Precision of methods for calculating identity-by-descent matrices using multiple markers

A rapid, deterministic method (DET) based on a recursive algorithm and a stochastic method based on Markov Chain Monte Carlo (MCMC) for calculating identity-by-descent (IBD) matrices conditional on multiple markers were compared using stochastic simulation. Precision was measured by the mean squared error (MSE) of the relationship coefficients in predicting the true IBD relationships, relative to MSE obtained from using pedigree only. Comparisons were made when varying marker density, allele numbers, allele frequencies, and the size of full-sib families. The precision of DET was 75-99% relative to MCMC, but was not simply related to the informativeness of individual loci. For situations mimicking microsatellite markers or dense SNP, the precision of DET was $\\geq 95\\%$ relative to MCMC. Relative precision declined for the SNP, but not microsatellites as marker density decreased. Full-sib family size did not affect the precision. The methods were tested in interval mapping and marker assisted selection, and the performance was very largely determined by the MSE. A multi-locus information index considering the type, number, and position of markers was developed to assess precision. It showed a marked empirical relationship with the observed precision for DET and MCMC and explained the complex relationship between relative precision and the informativeness of individual loci.