Originally published as Genetics Published Articles Ahead of Print on September 9, 2008.

Genetics, Vol. 180, 1661-1670, November 2008, Copyright © 2008
doi:10.1534/genetics.108.092932

Sequential Elimination of Major-Effect Contributors Identifies Additional Quantitative Trait Loci Conditioning High-Temperature Growth in Yeast

Himanshu Sinha^*,1,2, Lior David^,2, Renata C. Pascon, Sandra Clauder-Münster^*, Sujatha Krishnakumar, Michelle Nguyen, Getao Shi^*, Jed Dean, Ronald W. Davis, Peter J. Oefner^,**, John H. McCusker and Lars M. Steinmetz^*,,3

^* European Molecular Biology Laboratory, 69117 Heidelberg, Germany, Department of Animal Sciences, Hebrew University of Jerusalem, Rehovot 76100, Israel, Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina 27710, Stanford Genome Technology Center, Palo Alto, California 94304 and ^** Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany

³ Corresponding author: European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
E-mail: larsms{at}embl.de

Several quantitative trait loci (QTL) mapping strategies can successfully identify major-effect loci, but often have poor success detecting loci with minor effects, potentially due to the confounding effects of major loci, epistasis, and limited sample sizes. To overcome such difficulties, we used a targeted backcross mapping strategy that genetically eliminated the effect of a previously identified major QTL underlying high-temperature growth (Htg) in yeast. This strategy facilitated the mapping of three novel QTL contributing to Htg of a clinically derived yeast strain. One QTL, which is linked to the previously identified major-effect QTL, was dissected, and NCS2 was identified as the causative gene. The interaction of the NCS2 QTL with the first major-effect QTL was background dependent, revealing a complex QTL architecture spanning these two linked loci. Such complex architecture suggests that more genes than can be predicted are likely to contribute to quantitative traits. The targeted backcrossing approach overcomes the difficulties posed by sample size, genetic linkage, and epistatic effects and facilitates identification of additional alleles with smaller contributions to complex traits.

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