Principal Investigator: Dr Albert Tenesa
Department: Roslin Institute
Institution: University of Edinburgh
University of Edinburgh, Roslin Institute, Easter Bush, Roslin, EH25 9RGTags: 7908, cognition, genetics, GWAS, heritability, prediction
1a: The aim of the current study is to gain further understanding of the genetic factors that underlie human cognition and cognitive decline by identifying the genes that contribute to variation in cognitive function in the UK Biobank. The project will correlate genetic and phenotypic variation to identify genes that contribute to memory and processing speed (pairs matching, prospective, numeric, light pattern memory, reaction time and fluid intelligence), and develop predictors of cognitive impairment based on genetic markers. 1b: Cognitive impairment is a major health and social issue in ageing populations. Age-related cognitive decline is costly to the individual, his relatives and society in general. It represents a major financial burden to the health services and often precedes dementia, illness or death. Individual variation in cognitive ageing is partly genetic and partly environmental. About 50% of the cognitive variation is genetic. Identifying the genes that contribute to cognitive ageing would allow developing better prediction models of cognitive impairment, thereby facilitating early intervention; and better understanding of the molecular basis of disease that could eventually provide better or new treatments. 1c: Cognitive measurements and their change will be compared to the genetic variations measured from blood DNA. We expect that variation in DNA within, or nearby, genes relevant to cognitive function will correlate with differences in cognitive function among individuals. We will use statistical methods to test if any of the hundreds of thousands of single nucleotide polymorphisms (SNPs), a type of genetic variation, measured in the UK Biobank is associated with changes in cognition. 1d: Subset of the cohort with cognitive measurements that had cognitive measurements at recruitment.
Genes and environmental exposures determine susceptibility to common diseases such as diabetes or cancer. The relative contribution of genes to disease risk is known as the heritability. Heritability is often estimated using twin pairs. However, heritability estimates obtained from twins have limitations that could be overcome by using sibling and parental information on disease. That is, a person?s family history. We will calculate heritability by comparing the disease frequency among relatives to the frequency in the general population. We will use the full UKbiobank cohort data on family history to estimate the heritability of a broad range of medical conditions.
We are developing methods to perform GWAS of multiple traits using multivariate mixed linear models.
We will analyse data fields 21011-21016 and generated a set of novel data fields (i.e. phenotypes) characterising the structure and function of the retinal microvasculature in the scans. We will leverage the VAMPIRE software package to segment the blood vessels in fundus images (fields 21015-21016) and measure established structural phenotypes (e.g. vessel calibre, tortuousity, branching angle). Based on these segmented images, we will develop mathematical models of blood flow to estimate, for the first time, functional phenotypes such as vessel resistance to flow, flow rates, and pressure drops across the retinal vascular tree. We will develop novel approaches to quantify vascular structural phenotypes in OCT images (fields 21011-21014) such as vessel density, vessel calibre, and relative position within the different retinal layers. All the structural and functional phenotypes will be returned to UK Biobank as new data fields.
Last updated Feb 2, 2019