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Genetics. 2015 Jan;199(1):223-32. doi: 10.1534/genetics.114.171017. Epub 2014 Oct 31.

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

Author information

1
Queensland 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.
2
University 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.

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.

KEYWORDS:

experimental design; genomic relationship; heritability; maximum likelihood; sampling variance

PMID:
25361897
PMCID:
PMC4286686
DOI:
10.1534/genetics.114.171017
[Indexed for MEDLINE]
Free PMC Article

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