Evidence map›Paper›PMID 39362217›Full record

ArticleAmerican journal of human genetics2024

Modeling recent positive selection using identity-by-descent segments.

Seth D Temple, Ryan K Waples, Sharon R Browning

Abstract read
In one paragraph

Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Identity-by-descent segments in large samples.Theoretical population biology · 2025
    Article
  9. Article
  10. Accessible, realistic genome simulation with selection using stdpopsim.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. Article
  13. Fast simulation of identity-by-descent segments.Bulletin of mathematical biology · 2025
    Article
  14. Review
  15. Article
  16. Fast simulation of identity-by-descent segments.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Seth D TempleDepartment of Statistics, University of Washington, Seattle, WA, USA. Electronic address: sdtemple@uw.edu.
Ryan K WaplesDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Sharon R BrowningDepartment of Biostatistics, University of Washington, Seattle, WA, USA. Electronic address: sguy@uw.edu.

Funding

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
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
Predoctoral Research Training in Statistical Genetics Administrative SupplementT32GM081062 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2007 to 2021
$2.4M
NHGRI NIH HHS R01 HG005701NHLBI NIH HHS HHSN268201800001CNHLBI NIH HHS R01 HL117626NHLBI NIH HHS R01 HL120393NHLBI NIH HHS U01 HL120393NIGMS NIH HHS T32 GM081062
6 · The paper itself

Abstract

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Indexed as

Gene FrequencyHaplotypesModels, GeneticSelection, GeneticAllelesComputer SimulationGenetics, PopulationHumansWhite Peopleconfidence intervalsidentity-by-descentselection coefficientselective sweeps

Identifiers

PMID39362217
PMCPMC11568764

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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.