Leveraging genetic admixture and relatedness to advance rare variant discovery in the Mexico City Prospective Study

BACKGROUND

Genetic studies of rare damaging variants have revealed insights into the genetic basis of complex traits and diseases. However, these studies have mostly been conducted in populations of European descent and do not adequately capture risk variants present in other populations. Therefore, studies in diverse cohorts with large samples are needed.

The Mexico City Prospective Study (MCPS) is the largest blood-based prospective study in a Latin American country that includes 150,000 adult participants. Genetic data is available and includes genotyping and exome sequencing for the entire cohort, and whole genome sequencing on a subset of 10,000 participants.

Genetic analysis has revealed complex patterns of genetic relatedness and Mesoamerican admixture in the MCPS cohort, which present unique opportunities for advancing the discovery of rare variants that influence disease. 

The aim of this DPhil will be to leverage genetic features of MCPS to improve power of rare-variant analysis. Although the specific aims are subject to discussion and student interests, the project could include the following work: 

• Perform admixture mapping to identify genomic regions where DNA segments inherited from an ancestral population are significantly associated with disease
• Map “runs of homozygosity” (ROH) (i.e. contiguous regions of homozygous genotypes resulting from relatedness) and perform region-based tests
• Resolve genomic regions with pronounced identity-by-descent (IBD) sharing and identify IBD associations with disease.
• Leverage results from admixture, ROH and/or IBD mapping to improve power to implicate disease-relevant genes through rare variant association tests

Results from these analyses could be further combined with those from other large cohorts (e.g. UK Biobank) and engender downstream multi-omic analyses to resolve biological pathways and drug targets.  

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project will involve statistical analyses of large-scale genomic datasets on a high-performance computing cluster. The student will have opportunities to receive specialised training and present their work at internal and international meetings. The student will be encouraged to author peer-reviewed articles and participate in scientific collaborations.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based with the MCPS group at the Oxford Big Data Institute. On-site training opportunities will focus on deepening understanding of key concepts in genetic epidemiology, and developing practical skills in programming and high-performance computing.

PROSPECTIVE  STUDENT

The ideal candidate will have a passionate interest in human genetics, epidemiology and statistics, with relevant research experience completed as part of a Master's degree programme, research assistantship, or other role. A track record of self-directed work and a demonstrated ability to learn and apply new skills will further distinguish the candidate.