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.
Highlights
High-throughput sequencing technologies that take advantage of restriction endonuclease enzymes to generate reduced representations of genomes (Davey et al, 2011; Andrews et al, 2016) are enabling us to identify, sequence, and genotype thousands of single-nucleotide polymorphisms (SNP) in any kind of organism
As there is a direct association between co-ancestry and inbreeding, we investigated the effects of missing data (MD) and minor allele frequency (MAF) on Wright’s inbreeding coefficient
About 145 million single-end raw reads were produced on one sequencing lane of HiSeq 2000 Illumina for the 50 individuals included in the genomic library of D. jueirana-facao
Summary
High-throughput sequencing technologies that take advantage of restriction endonuclease enzymes to generate reduced representations of genomes (Davey et al, 2011; Andrews et al, 2016) are enabling us to identify, sequence, and genotype thousands of SNPs (i.e., single-nucleotide polymorphisms) in any kind of organism. This use of high-density biallelic SNP markers has heralded a plethora of evolutionary questions at a genomelevel in non-model organisms, improving our understanding of the underlying processes at micro- and macroevolutionary scales (Alencar and Quental, 2019; Myers et al, 2019). Analysis of hundreds of SNPs in an endangered salamander revealed the effects of fragmentation on genetic diversity and structure (McCartney-Melstad et al, 2018), but such effects went undetected in analyses of few microsatellite markers (Titus et al, 2014)
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