PCI Math Comp Biol

Title

An efficient algorithm for estimating population history from genetic datause asterix (*) to get italics

Authors

Alan R. RogersPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year

2021

Picture

Abstract

<p style="text-align: justify;">The Legofit statistical package uses genetic data to estimate parameters describing population history. Previous versions used computer simulations to estimate probabilities, an approach that limited both speed and accuracy. This article describes a new deterministic algorithm, which makes Legofit faster and more accurate. The speed of this algorithm declines as model complexity increases. With very complex models, the deterministic algorithm is slower than the stochastic one. In an application to simulated data sets, the estimates produced by the deterministic and stochastic algorithms were essentially identical. Reanalysis of a human data set replicated the findings of a previous study and provided increased support for the hypotheses that (a) early modern humans contributed genes to Neanderthals, and (b) a “superarchaic” population (which separated from all other humans early in the Pleistocene) was either large or deeply subdivided.</p>

Indicate the full web address (DOI or URL) giving public access to these data (if you have any problems with the deposit of your data, please contact contact@mcb.peercommunityin.org). In case all raw data are included in the preprint, indicate the DOI or URL of the preprint.

https:/doi.org/10.17605/OSF.IO/74BJFYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://

Indicate the full web address (DOI or URL) giving public access to these scripts (if you have any problems with the deposit of your scripts, please contact contact@mcb.peercommunityin.org). In case all raw scripts are included in the preprint, indicate the DOI or URL of the preprint.

You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

Indicate the full web address (DOI, SWHID or URL) giving public access to these codes (if you have any problems with the deposit of your codes, please contact contact@mcb.peercommunityin.org). In case all raw codes are included in the preprint, indicate the DOI or URL of the preprint.

You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

Keywords

population genetics, statistical inference, Markov chains, combinatorics, population history

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields

Combinatorics, Genetics and population Genetics

Suggested reviewers - Suggest up to 10 reviewers (provide names and Email addresses). (Optional)

No need for them to be recommenders of PCI Math Comp Biol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

Opposed reviewers - Suggest up to 5 people not to invite as reviewers. (Optional)

e.g. John Doe [john@doe.com]

Submission date

2021-01-26 20:04:35

Recommender

Matteo Fumagalli

Reviewers

or Register
Submit a preprint

MANAGING BOARD

MEMBER OF

INDEXED BY