Evidence map›Paper›PMID 40848714›Full record

ArticleAmerican journal of human genetics2025

Estimating gene conversion rates from population data using multi-individual identity by descent.

Sharon R Browning, Brian L Browning

Abstract read
In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Fast simulation of identity-by-descent segments.Bulletin of mathematical biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Sharon R BrowningDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA. Electronic address: sguy@uw.edu.
Brian L BrowningDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA. Electronic address: browning@uw.edu.

Funding

Large Scale Sequencing and Analysis of GenomesU54HG003067 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY, LANDER, ERIC S · 2004 to 2015
$568.6M
FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · 2019 to 2024
$29.8M
New Approaches for Empowering Studies of Asthma in Populations of African DescentR01HL104608 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI BARNES, KATHLEEN C, KENNY, EIMEAR ELIZABETH · 2011 to 2022
$23.1M
IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
Studies of Rare Genetic Variation in the Isolated Population of SardiniaR01HL117626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2013 to 2016
$10.5M
Rare variants and NHLBI traits in deeply phenotyped cohortsR01HL120393 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M, RICE, KENNETH M. · 2014 to 2016
$8.9M
Genome-Wide Association Studies of Asthma in Populations of African DescentR01HL087699 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI BARNES, KATHLEEN C · 2006 to 2008
$5.8M
Rare variants and NHLBI traits in deeply phenotyped cohortsU01HL120393 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M, RICE, KENNETH M. · 2017 to 2018
$5.6M
Improved gene mapping for whole genome dataR01HG005701 · NHGRI · UNIVERSITY OF WASHINGTON · PI BROWNING, BRIAN LEE · 2010 to 2024
$4.4M
Improved modeling of genotype dataR01HG008359 · NHGRI · UNIVERSITY OF WASHINGTON · PI BROWNING, BRIAN LEE · 2015 to 2022
$2.8M
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
NHGRI NIH HHS R01 HG005701NHGRI NIH HHS R01 HG008359NHGRI NIH HHS U54 HG003067NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS N01 HC025195NHLBI NIH HHS R01 HL087699NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL104608NHLBI NIH HHS R01 HL117626NHLBI NIH HHS R01 HL120393NHLBI NIH HHS U01 HL120393
6 · The paper itself

Abstract

In humans, homologous gene conversions occur at a higher rate than crossovers; however, gene conversion tracts are small and often unobservable. As a result, estimating gene conversion rates is more difficult than estimating crossover rates. We present a method for multi-individual identity-by-descent (IBD) inference that allows for mismatches due to genotype error and gene conversion. We use the inferred IBD to detect alleles that have changed due to gene conversion in the recent past. We analyze data from the TOPMed and UK Biobank studies to estimate autosome-wide maps of gene conversion rates. For 10 kb, 100 kb, and 1 Mb windows, the correlation between our TOPMed gene conversion map and the deCODE sex-averaged crossover map ranges from 0.56 to 0.67. We find that the strongest gene conversion hotspots typically fall back to the baseline gene conversion rate within 1 kb. In 100 kb and 1 Mb windows, our estimated gene conversion map has higher correlation than the deCODE sex-averaged crossover map with PRDM9 binding enrichment (0.34 vs. 0.29 for 100 kb windows and 0.52 vs. 0.34 for 1 Mb windows), suggesting that the effect of PRDM9 binding is greater on gene conversion than on crossover recombination. Our TOPMed gene conversion maps are constructed from 55-fold more observed allele conversions than the recently published deCODE gene conversion maps. Our maps provide sex-averaged estimates for 10 kb, 100 kb, and 1 Mb windows, whereas the deCODE gene conversion maps provide sex-specific estimates for 3 Mb windows.

Indexed as

Gene ConversionGenetics, PopulationAllelesCrossing Over, GeneticFemaleGenotypeHumansMaleModels, Geneticgene conversionidentity by descentTOPMedUK Biobank

Identifiers

PMID40848714
PMCPMC12380118

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Registered trials

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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.