Image1_Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes.pdf

Background: 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.Methods: 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.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 p-value ≤ 1 × 10−5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR Conclusion: 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.</p

Table1_Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes.XLSX

Background: 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.Methods: 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.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 p-value ≤ 1 × 10−5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR Conclusion: 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.</p

Driver Genes from GISTIC analysis showing more than two fold change in expression value as calculated by two different programs – AltAnalyze and Expression Console.

<p>N/A  =  No annotation for these genes were found in the analysis program.</p>a<p>Genes found to be differentially expressed in SAM/LIMMA analyses. CLDN7 and LOX genes were additional driver genes that were differentially expressed.</p>b<p>Genes found to have high splice index values.</p>c<p>Genes found to be eligible as biomarkers for colorectal cancer.</p><p>Driver Genes from GISTIC analysis showing more than two fold change in expression value as calculated by two different programs – AltAnalyze and Expression Console.</p

Integrated Exon Level Expression Analysis of Driver Genes Explain Their Role in Colorectal Cancer

<div><p>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.</p></div

Gene Regulatory Network Inference diagram for Tumor and Normal Groups.

<p>A hierarchical network topology is used to visualize the degrees of interaction between transcription factor genes and target genes. (a) The inferred network for tumour group showing RUVBL1 as master regulator. (b) The inferred network for normal group showing TSHZ1as master regulator.</p

Significant change in expression value at gene level was observed in 20/144 genes.

<p>Two different algorithms were used to measure expression values from Exon array data to support the results. AltAnalyze (1a) and Expression Console (1b) show complimentary results with maximum changes observed in BCAS1, INHBA, IL6 and MUC4 genes.</p

Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.

<p>Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.</p

Differentially regulated genes found to have incoherent expression levels and genomic changes.

<p>AA  =  Fold change value as calculated by AltAnalyze program.</p><p>EC  =  Fold change value as calculated by Expression Console program.</p><p>TF  =  Transcription Factor. Unknown is the TF that is not found in the driver genes.</p><p>Differentially regulated genes found to have incoherent expression levels and genomic changes.</p

Core Analysis of Differentially Expressed Genes using IPA.

<p>Core analysis using IPA was carried out using set of 760 genes that were differentially expressed in tumour samples. Important biological functions (a) pathways (b) and networks (c-e) were revealed by this analysis.</p

Significant changes in exon level expression of driver genes.

<p>List of 29 exons corresponding to 13 genes are listed along with their respective splice index and MiDAS p values.</p><p>Probeset IDs are according to Affymetrix database</p><p>SI  =  Splice Index</p><p>MiDAS  =  Microarray Detection of Alternative Splicing</p><p>Significant changes in exon level expression of driver genes.</p