Postgwas: Advanced GWAS Interpretation in R

Hiersche, Milan; Rühle, Frank; Stoll, Monika

doi:10.1371/journal.pone.0071775

Cited by 25 publications

(16 citation statements)

References 35 publications

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“…Finally, linear mixed models have been described as an alternative strategy for GWA analysis, which can account for family relatedness and population substructure [49][50][51][52][53][54]. An additional recommended resource for more in-depth post-processing of GWA findings, including gene and network-based analysis, is provided in [55].…”

Section: Broader Contemporary Context and Discussionmentioning

confidence: 99%

A guide to genome‐wide association analysis and post‐analytic interrogation

Reed

Núñez

Kulp

et al. 2015

Statistics in Medicine

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This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome‐wide association analysis. Approaches to post‐analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open‐source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome‐wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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Section: Broader Contemporary Context and Discussionmentioning

confidence: 99%

A guide to genome‐wide association analysis and post‐analytic interrogation

Reed

Núñez

Kulp

et al. 2015

Statistics in Medicine

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“…GSEA was performed for any bacterial abundance GWAS with at least one significantly associated SNP using the R package postgwas[ 45 ]. Each tested SNP was assigned to the closest gene using Ensembl release 75 and any SNP further than 10kb from a gene was eliminated from analysis.…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Studies of the Human Gut Microbiota

et al. 2015

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The bacterial composition of the human fecal microbiome is influenced by many lifestyle factors, notably diet. It is less clear, however, what role host genetics plays in dictating the composition of bacteria living in the gut. In this study, we examined the association of ~200K host genotypes with the relative abundance of fecal bacterial taxa in a founder population, the Hutterites, during two seasons (n = 91 summer, n = 93 winter, n = 57 individuals collected in both). These individuals live and eat communally, minimizing variation due to environmental exposures, including diet, which could potentially mask small genetic effects. Using a GWAS approach that takes into account the relatedness between subjects, we identified at least 8 bacterial taxa whose abundances were associated with single nucleotide polymorphisms in the host genome in each season (at genome-wide FDR of 20%). For example, we identified an association between a taxon known to affect obesity (genus Akkermansia) and a variant near PLD1, a gene previously associated with body mass index. Moreover, we replicate a previously reported association from a quantitative trait locus (QTL) mapping study of fecal microbiome abundance in mice (genus Lactococcus, rs3747113, P = 3.13 x 10−7). Finally, based on the significance distribution of the associated microbiome QTLs in our study with respect to chromatin accessibility profiles, we identified tissues in which host genetic variation may be acting to influence bacterial abundance in the gut.

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“…It is a well-known fact that the risk SNPs indicates haplotypes on which the functional variants reside; therefore, the next step was to identify their target genes. By adopting multi-annotations between risk SNPs and their surrounding genes, the snp2gene allowed conventional annotation due to their proximity, as well as linkage disequilibrium[ 9 ].…”

Section: Methodsmentioning

confidence: 99%

Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Zhang

Jiang

et al. 2015

PLoS ONE

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Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.

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Postgwas: Advanced GWAS Interpretation in R

Cited by 25 publications

References 35 publications

A guide to genome‐wide association analysis and post‐analytic interrogation

A guide to genome‐wide association analysis and post‐analytic interrogation

Genome-Wide Association Studies of the Human Gut Microbiota

Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

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