Novel functional variants at the GWAS-implicated loci might confer risk to major depressive disorder, bipolar affective disorder and schizophrenia

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Gene co-expression architecture in peripheral blood in a cohort of remitted first-episode schizophrenia patients

A better understanding of schizophrenia subtypes is necessary to stratify the patients according to clinical attributes. To explore the genomic architecture of schizophrenia symptomatology, we analyzed blood co-expression modules and their association with clinical data from patients in remission after a first episode of schizophrenia. In total, 91 participants of the 2EPS project were included. Gene expression was assessed using the Clariom S Human Array. Weighted-gene co-expression network analysis (WGCNA) was applied to identify modules of co-expressed genes and to test its correlation with global functioning, clinical symptomatology, and premorbid adjustment. Among the 25 modules identified, six modules were significantly correlated with clinical data. These modules could be clustered in two groups according to their correlation with clinical data. Hub genes in each group showing overlap with risk genes for schizophrenia were enriched in biological processes related to metabolic processes, regulation of gene expression, cellular localization and protein transport, immune processes, and neurotrophin pathways. Our results indicate that modules with significant associations with clinical data showed overlap with gene sets previously identified in differential gene-expression analysis in brain, indicating that peripheral tissues could reveal pathogenic mechanisms. Hub genes involved in these modules revealed multiple signaling pathways previously related to schizophrenia, which may represent the complex interplay in the pathological mechanisms behind the disease. These genes could represent potential targets for the development of peripheral biomarkers underlying illness traits in clinical remission stages after a first episode of schizophrenia.

The functional insight into the genetics of cardiovascular disease: results from the post-GWAS study

Cardiovascular diseases (CVDs), the leading cause of death worldwide, generally refer to a range of pathological conditions with the involvement of the heart and the blood vessels. A sizable fraction of the susceptibility loci is known, but the underlying mechanisms have been established only for a small proportion. Therefore, there is an increasing need to explore the functional relevance of trait-associated variants and, moreover, to search for novel risk genetic variation. We have reported the bioinformatic approach allowing effective identification of functional non-coding variants by integrated analysis of genome-wide data. Here, the analysis of 1361 previously identified regulatory SNPs (rSNPs) was performed to provide new insights into cardiovascular risk. We found 773,471 coding co-segregating markers for input rSNPs using the 1000 Genomes Project. The intersection of GWAS-derived SNPs with a relevance to cardiovascular traits with these markers was analyzed within a window of 10 Kbp. The effects on the transcription factor (TF) binding sites were explored by DeFine models. Functional pathway enrichment and protein– protein interaction (PPI) network analyses were performed on the targets and the extended genes by STRING and DAVID. Eighteen rSNPs were functionally linked to cardiovascular risk. A significant impact on binding sites of thirteen TFs including those involved in blood cells formation, hematopoiesis, macrophage function, inflammation, and vasoconstriction was found in K562 cells. 21 rSNP gene targets and 5 partners predicted by PPI were enriched for spliceosome and endocytosis KEGG pathways, endosome sorting complex and mRNA splicing REACTOME pathways. Related Gene Ontology terms included mRNA splicing and processing, endosome transport and protein catabolic processes. Together, the findings provide further insight into the biological basis of CVDs and highlight the importance of the precise regulation of splicing and alternative splicing.

Bioinformatics analysis of long non-coding RNA-associated competing endogenous RNA network in schizophrenia

Schizophrenia (SCZ) is a serious psychiatric condition with a 1% lifetime risk. SCZ is one of the top ten global causes of disabilities. Despite numerous attempts to understand the function of genetic factors in SCZ development, genetic components in SCZ pathophysiology remain unknown. The competing endogenous RNA (ceRNA) network has been demonstrated to be involved in the development of many kinds of diseases. The ceRNA hypothesis states that cross-talks between coding and non-coding RNAs, including long non-coding RNAs (lncRNAs), via miRNA complementary sequences known as miRNA response elements, creates a large regulatory network across the transcriptome. In the present study, we developed a lncRNA-related ceRNA network to elucidate molecular regulatory mechanisms involved in SCZ. Microarray datasets associated with brain regions (GSE53987) and lymphoblasts (LBs) derived from peripheral blood (sample set B from GSE73129) of SCZ patients and control subjects containing information about both mRNAs and lncRNAs were downloaded from the Gene Expression Omnibus database. The GSE53987 comprised 48 brain samples taken from SCZ patients (15 HPC: hippocampus, 15 BA46: Brodmann area 46, 18 STR: striatum) and 55 brain samples taken from control subjects (18 HPC, 19 BA46, 18 STR). The sample set B of GSE73129 comprised 30 LB samples (15 patients with SCZ and 15 controls). Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the limma package of the R software. Using DIANA-LncBase, Human MicroRNA Disease Database (HMDD), and miRTarBase, the lncRNA- associated ceRNA network was generated. Pathway enrichment of DEmRNAs was performed using the Enrichr tool. We developed a protein-protein interaction network of DEmRNAs and identified the top five hub genes by the use of STRING and Cytoscape, respectively. Eventually, the hub genes, DElncRNAs, and predictive miRNAs were chosen to reconstruct the subceRNA networks. Our bioinformatics analysis showed that twelve key DEmRNAs, including BDNF, VEGFA, FGF2, FOS, CD44, SOX2, NRAS, SPARC, ZFP36, FGG, ELAVL1, and STARD13, participate in the ceRNA network in SCZ. We also identified DLX6-AS1, NEAT1, MINCR, LINC01094, DLGAP1-AS1, BABAM2-AS1, PAX8-AS1, ZFHX4-AS1, XIST, and MALAT1 as key DElncRNAs regulating the genes mentioned above. Furthermore, expression of 15 DEmRNAs (e.g., ADM and HLA-DRB1) and one DElncRNA (XIST) were changed in both the brain and LB, suggesting that they could be regarded as candidates for future biomarker studies. The study indicated that ceRNAs could be research candidates for investigating SCZ molecular pathways.

Nucks1 gene polymorphism rs823114 is associated with the positive symptoms and neurocognitive function of patients with schizophrenia in parts of southern China

Nuclear casein kinase and cyclin-dependent kinase substrate 1 (nucks1) are considered a potential susceptibility gene for certain neurological diseases, such as Parkinson's disease (PD). In our study, we genotyped three single nucleotide polymorphisms (SNPs) (rs4951261, rs823114 and rs951366) of the nucks1 gene in 774 schizophrenic patients and 819 healthy controls using the improved multiplex ligation detection reaction (imLDR) technique. Furthermore, we also studied the relationship between the above SNPs and the clinical psychiatric symptoms and neurocognitive function of the patients. Genotype distributions and allele frequencies of these SNPs showed no significant differences and were found between patients and healthy controls. However, in an analysis of the positive symptom score of rs823114 among male patients, we found that the score of the A/A genotype was lower than that of the G/A+G/G genotypes (P = 0.001, P(corr) = 0.003]. Additionally, we also found that among the female patients, G allele carriers with rs823114 had lower semantic fluency scores than subjects with the A/A genotype (P = 0.010, P(corr) = 0.030]. Our data show for the first time that rs823114 polymorphism of nucks1 may affect positive symptoms and neurocognitive function in patients with schizophrenia in parts of southern China.

A Panel of rSNPs Demonstrating Allelic Asymmetry in Both ChIP-seq and RNA-seq Data and the Search for Their Phenotypic Outcomes through Analysis of DEGs

Currently, the detection of the allele asymmetry of gene expression from RNA-seq data or the transcription factor binding from ChIP-seq data is one of the approaches used to identify the functional genetic variants that can affect gene expression (regulatory SNPs or rSNPs). In this study, we searched for rSNPs using the data for human pulmonary arterial endothelial cells (PAECs) available from the Sequence Read Archive (SRA). Allele-asymmetric binding and expression events are analyzed in paired ChIP-seq data for H3K4me3 mark and RNA-seq data obtained for 19 individuals. Two statistical approaches, weighted z-scores and predicted probabilities, were used to improve the efficiency of finding rSNPs. In total, we identified 14,266 rSNPs associated with both allele-specific binding and expression. Among them, 645 rSNPs were associated with GWAS phenotypes; 4746 rSNPs were reported as eQTLs by GTEx, and 11,536 rSNPs were located in 374 candidate transcription factor binding motifs. Additionally, we searched for the rSNPs associated with gene expression using an SRA RNA-seq dataset for 281 clinically annotated human postmortem brain samples and detected eQTLs for 2505 rSNPs. Based on these results, we conducted Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and constructed the protein-protein interaction networks to represent the top-ranked biological processes with a possible contribution to the phenotypic outcome.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

Characterization of histone modification patterns and prediction of novel promoters using functional principal component analysis

Characterization of distinct histone methylation and acetylation binding patterns in promoters and prediction of novel regulatory regions remains an important area of genomic research, as it is hypothesized that distinct chromatin signatures may specify unique genomic functions. However, methods that have been proposed in the literature are either descriptive in nature or are fully parametric and hence more restrictive in pattern discovery. In this article, we propose a two-step non-parametric statistical inference procedure to characterize unique histone modification patterns and apply it to analyzing the binding patterns of four histone marks, H3K4me2, H3K4me3, H3K9ac, and H4K20me1, in human B-lymphoblastoid cells. In the first step, we used a functional principal component analysis method to represent the concatenated binding patterns of these four histone marks around the transcription start sites as smooth curves. In the second step, we clustered these curves to reveal several unique classes of binding patterns. These uncovered patterns were used in turn to scan the whole-genome to predict novel and alternative promoters. Our analyses show that there are three distinct promoter binding patterns of active genes. Further, 19654 regions not within known gene promoters were found to overlap with human ESTs, CpG islands, or common SNPs, indicative of their potential role in gene regulation, including being potential novel promoter regions.

Potential regulatory SNPs in the ATXN7L3B and KRT15 genes are associated with gender-specific colorectal cancer risk

Aim: According to the current data, a major factor for phenotypic variation of complex traits and disease susceptibility is the cis-acting effects of noncoding variants on gene expression. Our purpose was to evaluate the association between colorectal cancer (CRC) and six single nucleotide polymorphisms identified using our original bioinformatics approach as regulatory and putatively related to CRC. Materials: One hundred and sixty CRC patients and 185 healthy controls have been genotyped for rs590352, rs2072580, rs78317230, rs3829202, rs11542583 and rs4796672. Results: Genotypes and alleles distributions of rs590352 of ATXN7L3B gene were significantly different between the male CRC subjects and controls. Significant correlation of genotype with CRC is observable for women only for the rs4796672 of KRT15 gene. Analysis of haplotypes reveals that rs2072580 of the ISCU and SART3 genes can be also associated with CRC. Conclusion: We have identified three SNPs associated with CRC risk and demonstrated a gender specificity of rs590352 and rs4796672.

GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations

PURPOSE OF REVIEW

Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.

RECENT FINDINGS

Several common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.

SUMMARY

GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

Cognitive Deficits in Psychotic Disorders: A Lifespan Perspective

Individuals with disorders that include psychotic symptoms (i.e. psychotic disorders) experience broad cognitive impairments in the chronic state, indicating a dimension of abnormality associated with the experience of psychosis. These impairments negatively impact functional outcome, contributing to the disabling nature of schizophrenia, bipolar disorder, and psychotic depression. The robust and reliable nature of cognitive deficits has led researchers to explore the timing and profile of impairments, as this may elucidate different neurodevelopmental patterns in individuals who experience psychosis. Here, we review the literature on cognitive deficits across the life span of individuals with psychotic disorder and psychotic-like experiences, highlighting the dimensional nature of both psychosis and cognitive ability. We identify premorbid generalized cognitive impairment in schizophrenia that worsens throughout development, and stabilizes by the first-episode of psychosis, suggesting a neurodevelopmental course. Research in affective psychosis is less clear, with mixed evidence regarding premorbid deficits, but a fairly reliable generalized deficit at first-episode, which appears to worsen into the chronic state. In general, cognitive impairments are most severe in schizophrenia, intermediate in bipolar disorder, and the least severe in psychotic depression. In all groups, cognitive deficits are associated with poorer functional outcome. Finally, while the generalized deficit is the clearest and most reliable signal, data suggests specific deficits in verbal memory across all groups, specific processing speed impairments in schizophrenia and executive functioning impairments in bipolar disorder. Cognitive deficits are a core feature of psychotic disorders that provide a window into understanding developmental course and risk for psychosis.