title: Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray.
abstract: The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies would increase comparability and the potential for effective meta-analysis. This article provides a discussion of important aspects of quality control, imputation and analysis of genome-wide data from a low-coverage microarray, as well as a straight-forward guide to performing a genome-wide association study. A detailed protocol is provided online, with example scripts available at https://github.com/JoniColeman/gwas_scripts.
authors.forename: Jonathan R I
authors.initials: JR
authors.lastname: Coleman
authors.forename: Jack
authors.initials: J
authors.lastname: Euesden
authors.forename: Hamel
authors.initials: H
authors.lastname: Patel
authors.forename: Amos A
authors.initials: AA
authors.lastname: Folarin
authors.forename: Stephen
authors.initials: S
authors.lastname: Newhouse
authors.forename: Gerome
authors.initials: G
authors.lastname: Breen
mesh_terms: D000465:Algorithms; D003072:Cognition Disorders / Q000235:genetics* / Q000628:therapy; D015928:Cognitive Behavioral Therapy; D019295:Computational Biology / Q000379:methods*; D059472:Exome; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011786:Quality Control; D012984:Software
keywords: GWAS; analysis; imputation; low-coverage microarray; methods