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

Visscher, Peter M. and Goddard, Michael E. (2015) A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships. Genetics, 199 1: 223-232. doi:10.1534/genetics.114.171017


Author Visscher, Peter M.
Goddard, Michael E.
Title A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships
Journal name Genetics   Check publisher's open access policy
ISSN 1943-2631
Publication date 2015-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1534/genetics.114.171017
Volume 199
Issue 1
Start page 223
End page 232
Total pages 10
Place of publication Bethesda, MD, United States
Publisher Genetics Society of America
Collection year 2015
Formatted 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/N2, 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
UQ Diamantina Institute Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 13 times in Scopus Article | Citations
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