Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations

Abstract

Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample’s birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits.

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

“…In addition, we used a minimum length threshold of 3 cM for IBD estimation across our analyses in this manuscript because using hash-based algorithms such as iLASH, GERMLINE, and RaPID, to estimate short IBD segments (<1 cM) can result in an excess of false-positive edges with short segments, e.g., 36 . However, Saada et al have recently proposed FastSMC 37 , an IBD detection algorithm that uses coalescent based likelihood estimates to assess the validity of shorter segments. Development of a scalable pipeline where candidate matches found by iLASH’s LSH algorithm are evaluated and scored via the FastSMC method would yield a fast and more accurate approach for short segments.…”

Current Developments in Detection of Identity-by-Descent Methods and Applications

“…GERMLINE creates a hash table between short, exact matches of haplotypes and extending into longer, fuzzy (i.e., allowing for small SNP mismatches or genotype errors) IBD segments. This "seed and extend" paradigm, leveraging the inherent efficiency of short hashing functions for speedup beyond standard pairwise comparisons has been adopted by subsequent detection algorithms (Shemirani et al, 2019;Nait Saada et al, 2020), and improved efficiency over hidden Markov model (HMM)-based algorithms or simpler string matching approaches. The computational efficiency garnered by GERMLINE allows computational time to scale approximately linearly with the number of samples and genotyped variants.…”

“…Another novel algorithmic extension that builds on IBD detection and that shows high performance in accuracy as well as speed is FastSMC (Nait Saada et al, 2020). FastSMC builds upon the hash table GERMLINE method as a first identification step by also including a validation step that uses a approximate coalescent HMM (Palamara et al, 2018).…”

“…This is distinct from identity-by-state (IBS), in which a portion of two individual's genomes may appear identical, but not necessarily due to recent shared co-inheritance. Leveraging properties of IBD allows researchers to infer a vast amount of information about a population's demographic history (Carmi et al, 2013;Palamara and Pe'er, 2013;Nait Saada et al, 2020), allowing for evolutionary and pedigree-derived insights that can aid in the interpretation of genetic variation. Further, identifying these shared segments from a recent common ancestor can enrich for shared patterns of rare variation, due to the relationship between allele age and frequency (Slatkin and Rannala, 2000).…”

An Overview of the Use of Genotyping Techniques for Assessing Genetic Diversity in Local Farm Animal Breeds

“…Later, the program provided the platform for other was uses including the assessment of runs of homozygosity as a measure of autozygosity [152]. Peripolli et al [152] viewed Plink as superior to other programs such as GERMLINE [153] and BEAGLE [154], especially with regard to the analysis of runs of homozygosity. In fact, Plink is suitable for calculating most of the parameters described and it is replacing the use of a wide variety of other programs [155].…”