There Is No ‘Rule of Thumb’: Genomic Filter Settings for a Small Plant Population to Obtain Unbiased Gene Flow Estimates

Abstract

The application of high-density polymorphic single-nucleotide polymorphisms (SNP) markers derived from high-throughput sequencing methods has heralded plenty of biological questions about the linkages of processes operating at micro- and macroevolutionary scales. However, the effects of SNP filtering practices on population genetic inference have received much less attention. By performing sensitivity analyses, we empirically investigated how decisions about the percentage of missing data (MD) and the minor allele frequency (MAF) set in bioinformatic processing of genomic data affect direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial genetic structure – SGS) gene flow estimates. We focus specifically on these manifestations in small plant populations, and particularly, in the rare tropical plant species Dinizia jueirana-facao, where assumptions implicit to analytical procedures for accurate estimates of gene flow may not hold. Avoiding biases in dispersal estimates are essential given this species is facing extinction risks due to habitat loss, and so we also investigate the effects of forest fragmentation on the accuracy of dispersal estimates under different filtering criteria by testing for recent decrease in the scale of gene flow. Our sensitivity analyses demonstrate that gene flow estimates are robust to different setting of MAF (0.05–0.35) and MD (0–20%). Comparing the direct and indirect estimates of dispersal, we find that contemporary estimates of gene dispersal distance (σrt = 41.8 m) was ∼ fourfold smaller than the historical estimates, supporting the hypothesis of a temporal shift in the scale of gene flow in D. jueirana-facao, which is consistent with predictions based on recent, dramatic forest fragmentation process. While we identified settings for filtering genomic data to avoid biases in gene flow estimates, we stress that there is no ‘rule of thumb’ for bioinformatic filtering and that relying on default program settings is not advisable. Instead, we suggest that the approach implemented here be applied independently in each separate empirical study to confirm appropriate settings to obtain unbiased population genetics estimates.

Reference genome choice and filtering thresholds jointly influence phylogenomic analyses

“…Others demonstrate that filtering can bias important features of the resulting data (Nielsen, 2000; Mastretta-Yanes et al, 2015; Huang and Knowles, 2016; Chan et al, 2020), and therefore recommend against filtering stringently. It may also be the case that there is not a universally “best” approach to these decisions (Nazareno and Knowles, 2021); variation in empirical data attributes such as divergence times, incomplete lineage sorting, and genetic diversity may mean that different filtering decisions are optimal in different scenarios. For example, Huang and Knowles (2016) show that high missing data filters are particularly harmful for data sets with short coalescent times and it is likely that the consequences of other filters are similarly context dependent.…”