The empirical Bayes estimators of fine-scale population structure in high gene flow species.

An empirical Bayes (EB) pairwise F _ST estimator was previously introduced and evaluated for its performance by numerical simulation. In this study, we conducted coalescent simulations and generated genetic population structure mechanistically, and compared the performance of the EBF _ST with Nei's G _ST , Nei and Chesser's bias-corrected G _ST (G _ST_NC ), Weir and Cockerham's θ (θ _WC ) and θ with finite sample correction (θ _WC_F ). We also introduced EB estimators for Hedrick' G' _ST and Jost' D. We applied these estimators to publicly available SNP genotypes of Atlantic herring. We also examined the power to detect the environmental factors causing the population structure. Our coalescent simulations revealed that the finite sample correction of θ _WC is necessary to assess population structure using pairwise F _ST values. For microsatellite markers, EBF _ST performed the best among the present estimators regarding both bias and precision under high gene flow scenarios (FST≤0.032). For 300 SNPs, EBF _ST had the highest precision in all cases, but the bias was negative and greater than those for G _ST_NC and θ _WC_F in all cases. G _ST_NC and θ _WC_F performed very similarly at all levels of F _ST . As the number of loci increased up to 10 000, the precision of G _ST_NC and θ _WC_F became slightly better than for EBF _ST for cases with FST≥0.004, even though the size of the bias remained constant. The EB estimators described the fine-scale population structure of the herring and revealed that ~56% of the genetic differentiation was caused by sea surface temperature and salinity. The R package finepop for implementing all estimators used here is available on CRAN.
(© 2017 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.)