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Multi-view learning and quantitative genetics to identify the molecular basis of adaptation to chemical pollutants

Project description

Adaptation of natural populations to pesticides

The use of pesticides in agriculture is common practice, but little is known about the long-term effects it has on off-target species such as freshwater zooplankton. The EU-funded MultiOmicsTox project aims to investigate how the prolonged presence of chemicals in the environment for many generations alters the genetic variation and affects molecular evolution. Researchers will study the evolution of Daphnia over a period of 120 years and link molecular changes with pesticide exposure. This will provide important insight into the true susceptibility of populations to pesticides and their adaptive responses. Long term, the generated information will support the formulation of appropriate health and environmental policies.


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.



Net EU contribution
€ 224 933,76
B15 2TT Birmingham
United Kingdom

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West Midlands (England) West Midlands Birmingham
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00