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

Visscher PM, Goddard ME - Genetics (2014)

Bottom Line: We show that well-known results for balanced designs are special cases of a more general unified framework.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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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Standard error of estimates of heritability from different experimental designs in human populations, as a function of the population value of the heritability (x-axis), experimental sample size, and experimental design. For the within-family design (Within-family estimation using realized relationships estimates from markers), the variance in realized relationships was assumed to be 0.0392. For the population design (Random sampling from the population), the variance is relatedness was approximated assuming Ne = 10,000, a genome length of 35 M, and an average chromosome length of 1 M (Goddard 2009).
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fig1: Standard error of estimates of heritability from different experimental designs in human populations, as a function of the population value of the heritability (x-axis), experimental sample size, and experimental design. For the within-family design (Within-family estimation using realized relationships estimates from markers), the variance in realized relationships was assumed to be 0.0392. For the population design (Random sampling from the population), the variance is relatedness was approximated assuming Ne = 10,000, a genome length of 35 M, and an average chromosome length of 1 M (Goddard 2009).

Mentions: Figure 1 shows the approximation to the standard error of an estimate of heritability as a function of the population value, experimental sample size, and design. Four different designs were used: a pedigree design of unrelated full-sib pairs, a pedigree design with MZ and DZ twins pairs with a ratio of 1:2 MZ and DZ pairs, a within-family design using full-sib pairs, and a population design using nominally unrelated individuals. In the last two designs, GRM are estimated with SNP data. These designs are less powerful than the pedigree-based experimental designs, but make fewer assumptions. At N = 10,000 the sampling variance of the population design approaches that of the pedigree designs, and at N = 100,000 it becomes the most powerful design. Sample sizes of 100,000 are realistic in human population and even larger samples sizes are expected in the next few years. Therefore, strong inference on heritability can be drawn using random samples from the population, while not having to make assumptions about the resemblance between relatives due to common environmental factors. The within-family design, which is the most robust with respect to assumptions of the model, remains inaccurate even when the analysis is on 50,000 full-sib pairs. However, in species such as fish with huge full-sib family sizes, accurate estimation could be achieved (Odegard and Meuwissen 2012; Hill 2013).


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

Visscher PM, Goddard ME - Genetics (2014)

Standard error of estimates of heritability from different experimental designs in human populations, as a function of the population value of the heritability (x-axis), experimental sample size, and experimental design. For the within-family design (Within-family estimation using realized relationships estimates from markers), the variance in realized relationships was assumed to be 0.0392. For the population design (Random sampling from the population), the variance is relatedness was approximated assuming Ne = 10,000, a genome length of 35 M, and an average chromosome length of 1 M (Goddard 2009).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4286686&req=5

fig1: Standard error of estimates of heritability from different experimental designs in human populations, as a function of the population value of the heritability (x-axis), experimental sample size, and experimental design. For the within-family design (Within-family estimation using realized relationships estimates from markers), the variance in realized relationships was assumed to be 0.0392. For the population design (Random sampling from the population), the variance is relatedness was approximated assuming Ne = 10,000, a genome length of 35 M, and an average chromosome length of 1 M (Goddard 2009).
Mentions: Figure 1 shows the approximation to the standard error of an estimate of heritability as a function of the population value, experimental sample size, and design. Four different designs were used: a pedigree design of unrelated full-sib pairs, a pedigree design with MZ and DZ twins pairs with a ratio of 1:2 MZ and DZ pairs, a within-family design using full-sib pairs, and a population design using nominally unrelated individuals. In the last two designs, GRM are estimated with SNP data. These designs are less powerful than the pedigree-based experimental designs, but make fewer assumptions. At N = 10,000 the sampling variance of the population design approaches that of the pedigree designs, and at N = 100,000 it becomes the most powerful design. Sample sizes of 100,000 are realistic in human population and even larger samples sizes are expected in the next few years. Therefore, strong inference on heritability can be drawn using random samples from the population, while not having to make assumptions about the resemblance between relatives due to common environmental factors. The within-family design, which is the most robust with respect to assumptions of the model, remains inaccurate even when the analysis is on 50,000 full-sib pairs. However, in species such as fish with huge full-sib family sizes, accurate estimation could be achieved (Odegard and Meuwissen 2012; Hill 2013).

Bottom Line: We show that well-known results for balanced designs are special cases of a more general unified framework.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.

View Article: PubMed Central - PubMed

Affiliation: 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.

Show MeSH