Linkage between quantitative trait loci and marker loci: Resolution power of three statistical approaches in single marker analysis

Abraham B. Korol, Yefim I. Ronin, Valery M. Kirzhner

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a comparison of three methods of parameter estimation in analysis of linkage between a quantitative trait locus (QTL) and a marker locus: maximum likelihood, mean square for trait cumulative distribution function, and method of moments, employing simulated backcross data. The sensitivity of estimates to violation of assumptions of normality and equal variances was also studied. Some measures of discrepancy between the trait distributions in the QTL groups are considered to evaluate the potential dependence of the resolution capacity on the QTL substitution effect with respect to trait mean value and variance.

Original languageEnglish
Pages (from-to)426-441
Number of pages16
JournalBiometrics
Volume52
Issue number2
DOIs
StatePublished - Jun 1996

Bibliographical note

Funding Information:
\Ye are grateful to the many people who generously contributed unpublished manuscripts to assist in the preparation of this review and to our colleagues for numerous enlightening discussions. We wish especially to thank Michael Chamberlin, Joseph Coleman, David Dres-sler, Olga Kennard. Alexander Rich, Akiyoshi Wada, and David Ward for providing prints of figures. Also, Walter Fitch’s help with both coniputer searches and editorial matters (on Section VIII) is gratefully noted. The financial support of the N.I.H. (CA 20279) and the N.S.F. (PCM 77-15033) is gratefully acknowledged; W. H. was supported by the Max Kade Foundation and the Deutsche Forschungsgemeinschaft, and N. P. by an N.I.H. Postdoctoral Training Grant (T32 CAG9075).

Keywords

  • Discrepancy of distributions
  • Log-likelihood
  • Mapping
  • Marker-QTL linkage
  • Mean square
  • Moments
  • Nonequal variances
  • Normal mixture decomposition
  • Robustness

ASJC Scopus subject areas

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

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