Abstract
Identity-by-descent (IBD) inference is the problem of establishing a genetic connection between two individuals through a genomic segment that is inherited by both individuals from a recent common ancestor. IBD inference is an important preceding step in a variety of population genomic studies, ranging from demographic studies to linking genomic variation with phenotype and disease. The problem of accurate IBD detection has become increasingly challenging with the availability of large collections of human genotypes and genomes: Given a cohort's size, a quadratic number of pairwise genome comparisons must be performed. Therefore, computation time and the false discovery rate can also scale quadratically. To enable accurate and efficient large-scale IBD detection, we present Parente2, a novel method for detecting IBD segments. Parente2 is based on an embedded log-likelihood ratio and uses a model that accounts for linkage disequilibrium by explicitly modeling haplotype frequencies. Parente2 operates directly on genotype data without the need to phase data prior to IBD inference. We evaluate Parente2's performance through extensive simulations using real data, and we show that it provides substantially higher accuracy compared to previous state-of-the-art methods while maintaining high computational efficiency.
Original language | English (US) |
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Pages (from-to) | 280-289 |
Number of pages | 10 |
Journal | Genome Research |
Volume | 25 |
Issue number | 2 |
DOIs | |
State | Published - Oct 1 2014 |
Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This material is based upon work supported in part by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1147470. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work is also supported by a grant from the Stanford-KAUST alliance for academic excellence. L.H. was supported in part by a Stanford Graduate Fellowship. We thank Dorna Kashef-Haghighi for producing Figure 1.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.