A recursive approach to detect multivariable conditional variance components and conditional random effects

  • Authors:

  • Jixiang Wu;Dongfeng Wu;Johnie N. Jenkins;Jack C. McCarty, Jr.

  • Affiliations:

  • Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762, USA;Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762, USA;Crop Science Research Laboratory, USDA-ARS, P. O. Box 5367, Mississippi State, MS 39762, USA;Crop Science Research Laboratory, USDA-ARS, P. O. Box 5367, Mississippi State, MS 39762, USA

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2006

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Abstract

A complex trait like crop yield is determined by its component traits. Multivariable conditional analysis in a general mixed linear model is helpful in dissecting the gene expression for the complex trait due to different effects, such as environment, genotype, and genotype x environment interaction. A recursive approach is presented for constructing a new random vector that can be equivalently used to analyze multivariable conditional variance components and conditional effects. End-of-season plant mapping data, including lint yield and three yield components for nine cultivars of upland cotton (Gossypium hirsutum L.) were used to detect the conditional variance components and conditional effects using this new approach, which can help identify genotypes to be used in selection studies.