A phenome-wide study of ancestry-specific genetic heterogeneity in an admixed Mexican population

BACKGROUND

Genetic associations with complex traits and diseases have been shown to differ across global populations. Such heterogeneity in genetic effects may reflect differences in patterns of genetic variation or environmental interactions that exist within populations. Characterising and resolving the source of this heterogeneity is important to ensure methods that leverage genetic instruments (e.g. polygenic scores, Mendelian randomisation) are reliable across genetically diverse communities.

Contemporary populations with extensive admixture from two or more ancestral populations - such as those present in Latin America - provide unique and substantial opportunities to investigate ancestry-specific genetic heterogeneity. By resolving DNA segments inherited from distinct ancestral populations (i.e. 'local ancestry' inference), ancestry-specific genetic effects can be decomposed in a single cohort while controlling for sources of genetic confounding. 

The purpose of this DPhil project is to use data from the Mexico City Prospective Study, a large cohort with extensive genetic admixture from Indigenous American and Southern European ancestral populations, to:

• derive an atlas of local ancestry-informed genetic effects for a broad range of traits and diseases
• systematically assess genetic heterogeneity by ancestry
• explore the biological basis of observed heterogeneity through ancestry-specific colocalisation and Mendelian randomisation of molecular traits (e.g. gene expression)
• determine if observed genetic heterogeneity results from distinct patterns of gene-by-gene or gene-by-environment interactions.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will have the opportunity to engage in research that addresses the complexities of genetic admixture and its impact on health. The project will include gaining experience in a multidisciplinary setting, working across the areas of genetics, bioinformatics, epidemiology, and molecular biology, as well as developing programming skills using a wide range of software, bioinformatics analyses, and statistical programming. Support will be provided on a regular basis on programming and writing skills to publish related peer-reviewed papers.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based with the Mexico City Prospective Study group at the Big Data Institute. In-house training in statistical and epidemiological methods, programming, and scientific writing will be provided, and participation to in-house workshops and lectures will be expected. Based on needs, training in other institutions will be supported.

PROSPECTIVE  STUDENT

A background in statistics, epidemiology, genetics, biomedical sciences or related would be ideal. The candidate should be interested and keen to obtain advanced programming skills in a range of software (R, Python, Bash).