A simple procedure to detect, test for the presence of stuttering, and cure stuttered data with spreadsheet programs

Abstract

Microsatellites are powerful markers for empirical population genetics, but may be affected by amplification problems like stuttering that produces heterozygote deficits between alleles with one repeat difference. In this paper, we present a simple procedure that aims at detecting stuttering for each locus overall subsamples and only requires the use of a spreadsheet interactive application on any operating system. We compare the performances of this procedure with the one of MicroChecker on simulations of dioecious pangamic populations, monoecious selfing populations and clonal populations with or without stuttering, and on real data of vectors and parasites. We also propose a cure for loci affected and compare the results with those expected without stuttering. In sexual populations (dioecious or selfers), the new procedure appeared more than three times more efficient than MicroChecker. Cure was able to restore Wright's FIS of stuttered data to the expected value, and particularly so in selfing simulations. In clones, lack of segregation artificially increased false stuttering detection, and only highly significant stuttering tests and loci strongly deviating from others, could be usefully cured, in which case FIS estimate could be much improved. In doubt, and whenever possible, removal of affected and not curable loci may help to shift population genetics parameter estimates towards more reliable values.