Inferring whole-genome histories in large population datasets

Abstract

Inferring the full genealogical history of a set of DNA sequences is a core problem in evolutionary biology, because this history encodes information about the events and forces that have influenced a species. However, current methods are limited, and the most accurate techniques are able to process no more than a hundred samples. As datasets that consist of millions of genomes are now being collected, there is a need for scalable and efficient inference methods to fully utilize these resources. Here we introduce an algorithm that is able to not only infer whole-genome histories with comparable accuracy to the state-of-the-art but also process four orders of magnitude more sequences. The approach also provides an ‘evolutionary encoding’ of the data, enabling efficient calculation of relevant statistics. We apply the method to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the inferred genealogies are rich in biological signal and efficient to process.

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Fig. 1: Comparison of tree sequences with standard methods for storing genetic variation data.
Fig. 2: A schematic of the major steps of the inference algorithm.
Fig. 3: Accuracy of ancestry inference using different methods.
Fig. 4: Tree sequence characterization of global genome diversity.
Fig. 5: Tree sequence characterization of the UKB data.

Data availability

The TGP30, SGDP31 and UKB32 datasets used here are detailed in the relevant publications. Tree sequences inferred for all TGP (https://doi.org/10.5281/zenodo.3052359) and SGDP (https://doi.org/10.5281/zenodo.3052359) autosomes have been deposited on Zenodo. Tree sequences were compressed using the tszip utility; see the documentation at https://tszip.readthedocs.io/ for further details.

Code availability

tsinfer is freely available under the terms of the GNU GPL; see the documentation at https://tsinfer.readthedocs.io/ for further details. All code used to process data and run evaluations is available at https://github.com/mcveanlab/treeseq-inference.

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Acknowledgements

This work was supported by the Wellcome Trust grant 100956/Z/13/Z to G.M. A.W.W. and C.F. thank the Rhodes Trust for their support. We thank J. Novembre and P. Ralph for comments on earlier drafts of this manuscript; P. Ralph and K. Thornton for many useful discussions on tree sequence algorithms. Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

We used the CRediT taxonomy for contributions (https://casrai.org/credit). J.K.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. Y.W.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. A.W.W.: Formal analysis, Investigation, Validation, Visualization, Writing—review & editing. C.F.: Data Curation, Formal analysis, Visualization, Writing—review & editing. P.K.A.: Data curation, Resources, Visualization, Writing—review & editing. G.M.: Conceptualization, Funding acquisition, Methodology, Supervision, Writing—original draft, Writing—review & editing.

Correspondence to Jerome Kelleher.

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Competing interests

G.M. is a shareholder in and non-executive director of Genomics PLC, and is a partner in Peptide Groove LLP. The remaining authors declare no competing interests.

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