Postgwas: Advanced GWAS Interpretation in R

We present a comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. In the spirit of comparable tools for gene-expression analysis, we attempt to unify and simplify several procedures that are essential for the interpretation of GWAS results. This includes the generation of advanced Manhattan and regional association plots including rare variant display as well as novel interaction network analysis tools for the investigation of systems-biology aspects. Our package supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R. Previous software of that range is dispersed over a wide range of platforms and mostly not adaptable for custom work pipelines. We demonstrate the utility of this package by providing an example workflow on a publicly available dataset.

[1]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[2]  P. Sham,et al.  A Knowledge-Based Weighting Framework to Boost the Power of Genome-Wide Association Studies , 2010, PloS one.

[3]  Johnny S. H. Kwan,et al.  GATES: a rapid and powerful gene-based association test using extended Simes procedure. , 2011, American journal of human genetics.

[4]  Pablo Cingolani,et al.  © 2012 Landes Bioscience. Do not distribute. , 2022 .

[5]  R. Collins,et al.  Common variants at 30 loci contribute to polygenic dyslipidemia , 2009, Nature Genetics.

[6]  Thomas Lengauer,et al.  Improved scoring of functional groups from gene expression data by decorrelating GO graph structure , 2006, Bioinform..

[7]  E. G. de la Concha,et al.  IL4 in the 5q31 context: association studies of type 1 diabetes and rheumatoid arthritis in the Spanish population , 2007, Immunogenetics.

[8]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[9]  Zhongming Zhao,et al.  Enriched pathways for major depressive disorder identified from a genome-wide association study. , 2012, The international journal of neuropsychopharmacology.

[10]  A. Auton,et al.  Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel , 2011, Nature Genetics.

[11]  Ayellet V. Segrè,et al.  Hundreds of variants clustered in genomic loci and biological pathways affect human height , 2010, Nature.

[12]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  M. Stoll,et al.  A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke. , 2012, Blood.

[14]  Patrick F Sullivan,et al.  Modifiers and Subtype-Specific Analyses in Whole-Genome Association Studies: A Likelihood Framework , 2011, Human Heredity.

[15]  Annette Lee,et al.  Risk Alleles for Systemic Lupus Erythematosus in a Large Case-Control Collection and Associations with Clinical Subphenotypes , 2011, PLoS genetics.

[16]  Steven J. Schrodi,et al.  The 5q31 variants associated with psoriasis and Crohn's disease are distinct , 2008, Human molecular genetics.

[17]  S. Fisher,et al.  Genetic evidence for interaction of the 5q31 cytokine locus and the CARD15 gene in Crohn disease. , 2003, American journal of human genetics.

[18]  Holger Fröhlich,et al.  GOSim – an R-package for computation of information theoretic GO similarities between terms and gene products , 2007, BMC Bioinformatics.

[19]  Peter Kraft,et al.  Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis , 2012, Nature Genetics.

[20]  M. Daly,et al.  Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology , 2011, PLoS genetics.

[21]  X. Ke Presence of multiple independent effects in risk loci of common complex human diseases. , 2012, American journal of human genetics.

[22]  D. Goldman,et al.  Deconstruction of Vulnerability to Complex Diseases: Enhanced Effect Sizes and Power of Intermediate Phenotypes , 2007, TheScientificWorldJournal.

[23]  Mayetri Gupta,et al.  Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome‐wide associations , 2011, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[24]  E. Marcotte,et al.  Prioritizing candidate disease genes by network-based boosting of genome-wide association data. , 2011, Genome research.

[25]  Keyan Zhao,et al.  Genome-Wide Association Mapping in Arabidopsis Identifies Previously Known Flowering Time and Pathogen Resistance Genes , 2005, PLoS genetics.

[26]  Yurii S. Aulchenko,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm108 Genetics and population analysis GenABEL: an R library for genome-wide association analysis , 2022 .

[27]  Claudia Hemmelmann,et al.  Statistical analysis of rare sequence variants: an overview of collapsing methods , 2011, Genetic epidemiology.

[28]  D. Nyholt A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. , 2004, American journal of human genetics.

[29]  Jason Y. Liu,et al.  Analysis of genome-wide association study data using the protein knowledge base , 2011, BMC Genetics.

[30]  John P. A. Ioannidis,et al.  Validating, augmenting and refining genome-wide association signals , 2009, Nature Reviews Genetics.

[31]  Kai Wang,et al.  Pathway-based approaches for analysis of genomewide association studies. , 2007, American journal of human genetics.

[32]  Ralf H. Bortfeldt,et al.  CandiSNPer: a web tool for the identification of candidate SNPs for causal variants , 2010, Bioinform..

[33]  Mourad Sahbatou,et al.  Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease , 2001, Nature.

[34]  Nilanjan Chatterjee,et al.  Estimation of effect size distribution from genome-wide association studies and implications for future discoveries , 2010, Nature Genetics.

[35]  Steven J. Schrodi,et al.  Variants in the 5q31 cytokine gene cluster are associated with psoriasis , 2008, Genes and Immunity.

[36]  M. Inouye,et al.  Genome-wide association studies and systems biology: together at last. , 2011, Trends in genetics : TIG.

[37]  H. Hakonarson,et al.  Analysing biological pathways in genome-wide association studies , 2010, Nature Reviews Genetics.

[38]  Wei Zheng,et al.  dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks , 2011, Bioinform..