Originally published as Genetics Published Articles Ahead of Print on January 16, 2005.

Genetics, Vol. 169, 1649-1658, March 2005, Copyright © 2005
doi:10.1534/genetics.104.031948

Mapping Quantitative Trait Loci in Multiple Populations of Arabidopsis thaliana Identifies Natural Allelic Variation for Trichome Density

* Section of Molecular, Cell and Developmental Biology, Institute for Cellular and Molecular Biology
{ddagger} Section of Integrative Biology, University of Texas, Austin, Texas 78712
{dagger} IFEVA, Universidad de Buenos Aires, 1417-Buenos Aires, Argentina

1 Corresponding author: Section of Molecular, Cell and Developmental Biology, University of Texas, 2500 Speedway, MBB 1.448b, Austin, TX 78712.
E-mail: vsymonds{at}mail.utexas.edu

The majority of biological traits are genetically complex. Mapping the quantitative trait loci (QTL) that determine these phenotypes is a powerful means for estimating many parameters of the genetic architecture for a trait and potentially identifying the genes responsible for natural variation. Typically, such experiments are conducted in a single mapping population and, therefore, have only the potential to reveal genomic regions that are polymorphic between the progenitors of the population. What remains unclear is how well the QTL identified in any one mapping experiment characterize the genetics that underlie natural variation in traits. Here we provide QTL mapping data for trichome density from four recombinant inbred mapping populations of Arabidopsis thaliana. By aligning the linkage maps for these four populations onto a common physical map, the results from each experiment were directly compared. Seven of the nine QTL identified are population specific while two were mapped in all four populations. Our results show that many lineage-specific alleles that either increase or decrease trichome density persist in natural populations and that most of this genetic variation is additive. More generally, these findings suggest that the use of multiple populations holds great promise for better understanding the genetic architecture of natural variation.




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