Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of ...schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.
Most of them are components of signaling networks encoding the production of transcription factors (TFs), cell adhesion proteins, cell surface receptor proteins and morphogens. ...these genes ...function as master regulators by orchestrating other regulatory genes which regulate target/core genes involved in anatomical and physiological processes in the developing embryo. ...these deviations are predominantly due to the mechanisms by which the toolkit genes precisely regulate the core genes' expression in space and time (Carroll, 2008). ...single nucleotide polymorphisms (SNPs) in these regulatory regions can impact TF-DNA interactions and play a crucial role in regulating core gene expression. Dynamic Regulatory Event Miner (DREM) (Schulz et al., 2012), a unique approach, integrates disparate-omics data and time-series gene expression data (Ernst et al., 2007; Schulz et al., 2012, 2013) to identify target genes regulated by TFs. ...contributing to the capture of time and region-specific gene regulation in developmental studies such as human brain development. There was a significant similarity of TFs involved in human brain development across all brain regions (see Figure 1). ...we observed substantial gene regulatory activities during the prenatal stages of brain development.
The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological ...robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans.
We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results.
We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a
-value
1
10
. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia.
Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
This article proposes a novel approach to design a frequency selective surface (FSS) based on proximity coupling modes. Two closely placed modes given by two different structures are merged to ...achieve the coupled modes to improve the operation bandwidth (BW). This method reduces the radar cross section (RCS) of the patch antenna without deteriorating the radiation characteristics like gain, directivity, and radiation efficiency. A FSS unit cell of dimension 8 × 8 mm is made into a 3 × 2 array and is placed in the same layer around the microstrip patch antenna for RCS reduction in single layer. The simulated and measured results show the RCSR of 17 dB with a BW of 5.2 GHz in radar Ku band.
Genomic abnormalities leading to colorectal cancer (CRC) include somatic events causing copy number aberrations (CNAs) as well as copy neutral manifestations such as loss of heterozygosity (LOH) and ...uniparental disomy (UPD). We studied the causal effect of these events by analyzing high resolution cytogenetic microarray data of 15 tumor-normal paired samples. We detected 144 genes affected by CNAs. A subset of 91 genes are known to be CRC related yet high GISTIC scores indicate 24 genes on chromosomes 7, 8, 18 and 20 to be strongly relevant. Combining GISTIC ranking with functional analyses and degree of loss/gain we identify three genes in regions of significant loss (ATP8B1, NARS, and ATP5A1) and eight in regions of gain (CTCFL, SPO11, ZNF217, PLEKHA8, HOXA3, GPNMB, IGF2BP3 and PCAT1) as novel in their association with CRC. Pathway and target prediction analysis of CNA affected genes and microRNAs, respectively indicates TGF-β signaling pathway to be involved in causing CRC. Finally, LOH and UPD collectively affected nine cancer related genes. Transcription factor binding sites on regions of >35% copy number loss/gain influenced 16 CRC genes. Our analysis shows patient specific CRC manifestations at the genomic level and that these different events affect individual CRC patients differently.
Integrated analysis of genomic and transcriptomic level changes holds promise for a better understanding of colorectal cancer (CRC) biology. There is a pertinent need to explain the functional effect ...of genome level changes by integrating the information at the transcript level. Using high resolution cytogenetics array, we had earlier identified driver genes by 'Genomic Identification of Significant Targets In Cancer (GISTIC)' analysis of paired tumour-normal samples from colorectal cancer patients. In this study, we analyze these driver genes at three levels using exon array data--gene, exon and network. Gene level analysis revealed a small subset to experience differential expression. These results were reinforced by carrying out separate differential expression analyses (SAM and LIMMA). ATP8B1 was found to be the novel gene associated with CRC that shows changes at cytogenetic, gene and exon levels. Splice index of 29 exons corresponding to 13 genes was found to be significantly altered in tumour samples. Driver genes were used to construct regulatory networks for tumour and normal groups. There were rearrangements in transcription factor genes suggesting the presence of regulatory switching. The regulatory pattern of AHR gene was found to have the most significant alteration. Our results integrate data with focus on driver genes resulting in highly enriched novel molecules that need further studies to establish their role in CRC.
The interplay between genotype and phenotype is governed by a multitude of genetic interactions (GIs), and the mapping of GI networks holds significant importance for two main reasons: (1) GIs offer ...a valuable means to uncover compensatory biological mechanisms by modelling biological robustness, thereby identifying functional relationships between genes. This aspect is particularly relevant for biological exploration and translational research, as biological systems have evolved to compensate for genetic (i.e. variations, mutations) and environmental (i.e. drug efficacy) perturbations by leveraging compensatory relationships between genes, pathways, and biological processes; (2) GI facilitates the identification of the direction (positive/alleviating or negative/aggravating interactions) and magnitude of epistatic interactions that influence the resulting phenotype. While comprehensive GI databases exist for organisms like yeast, generating GIs for human diseases through experimental biology methods such as systematic deletion analysis is infeasible. Furthermore, generating disease-specific GIs in humans has not been previously attempted.
We used the Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement and test GI workflow. Standard GWAS sample quality control procedure was followed to check for ancestry and relatedness outliers. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. By using the odds ratio (OR) we identified the GIs that increase (OR >1) or decrease (OR < 1) the risk of a disease phenotype (i.e. schizophrenia). The SNP-based epistatic results were transformed into gene-based epistatic results.
We have developed a GI workflow for conducting gene-based statistical epistatic analysis and transforming these results to infer GIs. Spatial analysis of functional enrichment (SAFE) was used to detect the statistically overrepresented functional groups. There were ∼ 9.5 million GIs with a p-value ≤ 1 × 10-4. Approximately 4.8 million GIs showed an increased risk (Odds Ratio > 1.0), while ∼ 4.75 million GIs had decreased/no risk (Odds Ratio < 1.0) for schizophrenia. We identified many hub genes with numerous GIs, which increased and reduced the risk of Schizophrenia.
In contrast to model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. Despite limited power, meaningful GI data was generated with a small sample. SAFE and REVIGO analysis exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models for post-GWAS functional characterisation, potentially surpassing the limitations of conventional GWAS.
Colorectal cancer (CRC), which has high prevalence in Saudi Arabia and worldwide, needs better understanding by exploiting the latest available cytogenetic microarrays. We used biopsy tissue from ...consenting colorectal cancer patients to extract DNA and carry out microarray analysis using a CytoScan HD platform from Affymetrix. Patient specific comparisons of tumor–normal pairs were carried out. To find out the high probability key players, we performed Genomic Identification of Significant Targets in Cancer analysis and found 144 genes to form the list of driver genes. Of these, 24 genes attained high GISTIC scores and suggest being significantly associated with CRC. Loss of heterozygosity and uniparental disomy were found to affect 9 genes and suggest different mechanisms associated with CRC in every patient. Here we present the details of the methods used in carrying out the above analyses. Also, we provide some additional data on biomarker analysis that would complement the findings.
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