Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gwasurvivr: an R package for genome-wide survival analysis.

ABSTRACT:

Summary

To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival; covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases.

Availability and implementation

gwasurvivr, including source code, documentation and vignette are available at: http://bioconductor.org/packages/gwasurvivr.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Rizvi AA

PROVIDER: S-EPMC7963072 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles.

Project description:DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation, cellular specification and cancer development. Here, we describe an R package, methylKit, that rapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation and hydroxymethylation sequencing experiments. methylKit includes functions for clustering, sample quality visualization, differential methylation analysis and annotation features, thus automating and simplifying many of the steps for discerning statistically significant bases or regions of DNA methylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statistically significant regions of differential methylation and stratify tumor subtypes. methylKit is available at http://code.google.com/p/methylkit.

| S-EPMC3491415 | biostudies-literature

snpGeneSets: An R Package for Genome-Wide Study Annotation.

Project description:Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.

| S-EPMC5144977 | biostudies-literature

Genome-wide regression and prediction with the BGLR statistical package.

Project description:Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.

| S-EPMC4196607 | biostudies-literature

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival.

Project description:Glioblastoma (GBM), representing WHO grade IV astrocytoma, is a relatively common primary brain tumor in adults with an exceptionally dismal prognosis. With an incidence rate of over 10 000 cases in the United States annually, the median survival rate ranges from 10-15 months in IDH1/2-wildtype tumors and 24-31 months in IDH1/2-mutant tumors, with further variation depending on factors such as age, MGMT methylation status, and treatment regimen. We present a cohort of 4 patients, aged 37-60 at initial diagnosis, with IDH1-mutant GBMs that were associated with unusually long survival intervals after the initial diagnosis, currently ranging from 90 to 154 months (all still alive). We applied genome-wide profiling with a methylation array (Illumina EPIC Array 850k) and a next-generation sequencing panel to screen for genetic and epigenetic alterations in these tumors. All 4 tumors demonstrated methylation patterns and genomic alterations consistent with GBM. Three out of four cases showed focal amplification of the CCND2 gene or gain of the region on 12p that included CCND2, suggesting that this may be a favorable prognostic factor in GBM. As this study has a limited sample size, further evaluation of patients with similar favorable outcome is warranted to validate these findings.

| S-EPMC9891105 | biostudies-literature

bGWAS: an R package to perform Bayesian genome wide association studies.

Project description:SummaryIncreasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms.Availability and implementationbGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7520046 | biostudies-literature

GWAF: an R package for genome-wide association analyses with family data.

Project description:SummaryGWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Study's SNP Health Association Resource project.

| S-EPMC2852219 | biostudies-literature

GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

Project description:Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.

| S-EPMC5423544 | biostudies-literature

metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA.

Project description:Here we introduce metaseq, a software library written in Python, which enables loading multiple genomic data formats into standard Python data structures and allows flexible, customized manipulation and visualization of data from high-throughput sequencing studies. We demonstrate its practical use by analyzing multiple datasets related to chromatin insulators, which are DNA-protein complexes proposed to organize the genome into distinct transcriptional domains. Recent studies in Drosophila and mammals have implicated RNA in the regulation of chromatin insulator activities. Moreover, the Drosophila RNA-binding protein Shep has been shown to antagonize gypsy insulator activity in a tissue-specific manner, but the precise role of RNA in this process remains unclear. Better understanding of chromatin insulator regulation requires integration of multiple datasets, including those from chromatin-binding, RNA-binding, and gene expression experiments. We use metaseq to integrate RIP- and ChIP-seq data for Shep and the core gypsy insulator protein Su(Hw) in two different cell types, along with publicly available ChIP-chip and RNA-seq data. Based on the metaseq-enabled analysis presented here, we propose a model where Shep associates with chromatin cotranscriptionally, then is recruited to insulator complexes in trans where it plays a negative role in insulator activity.

| S-EPMC4132753 | biostudies-literature

gJLS2: an R package for generalized joint location and scale analysis in X-inclusive genome-wide association studies.

Project description:A joint analysis of location and scale can be a powerful tool in genome-wide association studies to uncover previously overlooked markers that influence a quantitative trait through both mean and variance, as well as to prioritize candidates for gene-environment interactions. This approach has recently been generalized to handle related samples, dosage data, and the analytically challenging X-chromosome. We disseminate the latest advances in methodology through a user-friendly R software package with added functionalities to support genome-wide analysis on individual-level or summary-level data. The implemented R package can be called from PLINK or directly in a scripting environment, to enable a streamlined genome-wide analysis for biobank-scale data. Application results on individual-level and summary-level data highlight the advantage of the joint test to discover more genome-wide signals as compared to a location or scale test alone. We hope the availability of gJLS2 software package will encourage more scale and/or joint analyses in large-scale datasets, and promote the standardized reporting of their P-values to be shared with the scientific community.

| S-EPMC8982384 | biostudies-literature

Genome wide analysis of acral melanoma (Illumina)

Project description:Assessment of mutation on expression levels Transcriptomic profile of a matched primary and metastatic acral melanoma One Primary and one metastatic acral melanoma transcript expression were assayed (no matched normal)

| S-ECPF-GEOD-28909 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data