Daniel W Belsky | Search bioRxiv

bioRxiv2016

Telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: Do they measure the same thing?

The geroscience hypothesis posits that therapies to retard biological processes of aging can prevent disease. To test such \"geroprotective\" therapies in humans, surrogate endpoints are needed for extension of disease-free lifespan. Methods to quantify biological aging could provide such surrogate endpoints, but different methods have not been systematically evaluated in the same humans. We studied seven measures of biological aging in 964 middle-aged humans in the Dunedin Study: telomere-length, three epigenetic-clocks, and three biomarker-composites. Agreement between these different measures of biological aging was low. We also compared associations between biological aging measures and outcomes that geroprotective therapies will seek to modify: physical functioning, cognitive decline, and subjective signs of aging. The 71-CpG epigenetic clock and the biomarker composites were consistently related to these outcomes. Effect-sizes were modest. Quantification of biological aging is a young field. Next steps are to move toward systematic evaluation and refinement of methods.

Epidemiology↗

bioRxiv2016

Mortality Selection in a Genetic Sample and Implications for Association Studies

Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection--the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection--has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.

Genetics↗