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It is clear that genetics has a major role in the ageing process of any species (de Magalhães 2003; Austad 2005). In fact, there are over hundreds of genetic manipulations that extends the lifespan in model organisms. Comparative genomics studies exploit the fact that different species show different phenotypes of a certain trait. Since ageing is tightly linked with the genome of an individual, by studying the genome of species with different ageing phenotypes, we may be able to find genes or functional regions involved in the molecular mechanisms of ageing. Researchers in ageing have already recognised the importance of exploiting the vast amount of data available in the literature by constructing the Human Ageing Genomic Resource (de Magalhães, Budovsky, et al. 2009), a collection of databases and tools to help researches understand the genetics of human ageing, including a database of ageing- and longevity-associated genes in model organisms (GenAge) and a compilation of data on ageing, longevity, and life history in over 4,000 species (AnAge). Comparative genomics studies in ageing are increasing in number, but we believe that it has the potential to grow much more.

One of the most recent and exciting studies in comparative genomics are genome wide association studies (GWAS) (Ku et al. 2010). In essence, GWAS are high-throughput hypothesis-free studies that examine genetic variation across the genome in order to associate genetic variations to phenotypic traits (Hunter et al. 2008). These studies usually involve two groups of the same species one with a certain phenotypic trait, commonly a disease, and the other without this trait. After analysing the genome of the individuals in each of the two groups, one can construct a set of markers such as single nucleotide polymorphisms (SNPs) for which one variation is significantly enriched in members of one group. These genetic variations are then considered as a pointers to which region of the genome are responsible for the trait differences and may then be further analysed. GWAS were successful in identifying genes associated to numerous diseases. For instance, in 2007 researchers have identified genes associated to type II diabetes in the first major GWAS study (Sladek et al. 2007). Also in 2007, the Wellcome Trust Case Control Consortium carried out genome-wide association studies for seven common diseases including bipolar disorder, coronary artery disease, Crohn's disease and type 1 and type 2 diabetes (WTCCConsortium 2007). The success of GWAS led ageing researchers to carry out their own and targeted genetic differences between centenarians and control individuals. So far, GWAS on centenarians have revealed few lipoproteins associated to long life in humans (Bergman et al. 2007), and the usage of SNPs could predict with up to 77% accuracy exceptional longevity (Sebastiani et al. 2010). Although there is still a lot of work to be done, the initial results obtained from GWAS on centenarians are encouraging. Although GWAS show great potential to discover genes responsible for ageing divergence within a single specie, we were more interested in discovering genes responsible for ageing divergence across multiple species and in particular across mammals.

More in line with our interests are Ka/Ks approaches which considers the evolutionary pressure on different coding regions of the genome across multiple

species. The Ka/Ks ratio is a proxy for the selective pressure on a protein coding gene and is defined by the ratio between the number of non-synonymous substitutions and the number of synonymous substitutions for a gene. A high Ka/Ks ratio suggests a selective pressure and a low one suggests purifying selection. Studies screen the whole genome in order to find genes with significantly higher or lower Ka/Ks ratio compared to a control orthologous gene and attribute these genes with the evolution of new traits or involvement in an important, well-conserved, pathway. This type of method was used in many studies such as a genome-wide survey of pseudogenes (Torrents et al. 2003) and the detection of of rapidly evolving genes in human (Wang et al. 2003) among others. To study the evolution of ageing, (de Magalhães and Church 2007) used a Ka/Ks approach on human- chimpanzee orthologous gene pairs and reported that genes associated with ageing in non-mammalian model organisms and cellular systems appear to be under stronger evolutionary constraints than those associated with ageing in mammal and also provided evidence suggesting the rapid evolution of Werner syndrome gene in the hominids. Though Ka/Ks is a good indicator of selective pressure at the sequence level, Ka/Ks based approaches cannot detect changes in the expression of genes nor account for the different type of amino acid substitutions.

Fortunately, it is possible to use other proxies for evolutionary selection based on substitution matrices that do take into account of the types of amino acid substitutions. The evolutionary landscape of a genome varies greatly from region to region and although the substitution rates for two neutrally evolving regions of sequence are usually well correlated, this correlation decreases rapidly as the genomic distance between the regions increases (Gaffney et al. 2005). Indeed, the rate of sequence mutation can depend on multiple factors such as the location of the region and the nucleotide composition of neighbouring sites (Hardison et al. 2003). Because of this, comparison of evolutionary rates within a single genome can be difficult. Rather, one usually compares the loci of multiple orthologs in a range of species in order to determine the relative mutational rates of the loci as a common

phylogenetic relationship and divergence times can be assumed for orthologs. This is the reason why most studies are based on the alignments of orthologous sequences. Needless to say, for these kind of analyses, constructing an accurate orthologous mapping of regions in different species is of critical importance in order to avoid biases due to sequence location and composition.