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MultiOmicsTox

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Multi view learning and quantitative genetics to identify the molecular basis of...

Multi view learning and quantitative genetics to identify the molecular basis of adaptation to chemical pollutants My project proposes to understand what genetic and functional genomic variation contribute to the process of adaptation and to the evolutionary fate of natural populations when confronted with modern threats, such as multi-generat... ver más

09/05/2023

UoB

225K€

Presupuesto del proyecto: 225K€

Líder del proyecto

THE UNIVERSITY OF BIRMINGHAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-05-09

MSCA-IF-2020: Individual Fellowships Objective:The goal of the Individual Fellowships is to enhance the creative and innovative potential...

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

UoB

224.93K€ | Lider

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Duración del proyecto: 24 meses Fecha Inicio: 2021-04-19
Fecha Fin: 2023-05-09

Líder del proyecto

THE UNIVERSITY OF BIRMINGHAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 225K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto My project proposes to understand what genetic and functional genomic variation contribute to the process of adaptation and to the evolutionary fate of natural populations when confronted with modern threats, such as multi-generational exposure to a chemical pollutant in the environment. The current environmental health policies and regulatory decisions are based on ad hoc methods and do not reflect true population susceptibility. My solution is to apply multi-view machine learning, combined with quantitative genetics, to analyse a huge volume of multi-omics data to advance Precision Toxicology that brings greater certainty in the causal links between chemicals and their adverse effects. My project focuses on the multi-generational effect of pesticides in shaping genetic variation and the molecular evolutionary trajectory of Daphnia obtained from resurrected subpopulations from within dated lake sediments spanning 120 years. The adaptive phenotypes at different doses of pesticides were scored in common garden experiments and samples were taken to produce associated multi-omics data (genomes, transcriptomes, regulomes and metabolomes). I propose utilizing this data to meet the following two objectives:(1) To use quantitative genetics for the determination of genetic susceptibility of the subpopulation to pesticide exposure; (2) To identify the mechanisms and forms of evolution that result in adaptation, by integrating multi-omics data using multi-view machine learning. Expected outcomes of this work will (a) fill a gap in mechanistic understanding of the adaptive responses of natural populations, (b) identify segregating genetic variation within genomes that regulates the pace and magnitude of an adaptive response to chemical pollutants, and (c) discover putative biomarkers that estimate exposure-related genetic susceptibility of populations to the multi-generational harmful effects of chemicals for setting site-specific controls on chemical pollutants.

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