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A guide to genome‐wide association analysis and post‐analytic interrogation |
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| Authors: | Eric Reed Sara Nunez David Kulp Jing Qian Muredach P. Reilly Andrea S. Foulkes |
| Affiliation: | 1. Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, U.S.A.;2. Department of Computer Science, University of Massachusetts, Amherst, MA, U.S.A.;3. Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, U.S.A.;4. Department of Medicine, University of Pennsylvania, Philadelphia, PA, U.S.A. |
| Abstract: | 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. |
| Keywords: | statistical genomics tutorial genome‐wide association (GWA) study R code Bioconductor SNP filtering sample filtering call rate minor allele frequency (MAF) Hardy– Weinberg equilibrium (HWE) heterozygosity relatedness IBD ancestry substructure principal component analysis (PCA) parallel processing imputation Manhattan plot Q– Q plot lambda statistic heatmap regional association plot UCSC Genome Browser |