Regression-based multi-trait QTL mapping using a structural equation model

Xiaojuan Mi, Kent Eskridge, Dong Wang, P. Stephen Baenziger, B. Todd Campbell, Kulvinder S. Gill, Ismail Dweikat, James Bovaird

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Quantitative trait loci (QTL) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits related to grain yield. Performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis and the multi-trait least-squares analysis, our multi-trait SEM improves statistical power of QTL detection and provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects. The approach also helps build biological models that more realistically reflect the complex relationships among QTL and traits and is more precise and efficient in QTL mapping than single trait analysis.

Original languageEnglish (US)
Article number38
JournalStatistical Applications in Genetics and Molecular Biology
Volume9
Issue number1
DOIs
StatePublished - Nov 12 2010

Keywords

  • QTL mapping
  • least squares
  • multiple traits
  • structural equation model

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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