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

Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform

Ardalan Naseri, Xiaoming Liu, Shaojie Zhang, View ORCID ProfileDegui Zhi
doi: https://doi.org/10.1101/103325
Ardalan Naseri
1Department of Computer Science, University of Central Florida, Orlando, Florida 32816, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaoming Liu
2Department of Epidemiology, Human Genetics & Environmental Science, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shaojie Zhang
1Department of Computer Science, University of Central Florida, Orlando, Florida 32816, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Degui Zhi
3School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Degui Zhi
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

With the availability of genotyping data of very large samples, there is an increasing need for tools that can efficiently identify genetic relationships among all individuals in the sample. One fundamental measure of genetic relationship of a pair of individuals is identity by descent (IBD), chromosomal segments that are shared among two individuals due to common ancestry. However, the efficient identification of IBD segments among a large number of genotyped individuals is a challenging computational problem. Most existing methods are not feasible for even thousands of individuals because they are based on pairwise comparisons of all individuals and thus scale up quadratically with sample size. Some methods, such as GERMLINE, use fast dictionary lookup of short seed sequence matches to achieve a near-linear time efficiency. However, the number of short seed matches often scales up super-linearly in real population data.

In this paper we describe a new approach for IBD detection. We take advantage of an efficient population genotype index, Positional BWT (PBWT), by Richard Durbin. PBWT achieves linear time query of perfectly identical subsequences among all samples. However, the original PBWT is not tolerant to genotyping errors which often interrupt long IBD segments into short fragments. We introduce a randomized strategy by running PBWTs over random projections of the original sequences. To boost the detection power we run PBWT multiple times and merge the identified IBD segments through interval tree algorithms. Given a target IBD segment length, RaPID adjust parameters to optimize detection power and accuracy.

Simulation results proved that our tool (RaPID) achieves almost linear scaling up to sample size and is orders of magnitude faster than GERMLINE. At the same time, RaPID maintains a detection power and accuracy comparable to existing mainstream algorithms, GERMLINE and IBDseq. Running multiple times with various target detection lengths over the 1000 Genomes Project data, RaPID can detect population events at different time scales. With our tool, it is feasible to identify IBDs among hundreds of thousands to millions of individuals, a sample size that will become reality in a few years.

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-NC 4.0 International license.
Back to top
PreviousNext
Posted January 26, 2017.
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.
Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform
(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
Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform
Ardalan Naseri, Xiaoming Liu, Shaojie Zhang, Degui Zhi
bioRxiv 103325; doi: https://doi.org/10.1101/103325
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform
Ardalan Naseri, Xiaoming Liu, Shaojie Zhang, Degui Zhi
bioRxiv 103325; doi: https://doi.org/10.1101/103325

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4381)
  • Biochemistry (9581)
  • Bioengineering (7086)
  • Bioinformatics (24844)
  • Biophysics (12597)
  • Cancer Biology (9952)
  • Cell Biology (14345)
  • Clinical Trials (138)
  • Developmental Biology (7944)
  • Ecology (12101)
  • Epidemiology (2067)
  • Evolutionary Biology (15984)
  • Genetics (10921)
  • Genomics (14735)
  • Immunology (9869)
  • Microbiology (23645)
  • Molecular Biology (9477)
  • Neuroscience (50838)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2681)
  • Physiology (4013)
  • Plant Biology (8655)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2391)
  • Systems Biology (6427)
  • Zoology (1346)