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 EBFST with Nei's G(ST), Nei and Chesser's bias-corrected G(ST) (G(ST_NC)), Weir and Cockerham's ((WC)) and with finite sample correction ((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 (WC) is necessary to assess population structure using pairwise F-ST values. For microsatellite markers, EBFST performed the best among the present estimators regarding both bias and precision under high gene flow scenarios (FST0.032). For 300 SNPs, EBFST had the highest precision in all cases, but the bias was negative and greater than those for G(ST_NC) and (WC_F) in all cases. G(ST_NC) and (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 (WC_F) became slightly better than for EBFST for cases with FST0.004, even though the size of the bias remained constant. The EB estimators described the fine-scale population structure of the herring and revealed that similar to 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.