Omics Data-based Dissection Of Disease-Related Genes And Functional Mechanisms Through Bioinformatics Analyses

The underlying risk factors contributing to the pathophysiology and clinical manifestations of complex diseases are multifactorial and largely remain enigmatic.In fact,the disease-related genes barely act in isolation thus it is of great importance to study the genetics of complex diseases at a comprehensive level.The huge volume of multi-omics data generated by the next-generation sequencing studies offers an unprecedented opportunity for geneticists and clinicians to help direct and improve treatment for complex diseases.Progress in next-generation sequencing and the rapid development in bioinformatics have made it possible to analyze large numbers of genes at an ultrahigh speed and a rather low price.Multiple repositories generated from collaborative bioinformatics studies including rich and detailed information about genetic mutations,gene expression,protein-protein interactions and epigenetic modifications have been deployed,which facilitate further researches to investigate the genes of interest underlying complex diseases in a systematic way.This dissertation presents the studies employing the techniques and data of next-generation sequencing,focusing mainly on the investigation of genetic mutations,gene expression as well as regulatory mechanisms and comprises of the following three parts:(1)Using whole-exome sequencing to identify and examine the role of genes with de novo mutations in primary biliary cholangitis.This study uncovered genes harboring de novo mutations in patients with primary biliary cholangitis using familial trios from Han Chinese.Integrative network and functional enrichment analyses have also been applied to unravel the role of genes with de novo mutations.The results from this study provided novel insights into the etiology of primary biliary cholangitis and expanded the pool of molecular candidates for discovering clinically actionable biomarkers.(2)Constructing predictive model for prognosis of patients with hepatocellular carcinoma using gene expression data.In this study,a predictive model have been generated using COX regression analysis with RNA-seq data and clinical information from TCGA depository and GEO database for predicting survival probabilities of patients diagnosed with hepatocellular carcinoma.Moreover,patients categorized as high-risk of poor survival exhibited significantly more abundant immunosuppressive cells than low-risk patients.In addition,we revealed a m RNA-mi RNA-circ RNA competing endogenous network that potentially played a pivotal role in the prognosis and pathogenesis of hepatocellular carcinoma.(3)Analyzing the role of de novo mutations in post-transcriptional regulation among four neuropsychiatric disorders with data from whole exome sequencing.In this study,we collected de novo mutations from previously reported studies on autism spectrum disorder(ASD),epileptic encephalopathy(EE),intellectual disability(ID),schizophrenia(SCZ),as well as unaffected control subjects.We subsequently utilized our inhouse workflow to evaluate the potential impact of these de novo mutations involved in post-transcriptional regulation in the four neuropsychiatric disorders based on experimental data from genome-wide association studies(GWAS),expression quantitative trait loci(e QTLs),CLIP-seq derived RBP binding sites,RNA editing and mi RNA targets.The results showed that post-transcriptionally impaired de novo mutations were significantly enriched in probands,highlighting the potential contribution of post-transcriptionally impaired de novo mutations in neuropsychiatric disorders.