Exploiting natural genetic variation to map QTLs of telomere properties in Yeast: Length silencing ageing and senescence

Most properties of an organism are under the control of many genes rather than single major genes. Genetics in the past 100 years has been split into two modes of analysis. The first is breeding genetics which generally dealt with complex traits that were inherited, such as egg production, milk yield and crop yields, but were not inherited in a simple fashion. Very complex statistical analyses have been generated over the decades to deal with the inheritance of these traits. The second mode of analysis deals with the simple single gene effects where a variant or mutant has a visible phenotypic effect. Modern molecular genetics has generally dealt with this single gene approach where a knockout or mutation of a gene has been assessed for the effect on particular phenotypes. This has grown to large scale genome wide approaches where the effect on a particular phenotype of every gene knockout is assessed. There are now concerted efforts to assess many if not all double gene knockouts to look at gene interactions. There hasn't yet been an effective marriage of the complex quantitative traits with the modern molecular approach though there have been some efforts with limited success. The advent of genome wide mapping of SNPs and other variation has improved the mapping of genes involved in complex traits but it is still a slow and arduous task. Nature has provided us with the necessary experimental material to undertake a modern molecular approach to complex traits in the form of populations that have diverged in sequence and phenotypic characteristics. In yeast populations we have found up to 4.5% sequence divergence as well as divergence in a number of phenotypes such as the length of telomeres. We can show that this difference in phenotypes is due to a number of genes rather than a few major genes. A global screen of all single gene knockouts has already been done on a laboratory strain of yeast which has identified a large number of genes that may be involved in telomere length. We will use the natural genetic variation to map all the genes involved in telomere length control, which will include essential genes not detectable in the previous studies, as well as their interactions. By comparing our approach to the previous approach we will identify new genetic factors but more importantly how these factors interact. We will assess the relative efficiency of our approach to the previous gene knockout approach and will also assess the relative workload required to determine the genetic factors of telomere length as well as related phenotypes of aging and nearby gene repression. The approach will definitely reveal further genetic factors involved in these traits but if it proves to be as feasible we will apply it to complex phenotypes in general and make the materials available to the community.

We will combine classical quantitative trait analysis with modern molecular genetics in the yeast system in order to determine the genes involved in telomere length regulation, telomere repression of nearby genes and genes involved in aging, both replicative and chronological which may or may not be related to telomeres. The ability to isolate all four meiotic products as F1 progeny along with having the genome sequence of several strains will allow us to map precisely the QTLs involved in these telomere properties and assess their complex multilocus interactions. The ability to manipulate the genomes of these yeasts in any way will allow us to test hypothesis of the role of specific genetic variants in specific genome contexts for their role in the quantitative traits. We will then expand this approach to phenotypes in general, particularly those easily tested by high throughput screening. We can compare this approach to the more classical gene knockout approach that has already been done for telomere length regulation in yeast. By using natural genetic variation that has passed the test of evolutionary constraints we will be able to identify new genetic factors that cannot be revealed via the gene knockout approach, such as essential genes and many genes with synthetic interactions. A resource of sequences, oligos, genotyped F1 progeny, and phenotypes will be provided to the community.