The prevalence of clonal haematopoiesis of indeterminate potential (CHIP) in healthy individuals increases rapidly from age 60 onwards and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in stem cells that are also drivers of myeloid malignancies. Since mutations in stem cells often drive leukaemia, we hypothesised that stem cell fitness substantially contributes to transformation from CHIP to leukaemia. Stem cell fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. We set out to quantify the fitness effects of CHIP drivers over a 15 year timespan in older age, using longitudinal error-corrected sequencing data. It is currently unknown whether mutations in different CHIP genes lead to distinct fitness advantages that could form the basis for patient stratification. We developed a new method based on drift-induced fluctuation (DIF) filtering to extract fitness effects from longitudinal data, and thus quantify the growth potential of variants within each individual. Our approach discriminates naturally drifting populations of cells and faster growing clones, while taking into account individual mutational context. We show that gene-specific fitness differences can outweigh inter-individual variation and therefore could form the basis for personalised clinical management.

We present a blood-based epigenome-wide association study and variance-components analysis of cognitive functions (n=9,162). Individual differences in DNA methylation (DNAm) accounted for up to 41.5% of the variance in cognitive functions; together, genetic and epigenetic markers accounted for up to 70.4% of the variance. A DNAm predictor accounted for 3.4% and 4.5% (P≤9.9x10-6) of the variance in general cognitive ability, independently of a polygenic score, in two external cohorts.

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvath’s across-tissue age predictor.

Identifying the biological correlates of late life cognitive function is important if we are to ascertain biomarkers for, and develop treatments to help reduce, age-related cognitive decline. This study investigated the associations between plasma levels of 91 neurology-related proteins (Olink® Proteomics) and general fluid cognitive ability in the Lothian Birth Cohort 1936 (LBC1936, N=798), the Lothian Birth Cohort 1921 (LBC1921, N=165), and the INTERVAL BioResource, (N=4,451). In LBC1936, we also examined mediation of protein-cognitive ability associations by MRI-derived indices of brain structure. In the LBC1936, 22 of the proteins and the first principal component (PC) created from a PC analysis of the 91 proteins, were associated with general fluid cognitive ability (β between -0.11 and -0.17, p<0.0029).Total brain volume partially mediated the association between 10 of these proteins and general fluid cognitive ability. Effect sizes for the 22 proteins, although smaller, were all in the same direction as in LBC1936 in an age-matched subsample of INTERVAL. Similar effect sizes were found for the majority of these 22 proteins in the older LBC1921. The associations were not replicated in a younger subset of INTERVAL. In conclusion, we identified plasma levels of a number of neurology-related proteins that were associated with general fluid cognitive ability in later life, some of which were mediated by brain volume.