Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Efficient pedigree recording for fast population genetics simulation

View ORCID ProfileJerome Kelleher, View ORCID ProfileKevin R. Thornton, View ORCID ProfileJaime Ashanderf, View ORCID ProfilePeter L. Ralph
doi: https://doi.org/10.1101/248500
Jerome Kelleher
*Big Data Institute, University of Oxford
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jerome Kelleher
Kevin R. Thornton
†Ecology and Evolutionary Biology, University of California at Irvine
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kevin R. Thornton
Jaime Ashanderf
‡Ecology and Evolutionary Biology, University of California at Los Angeles
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jaime Ashanderf
Peter L. Ralph
§Institute for Ecology and Evolution, University of Oregon
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Peter L. Ralph
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

In this paper we describe how to efficiently record the entire genetic history of a population in forwards-time, individual-based population genetics simulations with arbitrary breeding models, population structure and demography. This approach dramatically reduces the computational burden of tracking individual genomes by allowing us to simulate only those loci that may affect reproduction (those having non-neutral variants). The genetic history of the population is recorded as a succinct tree sequence as introduced in the software package msprime, on which neutral mutations can be quickly placed afterwards. Recording the results of each breeding event requires storage that grows linearly with time, but there is a great deal of redundancy in this information. We solve this storage problem by providing an algorithm to quickly ‘simplify’ a tree sequence by removing this irrelevant history for a given set of genomes. By periodically simplifying the history with respect to the extant population, we show that the total storage space required is modest and overall large efficiency gains can be made over classical forward-time simulations. We implement a general-purpose framework for recording and simplifying genealogical data, which can be used to make simulations of any population model more efficient. We modify two popular forwards-time simulation frameworks to use this new approach and observe efficiency gains in large, whole-genome simulations of one to two orders of magnitude. In addition to speed, our method for recording pedigrees has several advantages: (1) All marginal genealogies of the simulated individuals are recorded, rather than just genotypes. (2) A population of N individuals with M polymorphic sites can be stored in O(N log N + M) space, making it feasible to store a simulation’s entire final generation as well as its history. (3) A simulation can easily be initialized with a more efficient coalescent simulation of deep history. The software for recording and processing tree sequences is named tskit.

Author Summary Sexually reproducing organisms are related to the others in their species by the complex web of parent-offspring relationships that constitute the pedigree. In this paper, we describe a way to record all of these relationships, as well as how genetic material is passed down through the pedigree, during a forwards-time population genetic simulation. To make effective use of this information, we describe both efficient storage methods for this embellished pedigree as well as a way to remove all information that is irrelevant to the genetic history of a given set of individuals, which dramatically reduces the required amount of storage space. Storing this information allows us to produce whole-genome sequence from simulations of large populations in which we have not explicitly recorded new genomic mutations; we find that this results in computational run times of up to 50 times faster than simulations forced to explicitly carry along that information.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted June 07, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Efficient pedigree recording for fast population genetics simulation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Efficient pedigree recording for fast population genetics simulation
Jerome Kelleher, Kevin R. Thornton, Jaime Ashanderf, Peter L. Ralph
bioRxiv 248500; doi: https://doi.org/10.1101/248500
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Efficient pedigree recording for fast population genetics simulation
Jerome Kelleher, Kevin R. Thornton, Jaime Ashanderf, Peter L. Ralph
bioRxiv 248500; doi: https://doi.org/10.1101/248500

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3513)
  • Biochemistry (7359)
  • Bioengineering (5338)
  • Bioinformatics (20306)
  • Biophysics (10034)
  • Cancer Biology (7763)
  • Cell Biology (11331)
  • Clinical Trials (138)
  • Developmental Biology (6444)
  • Ecology (9968)
  • Epidemiology (2065)
  • Evolutionary Biology (13346)
  • Genetics (9365)
  • Genomics (12598)
  • Immunology (7718)
  • Microbiology (19059)
  • Molecular Biology (7452)
  • Neuroscience (41106)
  • Paleontology (300)
  • Pathology (1233)
  • Pharmacology and Toxicology (2141)
  • Physiology (3171)
  • Plant Biology (6869)
  • Scientific Communication and Education (1275)
  • Synthetic Biology (1899)
  • Systems Biology (5320)
  • Zoology (1090)