Oxford Team Reports Method for Genealogical Analysis of Complex Traits at Biobank Scale

A method for inferring genealogies from sequencing or genotyping array data at biobank-scale, along with strategies for using the genealogies to perform association and other complex trait analyses, are reported in Nature Genetics this week. Modeling genealogical relationships between individuals is a key aspect to studying natural selection, demographic history, and many other types of analyses. But the very large number of genealogical relationships that may give rise to observed genomic variation makes data-driven inference of these relationships computationally difficult. Aiming to overcome this challenge, University of Oxford researchers developed ARG-Needle, an algorithm that infers the ancestral recombination graph — or ARG — for large collections of genotyping or sequencing samples. They demonstrate ARG-Needle by building genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. They show that, despite being inferred using only array data, the ARG detects more independent associations to rare and ultra-rare variants than imputation based on a reference panel of roughly 65,000 sequenced haplotypes of matched ancestry. "In a subset of 138,039 exome sequencing samples, these associations strongly tag underlying sequencing variants enriched for loss-of-function variation," the team writes. "These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels."