Evidence map›Paper›PMID 24007429›Full record

ArticleGenetics, selection, evolution : GSE2013

On estimation of genetic variance within families using genome-wide identity-by-descent sharing.

William G Hill

Erratum issuedAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Correlational selection and genetic architecture shape the evolution of the leaf economics spectrum in a perennial grass.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. The genetic basis for panicle trait variation in switchgrass (Panicum virgatum).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022
    Article
  4. The genetic basis of the root economics spectrum in a perennial grass.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

William G HillInstitute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK. w.g.hill@ed.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditionally, heritability and other genetic parameters are estimated from between-family variation. With the advent of dense genotyping, it is now possible to compute the proportion of the genome that is shared by pairs of sibs and thus undertake the estimation within families, thereby avoiding environmental covariances of family members. Formulae for the sampling variance of estimates have been derived previously for families with two sibs, which are relevant for humans, but sampling errors are large. In livestock and plants much larger families can be obtained, and simulation has shown sampling variances are then much smaller.

methodsBased on the assumptions that realised relationship of sibs can be obtained from genomic data and that data are analyzed by restricted maximum likelihood, formulae were derived for the sampling variance of the estimates of genetic variance for arbitrary family sizes. The analysis used statistical differentiation, assuming the variance of relationships is small.

resultsThe variance of the estimate of the additive genetic variance was approximately proportional to 1/ (fn2σR2), for f families of size n and variance of relationships σR2.

conclusionsBecause the standard error of the estimate of heritability decreased in proportion to family size, the use of within-family information becomes increasingly efficient as the family size increases. There are however, limitations, such as near complete confounding of additive and dominance variances in full sib families.

Indexed as

Genetic VariationGenome, HumanModels, GeneticGenotypeHumansLikelihood FunctionsModels, StatisticalQuantitative Trait LociRegression AnalysisSiblings

Identifiers

PMID24007429
PMCPMC3871764

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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.