An effective filter for IBD detection in large data sets.

Lin Huang, Sivan Bercovici, Jesse M Rodriguez, Serafim Batzoglou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Identity by descent (IBD) inference is the task of computationally detecting genomic segments that are shared between individuals by means of common familial descent. Accurate IBD detection plays an important role in various genomic studies, ranging from mapping disease genes to exploring ancient population histories. The majority of recent work in the field has focused on improving the accuracy of inference, targeting shorter genomic segments that originate from a more ancient common ancestor. The accuracy of these methods, however, is achieved at the expense of high computational cost, resulting in a prohibitively long running time when applied to large cohorts. To enable the study of large cohorts, we introduce SpeeDB, a method that facilitates fast IBD detection in large unphased genotype data sets. Given a target individual and a database of individuals that potentially share IBD segments with the target, SpeeDB applies an efficient opposite-homozygous filter, which excludes chromosomal segments from the database that are highly unlikely to be IBD with the corresponding segments from the target individual. The remaining segments can then be evaluated by any IBD detection method of choice. When examining simulated individuals sharing 4 cM IBD regions, SpeeDB filtered out 99.5% of genomic regions from consideration while retaining 99% of the true IBD segments. Applying the SpeeDB filter prior to detecting IBD in simulated fourth cousins resulted in an overall running time that was 10,000x faster than inferring IBD without the filter and retained 99% of the true IBD segments in the output.
Original languageEnglish (US)
Pages (from-to)e92713
JournalPLoS ONE
Issue number3
StatePublished - Mar 25 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: LH is supported by a Pierre and Christine Lamond Stanford Graduate Fellowship. This material is also based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1147470. This work is also supported by a grant from the Stanford-KAUST alliance for academic excellence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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