title: SNiPer: improved SNP genotype calling for Affymetrix 10K GeneChip microarray data.
abstract: BACKGROUND
  High throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.
  
  
  RESULTS
  A database of 948 individuals genotyped on the GeneChip Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data.
  
  
  CONCLUSIONS
  The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip without sacrificing accuracy, thereby increasing the amount of valid data generated.
authors.affiliation: Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona 85004, USA. mhuentelman@tgen.org
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Huentelman
authors.forename: David W
authors.initials: DW
authors.lastname: Craig
authors.forename: Albert D
authors.initials: AD
authors.lastname: Shieh
authors.forename: Jason J
authors.initials: JJ
authors.lastname: Corneveaux
authors.forename: Diane
authors.initials: D
authors.lastname: Hu-Lince
authors.forename: John V
authors.initials: JV
authors.lastname: Pearson
authors.forename: Dietrich A
authors.initials: DA
authors.lastname: Stephan
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D002888:Chromosomes, Human, Pair 19 / Q000235:genetics; D016000:Cluster Analysis; D004252:DNA Mutational Analysis; D020869:Gene Expression Profiling; D005787:Gene Frequency; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016133:Polymerase Chain Reaction; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods; D012984:Software