A maximum likelihood-based method for mining major genes affecting a quantitative character

Rongling Wu, Bailian Li, Samuel S. Wu, George Casella

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this article, we present a maximum likelihood-based analytical approach for detecting a major gene of large effect on a quantitative trait in a progeny population derived from a mating design. Our analysis is based on a mixed genetic model specifying both major gene and background polygenic inheritance. The likelihood of the data is formulated by combining the information about population behaviors of the major gene during hybridization and its phenotypic distribution densities. The EM algorithm is implemented to obtain maximum likelihood estimates for population and quantitative genetic parameters of the major locus. This approach is applied to detect an overdominant gene governing stem volume growth in a factorial mating design of aspen trees. It is suggested that further molecular genetic research toward mapping single genes affecting aspen growth and production based on the same experimental data has a high probability of success.

Original languageEnglish (US)
Pages (from-to)764-768
Number of pages5
JournalBiometrics
Volume57
Issue number3
DOIs
StatePublished - Jan 1 2001

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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