Evidence map›Paper›PMID 40623485›Full record

ArticleTheoretical population biology2025

Identity-by-descent segments in large samples.

Seth D Temple, Elizabeth A Thompson

Abstract read
In one paragraph

Article in Theoretical population biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Fast simulation of identity-by-descent segments.Bulletin of mathematical biology · 2025
    Article
  8. Article
  9. Fast simulation of identity-by-descent segments.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Seth D TempleDepartment of Statistics, University of Washington, Seattle, WA, USA; Department of Statistics, University of Michigan, Ann Arbor, MI, USA; Michigan Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI, USA. Electronic address: sethtem@umich.edu.
Elizabeth A ThompsonDepartment of Statistics, University of Washington, Seattle, WA, USA.

Funding

Predoctoral Research Training in Statistical Genetics Administrative SupplementT32GM081062 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2007 to 2021
$2.4M
NIGMS NIH HHS T32 GM081062NIH HHS HG005701
6 · The paper itself

Abstract

If two haplotypes share the same alleles for an extended gene tract, these haplotypes are likely to be derived identical-by-descent from a recent common ancestor. Identity-by-descent segment lengths are correlated via unobserved ancestral tree and recombination processes, which commonly presents challenges to the derivation of theoretical results in population genetics. We show that the proportion of detectable identity-by-descent segments around a locus is normally distributed when the sample size and the scaled population size are large. We generalize this central limit theorem to cover flexible demographic scenarios, multi-way identity-by-descent segments, and multivariate identity-by-descent rates. The regularity conditions on sample size and scaled population size are unlikely to hold in genetic data from real populations, but provide intuition for when the Gaussian distribution may be a reasonable approximate model for the IBD rate. We use efficient simulations to study the distributional behavior of the detectable identity-by-descent rate. One consequence of non-normality in finite samples is that a genome-wide scan looking for excess identity-by-descent rates may be subject to anti-conservative control of family-wise error rates.

Indexed as

Genetics, PopulationHaplotypesModels, GeneticHumansPopulation DensityAsymptotic normalityCoalescentCovarianceIdentity-by-descent

Identifiers

PMID40623485
PMCPMC13308687

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.