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Am J Hum Genet ; 107(4): 622-635, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-32946763


Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

Carcinoma Epitelial do Ovário/genética , Proteínas Correpressoras/genética , Cistadenocarcinoma Seroso/genética , Elementos Facilitadores Genéticos , Histonas/genética , Proteínas do Tecido Nervoso/genética , Neoplasias Ovarianas/genética , Alelos , Sítios de Ligação , Carcinoma Epitelial do Ovário/diagnóstico , Carcinoma Epitelial do Ovário/patologia , Mapeamento Cromossômico , Proteínas Correpressoras/metabolismo , Cistadenocarcinoma Seroso/diagnóstico , Cistadenocarcinoma Seroso/patologia , Feminino , Predisposição Genética para Doença , Genoma Humano , Estudo de Associação Genômica Ampla , Histonas/metabolismo , Humanos , Padrões de Herança , Proteínas do Tecido Nervoso/metabolismo , Neoplasias Ovarianas/diagnóstico , Neoplasias Ovarianas/patologia , Penetrância , Polimorfismo de Nucleotídeo Único , Risco
BMC Genomics ; 20(1): 745, 2019 Oct 16.
Artigo em Inglês | MEDLINE | ID: mdl-31619158


BACKGROUND: The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. RESULTS: We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. CONCLUSION: The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Análise de Sequência de RNA/métodos , Software , Interpretação Estatística de Dados , Visualização de Dados , Internet , Reprodutibilidade dos Testes , Interface Usuário-Computador