RT Journal Article
SR Electronic
T1 Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.03.466843
DO 10.1101/2021.11.03.466843
A1 Brian C. Zhang
A1 Arjun Biddanda
A1 Pier Francesco Palamara
YR 2021
UL http://biorxiv.org/content/early/2021/11/04/2021.11.03.466843.abstract
AB Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from ∼65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3×) and loss-of-function (4.5×) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.Competing Interest StatementThe authors have declared no competing interest.