Principal Investigator: Dr Albert Tenesa
Department: Roslin Institute
Institution: University of Edinburgh
University of Edinburgh
Roslin, EH25 9RG
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
UK Biobank cohort data on family history to estimate the heritability of a
broad range of medical conditions. We will use information on smoking,
anthropometric and reproductive factors to understand to what degree
these risk factors are determined by genetics; and to what degree genes
influencing risky behaviours (e.g. smoking) and diseases are shared. In
addition, we will estimate the heritability of reproductive fitness (a
measure of natural selection).Estimates of heritability are important
because they set the potential utility of genetics to predict disease risk.
Stratification of the population by their level of risk would allow tailoring
the level of medical intervention to the level of risk and facilitate early
PROJECT EXTENSION – approved 16/08/2017:
“I would like to request a retrospective extension of project 788 to include all the traits included in projects 788, 4939, 6648, 7908 and 8447. The aims of the project extension are to:
1. Estimate heritability, genetic and environmental correlations for these traits.
2. Perform a GWAS for these traits.
3. Present the summary statistics in a database for the research community to query and download. We have currently analysed the traits described in the attached excel file.
4. To impute tissue-specific intermediate traits (e.g. using a reference panel of gene expression and methylation we have generated or obtained from public resources) and correlate them with disease status (yet to be done).
5. Present the summary statistics of the results in (4) in a database for the research community to query and download.
6. To write one or two papers with a few descriptive statistics of the data (similar to the one shown in the paper seen by UK Biobank).
7. Return to UK Biobank results from 3, 4, and 5.”