CHAPTER 9 Integrative Genomics of Aging João Pedro de Magalhães and Robi Tacutu Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Liverpool, UK OUTLINE Introduction 263 Finding Needles in Haystacks: Network Approaches and Multi-Dimensional Post-Genome Technologies and Data Integration 272 Biogerontology 264 Construction of Longevity Networks 273 Genome-Wide Approaches and the Topological Features 274 Genetics of Aging and Longevity 264 Network Modularity 276 Surveying the Aging Phenotype on a Multi-Dimensional Data Integration 276 Grand Scale 267 Predictive Methods and Models 278 Challenges in Data Analysis 270 Concluding Remarks 279 Data Integration 271 Acknowledgments 280 Data and Databases 271 References 280 INTRODUCTION processes are complex in the sense that they involve the interplay of multiple genes and The sequencing of genomes has revolution- proteins with each other and with the environ- ized biological and biomedical research. Thanks ment, surveying systems as a whole is impera- to various technologies and approaches that tive to fully comprehending them, and more take advantage of genome sequence knowl- accurately pinpointing how to intervene in edge, researchers can now focus on whole bio- them. Recent breakthroughs in developing logical systems rather than being limited to cheaper and quicker sequencing technolo- studying isolated parts. Because most biological gies have given further power to our capacity M. Kaeberlein & G.M. Martin (Eds) DOI: http://dx.doi.org/10.1016/B978-0-12-411596-5.00009-5 Handbook of the Biology of Aging, Eighth edition. 263 © 2016 Elsevier Inc. All rights reserved. 264 9. IntegratiVE GENOMICS OF AGING to survey biological systems in a holistic way sources, as this is one of the major challenges with multiple applications in aging research of the post-genome era, and also one of the (reviewed in de Magalhães et al., 2010). In addi- most promising. Various sources of data and tion to genomics, other omics approaches like approaches are discussed in this context. transcriptomics, proteomics, and epigenomics have allowed for a systematic profiling of bio- logical processes and disease states. POST-GENOME TECHNOLOGIES Aging is widely acknowledged as a complex AND BIOGERONTOLOGY process involving changes at various biological levels, interactions between them and feedback There are many open questions in biogeron- regulatory circuits. The underlying mechanis- tology, but arguably most researchers focus tic causes of aging remain a subject of debate, on two key questions (de Magalhães and and it is likely that multiple degenerative pro- Toussaint, 2004b): (i) What are the genetic cesses are involved, including organ-specific determinants of aging, both in terms of longev- processes but also interacting cell- and organ- ity differences between individuals and spe- level communications (Cevenini et al., 2010; de cies differences in aging? (ii) Which changes Magalhães, 2011; Lopez-Otin et al., 2013). While occur across the lifetime to increase vulnerabil- there are simple triggers to complex biological ity, for example, in a person from age 30 to age processes, such as telomere shortening trigger- 70 to increase the chance of dying by roughly ing replicative senescence in human fibroblasts 30-fold? Post-genome technologies may help us (de Magalhães, 2004), most researchers would answer them both. agree that organismal aging involves multiple processes and possibly the interplay between Genome-Wide Approaches and the various causal mechanisms. Likewise, hun- Genetics of Aging and Longevity dreds of genes have been associated with aging in model organisms (Tacutu et al., 2013), and Understanding human phenotypic variation yet the pathways involved are complex and in aging and longevity has been a long-term often interact in nonlinear ways (de Magalhães research goal. Studies in twins have shown that et al., 2012). One hypothesis is that aging and longevity in humans has a genetic component, longevity cannot be fully understood by stud- and the heritability of longevity has been esti- ying individual components and processes mated at approximately 25% (Christensen et al., (Cevenini et al., 2010). To understand aging we 2006). If we could identify genetic variants asso- must then account for the intrinsic complexity ciated with exceptional human longevity, these of biological systems. would likely be suitable for drug discovery (de Our goal in this chapter is to review poten- Magalhães et al., 2012). In 1994, APOE was asso- tial large-scale technologies in the context of ciated with longevity in a French population aging and longevity research and how data (Schachter et al., 1994). The sequencing of the can be analyzed and integrated to advance our human genome in 2001 allowed for much more understanding of these complex processes. We powerful whole-genome genotyping platforms first review the major technologies available capable of surveying hundreds of thousands for researchers to survey biological systems of genetic variants in a cost-effective way (de in a systematic fashion and their applications Magalhães, 2009). In spite of these recent tech- to advance the biology and genetics of aging, nological advances, the genetics of human lon- discuss issues in data analysis and statistics, gevity remains largely misunderstood. Several and discuss data integration between different genome-wide association studies (GWAS) have I. BASIC M E C HANISMS OF AGING: MODELS AND SYSTEMS POST-GENOME TECHNOLOGIES AND BIOGERONTOLOGY 265 FIGURE 9.1 Exponential growth in sequencing capacity as reflected in the dropping costs of sequencing from 2001 to 2013. Source: NHGRI (http://www.genome.gov/sequencingcosts/). been performed with thousands of individuals, with confidence with longevity, our understand- with largely disappointing results. For exam- ing of the genetics of longevity lags behind ple, one recent landmark study involving sev- our understanding of the genetics of complex eral European populations with a total of over age-related diseases, in itself made difficult by 2000 nonagenarian sibling pairs identified only numerous factors like multiple genes with small APOE as associated with longevity (Beekman effects. Intrinsic difficulties in longevity studies et al., 2013); and although APOE has been con- (e.g., lack of appropriate controls) or because sistently associated with longevity, it only longevity is a more complex trait may explain modestly explains the heritability of longevity. why our understanding of the heritability of GWAS focused on complex diseases and pro- longevity is still poor (de Magalhães, 2014). cesses have been on many occasions equally An even greater source of variation in aging disappointing to date, suggesting that common and longevity than that observed between genetic variants have a modest contribution to humans is observed across species. We know longevity and complex diseases (Manolio et al., that mice, for example, age 25–30 times faster 2009). than human beings, even under the best envi- The falling costs of DNA sequencing (Figure ronmental conditions (Finch, 1990). Even when 9.1) means that sequencing a human genome compared to chimpanzees, our closest living is rapidly becoming affordable. Therefore, in relative whose genome is about 95% similar the coming years researchers will move from to our own, aging is significantly retarded in genotyping platforms based on known genetic humans (de Magalhães, 2006). Therefore, there variants to genome sequencing of thousands of must be a genomic basis for species differ- individuals. It is possible that this will reveal ences in aging, and again the dropping costs rare variants with strong effects on longevity, of sequencing have permitted much more as has been predicted to be the case for complex affordable de novo sequencing of genomes diseases (Manolio et al., 2009). Nonetheless, con- (de Magalhães et al., 2010). For example, the sidering that only APOE has been associated sequencing of long-lived species, such as the I. BASIC M E C HANISMS OF AGING: MODELS AND SYSTEMS 266 9. IntegratiVE GENOMICS OF AGING naked mole-rat and bats (Keane et al., 2014; Aging is a particularly difficult process to Kim et al., 2011; Zhang et al., 2013b) can pro- unravel because it is much harder to study in vide candidate genes for selection in long- humans than most other processes and diseases. lived species, and it is interesting to observe Observational studies have been conducted but that genes involved in DNA damage responses are extremely time-consuming, and clinical tri- and repair have emerged from such studies (de als for longevity itself are nearly impossible, Magalhães and Keane, 2013). even though they can be performed for specific In addition to the analysis of genomes from age-related pathologies (de Magalhães et al., long-lived species, comparative analyses of 2012). Therefore most biogerontologists rely on genomes from species with different lifespans model systems: human cells; unicellular organ- are also beginning to provide further candi- isms such as the yeast Saccharomyces cerevisiae; date genes for a role in aging. We developed a the roundworm Caenorhabditis elegans; the fruit method to identify candidate genes involved in fly Drosophila melanogaster; rodents and in par- species differences in aging based on detecting ticular mice (Mus musculus) and rats (Rattus nor- proteins with accelerated evolution in multiple vegicus). The small size and short life cycles of lineages where