Rare variant analysis in multiply affected families, association studies and functional analysis suggest a role for the ITGΒ4 gene in schizophrenia and bipolar disorder

Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins

BACKGROUND

Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis.

METHODS

Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables.

RESULTS

We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways.

CONCLUSIONS

Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

RGS3 and IL1RAPL1 missense variants implicate defective neurotransmission in early-onset inherited schizophrenias

BACKGROUND

Schizophrenia is characterized by hallucinations, delusions and disorganized behaviour. Recessive or X-linked transmissions are rarely described for common psychiatric disorders. We examined the genetics of psychosis to identify rare large-effect variants in patients with extreme schizophrenia.

METHODS

We recruited 2 consanguineous families, each with patients affected by early-onset, severe, treatment-resistant schizophrenia. We performed exome sequencing for all participants. We checked variant rarity in public databases and with ethnically matched controls. We performed in silico analyses to assess the effects of the variants on proteins.

RESULTS

Structured clinical evaluations supported diagnoses of schizophrenia in all patients and phenotypic absence in the unaffected individuals. Data analyses identified multiple variants. Only 1 variant per family was predicted as pathogenic by prediction tools. A homozygous c.649C > T:p.(Arg217Cys) variant in RGS3 and a hemizygous c.700A > G:p.(Thr234Ala) variant in IL1RAPL1 affected evolutionary conserved amino acid residues and were the most likely causes of phenotype in the patients of each family. Variants were ultra-rare in publicly available databases and absent from the DNA of 400 ethnically matched controls. RGS3 is implicated in modulating sensory behaviour in Caenorhabditis elegans. Variants of IL1RAPL1 are known to cause nonsyndromic X-linked intellectual disability with or without human behavioural dysfunction.

LIMITATIONS

Each variant is unique to a particular family's patients, and findings may not be replicated.

CONCLUSION

Our work suggests that some rare variants may be involved in causing inherited psychosis or schizophrenia. Variant-specific functional studies will elucidate the pathophysiology relevant to schizophrenias and motivate translation to personalized therapeutics.

Transcriptomics and machine learning to advance schizophrenia genetics: A case-control study using post-mortem brain data

BACKGROUND AND OBJECTIVE

Alterations of the expression of a variety of genes have been reported in patients with schizophrenia (SCZ). Moreover, machine learning (ML) analysis of gene expression microarray data has shown promising preliminary results in the study of SCZ. Our objective was to evaluate the performance of ML in classifying SCZ cases and controls based on gene expression microarray data from the dorsolateral prefrontal cortex.

METHODS

We apply a state-of-the-art ML algorithm (XGBoost) to train and evaluate a classification model using 201 SCZ cases and 278 controls. We utilized 10-fold cross-validation for model selection, and a held-out testing set to evaluate the model. The performance metric utilizes to evaluate classification performance was the area under the receiver-operator characteristics curve (AUC).

RESULTS

We report an average AUC on 10-fold cross-validation of 0.76 and an AUC of 0.76 on testing data, not used during training. Analysis of the rolling balanced classification accuracy from high to low prediction confidence levels showed that the most certain subset of predictions ranged between 80-90%. The ML model utilized 182 gene expression probes. Further improvement to classification performance was observed when applying an automated ML strategy on the 182 features, which achieved an AUC of 0.79 on the same testing data. We found literature evidence linking all of the top ten ML ranked genes to SCZ. Furthermore, we leveraged information from the full set of microarray gene expressions available via univariate differential gene expression analysis. We then prioritized differentially expressed gene sets using the piano gene set analysis package. We augmented the ranking of the prioritized gene sets with genes from the complex multivariate ML model using hypergeometric tests to identify more robust gene sets. We identified two significant Gene Ontology molecular function gene sets: "oxidoreductase activity, acting on the CH-NH2 group of donors" and "integrin binding." Lastly, we present candidate treatments for SCZ based on findings from our study CONCLUSIONS: Overall, we observed above-chance performance from ML classification of SCZ cases and controls based on brain gene expression microarray data, and found that ML analysis of gene expressions could further our understanding of the pathophysiology of SCZ and help identify novel treatments.

Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.

Schizophrenia‑associated microRNA‑148b‑3p regulates COMT and PRSS16 expression by targeting the ZNF804A gene in human neuroblastoma cells

Zinc finger protein 804A (ZNF804A) has been identified by genome-wide association studies as a robust risk gene in schizophrenia, but how ZNF804A contributes to schizophrenia and its upstream regulation remains unknown. Previous studies have indicated that microRNAs (miRs) are key factors that regulate the expression levels of their target genes. The present study revealed significantly increased expression of miR-148b-3p in the peripheral blood of patients with first-onset schizophrenia compared with healthy controls, and bioinformatics analysis predicted that the ZNF804A gene is a target of miR-148b-3p. Therefore, the present study investigated the possible upstream regulation of ZNF804A by miR-148b-3p in the human neuroblastoma SH-SY5Y cell line, and assessed the implications for schizophrenia. The results revealed significantly reversed expression levels of miR-148b-3p (P=0.0051) and ZNF804A (P=0.0218) in the peripheral blood of patients with first-onset schizophrenia compared with healthy individuals. Furthermore, it was demonstrated that miR-148b-3p directly targeted ZNF804A via binding to conserved target sites in the 3′-untranslated region of ZNF804A mRNA, where it inhibited the endogenous expression of ZNF804A at both the mRNA (P=0.048) and protein levels (P=0.013) in SH-SY5Y cells. Furthermore, miR-148b-3p was revealed to regulate the expression levels of catechol-O-methyltransferase (COMT) and serine protease 16 (PRSS16) by targeting ZNF804A in SH-SY5Y cells. Collectively, the present results indicated that there was a direct upstream regulation of the schizophrenia risk gene ZNF804A by miR-148b-3p, which contributed to the regulation of the downstream genes COMT and PRSS16. Thus, the miR-148b-3p/ZNF804A/COMT/PRSS16 pathway may play an important role in the pathophysiology of schizophrenia, and may serve as a potential target in drug discovery and gene therapy for this disorder.

Discovery of rare variants implicated in schizophrenia using next-generation sequencing

Schizophrenia is a highly heritable psychiatric disorder that affects 1% of the population. Genome-wide association studies have identified common variants in candidate genes associated with schizophrenia, but the genetics mechanisms of this disorder have not yet been elucidated. The discovery of rare genetic variants that contribute to schizophrenia symptoms promises to help explain the missing heritability of the disease. Next generation sequencing techniques are revolutionizing the field of psychiatric genetics. Various statistical approaches have been developed for rare variant association testing in case-control and family studies. Targeted resequencing, whole exome sequencing and whole genome sequencing combined with these computational tools are used for the discovery of rare genetic variations in schizophrenia. The findings provide useful information for characterizing the rare mutations and elucidating the genetic mechanisms by which the variants cause schizophrenia.

Rare compound heterozygous missense SPATA7 variations and risk of schizophrenia; whole-exome sequencing in a consanguineous family with affected siblings, follow-up sequencing and a case-control study

PURPOSE

Whole-exome sequencing (WES) of multiplex families is a promising strategy for identifying causative variations for common diseases. To identify rare recessive risk variations for schizophrenia, we performed a WES study in a consanguineous family with affected siblings. We then performed follow-up sequencing of SPATA7 in schizophrenia-affected families. In addition, we performed a case-control study to investigate association between SPATA7 variations and schizophrenia.

PATIENTS AND METHODS

WES was performed on two affected siblings and their unaffected parents, who were second cousins, of a multiplex schizophrenia family. Subsequently, we sequenced the coding region of SPATA7, a potential risk gene identified by the WES analysis, in 142 affected offspring from 137 families for whom parental DNA samples were available. We further tested rare recessive SPATA7 variations, identified by WES and sequencing, for associations with schizophrenia in 2,756 patients and 2,646 controls.

RESULTS

Our WES analysis identified rare compound heterozygous missense SPATA7 variations, p.Asp134Gly and p.Ile332Thr, in both affected siblings. Sequencing SPATA7 coding regions from 137 families identified no rare recessive variations in affected offspring. In the case-control study, we did not detect the rare compound heterozygous SPATA7 missense variations in patients or controls.

CONCLUSION

Our data does not support the role of the rare compound heterozygous SPATA7 missense variations p.Asp134Gly and p.Ile332Thr in conferring a substantial risk of schizophrenia.