title: Identification of disease causing loci using an array-based genotyping approach on pooled DNA.
abstract: BACKGROUND
  Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.
  
  
  RESULTS
  We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.
  
  
  CONCLUSIONS
  Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.
authors.affiliation: Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, Arizona 85004, USA. dcraig@tgen.org
authors.forename: David W
authors.initials: DW
authors.lastname: Craig
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Huentelman
authors.forename: Diane
authors.initials: D
authors.lastname: Hu-Lince
authors.forename: Victoria L
authors.initials: VL
authors.lastname: Zismann
authors.forename: Michael C
authors.initials: MC
authors.lastname: Kruer
authors.forename: Anne M
authors.initials: AM
authors.lastname: Lee
authors.forename: Erik G
authors.initials: EG
authors.lastname: Puffenberger
authors.forename: John M
authors.initials: JM
authors.lastname: Pearson
authors.forename: Dietrich A
authors.initials: DA
authors.lastname: Stephan
mesh_terms: D000483:Alleles; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics* / Q000378:metabolism; D030541:Databases, Genetic; D005787:Gene Frequency; D016678:Genome; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D007231:Infant, Newborn; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D013398:Sudden Infant Death / Q000235:genetics
chemical_list: D004247:DNA