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Repetitive genomic regions and the inference of demographic history

Abstract

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.

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Fig. 1: Changes in effective population size (Ne) estimates from PSMC and MSMC due to masking of repeat regions in black cottonwood (Populus trichocarpa).
Fig. 2: Changes in effective population size (Ne) estimates from PSMC, SMC++, and the Stairway plot in humans.
Fig. 3: Changes in effective population size (Ne) estimates from PSMC after the inclusion of each repeat class in Populus trichocarpa separately.
Fig. 4: Changes in effective population size (Ne) estimates from PSMC due to masking of repeat regions in Populus trichocarpa are robust to bootstrapping.
Fig. 5: Abundance of different repeat classes across all atomic intervals in Populus trichocarpa PSMC.
Fig. 6: Fraction of genome contributing to each atomic interval of Populus trichocarpa PSMC quantified by repeat class.
Fig. 7: Comparison of heterozygosity and Ts/Tv ratio across atomic intervals of Populus trichocarpa PSMC.
Fig. 8: Correlation between repeat abundance and the effective population size (Ne).

Data availability

Data are archived at https://github.com/Ajinkya-IISERB/CoalRep/code.

Code availability

Code and the pipeline are archived at https://github.com/Ajinkya-IISERB/CoalRep.

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Acknowledgements

We thank the Ministry of Human Resource Development for fellowship to ABP. Computational analyses were done on the Har Gobind Khorana Computational Biology cluster established and maintained by combining funds from IISER Bhopal under Grant # INST/BIO/2017/019, IYBA 2018 from DBT, and ECRA from DST.

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Patil, A.B., Vijay, N. Repetitive genomic regions and the inference of demographic history. Heredity (2021). https://doi.org/10.1038/s41437-021-00443-8

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