The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, IBD by LocAlity-Sensitive Hashing, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to the current leading method and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for hundreds of thousands to millions of individuals. We applied iLASH to the Population Architecture using Genomics and Epidemiology (PAGE) dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, which identified IBD segments on a single machine in an hour (~3 minutes per chromosome compared to over 6 days per chromosome for a state-of-the-art algorithm). iLASH is able to efficiently estimate IBD tracts in very large-scale datasets, as demonstrated via IBD estimation across the entire UK Biobank (~500,000 individuals), detecting nearly 13 billion pairwise IBD tracts shared between ~11% of participants. In summary, iLASH enables fast and accurate detection of IBD, an upstream step in applications of IBD for population genetics and trait mapping.
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