Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays

• Type of Material: Article
• Authors: Shin Lin, Johns Hopkins University; Benilton Carvalho, Johns Hopkins University; David J Cutler, Emory University; Dan E Arking, Johns Hopkins University; Aravinda Chakravarti, Johns Hopkins University; Rafael A Irizarry, Johns Hopkins University
• Language: English
• Date: 2008-01-01
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: © 2008 Lin et al.; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://dx.doi.org/10.1186/gb-2008-9-4-r63
• Title of Journal or Parent Work: Genome Biology
• ISSN: 1474-7596
• Volume: 9
• Issue: 4
• Start Page: R63
• End Page: R63
• Grant/Funding Information: The work of Rafael A Irizarry was partially funded by NIH grants 1R01GM083084-01, 1P41HG004059 and P50 HL73994 (Core E); Benilton Carvalho was funded by NIH grant 1R01RR021967-01A2 and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Brazil); Shin Lin, David J Cutler, Dan E Arking, and Aravinda Chakravarti were supported by NIH grants HG02757, MH60007, and the DW Reynolds Foundation.
• Keywords: LOCI; DNA; Science & Technology; GENOME-WIDE ASSOCIATION; Life Sciences & Biomedicine; Genetics & Heredity; OLIGONUCLEOTIDE ARRAYS; POLYMORPHISM; ALGORITHMS; NORMALIZATION; EXPLORATION; Biotechnology & Applied Microbiology
• Research Categories: Biology, Genetics; Biology, Biostatistics

Abstract

Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.