Evidence map›Paper›PMID 25404112›Full record

ReviewNature reviews. Genetics2015

Relatedness in the post-genomic era: is it still useful?

Doug Speed, David J Balding

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 2 pooled it
–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

160 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
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  8. 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
    Article
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  12. Parameterizing the genetic architecture under stabilizing selection.bioRxiv : the preprint server for biology · 2026
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  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

100 more citing papers are in PubMed but not listed here.

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

2 authors.

Doug SpeedUCL Genetics Institute, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK.
David J Balding1] UCL Genetics Institute, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK. [2] Department of Genetics and Department of Mathematics and Statistics, University of Melbourne, Parkville VIC 3010, Australia.

Funding

Medical Research Council G0901388Medical Research Council MR/L012561/1
6 · The paper itself

Abstract

Relatedness is a fundamental concept in genetics but is surprisingly hard to define in a rigorous yet useful way. Traditional relatedness coefficients specify expected genome sharing between individuals in pedigrees, but actual genome sharing can differ considerably from these expected values, which in any case vary according to the pedigree that happens to be available. Nowadays, we can measure genome sharing directly from genome-wide single-nucleotide polymorphism (SNP) data; however, there are many such measures in current use, and we lack good criteria for choosing among them. Here, we review SNP-based measures of relatedness and criteria for comparing them. We discuss how useful pedigree-based concepts remain today and highlight opportunities for further advances in quantitative genetics, with a focus on heritability estimation and phenotype prediction.

Indexed as

Genetic VariationModels, GeneticPedigreePhenotypeComputer SimulationGenetics, PopulationHumansPolymorphism, Single Nucleotide

Identifiers

PMID25404112

What OpenQuestion holds

Textmetadata
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.