Discovery of runs-of-homozygosity diplotype clusters and their associations with diseases in UK Biobank

Abstract

Runs of homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE, to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 SNPs and shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended HLA region and autoimmune disorders. We found an association between a diplotype covering the HFE gene and haemochromatosis, even though the well-known causal SNP was not directly genotyped nor imputed. Using genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase of mortality among COVID-19 patients. In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at population scale.

FiMAP: A Fast Identity-by-Descent Mapping Test for Biobank-scale Cohorts

“…We computed all haplotypes on autosomes, with length ≥ 0.5 cM shared by 500 or more individuals from the UK Biobank using a PBWT-block algorithm. 47 In each simulation replicate, we used a random sample of N = 400,000 individuals and randomly sampled a causal haplotype. We simulated a continuous phenotype Y i for individual i as Y i = 0.05 age i + 0.5 sex i + H i γ + b i + ε i , where age i , sex i , b i and ε i were simulated using the same parameter settings as in the type I error simulations, and H i was the number of causal haplotypes carried by individual i , with possible values 0, 1, 2.…”

FastRecomb: Fast inference of genetic recombination rates in biobank scale data

“…PBWT facilitates an efficient approach to enumerate all pairwise haplotype matches longer than a given length. While Durbin's algorithm outputs all pairwise matches in O(N M + #matches), a block-based approach [14][15][16] can enumerate all matching blocks without explicitly outputting all pairs in O(N M ). By sorting the haplotype sequences based on their reversed prefix order, the longest match for each haplotype sequence is placed in the adjacent position.…”

“…Moreover, all pairwise haplotype sequences at the site k that are identical for at least L sites from k are separated by a haplotype sequence j with the condition d k [j] > k − L [13]. We define a L-block at a site k as a set of haplotype indices that share long matches with each other ending at site k with a minimum length of L. All L-blocks at any site k may be efficiently scanned by consensus PBWT algorithms [14][15][16].…”

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts

“…In addition, we simulated haplotype effects and compared FiMAP with IBD segments called with length ≥ 3 cM, 5 cM, 10 cM with the GWAS single-variant test. Specifically, we estimated the start and end positions for all haplotypes with length ≥ 0.5 cM shared by at least 500 individuals in the UK Biobank using a PBWT-block algorithm [ 58 ]. In each simulation replicate, we used a random sample of N = 400,000 individuals and randomly sampled a causal haplotype from an autosome.…”