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PMID: 11114174 Published · ppublish English Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Mixed model analysis of quantitative trait loci.

Proceedings of the National Academy of Sciences of the United States of America ·Vol. 97 ·No. 26 ·2000-12-19 ·Pages 14542-7

Xu S, Yi N

Abstract

We develop a mixed model approach of quantitative trait locus (QTL) mapping for a hybrid population derived from the crosses of two or more distinguished outbred populations. Under the mixed model, we treat the mean allelic value of each source population as the fixed effect and the allelic deviations from the mean as random effects so that we can partition the total genetic variance into between- and within-population variances. Statistical inference of the QTL parameters is obtained by using the Bayesian method implemented by Markov chain Monte Carlo (MCMC). This unified QTL mapping algorithm treats the fixed and random model approaches as special cases of the general mixed model methodology. Utility and flexibility of the method are demonstrated by using a set of simulated data.

MeSH Terms

Algorithms Alleles Chromosome Mapping Computer Simulation Crosses, Genetic Markov Chains Models, Genetic Monte Carlo Method Quantitative Trait, Heritable

Authors & Affiliations

2 authors, click to expand affiliations / ORCID

Xu S

Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA. xu@genetics.ucr.edu

Yi N

References (12)

12 references, click to expand

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Article Info

Journal

Proceedings of the National Academy of Sciences of the United States of America

Abbr.

Proc Natl Acad Sci U S A

ISSN

0027-8424

DOI

10.1073/pnas.250235197

xu@genetics.ucr.edu

Published

2000-12-19

Pages

14542-7

Language

English

Region

United States

NLM ID

7505876

PMCID

PMC18955

Subset

Grants

NIGMS NIH HHS · R01 GM055321 · United States

NIGMS NIH HHS · GM55321 · United States

External Links

PubMed source DOI: 10.1073/pnas.250235197 PMC Full Text View journal details

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