Quantitative trait locus analyses and the study of evolutionary process

Mol Ecol. 2004 Sep;13(9):2505-22. doi: 10.1111/j.1365-294X.2004.02254.x.


The past decade has seen a proliferation of studies that employ quantitative trait locus (QTL) approaches to diagnose the genetic basis of trait evolution. Advances in molecular techniques and analytical methods have suggested that an exact genetic description of the number and distribution of genes affecting a trait can be obtained. Although this possibility has met with some success in model systems such as Drosophila and Arabidopsis, the pursuit of an exact description of QTL effects, i.e. individual gene effect, in most cases has proven problematic. We discuss why QTL methods will have difficulty in identifying individual genes contributing to trait variation, and distinguish between the identification of QTL (or marker intervals) and the identification of individual genes or nucleotide differences within genes (QTN). This review focuses on what ecologists and evolutionary biologists working with natural populations can realistically expect to learn from QTL studies. We highlight representative issues in ecology and evolutionary biology and discuss the range of questions that can be addressed satisfactorily using QTL approaches. We specifically address developing approaches to QTL analysis in outbred populations, and discuss practical considerations of experimental (cross) design and application of different marker types. Throughout this review we attempt to provide a balanced description of the benefits of QTL methodology to studies in ecology and evolution as well as the inherent assumptions and limitations that may constrain its application.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Adaptation, Biological / genetics
  • Biological Evolution*
  • Epistasis, Genetic
  • Gene Expression
  • Genetic Markers / genetics
  • Genetics, Population*
  • Quantitative Trait Loci / genetics*
  • Research Design*


  • Genetic Markers