Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites

• Main Authors: Stephan Michael Funk, Sonya Guedaoura, Rytis Juras, Absul Raziq, Faouzi Landolsi, Cristina Luís, Amparo Martínez Martínez, Abubakar Musa Mayaki, Fernando Mujica, Maria do Mar Oom, Lahoussine Ouragh, Yves‐Marie Stranger, Jose Luis Vega‐Pla, Ernest Gus Cothran
• Format: Article
• Language: English
• Published: Wiley 2020-05-01
• Series: Ecology and Evolution
• Subjects: domestic horse; population genetic structure; Przewalski horse; STRUCTURE analysis
• Online Access: https://doi.org/10.1002/ece3.6195

Abstract STRUCTURE remains the most applied software aimed at recovering the true, but unknown, population structure from microsatellite or other genetic markers. About 30% of STRUCTURE‐based studies could not be reproduced (Molecular Ecology, 21, 2012, 4925). Here we use a large set of data from 2,323 horses from 93 domestic breeds plus the Przewalski horse, typed at 15 microsatellites, to evaluate how program settings impact the estimation of the optimal number of population clusters Kopt that best describe the observed data. Domestic horses are suited as a test case as there is extensive background knowledge on the history of many breeds and extensive phylogenetic analyses. Different methods based on different genetic assumptions and statistical procedures (DAPC, FLOCK, PCoA, and STRUCTURE with different run scenarios) all revealed general, broad‐scale breed relationships that largely reflect known breed histories but diverged how they characterized small‐scale patterns. STRUCTURE failed to consistently identify Kopt using the most widespread approach, the ΔK method, despite very large numbers of MCMC iterations (3,000,000) and replicates (100). The interpretation of breed structure over increasing numbers of K, without assuming a Kopt, was consistent with known breed histories. The over‐reliance on Kopt should be replaced by a qualitative description of clustering over increasing K, which is scientifically more honest and has the advantage of being much faster and less computer intensive as lower numbers of MCMC iterations and repetitions suffice for stable results. Very large data sets are highly challenging for cluster analyses, especially when populations with complex genetic histories are investigated.