Toward fully automated genotyping of microsatellite markers by deconvolution (GMBD)

ABSTRACT

Dense genetic linkage maps have been constructed for the human and mouse genomes, with average densities of 2.9 centiMorgans (cM) and 0.35cM, respectively. These genetic maps are crucial for mapping both Mendelian and complex traits, and are useful in clinical genetic diagnosis . Current maps are largely comprised of abundant, easily assayed, and highly polymorphic PCR-based microsatellite markers, primarily dinucleotide (CA)n repeats .

One key limitation of these length polymorphisms is the PCR stutter (or slippage) artifact that introduces additional stutter bands. With two (or more) closely spaced alleles, the stutter bands overlap and it is difficult to accurately determine the correct alleles; this stutter phenomenon has all but precluded full automation, since a human must visually inspect the allele data.

We have developed novel deconvolution methods for accurate genotyping which mathematically remove PCR stutter artifact from microsatellite markers. Ten different deconvolution algorithms were assessed for relative speed and accuracy on simulated and empirical data. Efficacy was demonstrated on gel data obtained using automated fluorescence-based DNA sequencers, including ABI, Pharmacia, and DuPont machines. Our deconvolution methods overcome the manual interpretation bottleneck, and thereby enable full automation of genetic map construction and use. We also implemented new deconvolution-based genotyping functionalities, including the analysis of pooled DNAs and of pooled markers in PCR studies, which may greatly reduce overall experimentation requirements.