Rapid detection of identity-by-descent tracts for mega-scale datasets

Abstract

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, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections.

Leveraging genomic diversity for discovery in an EHR-linked biobank: the UCLA ATLAS Community Health Initiative

“…The merged dataset was then statistically phased using Shapeit4 [81]. IBD was called using iLASH using default parameters [82]. For downstream analysis, IBD segments were summed between individuals to create an adjacency matrix, where each row represented a pair of individuals, and each column represented the total genome-wide IBD between those two individuals.…”

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

“…They can be divided into two distinct groups: those that need phased data and those that do not. In the first group there is GERMLINE [ 51 ] that deals with errors in the genotypes and iLASH [ 52 ], RaPID [ 53 ], hap-IBD [ 54 ], FastSMC [ 55 ] and fastIBD [ 56 ], all of them reporting improved speed. Also in this group, there is RefinedIBD [ 57 ], which does not allow for genotype errors but uses the GERMLINE algorithm for the identification of shared haplotypes exceeding a threshold length.…”

Health care utilization of fine-scale identity by descent clusters in a Los Angeles biobank

“…To define our IBD clusters, we called pairwise IBD between all ATLAS participants and reference individuals sourced from the 1000 Genomes Project [26], the Simons Genome Diversity Project [27], and the Human Genome Diversity Project [28]. IBD segments were estimated using iLASH [29], identifying in total, over 95 million shared IBD segments. All participants in the biobank had at least one IBD segment detected, with the mean amount of IBD sharing being 14.80cM (IQR: 3.84-21.57cM).…”

“…IBD was called using iLASH [29] with the following parameters: slice_size 350, step_size 350, perm_count 20, shingle_size 15, shingle_overlap 0, bucket_count 5, max_thread 20, match_threshold 0.99, interest_threshold 0.70, min_length 2.9, auto_slice 1, slice_length 2.9, cm_overlap 1, minhash_threshold 55. IBD was called for one chromosome at a time.…”