CoalQC - Quality control while inferring demographic histories from genomic data: Application to forest tree genomes

Abstract

Estimating demographic histories using genomic datasets has proven to be useful in addressing diverse evolutionary questions. Despite improvements in inference methods and availability of large genomic datasets, quality control steps to be performed prior to the use of sequentially Markovian coalescent (SMC) based methods remains understudied. While various filtering and masking steps have been used by previous studies, the rationale for such filtering and its consequences have not been assessed systematically. In this study, we have developed a reusable pipeline called "CoalQC", to investigate potential sources of bias (such as repeat regions, heterogeneous coverage, and callability). First, we demonstrate that genome assembly quality can affect the estimation of demographic history using the genomes of several species. We then use the CoalQC pipeline to evaluate how different repeat classes affect the inference of demographic history in the plant species Populus trichocarpa. Next, we assemble a draft genome by generating whole-genome sequencing data for Mesua ferrea (sampled from Western Ghats, India), a multipurpose forest plant distributed across tropical south-east Asia and use it as an example to evaluate several technical (sequencing technology, PSMC parameter settings) and biological aspects that need to be considered while comparing demographic histories. Finally, we collate the genomic datasets of 14 additional forest tree species to compare the temporal dynamics of Ne and find evidence of a strong bottleneck in all tropical forest plants during Mid-Pleistocene glaciations. Our findings suggest that quality control prior to the use of SMC based methods is important and needs to be standardised.