AI Article Synopsis

  • The software STRUCTURE is widely used for determining population structure in genetic studies, but about 30% of its findings cannot be consistently reproduced, raising concerns about its reliability.* -
  • An analysis of 2,323 horses from various domestic breeds and the Przewalski horse showed that different methods yielded similar broad breed relationships but varied in identifying finer details.* -
  • The study suggests moving away from complex methods like the Δ method in STRUCTURE, advocating for simpler qualitative approaches that provide consistent results with fewer computational demands.*

Article 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 (, 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 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 using the most widespread approach, the Δ method, despite very large numbers of MCMC iterations (3,000,000) and replicates (100). The interpretation of breed structure over increasing numbers of , without assuming a , was consistent with known breed histories. The over-reliance on should be replaced by a qualitative description of clustering over increasing , 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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246218PMC
http://dx.doi.org/10.1002/ece3.6195DOI Listing

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