From 1 January, 2019 through 31 December, 2021 we will be working on a BBSRC-funded project (BB/S001824/1) to develop statistical tools to extract ageing signals from disparate longitudinal data.
Project description
Personnel
David Steinsaltz
Maria Christodoulou
Hamish Patten
Abstract
How do we age as individuals? What are the hidden trajectories each of us follow as we grow old? How much of that process is genetic and how much is it the reflection of our experiences and chosen lifestyles? Complex questions such as these will be in the forefront of our proposed research. To answer them we need to combine complicated datasets with novel statistical methods. The datasets already exist, in the form of longitudinal studies. The methods still need to be developed, either from first principles or as adaptations of existing tools. In this multifaceted project, we will be both developing the methodological foundation to answer such questions as well as testing them on existing data.
Longitudinal studies, such as the British Birth Cohorts, are rich, carefully curated datasets with great potential. Collected through the decades, these large datasets have been used to provide us with snapshots of the lives we lead. In a topic as complex as ageing however, a snapshot is simply not enough. We live varied lives, have different experiences and different genetic endowments, and as such we age differently. To study our life trajectories we need to exploit the available datasets to their full extent. The primary limiting factor to such exploitation is the lack of appropriate statistical tools. For this project, we propose to design a suite of statistical tools, together with relevant user-friendly software, that will make the exploration of these elaborate datasets more efficient.
Our statistical tools build on the intuition that beneath all the ups and downs of an individual life there is a clock that measures the individual rate of ageing. Does everyone’s clock tick at the same rate? Is the rate predetermined or driven by random exigencies? We call this hidden clock that drives all the observable phenomena of ageing a “latent process”. The challenge is to work backward from what is observable to estimate the latent process. Although until now their usage has been mostly limited to medical applications, modern computational tools make it feasible to apply such methods to complex datasets, such as longitudinal studies.
Our aim is to work on three fronts:
1. Develop relevant methodology, with appropriate diagnostic tools and performance metrics, that can be used to answer a large range of scientific questions that focus on life histories and individual trajectories. We intend to make these tools available to researchers together with easy to follow tutorials and appropriate software.
2. Apply these tools to the complex topic of ageing. Using human and model organism datasets (such as guppies), we will test our developed methodology, and interpret the findings within the context of modern evolutionary theory. Our findings will then be disseminated both through scientific publications, through popular science press to the general public and relevant charities.
3. Use these tools to develop our understanding of genetic heritability and its variability as organisms senesce. Through the combination of phenotypic and genetic datasets we can estimate how the genetic impact of a particular character changes through a lifetime.
Technical Summary
Our goal is to operationalise core models, the “latent Markov process” models, from the mathematical theory of ageing by means of novel statistical methodologies. We also plan to develop statistical tools to help testing and calibrating the mutation-selection models central to the theory of ageing.
A key element of many theoretical treatments of ageing is a hidden “senescence” that determines characters such as an organism’s response to shocks and challenges, or its likelihood of reproducing. We aim to use sequential Monte Carlo methods to estimate this for individuals from complex data. The main objectives are:
1. To parcel out the variability in senescence among time-scales and population scales. In principle, individuals may differ in their initial condition, their inherent rate of ageing, the random shocks from which they suffer, and the age-related deterioration that they accumulate. Appropriate statistical models allow us to distinguish these different kinds of variation.
2. Filtering: We can distil complex longitudinal data into model-based estimates of simple senescence trajectories, that can be used as the basis for optimal prediction, or as phenotypes for genomic investigations.
3. To produce an accessible software package that will enable a wide range of researchers to fit latent-process models in their research.
4. To apply this software to problems of individual choice and population-level screening design.
Standard theories of the evolution of ageing depend on persistent genetic heterogeneity whose effect varies by age. It is difficult to identify such variants, much less to assess their overall contribution to the genetic load. We aim to meld a variety of standard survival models with random-effects models for heritability estimation to define age-dependent heritability and derive estimates. These methods will be applied to a variety ofdata, such as guppies, through our collaboration with the Guppy Project, and human longitudinal datasets.
Finally, an indirect but crucial impact of our work is that by designing tools to exploit the longitudinal surveys, that have been carefully collected and curated through decades of hard work, we help to make them available to their full potential for a wide range of research questions, from health policy making to initiative development for an ageing population.
Statistics and Biodemography 