MultiOmicsTox · Multi-view learning and quantitative genetics to identify the molecular basis of adaptation to chemical pollutants
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-05-01 → 2023-05-09
- EU contribution
- €224,934
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Multi-view learning and quantitative genetics to identify the molecular basis of adaptation to chemical pollutants
The existing methods for setting regulatory limits on hazardous chemicals in the environment currently overlook the impact of population genetic variability on toxicity endpoints. These methods rely on arbitrary adjustments to account for this variability, which can lead to either over-regulation or under-regulation. To ensure accurate environmental risk assessment, it is best to consider the evolutionary potential and adaptive capacity of natural populations. MultiOmicsTox is a project that measures the genetic basis of chemical toxicity at the population level, considering natural selection. This understanding is essential for the development of evidence-based environmental protection policies and regulations pertaining to chemicals. The primary objective of the MultiOmicsTox project was to explore toxicity response pathways and investigate their roles in the adaptation process, using the model species Daphnia. By addressing the knowledge gap regarding the influence of genetic diversity on population-level responses to chemicals, this project has contributed valuable insights to the fields of evolutionary and toxicological genomics.
Data: CORDIS, © European Union
Project objective
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.
Original text from CORDIS.
Participants
- THE UNIVERSITY OF BIRMINGHAM · BirminghamCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union