Natural selection during recent phenotypic evolution in domesticated species

Theme: Evolution & Adaptation

Primary Supervisor:

Matteo Fumagalli

School of Biological and Chemical Sciences, QMUL

Matteo Fumagalli's Profile Picture

Secondary Supervisor:

Laurent Frantz

School of Biological and Chemical Sciences, QMUL

Laurent Frantz's Profile Picture

Project Description:

Understanding how species adapt to rapid changes in their environment is critical for biodiversity conservation strategies. The importance of adaptive evolution during rapid phenotypic evolution, however, is still heavily debated. In fact, detecting selection in the genome of an organism that has underwent large demographic changes (i.e. bottleneck or expansion) remains challenging. This make it difficult to predict how species may be able to cope with rapid changes such a climate change.

Recently, we successfully deployed artificial intelligence, and in particular deep learning, algorithms to detect signatures of natural selection from population genomic data of contemporary samples. Additionally, in recent years, the generation of sequencing data from ancient and historical samples allow for a direction observation of how genetic diversity and allele frequencies change over time.

This project aims to infer signatures of selection from population genomic data of contemporary and ancient samples of domesticated species using machine learning.

The overarching aim is to shed novel insights onto the biological mechanisms underpinning rapid genetic adaptation in domesticated species. The student will use available published and unpublished data from species that underwent complex demographic history and recent adaptive evolution, including wild and domestic bovids, suids and canids.

Policy Impact of Research:

From these analyses, it will be possible to discern genes under selection in the past (e.g., during domestication or during ancient climatic events) from genes affected by recent selective processes (e.g., during deforestation, or as a result of artificial selection) to inform on molecular monitoring strategies.


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