PhD Research Project: Automated linking of genotype and environmental covariates to predict the eff

The recent explosion in wild populations’ genomic data enables vast improvements in our understanding of how genotypes affect a wide range of biological phenomena. Research has successfully linked genotypes to longitude and latitude coordinates (Elhaik et al. 2014, Nature Communications), but to understand the role of phenotypic plasticity in adaptation to different geographical situations, it is important to link genotypes with other environmental-geographical factors, such as altitude, habitat type, and climate.

We will build on our models (e.g., Elhaik et al. 2014a; Das et al. 2016; Marshall et al. 2016): by 1) Widening the genotype-environment link to incorporate a variety of published geophysical, ecological and meteorological data, 2) Making them species independent, 3) extending them to haplotype-level. The resulting gene-landscape correlations will enable us to predict which individuals are more likely to survive under landscape- and climate-change scenarios, therefore determine the likely evolutionary path a population would take.

This project will exploit existing two major genomic/environmental/phenotypic datasets for humans and Medicago truncatula. It will run alongside Elhaik’s recently funded NSF/MRC/Industry grants for developing tools to infer human ancestry from genomics. The student will collaborate with anthropologists at Central Washington University and develop highly demanded cutting-edge bioinformatics tools.

A brief description of the supervisors and their roles in the proposed project

Eran Elhaik is a computational biologists in The Bioinformatics Hub and The Department of Animal and Plant Sciences at the University of Sheffield, whose research focusses on complex traits (or disorders) and population genetics. As the primary PI, his role will be in training the student in genetics and introduce relevant bioinformatics tools and methods necessary to carry out the tasks. He has much experience analyzing non-human (Elhaik, Pellegrini, and Tatarinova 2014) and human (Elhaik et al. 2014a; Elhaik et al. 2014b) population genetic data and studying complex disorders (Elhaik and Zandi 2015).

How to apply: Go to http://www.sheffield.ac.uk/postgraduate/research/apply/applying after reading the information contained on that page click the link to the Postgraduate online application form

Funding Notes

This project will be funded by the Leverhulme Trust Centre for Advanced Biological Modelling