INVESTIGATION A Noncomplementation Screen for Quantitative Trait Alleles in Saccharomyces cerevisiae Hyun Seok Kim,* ,† Juyoung Huh,* Linda Riles,* Alejandro Reyes, † and Justin C. Fay* ,†,‡,1 *Department of Genetics, † Computational Biology Program, and ‡ Center for Genome Sciences and Systems Biology, Washington University, St. Louis, Missouri 63108 ABSTRACT Both linkage and linkage disequilibrium mapping provide well-defined approaches to mapping quantitative trait alleles. However, alleles of small effect are particularly difficult to refine to individual genes and causative mutations. Quantitative noncomplementation provides a means of directly testing individual genes for quantitative trait alleles in a fixed genetic background. Here, we implement a genome-wide noncomplementation screen for quantitative trait alleles that affect colony color or size by using the yeast deletion collection. As proof of principle, we find a previously known allele of CYS4 that affects colony color and a novel allele of CTT1 that affects resistance to hydrogen peroxide. To screen nearly 4700 genes in nine diverse yeast strains, we developed a high-throughput robotic plating assay to quantify colony color and size. Although we found hundreds of candidate alleles, reciprocal hemizygosity analysis of a select subset revealed that many of the candidates were false positives, in part the result of background-dependent haploinsuffi- ciency or second-site mutations within the yeast deletion collection. Our results highlight the difficulty of identifying small-effect alleles but support the use of noncomplementation as a rapid means of identifying quantitative trait alleles of large effect. KEYWORDS complex trait copper sulfate hydrogen sulfide Identifying genes responsible for phenotypic variation in natural pop- ulations is difficult because most traits are influenced by multiple genes and because the effects of each gene must be mapped within a hetero- geneous genetic background. Both linkage mapping and genome-wide association studies overcome this heterogeneity by measuring the av- erage effect of a gene over a large number of samples. However, the two approaches detect qualitatively different types of alleles. Linkage map- ping often reveals alleles with large and in some cases epistatic effects that are rare in the general population (e.g., Deutschbauer and Davis 2005; Ben-Ari et al. 2006; Sinha et al. 2006; Gerke et al. 2009). In contrast, genome-wide association studies often identify small-effect associations with common alleles and find little evidence of epistasis (Altshuler et al. 2008). Although many factors likely contribute to these differences (e.g., Gerke et al. 2010), our understanding of quan- titative trait alleles depends on both how they are mapped and our ability to map them (Rockman 2012). One particularly undersampled source of variation is rare alleles of moderate or small effect (Pritchard 2001; Wang et al. 2005). Under a rare alleles model, alleles segregating in one cross are expected to be absent in other crosses because they are rare in the general population. Furthermore, most rare alleles are not detected by population associa- tion because power is a function of allele frequency. The larger number of rare missense or nonsense alleles in case compared with control samples supports the contribution of rare alleles to a number of com- plex human genetic diseases (e.g., Cohen et al. 2004; Fearnhead et al. 2004; Ahituv et al. 2007). However, without a population-based screen for quantitative trait alleles that does not depend on their frequency, the amount of variation explained by rare alleles has been difficult to assess. Quantitative noncomplementation provides a means of identifying and measuring the effect of an allele. The idea is that the effect of a recessive or partially recessive allele will be revealed in the absence of a wild-type allele, whereas the effect of a dominant allele, typically wild type, will remain unchanged (Figure 1). Quantitative noncomplemen- tation has been predominantly used to fine-map quantitative trait loci (Mackay 2004). However, it can also be used to screen the genome when a large number of mutations are available (e.g., Coyne et al. 1998; Takahashi et al. 2011). In the context of a genome-wide screen, quantitative noncomplementation offers two distinct advantages over linkage and association studies. First, it can be applied to multiple Copyright © 2012 Kim et al. doi: 10.1534/g3.112.002550 Manuscript received March 15, 2012; accepted for publication April 30, 2012 This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/ by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Supporting information is available online at http://www.g3journal.org/lookup/ suppl/doi:10.1534/g3.112.002550/-/DC1 1 Corresponding author: 4444 Forest Park Avenue, St. Louis, MO 63108. E-mail: jfay@genetics.wustl.edu Volume 2 | July 2012 | 753 Downloaded from https://academic.oup.com/g3journal/article/2/7/753/5986885 by guest on 21 December 2022