Ageing is the highest risk factor for a large number of diseases and yet surprisingly little is known about the underlying molecular mechanisms. Ageing is a classical complex trait affected by both the genetic background and the environment, and varies quantitatively among individuals. Identifying the causal genetic variants underlying variation in longevity is a major challenge. I propose to use the budding yeast, Saccharomyces cerevisiae, to identify quantitative trait loci (QTLs) contributing to natural variation in replicative life span, telomerase negative cellular senescence and chronological life span in a vast array of environmental conditions. Many of these longevity pathways are conserved from yeast to higher eukaryotes making S. cerevisiae an attractive model system for ageing.Recently, I developed groundbreaking approaches in population genomics and complex traits that open the door to next generation QTL mapping in yeast. I propose to combine my unique high resolution QTL mapping with an artificial multiparent population that broadly captures the genetic and phenotypic species diversity. This population will contain all the ideal features for QTL mapping and will resemble samples used in human genome-wide association studies. The outcomes of this research will help to resolve some long standing questions including what types of alleles are responsible for natural variation in ageing; how do the alleles interact among themselves and with the environment; and how they vary and are distributed across natural populations. Once the broad structure of longevity traits has been dissected, we will extrapolate this knowledge to make predictions and to understand what evolutionary forces maintain the variability in natural populations. Revealing how these elusive natural genetic variants affect longevity will deepen on our knowledge of the cellular mechanisms that drive ageing, offering new possibilities to promote health span in humans and livestock.