PROJECT SUMMARY/ABSTRACT Individuals differ from each other in many traits, and very few trait differences have simple genetic causes. Indeed, traits associated with common diseases in humans tend to be quite complex, with variation caused by the combined effects of many genetic variants as well as environmental influences and random chance. Determining the genetic contributions to variation in complex traits therefore remains challenging. One approach to meeting this challenge is to perform genetic analysis in laboratory organisms. Laboratory experiments can control for sources of variation that human studies cannot, and can serve as a test bed for developing new methods to determine genotypes and phenotypes at large scale. The budding yeast, Saccharomyces cerevisiae, long used as a model for eukaryotic cell biology, has emerged as a key organism for such experiments. Current yeast experiments achieve high statistical power for detecting genetic effects on trait variation by sampling thousands to millions of individuals. However, to achieve these sample sizes the experiments focus on traits that are easy to measure or select for, such as resistance to toxic environments. This limited repertoire leaves a big gap in understanding the genetic basis of differences in complex cellular traits such as morphological ones. The shapes and sizes of cells are highly relevant to various disease processes but are understudied by quantitative geneticists. To fill this gap, this project will use a combination of high- throughput microscopy, automated image analysis, and photoactivatable cell sorting to sample individuals for high-power genetic analysis. Genetic crosses between natural-isolate strains of budding yeast will generate large numbers of recombinant progeny. Real-time image analysis and microscope control will be used to identify cells with extreme trait values and label them via photoactivation of a genetically encoded or experimentally applied convertible fluorophore. Selected cells will then be recovered using fluorescence activated cell sorting and pooled for genome sequencing. Genetic variants that contribute to differences in cell morphology will be identified as those that are over-represented in selected pools relative to unselected pools. The project will produce a broadly applicable method for linking complex cellular traits with genetic differences. It will also yield new insights into the genetic basis of variation in such traits, and thereby advance understanding of the genetic underpinnings of complex diseases.