Multi-locus match probability in a finite population: a fundamental difference between the Moran and Wright-Fisher models.
BACKGROUND
A fundamental problem in population genetics, which being also of importance to forensic science, is to compute the match probability (MP) that two individuals randomly chosen from a population have identical alleles at a collection of loci. At present, 11-13 unlinked autosomal microsatellite loci are typed for forensic use. In a finite population, the genealogical relationships of individuals can create statistical non-independence of alleles at unlinked loci. However, the so-called product rule, which is used in courts in the USA, computes the MP for multiple unlinked loci by assuming statistical independence, multiplying the one-locus MPs at those loci. Analytically testing the accuracy of the product rule for more than five loci has hitherto remained an open problem.


RESULTS
In this article, we adopt a flexible graphical framework to compute multi-locus MPs analytically. We consider two standard models of random mating, namely the Wright-Fisher (WF) and Moran models. We succeed in computing haplotypic MPs for up to 10 loci in the WF model, and up to 13 loci in the Moran model. For a finite population and a large number of loci, we show that the MPs predicted by the product rule are highly sensitive to mutation rates in the range of interest, while the true MPs computed using our graphical framework are not. Furthermore, we show that the WF and Moran models may produce drastically different MPs for a finite population, and that this difference grows with the number of loci and mutation rates. Although the two models converge to the same coalescent or diffusion limit, in which the population size approaches infinity, we demonstrate that, when multiple loci are considered, the rate of convergence in the Moran model is significantly slower than that in the WF model.


BACKGROUND
A C++ implementation of the algorithms discussed in this article is available at http://www.cs.berkeley.edu/ approximately yss/software.html.
Bhaskar
Anand
A
Computer Science Division and Department of Statistics, University of California, Berkeley, CA, USA.
Song
Yun S
YS
D019295:Computational Biology / Q000379:methods*; D005828:Genetics, Population / Q000379:methods*; D008957:Models, Genetic; D011156:Population Density; D011157:Population Dynamics; D011336:Probability