KT
K.N.I. Timmerman
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1
Mutational signatures in the general population
Population-scale mutational signature analysis of blood-derived genomes from the UK Biobank
Mutational processes leave characteristic patterns of somatic mutations, traditionally studied in tumour tissue. Far less is known about whether they can be observed in normal tissue, particularly blood. Detecting the mutational imprint of disease-associated processes there could enable earlier detection and intervention. Here, we investigate whether the mutational signal detected in blood can be explained by biological and clinical factors, in particular age, DNA repair deficiencies, and cancer diagnoses. Using whole-genome sequencing of blood-derived DNA from 17,419 UK Biobank participants, we developed a filtering strategy to isolate somatic mutations and analysed four views of the mutational landscape: mutation burden, mutation channel composition, exposures to de novo signatures and exposures to COSMIC signatures. We modelled their relation to these factors using regression analysis. Across all four views, sequencing provider was the dominant predictor, far outweighing other predictors. Among non-technical predictors, BRCA (p = 0.024) and POLE (p = 0.030) variants were significantly associated with a higher mutation burden. A leukaemia diagnosis was the strongest signal across the remaining views, appearing in both the mutation channel composition and the exposure to the clock-like signature SBS1 (p = 2.10e-06). De novo signature exposures clustered by sequencing provider, and no association survived when extraction was performed separately for each provider. Our results show that some biological and clinical factors do explain part of the mutational signal in blood, but that technical variation between sequencing providers dominates the mutational landscape and must be addressed before blood can serve as a reliable substrate for mutational analysis.
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Mutational processes leave characteristic patterns of somatic mutations, traditionally studied in tumour tissue. Far less is known about whether they can be observed in normal tissue, particularly blood. Detecting the mutational imprint of disease-associated processes there could enable earlier detection and intervention. Here, we investigate whether the mutational signal detected in blood can be explained by biological and clinical factors, in particular age, DNA repair deficiencies, and cancer diagnoses. Using whole-genome sequencing of blood-derived DNA from 17,419 UK Biobank participants, we developed a filtering strategy to isolate somatic mutations and analysed four views of the mutational landscape: mutation burden, mutation channel composition, exposures to de novo signatures and exposures to COSMIC signatures. We modelled their relation to these factors using regression analysis. Across all four views, sequencing provider was the dominant predictor, far outweighing other predictors. Among non-technical predictors, BRCA (p = 0.024) and POLE (p = 0.030) variants were significantly associated with a higher mutation burden. A leukaemia diagnosis was the strongest signal across the remaining views, appearing in both the mutation channel composition and the exposure to the clock-like signature SBS1 (p = 2.10e-06). De novo signature exposures clustered by sequencing provider, and no association survived when extraction was performed separately for each provider. Our results show that some biological and clinical factors do explain part of the mutational signal in blood, but that technical variation between sequencing providers dominates the mutational landscape and must be addressed before blood can serve as a reliable substrate for mutational analysis.
What would Jiminy Cricket do?
A pluralist approach in generating and processing morally-aligned text
When making decisions, people are automatically guided by their moral compass. However, AI agents need to be conditioned in order to be steered towards moral behaviour. An environment that can be used to train and test agents is the Jiminy Cricket environment. The Jiminy Cricket environment consists of a set of text-based narrative games, where every action possible is annotated with the morality of that action. However, to create a more morally nuanced agent, we have annotated all of the actions according to the following moral values: Care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. To morally condition the agent, we calculate the predicted progress of a potential action and combine it with an oracle to retrieve the moral annotation of the potential action. Using both of these components, the score per generated action is calculated and based on the score the eventual action is chosen. The score can be calculated differently based on the weights assigned to the overall progress and morality, as well as based on the sub-weights assigned to each moral value. Using this environment we pose the question, if we focus on only one moral value, what is the most optimal configuration that can be achieved in order to maximize both progress and morality? From the results we can observe that the lowest relative immorality can be achieved by imposing no moral constraints on the agent. Posing constraints on the agent will lead to a relatively bigger decrease of the completion percentage than to the immorality decrease. One-hot encoding the moral values will reveal which immoral actions are needed to progress in the game, and which immoral actions should to be prevented to lower immorality.
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When making decisions, people are automatically guided by their moral compass. However, AI agents need to be conditioned in order to be steered towards moral behaviour. An environment that can be used to train and test agents is the Jiminy Cricket environment. The Jiminy Cricket environment consists of a set of text-based narrative games, where every action possible is annotated with the morality of that action. However, to create a more morally nuanced agent, we have annotated all of the actions according to the following moral values: Care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. To morally condition the agent, we calculate the predicted progress of a potential action and combine it with an oracle to retrieve the moral annotation of the potential action. Using both of these components, the score per generated action is calculated and based on the score the eventual action is chosen. The score can be calculated differently based on the weights assigned to the overall progress and morality, as well as based on the sub-weights assigned to each moral value. Using this environment we pose the question, if we focus on only one moral value, what is the most optimal configuration that can be achieved in order to maximize both progress and morality? From the results we can observe that the lowest relative immorality can be achieved by imposing no moral constraints on the agent. Posing constraints on the agent will lead to a relatively bigger decrease of the completion percentage than to the immorality decrease. One-hot encoding the moral values will reveal which immoral actions are needed to progress in the game, and which immoral actions should to be prevented to lower immorality.