IBD

IBD detects segments of genomes that are identical-by-descent (IBD) using locality-sensitive hashing to enable rapid, scalable analysis of large-scale genotype datasets.

Key Features:

• Locality-Sensitive Hashing Algorithm: iLASH (IBD by LocAlity-Sensitive Hashing) leverages a locality-sensitive hashing algorithm to detect similarities within genotype data.
• Efficiency and Scalability: The method processed the PAGE dataset (~52,000 multi-ethnic participants) in about one hour on a single machine, compared with traditional methods that can take over six days per chromosome.
• Accuracy: In simulations, iLASH shows equal or improved accuracy relative to leading IBD detection methods.
• Application to Large Datasets: Applied to the UK Biobank (~500,000 individuals), it detected nearly 13 billion pairwise IBD tracts shared among approximately 11% of participants.

Scientific Applications:

• Population Genetics Studies: Exploration of genetic relationships and ancestry patterns across large populations using detected IBD segments.
• Trait Mapping: Association of identified IBD tracts with genomic regions linked to phenotypic traits and disease.

Methodology:

Genotype data are divided into consecutive slices/windows that are processed with a locality-sensitive hashing algorithm (iLASH) to identify and construct IBD tracts by comparing segments across all pairs of individuals.