Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T11:46:11.710Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-parametric exploratory analysis of the covariance structure for genetic analysis of repeated measures and other function-value traits

Published online by Cambridge University Press:  16 September 2002

FLORENCE JAFFRÉZIC ,
SCOTT D. PLETCHER  and
WILLIAM G. HILL
FLORENCE JAFFRÉZIC
Affiliation:
INRA Quantitative and Applied Genetics, 78352 Jouy-en-Josas Cedex, France Institute of Cell Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
SCOTT D. PLETCHER
Affiliation:
Department of Biology, Galton Laboratory, University College, London NW1 2HE, UK
WILLIAM G. HILL
Affiliation:
Institute of Cell Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In the analysis of longitudinal data, before assuming a parametric model, an idea of the shape of the variance and correlation functions for both the genetic and environmental parts should be known. When a small number of observations is available for each subject at a fixed set of times, it is possible to estimate unstructured covariance matrices, but not when the number of observations over time is large and when individuals are not measured at all times. The non-parametric approach, based on the variogram, presented by Diggle & Verbyla (1998), is specially adapted for exploratory analysis of such data. This paper presents a generalization of their approach to genetic analyses. The methodology is applied to daily records for milk production in dairy cattle and data on age-specific fertility in Drosophila.

Type
Research Article
Information
Genetics Research , Volume 80 , Issue 1 , August 2002 , pp. 47 - 53
Copyright
© 2002 Cambridge University Press