Association analysis of PALB2 and BRCA2 in bipolar disorder and schizophrenia in a scandinavian case-control sample

The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies

The X‐chromosome is often excluded from genome‐wide association studies because of analytical challenges. Some of the problems, such as the random, skewed, or no X‐inactivation model uncertainty, have been investigated. Other considerations have received little to no attention, such as the value in considering nonadditive and gene–sex interaction effects, and the inferential consequence of choosing different baseline alleles (i.e., the reference vs. the alternative allele). Here we propose a unified and flexible regression‐based association test for X‐chromosomal variants. We provide theoretical justifications for its robustness in the presence of various model uncertainties, as well as for its improved power when compared with the existing approaches under certain scenarios. For completeness, we also revisit the autosomes and show that the proposed framework leads to a more robust approach than the standard method. Finally, we provide supporting evidence by revisiting several published association studies. Supporting Information for this article are available online.

Structure analysis of deleterious nsSNPs in human PALB2 protein for functional inference

Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygous mutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functional impacts of SNPs located in the PALB2 gene. Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silico tools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combination of in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhD-SNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were further selected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline of the structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutant models (rs78179744, rs180177123 and rs45525135) were identified. The non-coding SNPs in the 3' UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensive characterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through genetic association study will reveal their clinical significance.

Genetic Intersections of Language and Neuropsychiatric Conditions

PURPOSE OF REVIEW

To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes implicated in both language and neuropsychiatric disorders, as they reveal biological domains likely to underpin the processes controlling both.

RECENT FINDINGS

Abnormal language and communication are common manifestations of neuropsychiatric conditions, and children with impaired language are more likely to develop psychiatric disorders than their peers. Major themes in the genetics of both language and psychiatry include master transcriptional regulators, like FOXP2; key developmental regulators, like AUTS2; and mediators of neurotransmission, like GRIN2A and CACNA1C.

Double-Edged Sword of Tumour Suppressor Genes in Schizophrenia

Schizophrenia (SCZ) is a common psychiatric disorder with polygenetic pathogenesis. Among the many identified candidate genes and loci, the group of tumour suppressor genes has drawn our interest. In this mini-review article, we describe evidence of a correlation between major tumour suppressor genes and SCZ development. Genetic mutations ranging from single nucleotide polymorphisms to large structural alterations have been found in tumour-related genes in patients with SCZ. Epigenetic mechanisms, including DNA methylation/acetylation and microRNA regulation of tumour suppressor genes, have also been implicated in SCZ. Beyond genetic correlations, we hope to establish causal relationships between tumour suppressor gene function and SCZ risk. Accumulating evidence shows that tumour suppressor genes may mediate cell survival and neural development, both of which contribute to SCZ aetiology. Moreover, converging intracellular signalling pathways indicate a role of tumour suppressor genes in SCZ pathogenesis. Tumour suppressor gene function may mediate a direct link between neural development and function and psychiatric disorders, including SCZ. A deeper understanding of how neural cell development is affected by tumour suppressors may lead to improved anti-psychotic drugs.

Gain of power of the general regression model compared to Cochran-Armitage Trend tests: simulation study and application to bipolar disorder

Background

Most genome-wide association studies assumed an additive model of inheritance which may result in significant loss of power when there is a strong departure from additivity. The General Regression Model (GRM), which allows performing an assumption-free test for association by testing for both additive effect and deviation from additive effect, may be more appropriate for association tests. Additionally, GRM allows testing the underlying genetic model. We compared the power of GRM association test to additive and other Cochran-Armitage Trend (CAT) tests through simulations and by applying GRM to a large case/control sample, the bipolar Welcome Trust Case Control Cohort data. Simulations were performed on two sets of case/control samples (1000/1000 and 2000/2000), using a large panel of genetic models. Four association tests (GRM and additive, recessive and dominant CAT tests) were applied to all replicates.

Results

We showed that GRM power to detect association was similar or greater than the additive CAT test, in particular in case of recessive inheritance, with up to 67% gain in power. GRM analysis of genome-wide bipolar disorder Welcome Trust Consortium data (1998 cases/3004 controls) showed significant association in the 16p12 region (rs420259; P = 3.4E-7) which has not been identified using the additive CAT test. As expected, rs42025 fitted a non-additive (recessive) model.

Conclusions

GRM provides increased power compared to the additive CAT test for association studies and is easily applicable.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-017-0486-6) contains supplementary material, which is available to authorized users.

High-resolution chromosome ideogram representation of recognized genes for bipolar disorder

Bipolar disorder (BPD) is genetically heterogeneous with a growing list of BPD associated genes reported in recent years resulting from increased genetic testing using advanced genetic technology, expanded genomic databases, and better awareness of the disorder. We compiled a master list of recognized susceptibility and genes associated with BPD identified from peer-reviewed medical literature sources using PubMed and by searching online databases, such as OMIM. Searched keywords were related to bipolar disorder and genetics. Our compiled list consisted of 290 genes with gene names arranged in alphabetical order in tabular form with source documents and their chromosome location and gene symbols plotted on high-resolution human chromosome ideograms. The identified genes impacted a broad range of biological pathways and processes including cellular signaling pathways particularly cAMP and calcium (e.g., CACNA1C, CAMK2A, CAMK2D, ADCY1, ADCY2); glutamatergic (e.g., GRIK1, GRM3, GRM7), dopaminergic (e.g., DRD2, DRD4, COMT, MAOA) and serotonergic (e.g., HTR1A, HTR2A, HTR3B) neurotransmission; molecular transporters (e.g., SLC39A3, SLC6A3, SLC8A1); and neuronal growth (e.g., BDNF, IGFBP1, NRG1, NRG3). The increasing prevalence of BPD calls for better understanding of the genetic etiology of this disorder and associations between the observed BPD phenotype and genes. Visual representation of genes for bipolar disorder becomes a tool enabling clinical and laboratory geneticists, genetic counselors, and other health care providers and researchers easy access to the location and distribution of currently recognized BPD associated genes. Our study may also help inform diagnosis and advance treatment developments for those affected with this disorder and improve genetic counseling for families.

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.

Genome-wide and gene-based association studies of anxiety disorders in European and African American samples

Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.

A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation

BACKGROUND

Genome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories.

METHODS

1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls).

RESULTS

No individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p = .003). A polygenic score analysis found that the Psychiatric GWAS Consortium's panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 × 10(-14)) and explained approximately 2% of the phenotypic variance.

CONCLUSIONS

Although narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.

First manic episode in a patient with breast cancer

Mental disorders occur in as much as 50% of patients with cancer, impairing the oncologic prognosis and quality of life. The diagnostic investigation and treatment planning of this comorbidity impose a clinical challenge once complications related to the neoplasm, such as brain metastasis and paraneoplastic syndromes, must be excluded. In addition, psychotropic medications may interfere with oncologic treatment. We report the case of a 39-year-old female patient who presented with a first episode of psychotic mania after having been recently diagnosed with breast cancer. Also, we present a literature review of this comorbidity and point out a possible pathophysiological link between the two diseases.

A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation

BACKGROUND

Genome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories.

METHODS

1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls).

RESULTS

No individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p = .003). A polygenic score analysis found that the Psychiatric GWAS Consortium's panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 × 10(-14)) and explained approximately 2% of the phenotypic variance.

CONCLUSIONS

Although narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.

Incidence and risk of mood disorders in patients with breast cancers in Taiwan: a nationwide population-based study

OBJECTIVE

The objective of this study is to assess the incidence and risk of mood disorders, including major depression, anxiety, and bipolar disorders, in Taiwanese patients after the diagnosis of breast cancer compared with a matched cohort.

METHODS

From January 2000 to December 2005, 26,629 newly diagnosed breast cancer patients were enrolled by the Taiwan National Health Insurance program database. The control cohort was selected randomly from 1,000,000 National Health Insurance beneficiaries from a population of 21,400,826 enrolled throughout Taiwan. Each patient was matched with one subject without breast cancer by age, sex, and presence of comorbidities with the same diagnosis index date. The diagnosis of mood disorders was defined by compatible International Classification of Diseases, 9th revision, clinical modification codes plus the prescription of antidepressants for at least 30 days.

RESULTS

The overall incidence rate ratio of mood disorders was 1.33 (95% CI 1.28-1.39, p < 0.001) in the breast cancer cohort compared with the matched cohort. The incidence rate ratios for specific mood disorders were 2.06 for bipolar disorder (95% CI 1.37-3.15 p = 0.0003), 1.94 for major depressive disorder (95% CI 1.76-2.13 p < 0.001), and 1.22 for anxiety (95% CI 1.16-1.27 p < 0.001). Independent risk factors for developing mood disorders included breast cancer, as well as age, hypertension, chronic obstructive pulmonary disease, autoimmune disease, ischemic heart disease, and cerebrovascular disease.

CONCLUSIONS

Breast cancer is a prominent risk factor for mood disorders, including major depressive disorder, anxiety, and bipolar disorder. The impact is most potent in the first year after diagnosis. Psychological support is a critical issue in these patients.

An unbiased evaluation of gene prioritization tools

MOTIVATION

Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated.

RESULTS

Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful.

CONTACT

yves.moreau@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Behavioral genetics of affective and anxiety disorders

As shown by clinical genetic studies, affective and anxiety disorders are complex genetic disorders with genetic and environmental factors interactively determining their respective pathomechanism. Advances in molecular genetic techniques including linkage studies, association studies, and genome-wide association studies allow for the detailed dissection of the genetic influence on the development of these disorders. Besides the molecular genetic investigation of categorical entities according to standardized diagnostic criteria, intermediate phenotypes comprising neurobiological or neuropsychological traits (e.g., neuronal correlates of emotional processing) that are linked to the disease of interest and that are heritable, have been proposed to be closer to the underlying genotype than the overall disease phenotype. These intermediate phenotypes are dimensional and more precisely defined than the categorical disease phenotype, and therefore have attracted much interest in the genetic investigation of affective and anxiety disorders. Given the complex genetic nature of affective and anxiety disorders with an interaction of multiple risk genes and environmental influences, the interplay of genetic factors with environmental factors is investigated by means of gene-environment interaction (GxE) studies. Pharmacogenetic studies aid in the dissection of the genetically influenced heterogeneity of psychotropic drug response and may contribute to the development of a more individualized treatment of affective and anxiety disorders. Finally, there is some evidence for genetic factors potentially shared between affective and anxiety disorders pointing to a possible overlapping phenotype between anxiety disorders and depression.

[Systematic review of the Genomewide Association Studies (GWAS) in schizophrenia]

INTRODUCTION

Heritability in schizophrenia can reach up to 80% and the risk in families is 5-10 times higher than in the general population. The large contribution of genetics in this disorder has led to a growing interest in its study.

OBJECTIVES

To review the findings of genetic studies known as Genomewide Association Studies (GWAS) on schizophrenia.

METHOD

Systematic search using Pubmed with the key words GWAS and (psychosis) or (schizophrenia). The following web pages have been reviewed: http://www.szgene.org/largescale.asp and www.genome.gov/gwastudies/.

RESULTS

The GWAS have focused on causal biological aspects, such as the histocompatibility complex, glutamate metabolism, apoptosis and inflammatory processes, and the immune system (TNF-β, TNFR1). Also focused in the search were the genes that modulate the appearance of secondary metabolic and cardiac effects and secondary effects in subjects with schizophrenia and on anti-psychotic treatment. In neurorecognition, over-expression of the MET proto-oncogene (MET) has been associated with a low susceptibility for schizophrenia and a better cognitive performance, as well as a lower susceptibility for the incidence of cancer. Mention is also made of the different genes that mediate in cognitive functioning depending on the anti-psychotic treatment received.

CONCLUSIONS

The main interests of the GWAS during the last few years have been the neurobiological pathways involved in schizophrenia. The discoveries arising from these studies have been limited. This has led to an innovative approach on the aetiological study of the disorder by studying gene-environment interactions.