PROJECT SUMMARY Variation in survival and reproduction is due largely to variation in polygenic traits. Therefore, identifying the genetic basis of adaptive divergence in complex phenotypes is of paramount importance not only in evolutionary biology, but also in the study and treatment of human disease. Few studies have been able to identify the individual genes that underlie adaptive differences in quantitative traits. The overarching goal of this work is to identify the individual loci involved in quantitative trait variation and link those loci to fitness in natural populations. Previous efforts have fallen short either because (1) quantitative trait locus (QTL) mapping studies provided limited resolution and failed to pinpoint particular genes, or (2) the effects of variation in candidate genes and traits on fitness could not be tested in natural populations. The proposed work will overcome these limitations in two ways. First, to identify individual loci involved in ecologically relevant complex traits such as phenology, mating system, and morphology, we plan to combine QTL mapping in advanced generation hybrids with genome wide association studies and population genomic scans for selection in natural populations. Second, we will perform reciprocal transplant studies using genetically recombinant individuals to causally link genetic variation in quantitative traits to variation in fitness in native habitats. The Mimulus guttatus species complex is an ideal system in which to study the genetics of adaptation in quantitative traits because it is an ecologically diverse, experimentally tractable, and highly inter-fertile group of wildflowers with many genetic and genomic tools. Using this combination of approaches in Mimulus will allow us to address the following fundamental evolutionary questions: What is the genetic architecture of local adaptation under spatially and temporally varying selection? Is evolution in complex traits predictable at the molecular level? What is the genetic basis of genotype by environment interactions? Our work uses a powerful integrative approach to examine the evolution of complex traits in natural populations. How quantitative traits respond to environmental variation in time and space has important implications for the evolution of complex diseases and agriculturally important plants and animals in a changing climate.