Mapping Small Effect Mutations inSaccharomyces cerevisiae: Impacts of Experimental Design and Mutational Properties

Abstract

Genetic variants identified by mapping are biased toward large phenotypic effects because of methodologic challenges for detecting genetic variants with small phenotypic effects. Recently, bulk segregant analysis combined with next-generation sequencing (BSA-seq) was shown to be a powerful and cost-effective way to map small effect variants in natural populations. Here, we examine the power of BSA-seq for efficiently mapping small effect mutations isolated from a mutagenesis screen. Specifically, we determined the impact of segregant population size, intensity of phenotypic selection to collect segregants, number of mitotic generations between meiosis and sequencing, and average sequencing depth on power for mapping mutations with a range of effects on the phenotypic mean and standard deviation as well as relative fitness. We then used BSA-seq to map the mutations responsible for three ethyl methanesulfonate−induced mutant phenotypes in Saccharomyces cerevisiae. These mutants display small quantitative variation in the mean expression of a fluorescent reporter gene (−3%, +7%, and +10%). Using a genetic background with increased meiosis rate, a reliable mating type marker, and fluorescence-activated cell sorting to efficiently score large segregating populations and isolate cells with extreme phenotypes, we successfully mapped and functionally confirmed a single point mutation responsible for the mutant phenotype in all three cases. Our simulations and experimental data show that the effects of a causative site not only on the mean phenotype, but also on its standard deviation and relative fitness should be considered when mapping genetic variants in microorganisms such as yeast that require population growth steps for BSA-seq.

Compensatorytrans-regulatory alleles minimizing variation inTDH3expression are common withinSaccharomyces cerevisiae

“…For each focal strain, we used extreme QTL mapping with three rounds of crossing followed by three rounds of selection on P TDH3 -YFP expression using fluorescence-assisted cell sorting (FACS; Fig. 3A; Ehrenreich et al 2010Ehrenreich et al , 2012Kofler et al 2011;Parts et al 2011;Cubillos et al 2013;Albert et al 2014;Duveau et al 2014;Schlötterer et al 2014).…”

The Genomic Landscape of Position Effects on Protein Expression Level and Noise in Yeast

“…The GFP protein concentration of each cell was estimated using FACS following a recent study (Duveau et al, 2014), with experimental details provided in Supplementary Experimental Procedures.…”

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

“…Furthermore, low sequencing depth of some of our segregant pools may have limited our power to detect small-effect alleles (Materials and methods). However, even the lowest sequencing depth we obtained (25x) is still sufficiently powered to map alleles with effects as low as 5% of phenotypic variance [44]. More importantly, we performed a complementary analysis of segregants to directly measure the contribution of GAL3 to the phenotypic variance in a cross (see below).…”