Biology subjects

Escabi, J.

Publications and source records attributed to Escabi, J..

3 recordsLinked to original sources

bioRxiv2025

Robustness of Selection and Timing Inference under Model Variation in Population Genetics

1In population genetics, accurately inferring the selection coefficient and the time of onset of advantageous mutations from genetic data is fundamental for understanding evolutionary processes. Here, we investigate how mismatches between the true evolutionary process and the inference model--specifically in the reproductive variance ({sigma}2) and the number of generations (L)--affect the posterior distributions of the selection coefficient and the time of onset. Using the Kolmogorov forward and backward equations, we model the stochastic dynamics of gene frequencies under selection and drift. We show that while the posterior distribution of the selection coefficient remains unaffected by changes in{sigma} 2 and L, this invariance does not apply to the time of onset. By framing the problem as a first passage time issue, we derive explicit expressions for the offsets in the posterior mean and variance of the time of onset that result from incorrect assumptions about{sigma} 2 and L. Our analysis reveals that these offsets are related to the mean and variance of the first passage time required for the allele frequency to reach a certain threshold, starting from an initial frequency determined by the model parameters. Under the assumption of a uniform prior for the time of onset, we find that the offset in the inferred mean is given by the difference in the effective generation duration ({Delta} = 1/{sigma}2) between the true process and the inference model. We validate our theoretical findings through simulations, demonstrating that the empirical offsets closely match our predictions. Furthermore, we generalize our results to accommodate non-uniform prior distributions, such as exponential priors, and provide numerical methods for calculating offsets under arbitrary priors. Stochastic fluctuations due to genetic drift, which are influenced by the reproductive variance and generational structure, can introduce significant biases in the posterior distribution of time of onset of advantageous mutations. By quantifying these biases, our framework enables more accurate adjustments to inferences drawn from genetic data, thereby enhancing our understanding of evolutionary dynamics and improving the reliability of population genetic analyses.

genetics↗

bioRxiv2022

Fitness of a clonal population can be inferred from lineage trees without knowledge of the biological details

Inferring the rate at which a clonal population grows, or its fitness, is important for many biomedical applications. For example, measuring the fitness of mutated cells in a patient with cancer may provide important information about prognosis and treatment. Similarly, measuring the fitness of new viral strains that emerge during a pandemic can inform how to plan an effective response. In previous work, the lineage trees constructed from individuals randomly sampled from the population at the final time-point have been used to infer the fitness and the times at which the mutation providing the fitness advantage arose in a diverse set of systems, such as blood cancers [1], [2] and the influenza virus [3]. However, it is not clear to what extent the inferred values depend on the exact biological details assumed in the models used for the inference. In this paper we show that coalescent statistics of lineage trees are invariant to changes in key parameters underlying the expansion, such as the distribution of the number of progenies produced by each individual and heterogeneity in the expansion rate. In addition, we show that competition between drift and selection imply that the fitness of the mutated population and when the mutation occurred can be inferred without knowledge of the mutation rate per generation even though the population size itself cannot be inferred. Lastly, we show that our results also generalize to cases where multiple competing mutations result in multiple distinct subclones with different values of fitness. Taken together, our results show that inferring fitness from lineage trees is robust to most model assumptions.

evolutionary biology↗

bioRxiv2020

Reconstructing the lineage histories and differentiation trajectories of individual cancer cells in JAK2-mutant myeloproliferative neoplasms

Some cancers originate from a single mutation event in a single cell. For example, blood cancers known as myeloproliferative neoplasms (MPN) are thought to originate through the acquisition of a driver mutation (most commonly JAK2-V617F) in a hematopoietic stem cell (HSC). However, when the mutation first occurs in individual patients and how it impacts the behavior of HSCs in their native context is not known. Here we quantified the impact of the JAK2-V617F mutation on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual MPN patients. We reconstructed the lineage history of individual HSCs obtained from MPN patients using the patterns of spontaneous somatic mutations accrued in their genomes over time. Strikingly, we found that the JAK2-V617F mutation occurred in a single HSC several decades before MPN diagnosis -- at age 9{+/-}2 years in a 34-year-old patient, and at age 19{+/-}3 years in a 63-year-old patient. For each patient, we inferred the number of mutated HSCs over time and computed their fitness. The population of JAK2-mutated HSCs grew exponentially by 63{+/-}15% and 44{+/-}13% every year in the two patients, respectively. To contrast the differentiation trajectories of the JAK2-mutated HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single hematopoietic stem and progenitor cells (HSPCs). We found that the fraction of JAK2-mutant HSPCs varied significantly across different myeloid cell types within the same patient. The erythroid progenitor cells were often entirely JAK2-mutant, even when the peripheral blood JAK2-V617F allele burden was low. The novel biological insights uncovered by this work have implications for the prevention and treatment of MPN, as well as the accurate assessment of disease burden in patients. The technology platforms and computational frameworks developed here are broadly applicable to other types of hematological malignancies and cancers.

cancer biology↗