<scp>Clumpak</scp>: a program for identifying clustering modes and packaging population structure inferences across <i>K</i>

TLDR

Identifying genetic structure from multilocus genotype data is central to modern population genetics, yet model‑based clustering requires multiple steps—choosing assumptions, testing different K values, running replicates, and distinguishing distinct clustering solutions. Clumpak automates the post‑processing of model‑based population structure analyses. It groups highly similar runs at each K, generates consensus solutions for each mode via a Markov clustering algorithm on a similarity matrix from Clumpp, aligns clusters across K values, and offers tools for selecting K and comparing results from different programs or data subsets. Clumpak, available at http://clumpak.tau.ac.il, streamlines the use of model‑based population structure methods in genetics and ecology.

Abstract

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.