BRLMM: an Improved Genotype Calling Method for the GeneChip® Human Mapping 500K Array Set

Highly accurate and reliable genotype calling is an essential component of any highthroughput SNP genotyping technology. The Dynamic Model (DM, [1]) which has been extensively used for the GeneChip® Human Mapping 100K Array Set and the GeneChip® Human Mapping 500K Array Set has proven to be very effective, however it is possible to do better. Rabbee & Speed recently developed a model called the Robust Linear Model with Mahalanobis distance classifier (RLMM, pronounced ‘realm’) which provided an improvement over DM on the Mapping 100K set [2,3,4]. We present here an extension of the RLMM model developed for the Mapping 500K product which provides a significant improvement over DM in two important areas – it improves overall performance (call rates and accuracy) and it equalizes the performance on homozygous and heterozygous genotypes. The difference between RLMM and this approach is the addition of a Bayesian step which provides improved estimates of cluster centers and variances, the new model is called BRLMM (pronounced ‘B-realm’).