RT Journal Article
SR Electronic
T1 Understanding population structure in an evolutionary context: population-specific <em>F</em><sub>ST</sub> and pairwise <em>F</em><sub>ST</sub>
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.30.927186
DO 10.1101/2020.01.30.927186
A1 Shuichi Kitada
A1 Reiichiro Nakamichi
A1 Hirohisa Kishino
YR 2020
UL http://biorxiv.org/content/early/2020/08/04/2020.01.30.927186.abstract
AB Populations are shaped by their history. Therefore, it is crucial to interpret population structure in an evolutionary context. Wright’s FST measures current population structure, whereas population-specific FST measures deviation from the ancestral population. To understand current population structure and a population’s history of range expansion, we propose a novel representation method that overlays population-specific FST estimates on an unrooted neighbor-joining tree inferred from a pairwise FST distance matrix and on a map of sampling locations. We examined the usefulness of our procedure by conducting simulations that mimicked population colonization from an ancestral population and analyzing published human, Atlantic cod, and wild poplar genotype data sets. Our results demonstrated that population-specific FST values identify the source population and trace the evolutionary history of its derived populations based on genetic diversity. In contrast, pairwise FST values represent the current population structure. By integrating results of both estimators, we obtained a new picture of current population structure that incorporates evolutionary history. The generalized least squares of genome-wide population-specific FST indicated that the wild poplar population expanded its distribution to the north where it adapted to longer day lengths, to seashores where it adapted to abundant rainfall, and to the south where it adapted to dry summers. Genomic data highlight the power of the bias-corrected moment estimators of FST. All FST moment estimators described in this paper have reasonable CPU times and are useful in population genomics studies. The R codes for our representation method and simulations are available in the Supporting Information.Competing Interest StatementThe authors have declared no competing interest.