Evidence map›Paper›PMID 34567074›Full record

ReviewFrontiers in genetics2021

Current Developments in Detection of Identity-by-Descent Methods and Applications.

Evan L Sticca, Gillian M Belbin, Christopher R Gignoux

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Article
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  4. Leveraging Identity-by-Descent for Disease Gene Discovery.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  5. Article
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  12. Estimating evolutionary and demographic parameters via ARG-derived IBD.bioRxiv : the preprint server for biology · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
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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.

Evan L SticcaHuman Medical Genetics and Genomics Program and Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Gillian M BelbinInstitute for Genomic Health, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Christopher R GignouxHuman Medical Genetics and Genomics Program and Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identity-by-descent (IBD), the detection of shared segments inherited from a common ancestor, is a fundamental concept in genomics with broad applications in the characterization and analysis of genomes. While historically the concept of IBD was extensively utilized through linkage analyses and in studies of founder populations, applications of IBD-based methods subsided during the genome-wide association study era. This was primarily due to the computational expense of IBD detection, which becomes increasingly relevant as the field moves toward the analysis of biobank-scale datasets that encompass individuals from highly diverse backgrounds. To address these computational barriers, the past several years have seen new methodological advances enabling IBD detection for datasets in the hundreds of thousands to millions of individuals, enabling novel analyses at an unprecedented scale. Here, we describe the latest innovations in IBD detection and describe opportunities for the application of IBD-based methods across a broad range of questions in the field of genomics.

Indexed as

biobankgeneticsidentity-by-descentpedigreerelatedness inference

Identifiers

PMID34567074
PMCPMC8461052

What OpenQuestion holds

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LicenceCC BY
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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.