Estimation of variance components of quantitative traits in inbred populations

Am J Hum Genet. 2000 Feb;66(2):629-50. doi: 10.1086/302759.


Use of variance-component estimation for mapping of quantitative-trait loci in humans is a subject of great current interest. When only trait values, not genotypic information, are considered, variance-component estimation can also be used to estimate heritability of a quantitative trait. Inbred pedigrees present special challenges for variance-component estimation. First, there are more variance components to be estimated in the inbred case, even for a relatively simple model including additive, dominance, and environmental effects. Second, more identity coefficients need to be calculated from an inbred pedigree in order to perform the estimation, and these are computationally more difficult to obtain in the inbred than in the outbred case. As a result, inbreeding effects have generally been ignored in practice. We describe here the calculation of identity coefficients and estimation of variance components of quantitative traits in large inbred pedigrees, using the example of HDL in the Hutterites. We use a multivariate normal model for the genetic effects, extending the central-limit theorem of Lange to allow for both inbreeding and dominance under the assumptions of our variance-component model. We use simulated examples to give an indication of under what conditions one has the power to detect the additional variance components and to examine their impact on variance-component estimation. We discuss the implications for mapping and heritability estimation by use of variance components in inbred populations.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms
  • Alleles
  • Child
  • Christianity
  • Computer Simulation*
  • Consanguinity*
  • Environment
  • Female
  • Genes, Dominant / genetics
  • Humans
  • Likelihood Functions
  • Lipoproteins, HDL / genetics
  • Male
  • Matched-Pair Analysis
  • Models, Genetic*
  • Multivariate Analysis
  • Pedigree
  • Quantitative Trait, Heritable*
  • Sample Size
  • South Dakota


  • Lipoproteins, HDL