FLOCK Provides Reliable Solutions to the “Number of Populations” Problem

摘要

Identifying groups of individuals forming coherent genetic clusters is relevant to many fields of biology. This paper addresses the K-partition problem: given a collection of genotypes, partition those genotypes into K groups, each group being a sample of the K source populations that are represented in the collection of genotypes. This problem involves allocating genotypes to genetic groups while building those groups at the same time without the use of any other a priori information. FLOCK is a non-Markov chain Monte Carlo (MCMC) algorithm that uses an iterative method to partition a collection of genotypes into k groups. Rules to estimate K are formulated and their validity firmly established by running simulations under several migration rates, migration regimes, number of loci, and values of K. FLOCK tended to build clusters largely consistent with the source samples. The performance of FLOCK was also compared with that of STRUCTURE and BAPS. FLOCK provided more accurate allocations to clusters and more reliable estimates of K; it also ran much faster than STRUCTURE. FLOCK is based on an entirely novel approach and provides a true alternative to the existing, MCMC based, algorithms. FLOCK v.2.0 for microsatellites or for AFLP markers can be downloaded from http://www.bio.ulaval.cao_cache/departement/professeurs/fiche_des_professeurs/professeur/11/13/.