) and German-Dutch (1169 cases, 3714 controls) samples. The SNPs most strongly associated in the single-marker analysis of the combined Danish samples were rs4757144 in ARNTL (P=3.78 × 10(-6)) and rs8057927 in CDH13 (P=1.39 × 10(-5)). Both genes have previously been linked to schizophrenia or other psychiatric disorders. The strongest associated SNP in the combined analysis, including Danish and German-Dutch samples, was rs12922317 in RUNDC2A (P=9.04 × 10(-7)). A region-based analysis summarizing independent signals in segments of 100 kb identified a new region-based genome-wide significant locus overlapping the gene ZEB1 (P=7.0 × 10(-7)). This signal was replicated in the follow-up analysis (P=2.3 × 10(-2)). Significant interaction with maternal CMV infection was found for rs7902091 (P(SNP × CMV)=7.3 × 10(-7)) in CTNNA3, a gene not previously implicated in schizophrenia, stressing the importance of including environmental factors in genetic studies.

Application of array comparative genomic hybridization in 256 patients with developmental delay or intellectual disability

We used whole-genome exon-targeted oligonucleotide array comparative genomic hybridization (array CGH) in a cohort of 256 patients with developmental delay (DD)/intellectual disability (ID) with or without dysmorphic features, additional neurodevelopmental abnormalities, and/or congenital malformations. In 69 patients, we identified 84 non-polymorphic copy-number variants, among which 41 are known to be clinically relevant, including two recently described deletions, 4q21.21q21.22 and 17q24.2. Chromosomal microarray analysis revealed also 15 potentially pathogenic changes, including three rare deletions, 5q35.3, 10q21.3, and 13q12.11. Additionally, we found 28 copy-number variants of unknown clinical significance. Our results further support the notion that copy-number variants significantly contribute to the genetic etiology of DD/ID and emphasize the efficacy of the detection of novel candidate genes for neurodevelopmental disorders by whole-genome array CGH.

Evidence of recessive Alzheimer disease loci in a Caribbean Hispanic data set: genome-wide survey of runs of homozygosity

IMPORTANCE The search for novel Alzheimer disease (AD) genes or pathologic mutations within known AD loci is ongoing. The development of array technologies has helped to identify rare recessive mutations among long runs of homozygosity (ROHs), in which both parental alleles are identical. Caribbean Hispanics are known to have an elevated risk for AD and tend to have large families with evidence of inbreeding. OBJECTIVE To test the hypothesis that the late-onset AD in a Caribbean Hispanic population might be explained in part by the homozygosity of unknown loci that could harbor recessive AD risk haplotypes or pathologic mutations. DESIGN We used genome-wide array data to identify ROHs (>1 megabase) and conducted global burden and locus-specific ROH analyses. SETTING A whole-genome case-control ROH study. PARTICIPANTS A Caribbean Hispanic data set of 547 unrelated cases (48.8% with familial AD) and 542 controls collected from a population known to have a 3-fold higher risk of AD vs non-Hispanics in the same community. Based on a Structure program analysis, our data set consisted of African Hispanic (207 cases and 192 controls) and European Hispanic (329 cases and 326 controls) participants. EXPOSURE Alzheimer disease risk genes. MAIN OUTCOMES AND MEASURES We calculated the total and mean lengths of the ROHs per sample. Global burden measurements among autosomal chromosomes were investigated in cases vs controls. Pools of overlapping ROH segments (consensus regions) were identified, and the case to control ratio was calculated for each consensus region. We formulated the tested hypothesis before data collection. RESULTS In total, we identified 17 137 autosomal regions with ROHs. The mean length of the ROH per person was significantly greater in cases vs controls (P = .0039), and this association was stronger with familial AD (P = .0005). Among the European Hispanics, a consensus region at the EXOC4 locus was significantly associated with AD even after correction for multiple testing (empirical P value 1 [EMP1], .0001; EMP2, .002; 21 AD cases vs 2 controls). Among the African Hispanic subset, the most significant but nominal association was observed for CTNNA3, a well-known AD gene candidate (EMP1, .002; 10 AD cases vs 0 controls). CONCLUSIONS AND RELEVANCE Our results show that ROHs could significantly contribute to the etiology of AD. Future studies would require the analysis of larger, relatively inbred data sets that might reveal novel recessive AD genes. The next step is to conduct sequencing of top significant loci in a subset of samples with overlapping ROHs.

LRRTM3 interacts with APP and BACE1 and has variants associating with late-onset Alzheimer's disease (LOAD)

Leucine rich repeat transmembrane protein 3 (LRRTM3) is member of a synaptic protein family. LRRTM3 is a nested gene within α-T catenin (CTNNA3) and resides at the linkage peak for late-onset Alzheimer’s disease (LOAD) risk and plasma amyloid β (Aβ) levels. In-vitro knock-down of LRRTM3 was previously shown to decrease secreted Aβ, although the mechanism of this is unclear. In SH-SY5Y cells overexpressing APP and transiently transfected with LRRTM3 alone or with BACE1, we showed that LRRTM3 co-localizes with both APP and BACE1 in early endosomes, where BACE1 processing of APP occurs. Additionally, LRRTM3 co-localizes with APP in primary neuronal cultures from Tg2576 mice transduced with LRRTM3-expressing adeno-associated virus. Moreover, LRRTM3 co-immunoprecipitates with both endogenous APP and overexpressed BACE1, in HEK293T cells transfected with LRRTM3. SH-SY5Y cells with knock-down of LRRTM3 had lower BACE1 and higher CTNNA3 mRNA levels, but no change in APP. Brain mRNA levels of LRRTM3 showed significant correlations with BACE1, CTNNA3 and APP in ∼400 humans, but not in LRRTM3 knock-out mice. Finally, we assessed 69 single nucleotide polymorphisms (SNPs) within and flanking LRRTM3 in 1,567 LOADs and 2,082 controls and identified 8 SNPs within a linkage disequilibrium block encompassing 5′UTR-Intron 1 of LRRTM3 that formed multilocus genotypes (MLG) with suggestive global association with LOAD risk (p = 0.06), and significant individual MLGs. These 8 SNPs were genotyped in an independent series (1,258 LOADs and 718 controls) and had significant global and individual MLG associations in the combined dataset (p = 0.02–0.05). Collectively, these results suggest that protein interactions between LRRTM3, APP and BACE1, as well as complex associations between mRNA levels of LRRTM3, CTNNA3, APP and BACE1 in humans might influence APP metabolism and ultimately risk of AD.

George-Hyslop P, Mayeux R, Haines JL, Pericak-Vance MA, Yoshida M, Nishida N, Tokunaga K, Yamamoto K, Tsuji S, Kanazawa I, Ihara Y, Schellenberg GD, Farrer LA, Kuwano R. SORL1 is genetically associated with late-onset Alzheimer's disease in Japanese, Koreans and Caucasians

To discover susceptibility genes of late-onset Alzheimer's disease (LOAD), we conducted a 3-stage genome-wide association study (GWAS) using three populations: Japanese from the Japanese Genetic Consortium for Alzheimer Disease (JGSCAD), Koreans, and Caucasians from the Alzheimer Disease Genetic Consortium (ADGC). In Stage 1, we evaluated data for 5,877,918 genotyped and imputed SNPs in Japanese cases (n = 1,008) and controls (n = 1,016). Genome-wide significance was observed with 12 SNPs in the APOE region. Seven SNPs from other distinct regions with p-values <2×10(-5) were genotyped in a second Japanese sample (885 cases, 985 controls), and evidence of association was confirmed for one SORL1 SNP (rs3781834, P = 7.33×10(-7) in the combined sample). Subsequent analysis combining results for several SORL1 SNPs in the Japanese, Korean (339 cases, 1,129 controls) and Caucasians (11,840 AD cases, 10,931 controls) revealed genome wide significance with rs11218343 (P = 1.77×10(-9)) and rs3781834 (P = 1.04×10(-8)). SNPs in previously established AD loci in Caucasians showed strong evidence of association in Japanese including rs3851179 near PICALM (P = 1.71×10(-5)) and rs744373 near BIN1 (P = 1.39×10(-4)). The associated allele for each of these SNPs was the same as in Caucasians. These data demonstrate for the first time genome-wide significance of LOAD with SORL1 and confirm the role of other known loci for LOAD in Japanese. Our study highlights the importance of examining associations in multiple ethnic populations.

BCL9 and C9orf5 are associated with negative symptoms in schizophrenia: meta-analysis of two genome-wide association studies

Schizophrenia is a chronic and debilitating psychiatric condition affecting slightly more than 1% of the population worldwide and it is a multifactorial disorder with a high degree of heritability (80%) based on family and twin studies. Increasing lines of evidence suggest intermediate phenotypes/endophenotypes are more associated with causes of the disease and are less genetically complex than the broader disease spectrum. Negative symptoms in schizophrenia are attractive intermediate phenotypes based on their clinical and treatment response features. Therefore, our objective was to identify genetic variants underlying the negative symptoms of schizophrenia by analyzing two genome-wide association (GWA) data sets consisting of a total of 1,774 European-American patients and 2,726 controls. Logistic regression analysis of negative symptoms as a binary trait (adjusted for age and sex) was performed using PLINK. For meta-analysis of two datasets, the fixed-effect model in PLINK was applied. Through meta-analysis we identified 25 single nucleotide polymorphisms (SNPs) associated with negative symptoms with p<5×10(-5). Especially we detected five SNPs in the first two genes/loci strongly associated with negative symptoms of schizophrenia (P(meta-analysis)<6.22×10(-6)), which included three SNPs in the BCL9 gene: rs583583 showed the strongest association at a P(meta-analysis) of 6.00×10(-7) and two SNPs in the C9orf5 (the top SNP is rs643410 with a p = 1.29 ×10(-6)). Through meta-analysis, we identified several additional negative symptoms associated genes (ST3GAL1, RNF144, CTNNA3 and ZNF385D). This is the first report of the common variants influencing negative symptoms of schizophrenia. These results provide direct evidence of using of negative symptoms as an intermediate phenotype to dissect the complex genetics of schizophrenia. However, additional studies are warranted to examine the underlying mechanisms of these disease-associated SNPs in these genes.

Epileptic encephalopathies of the Landau-Kleffner and continuous spike and waves during slow-wave sleep types: genomic dissection makes the link with autism

PURPOSE

The continuous spike and waves during slow-wave sleep syndrome (CSWSS) and the Landau-Kleffner (LKS) syndrome are two rare epileptic encephalopathies sharing common clinical features including seizures and regression. Both CSWSS and LKS can be associated with the electroencephalography pattern of electrical status epilepticus during slow-wave sleep and are part of a clinical continuum that at its benign end also includes rolandic epilepsy (RE) with centrotemporal spikes. The CSWSS and LKS patients can also have behavioral manifestations that overlap the spectrum of autism disorders (ASD). An impairment of brain development and/or maturation with complex interplay between genetic predisposition and nongenetic factors has been suspected. A role for autoimmunity has been proposed but the pathophysiology of CSWSS and of LKS remains uncharacterized.

METHODS

In recent years, the participation of rare genomic alterations in the susceptibility to epileptic and autistic disorders has been demonstrated. The involvement of copy number variations (CNVs) in 61 CSWSS and LKS patients was questioned using comparative genomic hybridization assays coupled with validation by quantitative polymerase chain reaction (PCR).

KEY FINDINGS

Whereas the patients showed highly heterogeneous in genomic architecture, several potentially pathogenic alterations were detected. A large number of these corresponded to genomic regions or genes (ATP13A4, CDH9, CDH13, CNTNAP2, CTNNA3, DIAPH3, GRIN2A, MDGA2, SHANK3) that have been either associated with ASD for most of them, or involved in speech or language impairment, or in RE. Particularly, CNVs encoding cell adhesion proteins (cadherins, protocadherins, contactins, catenins) were detected with high frequency (≈20% of the patients) and significant enrichment (cell adhesion: p = 0.027; cell adhesion molecule binding: p = 9.27 × 10(-7)).

SIGNIFICANCE

Overall our data bring the first insights into the possible molecular pathophysiology of CSWSS and LKS. The overrepresentation of cell adhesion genes and the strong overlap with the genetic, genomic and molecular ASD networks, provide an exciting and unifying view on the clinical links among CSWSS, LKS, and ASD.

Butyrylcholinesterase genotype and gender influence Alzheimer's disease phenotype

Retrospective data are presented to support a spectrum of early Alzheimer's disease (AD) along a continuum defined by gender and genotype. The putative neurodegenerative mechanisms driving distinct phenotypes at each end of the spectrum are glial hypoactivity associated with early failure of synaptic cholinergic neurotransmission and glial overactivation associated with loss of neural network connectivity due to accelerated age-related breakdown of myelin. In early AD, male butyrylcholinesterase K-variant carriers with one or two apolipoprotein ɛ4 alleles have prominent medial temporal atrophy, synaptic failure, cognitive decline, and accumulation of aggregated beta-amyloid peptide. Increasing synaptic acetylcholine in damaged but still functional cholinergic synapses improves cognitive symptoms, whereas increasing the ability of glia to support synapses and to clear beta-amyloid peptide might be disease-modifying. Conversely, chronic glial overactivation can also drive degenerative processes and in butyrylcholinesterase K-variant negative females generalized glial overactivation may be the main driver from mild cognitive impairment to AD. Females are more likely than males to have accelerated age-related myelin breakdown, more widespread white matter loss, loss of neural network connectivity, whole brain atrophy, and functional decline. Increasing extracellular acetylcholine levels blocks glial activation, reduces myelin loss and damage to neural network connectivity, and is disease-modifying. Between extremes characterized by gender, genotype, and age, pathophysiology may be mixed and this spectrum may explain much of the heterogeneity of amnestic mild cognitive impairment. Preservation of the functional integrity of the neural network may be an important component of strengthening cognitive reserve and significantly delaying the onset and progression of dementia, particularly in females. Prospective confirmation of these hypotheses is required. Implications for future research and therapeutic opportunities are discussed.

Alpha T-catenin (CTNNA3): a gene in the hand is worth two in the nest

Alpha-T-Catenin (CTNNA3) is a key protein of the adherens junctional complex in epithelial cells playing a crucial role in cellular adherence. What makes this gene particularly interesting is that it is located within a common fragile site, is epigenetically regulated, is transcribed through multiple promoters, and generates a variety of alternate transcripts. Finally, CTNNA3 has a nested gene (LRTMM3) embedded within its genomic context transcribed in the opposite direction. Apart from the complexity of its regulation, alterations in both CTNNA3 and LRTMM3 are implicated in human disease.

A common variant in TFB1M is associated with reduced insulin secretion and increased future risk of type 2 diabetes

Type 2 diabetes (T2D) evolves when insulin secretion fails. Insulin release from the pancreatic β cell is controlled by mitochondrial metabolism, which translates fluctuations in blood glucose into metabolic coupling signals. We identified a common variant (rs950994) in the human transcription factor B1 mitochondrial (TFB1M) gene associated with reduced insulin secretion, elevated postprandial glucose levels, and future risk of T2D. Because islet TFB1M mRNA levels were lower in carriers of the risk allele and correlated with insulin secretion, we examined mice heterozygous for Tfb1m deficiency. These mice displayed lower expression of TFB1M in islets and impaired mitochondrial function and released less insulin in response to glucose in vivo and in vitro. Reducing TFB1M mRNA and protein in clonal β cells by RNA interference impaired complexes of the mitochondrial oxidative phosphorylation system. Consequently, nutrient-stimulated ATP generation was reduced, leading to perturbed insulin secretion. We conclude that a deficiency in TFB1M and impaired mitochondrial function contribute to the pathogenesis of T2D.

ChIP-chip analysis of neurexins and other candidate genes for addiction and neuropsychiatric disorders

Several addiction susceptibility genes have been mapped by linkage and genomewide association. However, functional alleles associated with disease risk have not been identified, with a few possible exceptions. In addition, little is known about the cis- and trans-acting factors involved in regulating their expression. To address these issues, we used a ChIP-chip approach to identify regulatory elements in fetal-brain- targeting genes implicated in addiction and other neuropsychiatric conditions. Our data point to a number of putative regulatory elements, several of which, we show, are functionally significant. Many established or putative regulatory elements map near-disease-associated SNPs. These regions would be of interest to survey for patient-specific functional variants involved in disease susceptibility.

Hotspots of large rare deletions in the human genome

Background

We have examined the genomic distribution of large rare autosomal deletions in a sample of 440 parent-parent-child trios from the Quebec founder population (QFP) which was recruited for a study of Attention Deficit Hyperactivity Disorder.

Methodology/Principal Findings

DNA isolated from blood was genotyped on Illumina Hap300 arrays. PennCNV combined with visual evaluation of images generated by the Beadstudio program was used to determine deletion boundary definition of sufficient precision to discern independent events, with near-perfect concordance between parent and child in about 98% of the 399 events detected in the offspring; the remaining 7 deletions were considered de novo. We defined several genomic regions of very high deletion frequency (‘hotspots’), usually of 0.4–0.6 Mb in length where independent rare deletions were found at frequencies of up to 100 fold higher than the average for the genome as a whole. Five of the 7 de novo deletions were in these hotspots. The same hotspots were also observed in three other studies on members of the QFP, those with schizophrenia, with endometriosis and those from a longevity cohort.

Conclusions/Significance

Nine of the 13 hotspots carry one gene (7 of which are very long), while the rest contain no known genes. All nine genes have been implicated in disease. The patterns of exon deletions support the proposed roles for some of these genes in human disease, such as NRXN1 and PARKIN, and suggest limited roles or no role at all, for others, including MACROD2 and CTNNA3. Our results also offer an alternative interpretation for the observations of deletions in tumors which have been proposed as reflecting tumor-suppressive activity of genes in these hotspots.

Progression from mild cognitive impairment to Alzheimer's disease: effects of sex, butyrylcholinesterase genotype, and rivastigmine treatment

OBJECTIVE

Evaluate the effect of sex and butyrylcholinesterase (BuChE) genotype on the incidence of Alzheimer's disease (AD), cognitive and functional decline, brain volume changes, and response to rivastigmine treatment in individuals with mild cognitive impairment (MCI).

METHODS

This retrospective exploratory analysis from a 3-4 year, randomized, placebo-controlled study of rivastigmine in MCI patients included participants who consented to pharmacogenetic testing.

RESULTS

Of a total of 1018 patients, 490 [253 (52%) female] were successfully genotyped for BuChE. In patients receiving placebo, the BuChE wt/wt genotype was associated with a statistically significant higher incidence of progression to AD and functional decline in women, compared with men with the BuChE wt/wt genotype. In patients with a BuChE-K allele receiving placebo, incidence of progression to AD and rate of functional decline were not significantly different by sex; however, cognitive decline was significantly faster in men. Statistically significant benefits of rivastigmine treatment on incident AD, functional decline, ventricular volume expansion, whole-brain atrophy, and white matter loss were evident in female BuChE wt/wt.

CONCLUSION

Sex and BuChE genotype seem to differentially influence the type of decline in MCI patients, with more rapid progression of cognitive decline in male BuChE-K, and more incident AD and functional decline in female BuChE wt/wt. Cognitive decline in male BuChE-K and functional decline and incident AD in female BuChE wt/wt were significantly attenuated by rivastigmine. Rivastigmine treatment also significantly reduced ventricular expansion, whole-brain atrophy rate, and white matter loss in female BuChE wt/wt, suggesting a possible disease-modifying effect.

Alpha-T-catenin (CTNNA3) gene was identified as a risk variant for toluene diisocyanate-induced asthma by genome-wide association analysis

BACKGROUND

Toluene diisocyanate (TDI) is the most important cause of occupational asthma, but the genetic mechanism of TDI-induced asthma is still unknown.

OBJECTIVE

The objective of the study was to identify susceptibility alleles associated with the TDI-induced asthma phenotype.

METHODS

We conducted a genome-wide association study in 84 patients with TDI-induced asthma and 263 unexposed healthy normal controls using Affymetrix 500K SNPchip. We also investigated the relationships between genetic polymorphisms and transcript levels in Epstein-Barr virus-transformed lymphoblastoid cell lines from patients with TDI-induced asthma enrolled in this study.

RESULTS

Genetic polymorphisms of CTNNA3 (catenin alpha 3, alpha-T catenin) were significantly associated with the TDI-induced asthma phenotype (5.84 x 10(-6) for rs10762058, 1.41 x 10(-5) for rs7088181, 2.03 x 10(-5) for rs4378283). Carriers with the minor haplotype, HT2 [GG], of two genetic polymorphisms (rs10762058 and rs7088181) showed significantly lower PC(20) methacholine level (P=0.041) and lower mRNA expression of CTNNA3 than non-carriers (P=0.040). A genetic polymorphism in the 3' downstream region of CTNNA3 (rs1786929), as identified by DNA direct sequencing, was significantly associated with the TDI-induced asthma phenotype (P=0.015 in recessive analysis model) and the prevalence of serum-specific IgG to cytokeratin 19 (P=0.031).

CONCLUSION

These findings suggested that multiple genetic polymorphisms of CTNNA3 may be determinants of susceptibility to TDI-induced asthma.

GAB2 is not associated with late-onset Alzheimer's disease in Japanese

The varepsilon4 allele of the apolipoprotein E gene (APOE) is unequivocally recognized as a genetic risk factor for late-onset Alzheimer's disease (LOAD). Recently, single-nucleotide polymorphisms (SNPs) of the GRB2-associated binding protein 2 gene (GAB2) were shown to be associated with LOAD in Caucasians carrying the APOE-varepsilon4 allele through a genome-wide association study. Here, we attempted to replicate the finding by genotyping these SNPs in a large clinical cohort of Japanese. We observed no association of any of the SNPs with LOAD. GAB2 may not be a disease susceptibility gene for LOAD in Japanese.

Genomic imprinting in the development and evolution of psychotic spectrum conditions

I review and evaluate genetic and genomic evidence salient to the hypothesis that the development and evolution of psychotic spectrum conditions have been mediated in part by alterations of imprinted genes expressed in the brain. Evidence from the genetics and genomics of schizophrenia, bipolar disorder, major depression, Prader-Willi syndrome, Klinefelter syndrome, and other neurogenetic conditions support the hypothesis that the etiologies of psychotic spectrum conditions commonly involve genetic and epigenetic imbalances in the effects of imprinted genes, with a bias towards increased relative effects from imprinted genes with maternal expression or other genes favouring maternal interests. By contrast, autistic spectrum conditions, including Kanner autism, Asperger syndrome, Rett syndrome, Turner syndrome, Angelman syndrome, and Beckwith-Wiedemann syndrome, commonly engender increased relative effects from paternally expressed imprinted genes, or reduced effects from genes favouring maternal interests. Imprinted-gene effects on the etiologies of autistic and psychotic spectrum conditions parallel the diametric effects of imprinted genes in placental and foetal development, in that psychotic spectrum conditions tend to be associated with undergrowth and relatively-slow brain development, whereas some autistic spectrum conditions involve brain and body overgrowth, especially in foetal development and early childhood. An important role for imprinted genes in the etiologies of psychotic and autistic spectrum conditions is consistent with neurodevelopmental models of these disorders, and with predictions from the conflict theory of genomic imprinting.

Pharmacogenomics and treatment for dementia induced by Alzheimer’s disease

Pharmacogenomics is the study of interindividual genetic variability, which plays a significant role in defining drug response and toxicity. As research has graduated from studying single candidate genes to whole-genome scans, pharmacogenomics is beginning to make its impact on the therapeutics of complex CNS disorders, such as schizophrenia, Parkinson’s disease and Alzheimer’s disease. Alzheimer’s disease is a progressive complex disorder, where genetic predisposition interacts with environmental factors. With conventional therapeutics only providing symptomatic treatment, the current focus of the pharmaceutical industry is on novel strategies with an etiopathogenic orientation. In this review, we have summarized the current knowledge of pathogenetic mechanisms of Alzheimer’s disease, with a focus on the known relevant molecules and the potential of pharmacogenomics in translating this knowledge of human genome variability into efficacious and safer therapeutics.