Evidence map›Paper›PMID 37127670›Full record

ArticleNature genetics2023

Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits.

Brian C Zhang, Arjun Biddanda, Árni Freyr Gunnarsson, Fergus Cooper, Pier Francesco Palamara

Abstract read
In one paragraph

Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

93 citing papers in PubMed.

  1. Review
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  10. Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  15. Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Brian C ZhangDepartment of Statistics, University of Oxford, Oxford, UK.
Arjun BiddandaDepartment of Statistics, University of Oxford, Oxford, UK.
Árni Freyr GunnarssonDepartment of Statistics, University of Oxford, Oxford, UK.
Fergus CooperDepartment of Computer Science, University of Oxford, Oxford, UK.
Pier Francesco PalamaraDepartment of Statistics, University of Oxford, Oxford, UK. palamara@stats.ox.ac.uk.ORCID http://orcid.org/0000-0002-7999-1972

Funding

Efficient methods for identifying cryptic relatedness in millions of individualsR21HG010748 · NHGRI · DANA-FARBER CANCER INST · PI GUSEV, ALEXANDER · 2019 to 2020
$372k
U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R21-HG010748-01Wellcome Trust (Wellcome) 222336/Z/21/Z
6 · The paper itself

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.

Indexed as

Genetics, PopulationMultifactorial InheritanceBiological Specimen BanksGenome-Wide Association StudyGenotypeHumansPolymorphism, Single NucleotideRecombination, Genetic

Identifiers

PMID37127670
PMCPMC10181934

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.