Understanding population structure in an evolutionary context: population-specific F ST and pairwise F ST

• Published in: G3 Genes|Genomes|Genetics
• Main Authors: Kitada, Shuichi, Nakamichi, Reiichiro, Kishino, Hirohisa
• Other Authors: Ross-Ibarra, J, Japan Society for the Promotion of Science
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2021
• Subjects: Genetics (clinical); Genetics; Molecular Biology; atlantic cod
• Online Access: http://dx.doi.org/10.1093/g3journal/jkab316; http://academic.oup.com/g3journal/advance-article-pdf/doi/10.1093/g3journal/jkab316/40531199/jkab316.pdf; http://academic.oup.com/g3journal/article-pdf/11/11/jkab316/40770287/jkab316.pdf

Abstract Populations are shaped by their history. It is crucial to interpret population structure in an evolutionary context. Pairwise FST measures population structure, whereas population-specific FST measures deviation from the ancestral population. To understand the current population structure and a population’s history of range expansion, we propose a representation method that overlays population-specific FST estimates on a sampling location map, and on an unrooted neighbor-joining tree and a multi-dimensional scaling plot inferred from a pairwise FST distance matrix. We examined the usefulness of our procedure using simulations that mimicked population colonization from an ancestral population and by analyzing published human, Atlantic cod, and wild poplar data. Our results demonstrated that population-specific FST values identify the source population and trace the evolutionary history of its derived populations. Conversely, pairwise FST values represent the current population structure. By integrating the results of both estimators, we obtained a new picture of the population structure that incorporates evolutionary history. The generalized least squares estimate of genome-wide population-specific FST indicated that the wild poplar population expanded its distribution to the north, where daylight hours are long in summer, to coastal areas with abundant rainfall, and to the south where summers are dry. Genomic data highlight the power of the bias-corrected moment estimators of FST, whether global, pairwise, or population-specific, that provide unbiased estimates of FST. All FST moment estimators described in this paper have reasonable processing times and are useful in population genomics studies.