Genome-wide association analysis of anthropomorphic traits
Project reference: NDPH/MT16/026
Many physical traits (e.g. body mass index, waist circumference) are established risk factors for human diseases including diabetes, stroke and cancer. These traits have a substantial genetic component, but only a small proportion of this is explained. Even for known genetic associations, in the large majority of cases the causal basis is unknown. Previous studies of the genetics determinants of anthropomorphic traits have mainly involved cohorts of European descent, with much less known about other ethnicities.
The China Kadoorie Biobank Study (CKB, www.ckbiobank.org) of over 0.5 million adults includes extensive questionnaire-based health and lifestyle data and detailed physical measurements at baseline, and prospective follow-up for incident events from disease registries and health insurance data. The cohort is currently undergoing genome-wide genotyping (>100,000 individuals by mid-2016).
Research experience, research methods, and training
The project will involve experience and training in literature review, study design and planning, novel analytic methods, and statistical analysis, interpretation and reporting of large-scale multi-dimensional ‘omics data. It will include:
Genome-wide association analysis of anthropomorphic traits in CKB, including experience in all stages of data QC, genotype imputation and association analysis.
Meta-analysis of association data from multiple studies, including involvement in international consortia such as GIANT (www.broadinstitute.org/collaboration/giant/).
Construction of genetic risk scores for different traits, and investigation of their association with risk of disease.
Field work, secondments, industry placement, and training
In-house training in statistical and computational genetics. Attendance at relevant courses including the Wellcome Trust course “Design and Analysis of Genetic-based Association Studies”.
In addition to the supervisor noted above, this project will be co-supervised by Professor Mark McCarthy of the Wellcome Trust Centre for Human Genetics
Candidates should have a 2.1 or higher degree in genetics, statistics and/or computational biology, with an interest in the causes of human disease.