Project No. 2116

Primary Supervisor

Dr Marta Farré – University of Kent

Co-Supervisor(s)

Dr Suzanne Litthauer – NIAB-EMR

Dr Alessia Buscaino – University of Kent

Summary

Structural genomic variants (SVs) have been studied going back to the discovery of chromosomal inversions in Drosophila in the early 20th century.

Yet, this rich knowledge from comparative genetics and cytogenetics has been widely overlooked since the ‘70s. Researchers attention moved to molecular markers that quantify patterns defined by one or few single nucleotide polymorphisms (SNPs). Thanks to the introduction of nextgeneration sequencing (NGS), mounting evidence suggests that SVs are taxonomically ubiquitous and key contributors to a multitude of evolutionary processes, including adaptation to salinity in fishes and flowering time in wheat. However, the correct detection of SVs is still a prominent issue in these studies because the resolution of the current methodologies allows only the detection of short SVs. The introduction of affordable long-read sequencing in the past couple of years opened up research into SVs. Therefore, now it is the right time to develop and implement a new methodology to detect short and long SVs in non-model species.

In this project, the student will develop a new approach to detect SVs in plant and animal genomes using state-of-the-art long-read Oxford Nanopore sequencing. Focusing on cattle breeds adapted to hot environments and members of the Rosoideae sub-section of the Rosaceae in plants (strawberry, raspberry, blackberry and potentilla species), the student will study the impact of SVs in these adaptations. First, s/he will optimise DNA extraction and sequencing protocols to determine the depth of coverage needed to detect SVs. S/he will then develop new bioinformatic analysis protocols to finally identify the role of SVs in both systems.

This project will not only produce important biological insights, the methodology developed during this project will have a great impact in the agricultural and farm sectors by being able to detect SVs in a rapid and affordable manner for livestock and horticultural species.