SOURCE-LINKED INTELLIGENCE
Mapping the behavioural causes of weight change variability with genetic lottery
ictors or intervention targets. To move the field forward, we propose a novel 3-step OBECAUSE pipeline consisting of consolidation, genomic causation, and validation. 1) In consolidation, we will use machine learning to find best-predicting PEBBL measures in several large-scale weight loss datasets. The PEBBL measures will be integrated into a new PEBBL short questionnaire with wide coverage and good psychometric properties. The questionnaire will be then distributed to all participants of Estonian Biobank to study the genomics of PEBBL. 2) For genomic causation, we will detect genetic variants behind PEBBL measures and weight change. Knowing these variants enables discovering additions to the PEBBL framework through genetic correlations and functional mapping. Importantly, as genetic variants are randomised through genetic lottery, they enable systematic causal mapping of PEBBL measures that have causal effects on weight change. 3) For validation, these causal measures will be used as inputs to design an OBECAUSE toolbox of weight loss interventions. The value of these interventions
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 1497500
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T02:21:08.944Z. This is not the publication date.