The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets

The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE out- put, the ΔK method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of K and is sensitive to uneven sampling. If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described es- timators of K to re-estimate genetic structure in 41 empirical microsatellite data sets; 15 from a broad range of taxa and 26 from one phylogenetic group, coral. We com- pared alternative estimates of K (Puechmaille statistics) with traditional (ΔK and pos- terior probability) estimates and found widespread disagreement of estimators across data sets. Thus, one estimator alone is insufficient for determining the optimal num- ber of clusters; this was regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) did not necessarily clarify which clustering solution was best. To better infer population structure, we suggest a com- bination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to ΔK. Disagreement between traditional and recent estimators may have important biological implications, such as previously unrecognized population structure, as was the case for many studies reanalysed here

The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets

14 pages, 5 figures, 2 tables, 1 appendix, supporting information https://doi.org/10.1111/1755-0998.13522.-- Data Availability Statement: All Supporting Information figures and their corresponding raw data can be accessed on Dryad (https://doi.org/10.5061/dryad.zgmsbccck)The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the ΔK method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of K and is sensitive to uneven sampling. If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of K to re-estimate genetic structure in 41 empirical microsatellite data sets; 15 from a broad range of taxa and 26 from one phylogenetic group, coral. We compared alternative estimates of K (Puechmaille statistics) with traditional (ΔK and posterior probability) estimates and found widespread disagreement of estimators across data sets. Thus, one estimator alone is insufficient for determining the optimal number of clusters; this was regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) did not necessarily clarify which clustering solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to ΔK. Disagreement between traditional and recent estimators may have important biological implications, such as previously unrecognized population structure, as was the case for many studies reanalysed hereThis work was made possible by NSF grant OCE-1537959 to IBB, NIH grant T32: Computation, Bioinformatics, and Statistics (CBIOS) Training Program to KHS, a Bunton-Waller fellowship to KLVK, the strategic Funding UIDB/04423/2020 and UIDP/04423/2020 to JBL, and the Pennsylvania State University Biology Department. The project leading to this publication has received funding from European FEDER Fund under project 1166-39417 to DA. We acknowledge the funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S)Peer reviewe

Juvenile corals inherit mutations acquired during the parents lifespan

128 years ago, August Weismann proposed that the only source of inherited genetic variation in animals is the germline. Julian Huxley reasoned that if this were true, it would falsify Jean-Baptiste Lamarck′s theory that acquired characteristics are heritable. Since then, scientists have discovered that not all animals segregate germline cells from somatic cells permanently and early in development. In fact, throughout their lives, Cnidaria and Porifera maintain primordial stem cells that continuously give rise to both germline and somatic cells. The fate of mutations generated in this primordial stem cell line during adulthood remains an open question. It was unknown whether post−embryonic mutations could be heritable in animals−until now. Here we use two independent genetic marker analyses to show that post-embryonic mutations are inherited in the coral Acropora palmata (Cnidaria, Anthozoa). This discovery upends the long-held supposition that post-embryonic genetic mutations acquired over an animal′s lifetime in non-germline tissues are not heritable2. Over the centuries-long lifespan of a coral, the inheritance of post-embryonic mutations may not only change allele frequencies in the local larval pool but may also spread novel alleles across great distances via larval dispersal. Thus, corals may have the potential to adapt to changing environments via heritable somatic mutations. This mechanism challenges our understanding of animal adaptation and prompts a deeper examination of both the process of germline determination in clonal animals and the role of post−embryonic genetic mutations in adaptation and epigenetics. Understanding the role of post−embryonic mutations in animal adaptation will be crucial as ecological change accelerates in the Anthropocene