A Model-Driven Quantitative Analysis of Retrotransposon Distributions in the Human Genome

Repetitive genomic regions and the inference of demographic history

Inference of demographic histories using whole-genome datasets has provided insights into diversification, adaptation, hybridization, and plant-pathogen interactions, and stimulated debate on the impact of anthropogenic interventions and past climate on species demography. However, the impact of repetitive genomic regions on these inferences has mostly been ignored by masking of repeats. We use the Populus trichocarpa genome (Pop_tri_v3) to show that masking of repeat regions leads to lower estimates of effective population size (Ne) in the distant past in contrast to an increase in Ne estimates in recent times. However, in human datasets, masking of repeats resulted in lower estimates of Ne at all time points. We demonstrate that repeats affect demographic inferences using diverse methods like PSMC, MSMC, SMC++, and the Stairway plot. Our genomic analysis revealed that the biases in Ne estimates were dependent on the repeat class type and its abundance in each atomic interval. Notably, we observed a weak, yet consistently significant negative correlation between the repeat abundance of an atomic interval and the Ne estimates for that interval, which potentially reflects the recombination rate variation within the genome. The rationale for the masking of repeats has been that variants identified within these regions are erroneous. We find that polymorphisms in some repeat classes occur in callable regions and reflect reliable coalescence histories (e.g., LTR Gypsy, LTR Copia). The current demography inference methods do not handle repeats explicitly, and hence the effect of individual repeat classes needs careful consideration in comparative analysis. Deciphering the repeat demographic histories might provide a clear understanding of the processes involved in repeat accumulation.