Multi-omics prediction of cognitive decline, Alzheimer’s disease, and death
Project Details - Multi-omics prediction of cognitive decline, Alzheimer’s disease, and death
|Supervisor(s): Dr Riccardo Marioni, Prof Ian Deary & Ass Prof Sara Hägg|
|Centre/Institute: Centre for Genomic and Experimental Medicine, IGMM|
The social and economic impact of Alzheimer’s disease (AD) makes it a global priority for health and policy research. This is becoming increasingly important as life expectancies rise. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. However, chronological age remains the biggest risk factor for AD.
This project will integrate multiple biomarkers of ageing with cognitive and brain changes across the 8th decade to stratify individuals into risk groups for AD.
We will leverage omics data from consortia projects and a large local dataset, Generation Scotland (n=24,000), to investigate causal pathways using the latest biostatistical methods applied to big data in epi/genetic epidemiology.
The primary analysis dataset is the Lothian Birth Cohort 1936, a uniquely-richly phenotyped cohort with longitudinal multi-omics and health data. Participants (n=1,091) were born in 1936, completed an IQ test at age-11 years, along with four waves of physical, cognitive, pyscho-social, health and biological collection at ages 70, 73, 76, and 79 years. Biological data include four waves of leukocyte telomere length and genome-wide DNA methylation, three waves of structural brain imaging, and two waves of gene expression. There are also data available on glycomics and lipidomics at age-70 baseline, and whole-genome sequencing on all individuals.
The power of the LBC1936 study is in its longitudinal data. It is rare to have deeply-phenotyped and multi-omics cross-sectional data let alone repeated measures, particularly at four time points across the whole of eighth decade of life when the incidence of AD rises exponentially.
To gain a deeper understanding of biological ageing and its associations with cognitive, brain, and health changes over the eighth decade.
In the LBC1936 and beyond, we have shown DNA methylation-based biological clocks to correlate with health and disease phenotypes (Marioni et al. 2015) and to be independent of other biological clocks, such as brain age and telomere length (Cole et al. 2017, Marioni et al. 2016).
The number of CpG outliers, defined as data points more than 3 times the inter-quartile range from the the 25th/75th percentiles, have been observed to increase exponentially with age in cross-sectional data (Gentilini et al. 2015). It is currently unknown how this epigenetic-outlier burden changes longitudinally within individuals and if it correlates with biological clock measures and age-related health outcomes.
This project will examine:
Where possible, we will replicate our epigenetic-outlier burden findings from LBC1936 in an additional longitudinal cohort, the Swedish Adoption/Twin Study of Aging (SATSA). SATSA has collected information over 30 years’ time with up to 10 waves of health questionnaire data and cognitive testing occasions. DNA methylation data exist for 550 individuals at up to 6 occasions.
Project extensions include the application of machine learning methods (elastic net/LASSO) to derive new multi-omic predictors of ageing and disease. We can also consider approaches such as Mendelian Randomisation to test for the causal association associations between our biological clock measures and health/disease outcomes.
The student will join one of the world's leading research groups in genetic and epigenetic epidemiology. The Wray/Visscher labs at the University of Queensland co-fund and co-direct our epigenetics work, providing additional, cutting edge statistical genetics expertise. The analyses on the SATSA cohort data will be carried out at the Karolinska Institutet, at the Department of Medical Epidemiology and Biostatistics, where the student will receive excellent training in epidemiology and be part of a research group in Molecular Epidemiology of Ageing (Dr. Hägg’s lab).
The successful candidate will receive training in biostatistics, with a particular focus on longitudinal analysis of omics datasets, in addition to working with the latest methods in ageing epidemiology.
We also collaborate closely with Steve Horvath (UCLA), who helped pioneer the development of the epigenetic biological clock, and Caroline Relton (Bristol), whose team leads the Go-DMC consortium and are experts in causal inference (Mendelian Randomisation) in epigenetic epidemiology.
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- The deadline for 18/19 applications is 5pm on Wednesday 10th January 2018.
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