scholarly journals Exploratory Gene Ontology Analysis with Interactive Visualization

2018 ◽  
Author(s):  
Junjie Zhu ◽  
Qian Zhao ◽  
Eugene Katsevich ◽  
Chiara Sabatti
Keyword(s):  
Gene Ontology ◽  
High Throughput ◽  
Association Studies ◽  
Biological Data ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Ontology Structure ◽  
Comprehensive Picture ◽  
Power Studies ◽  
Gene Expression Studies

AbstractThe Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

2021 ◽  
pp. 1-10
Author(s):  
Zoe Guan ◽  
Ronglai Shen ◽  
Colin B. Begg
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
The Cancer Genome Atlas ◽  
Considerable Proportion ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Risk Variants ◽  
Cancer Types ◽  
Pan Cancer

<b><i>Background:</i></b> Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. <b><i>Objectives:</i></b> In this study, we investigated associations between rare variants and 14 cancer types. <b><i>Methods:</i></b> We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). <b><i>Results:</i></b> We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). <b><i>Conclusions:</i></b> Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

scholarly journals HAPPI GWAS: Holistic Analysis with Pre and Post Integration GWAS

2020 ◽  
Author(s):  
Marianne L. Slaten ◽  
Yen On Chan ◽  
Vivek Shrestha ◽  
Alexander E. Lipka ◽  
Ruthie Angelovici
Keyword(s):  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Gwas Analysis ◽  
Genome Wide ◽  
Large Populations ◽  
Unbiased Estimates ◽  
Best Linear Unbiased ◽  
Automated Pipeline

AbstractMotivationAdvanced publicly available sequencing data from large populations have enabled in-formative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking.ResultsHere, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface.AvailabilityHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git)[email protected]

scholarly journals High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 374 ◽  
Author(s):  
Anna Dziewulska ◽  
Aneta Dobosz ◽  
Agnieszka Dobrzyn
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Islets ◽  
High Throughput ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genomic Landscape ◽  
A Genome ◽  
Next Generation Sequencing Ngs

Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.

scholarly journals Cystic Fibrosis Disease Modifiers: Complex Genetics Defines the Phenotypic Diversity in a Monogenic Disease

2018 ◽  
Vol 19 (1) ◽  
pp. 201-222 ◽  
Author(s):  
Wanda K. O'Neal ◽  
Michael R. Knowles
Keyword(s):  
Cystic Fibrosis ◽  
Phenotypic Diversity ◽  
Association Studies ◽  
Gene Mutations ◽  
Monogenic Disease ◽  
Genome Wide Association Studies ◽  
Complex Genetics ◽  
Genetic Components ◽  
Significant Gene ◽  
Gene Expression Studies

In many respects, genetic studies in cystic fibrosis (CF) serve as a paradigm for a human Mendelian genetic success story. From recognition of the condition as a heritable pathological entity to implementation of personalized treatments based on genetic findings, this multistep pathway of progress has focused on the genetic underpinnings of CF clinical disease. Along this path was the recognition that not all CFTR gene mutations produce the same disease and the recognition of the complex, multifactorial nature of CF genotype–phenotype relationships. The non- CFTR genetic components (gene modifiers) that contribute to variation in phenotype are the focus of this review. A multifaceted approach involving candidate gene studies, genome-wide association studies, and gene expression studies has revealed significant gene modifiers for multiple CF phenotypes. The bold challenges for the future are to integrate the findings into our understanding of CF pathogenesis and to use the knowledge to develop novel therapies.

scholarly journals Genes identified through genome-wide association studies of osteonecrosis in childhood acute lymphoblastic leukemia patients

2019 ◽  
Vol 20 (17) ◽  
pp. 1189-1197 ◽  
Author(s):  
Vincent Gagné ◽  
Anne Aubry-Morin ◽  
Maria Plesa ◽  
Rachid Abaji ◽  
Kateryna Petrykey ◽  
...  
Keyword(s):  
Association Studies ◽  
Lymphoblastic Leukemia ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Childhood All ◽  
Exome Sequencing Data ◽  
Genome Wide ◽  
Whole Exome ◽  
Whole Exome Sequencing Data

Aim: To evaluate top-ranking genes identified through genome-wide association studies for an association with corticosteroid-related osteonecrosis in children with acute lymphoblastic leukemia (ALL) who received Dana–Farber Cancer Institute treatment protocols. Patients & methods: Lead SNPs from these studies, as well as other variants in the same genes, pooled from whole exome sequencing data, were analyzed for an association with osteonecrosis in childhood ALL patients from Quebec cohort. Top-ranking variants were verified in the replication patient group. Results: The analyses of variants in the ACP1-SH3YL1 locus derived from whole exome sequencing data showed an association of several correlated SNPs (rs11553746, rs2290911, rs7595075, rs2306060 and rs79716074). The rs79716074 defines *B haplotype of the APC1 gene, which is well known for its functional role. Conclusion: This study confirms implication of the ACP1 gene in the treatment-related osteonecrosis in childhood ALL and identifies novel, potentially causal variant of this complication.

Microglia in Brain Development, Homeostasis, and Neurodegeneration

2019 ◽  
Vol 53 (1) ◽  
pp. 263-288 ◽  
Author(s):  
Christopher J. Bohlen ◽  
Brad A. Friedman ◽  
Borislav Dejanovic ◽  
Morgan Sheng
Keyword(s):  
Alzheimer Disease ◽  
Brain Development ◽  
Neurodegenerative Disease ◽  
Human Genetics ◽  
Association Studies ◽  
Therapeutic Interventions ◽  
Genome Wide Association Studies ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Expression Studies

Advances in human genetics have implicated a growing number of genes in neurodegenerative diseases, providing insight into pathological processes. For Alzheimer disease in particular, genome-wide association studies and gene expression studies have emphasized the pathogenic contributions from microglial cells and motivated studies of microglial function/dysfunction. Here, we summarize recent genetic evidence for microglial involvement in neurodegenerative disease with a focus on Alzheimer disease, for which the evidence is most compelling. To provide context for these genetic discoveries, we discuss how microglia influence brain development and homeostasis, how microglial characteristics change in disease, and which microglial activities likely influence the course of neurodegeneration. In all, we aim to synthesize varied aspects of microglial biology and highlight microglia as possible targets for therapeutic interventions in neurodegenerative disease.

scholarly journals Dissecting meningococcal disease and carriage traits using high throughput phenotypic testing

2020 ◽  
Vol 2 (7A) ◽  
Author(s):  
Megan De Ste Croix ◽  
Dave Neelam ◽  
Neil Oldfield ◽  
Jay Lucidarme ◽  
David Turner ◽  
...  
Keyword(s):  
High Throughput ◽  
Meningococcal Disease ◽  
Association Studies ◽  
Phase Variation ◽  
Genome Wide Association Studies ◽  
Current Policy ◽  
Genome Sequences ◽  
Phenotypic Differences ◽  
Genome Wide ◽  
The Uk

Despite on-going vaccination programmes, Neisseria meningitidis causes over 700 cases of invasive meningococcal disease (IMD) in the UK each year. In 2017-18, the MenW and MenY capsular groups caused 38% of all IMD cases. Current policy is to generate genome sequences of all meningococcal disease isolates. Using this resource, we aim to understand how genetic variation contributes to phenotypic differences between carriage and disease isolates. We are adapting a variety of assays, designed to mimic carriage and disease behaviours, for high throughput phenotypic testing of multiple meningococcal isolates from carriage and cases of IMD. We have selected 335 MenW cc11 and MenY cc23 isolates and are currently testing subsets of isolates in cell culture (CaLu3), growth and biofilm assays. Phenotypic differences will be utilised as input data for Genome Wide Association Studies that aim to identify the specific genomic variants, or combinations of variants, determining observed differences. Genomic data will include whole genome sequences and repeat-mediated phase variation states. Our preliminary data has detected variation in the ability of cc11 and cc23 isolates to disrupt monolayers of CaLu3 cells, indicating that minor genetic differences in phylogentically similar organisms may be physiologically important for both carriage and disease. We will also discuss progress in establishing successful, high-throughput assays for testing multiple isolates.

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