Evidence map›Paper›PMID 25361897›Full record

ArticleGenetics2015

A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships.

Peter M Visscher, Michael E Goddard

Abstract read
In one paragraph

Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

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

  1. Pooled it
  2. Pooled it
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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

2 authors.

Peter M VisscherQueensland Brain Institute, University of Queensland, Brisbane, Queensland 4072, Australia The University of Queensland Diamantina Institute, The Translational Research Institute, Brisbane, Queensland 4102, Australia peter.visscher@uq.edu.au.
Michael E GoddardUniversity of Melbourne, Department of Food and Agricultural Systems, Parkville, Victoria 3010, Australia Biosciences Research Division, Department of Environment and Primary Industries, Bundoora, Victoria 3001, Australia.

Funding

Theoretical Population Genetics SupplementR01GM075091 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2006 to 2020
$4.7M
NIGMS NIH HHS R01 GM075091
6 · The paper itself

Abstract

Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the resemblance between relatives is solely due to additive genetic variation. We show that well-known results for balanced designs are special cases of a more general unified framework. For pedigree designs, the sampling variance is inversely proportional to the variance of relationship in the pedigree and it is proportional to 1/N, whereas for population samples it is approximately proportional to 1/N(2), where N is the sample size. Variation in relatedness is a key parameter in the quantification of the sampling variance of heritability. Consequently, the sampling variance is high for populations with large recent effective population size (e.g., humans) because this causes low variation in relationship. However, even using human population samples, low sampling variance is possible with high N.

Indexed as

Genetic VariationModels, GeneticPedigreeQuantitative Trait, HeritableGenetic MarkersGenome, HumanHumansLikelihood FunctionsSampling StudiesGenetic Markersexperimental designgenomic relationshipheritabilitymaximum likelihoodsampling variance

Identifiers

PMID25361897
PMCPMC4286686

What OpenQuestion holds

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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.