Selecting Clustering Algorithms for Identity-By-Descent Mapping

Abstract

Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks using IBD mapping. Clustering algorithms play an important role in finding these groups accurately and at scale. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare the statistical power of clustering algorithms via simulating 2.3 million clusters across 850 experiments. We found Infomap and Markov Clustering (MCL) community detection methods to have high statistical power in most of the scenarios. They yield a 30% increase in power compared to the current state-of-art approach, with a 3 orders of magnitude lower runtime. We also found that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications. We extend our findings to real datasets by analyzing the Population Architecture using Genomics and Epidemiology (PAGE) Study dataset with 51,000 samples and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters. We demonstrate the power of our approach by recovering signals of rare genetic variation in the Whole-Exome Sequence data of 200,000 individuals in the UK Biobank. We provide an efficient implementation to enable clustering at scale for IBD mapping for various populations and scenarios.

Modeling recent positive selection in Americans of European ancestry

“…Let haplotypes be nodes and IBD segments be edges in the context of a network graph. Because IBD segments longer than 1.0 cM are infrequent in a population sample, this graph should contain many disconnected haplotype clusters 48 . We also impose the rule that a node in a cluster may only be three edges away from its highest degree node (the haplotype sharing the greatest number of IBD segments).…”

“…Let haplotypes be nodes and IBD segments be edges in the context of a network graph. Because extended IBD is infrequent in a population sample, this graph should contain many disconnected haplotype clusters 80 . Sizes of these haplotype clusters C 1 , C 2 , … amount to an empirical distribution for IBD cluster size.…”

Biobank-scale inference of multi-individual identity by descent and gene conversion

“…Previous approaches to determining multi-individual IBD have generally looked for highly-connected clusters of pairwise IBD, and then added or removed some pairwise IBD to obtain IBD transitivity. 6; 32; 33 This approach scales quadratically with cluster size. In this work, we solve the problem by trimming a fixed genetic distance (e.g.…”