| Title: | Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits |
| Journal: | Nature Genetics |
| Published: | 1 May 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37127670/ |
| DOI: | https://doi.org/10.1038/s41588-023-01379-x |
| URL: | https://www.nature.com/articles/s41588-023-01379-x.pdf |
| Citations: | 35 (35 in last 2 years) as of 8 Aug 2024 |
Publication 11401
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Abstract
Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 134, frequency range 0.0007−0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. 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.</p>
5 Authors
- Brian C. Zhang
- Arjun Biddanda
- Árni Freyr Gunnarsson
- Fergus Cooper
- Pier Francesco Palamara
1 Application
| Application ID | Title |
|---|---|
| 43206 | Statistical methods for large-scale genomic analysis |
Enabling scientific discoveries that improve human health