Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes

bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-04 · 1 citations

Abstract Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics largely lacks theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework, we show how Markov decision processes (MDPs) can be applied to jointly model a population’s dynamics along with the sampling process. Critically, these MDPs allow us to compute the expected long-term value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies. To illustrate our framework, we develop MDPs for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the MDP allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing costs.

Experimental Evolution of <i>Phytophthora infestans</i> on Tomato Reveals Rapid Genotypic and Phenotypic Adaptation and Dynamic RXLR Genome Variation

Phytopathology · 2025-05-05 · 1 citations

The late blight pathogen, Phytophthora infestans, poses a significant threat to tomato crops worldwide. To understand the potential for rapid adaptation of this pathogen, we conducted an in vitro experimental evolution study with four P. infestans US-23 lineages collected from tomato hosts with different combinations of resistance genes ( Ph genes). These isolates were passed serially over eight generations on five tomato cultivars. After infection, targeted sequencing of the pathogen's RXLR effector genes was done. In just eight generations, we observed both phenotypic and genotypic changes in the US-23 lineages, with differences in disease severity among pathogen isolates and alterations in the RXLR genome. Our findings suggest rapid mutation even in a clonally reproducing lineage, highlighting the potential for adaptation of P. infestans within a single growing season on tomato. These insights shed light on the adaptability of this devastating pathogen and emphasize the importance of considering tomato host resistance in late blight management strategies.

Phylodynamics beyond neutrality: the impact of incomplete purifying selection on viral phylogenies and inference

Philosophical Transactions of the Royal Society B Biological Sciences · 2025-02-13 · 3 citations

Viral phylodynamics focuses on using sequence data to make inferences about the population dynamics of viral diseases. These inferences commonly include estimation of growth rates, reproduction numbers and times of most recent common ancestor. With few exceptions, existing phylodynamic inference approaches assume that all observed and ancestral viral genetic variation is fitness-neutral. This assumption is commonly violated, with a large body of analyses indicating that fitness varies substantially among genotypes circulating in viral populations. Here, we focus on fitness variation arising from deleterious mutations, asking whether incomplete purifying selection of deleterious mutations has the potential to bias phylodynamic inference. We use simulations of an exponentially growing population to explore how incomplete purifying selection distorts tree shape and shifts the distribution of mutations over trees. We find that incomplete purifying selection strongly shapes the distribution of mutations while only weakly impacting tree shape. Despite incomplete purifying selection shifting the distribution of deleterious mutations, we find little discernible bias in estimates of viral growth rates and times of the most recent common ancestor. Our results reassuringly indicate that existing phylodynamic inference approaches that assume neutrality may nevertheless yield accurate epidemiological estimates in the face of incomplete purifying selection. More work is needed to assess the robustness of these findings to alternative epidemiological parametrizations.This article is part of the theme issue ''"A mathematical theory of evolution": phylogenetic models dating back 100 years'.

Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes

Genetics · 2025-11-11

Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet, predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics and related fields such as phylodynamics and phylogeography largely lack theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework based on Markov decision processes, we model how sampling interacts with a population's demographic history to shape the ancestral or genealogical relationships of sampled individuals. By probabilistically considering these ancestral relationships, we can use Markov decision processes to predict the expected value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies even when the informational value of sampling depends on past or future sampling events. To illustrate our framework, we develop Markov decision processes for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the Markov decision process allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing the associated costs of sampling.

Quantifying the strength of viral fitness trade-offs between hosts: a meta-analysis of pleiotropic fitness effects

Evolution Letters · 2024-07-29 · 4 citations

The range of hosts a given virus can infect is widely presumed to be limited by fitness trade-offs between alternative hosts. These fitness trade-offs may arise naturally due to antagonistic pleiotropy if mutations that increase fitness in one host tend to decrease fitness in alternate hosts. Yet there is also growing recognition that positive pleiotropy may be more common than previously appreciated. With positive pleiotropy, mutations have concordant fitness effects such that a beneficial mutation can simultaneously increase fitness in different hosts, providing a genetic mechanism by which selection can overcome fitness trade-offs. How readily evolution can overcome fitness trade-offs therefore depends on the overall distribution of mutational fitness effects between hosts, including the relative frequency of antagonistic versus positive pleiotropy. We therefore conducted a systematic meta-analysis of the pleiotropic fitness effects of viral mutations reported in different hosts. Our analysis indicates that while both antagonistic and positive pleiotropy are common, fitness effects are overall positively correlated between hosts and unconditionally beneficial mutations are not uncommon. Moreover, the relative frequency of antagonistic versus positive pleiotropy may simply reflect the underlying frequency of beneficial and deleterious mutations in individual hosts. Given a mutation is beneficial in one host, the probability that it is deleterious in another host is roughly equal to the probability that any mutation is deleterious, suggesting there is no natural tendency toward antagonistic pleiotropy. The widespread prevalence of positive pleiotropy suggests that many fitness trade-offs may be readily overcome by evolution given the right selection pressures.

Disease Progress and Detection of a California Resistance-Breaking Strain of Tomato Spotted Wilt Virus in Tomato with LAMP and CRISPR-Cas12a Assays

PhytoFrontiers™ · 2024-01-24 · 12 citations

Use of tomato cultivars with the Sw-5 resistance gene cluster has led to the occurrence of resistance-breaking (RB) tomato spotted wilt virus (TSWV) strains globally, including California and, recently, North Carolina and Texas. We documented disease on tomato infected with either an RB strain from California (CA-RB) or a wild type (CA-WT) strain of TSWV on tomato with (cultivar Mountain Merit) or without (cultivar Mountain Fresh Plus) the Sw-5b resistance gene and detected virus incidence over time using microneedle RNA extractions and LAMP. We developed a LAMP/Cas12a assay for detection of the CA-C118Y mutation in a CA-RB strain and tested the assay with field samples. Disease in the susceptible cultivar was less severe with CA-RB than with the CA-WT strain. In contrast, the resistant cultivar had little disease when inoculated with the CA-WT strain but exhibited stunting of greater than 50% when inoculated with the CA-RB strain. In the susceptible tomatoes, the detection rates over time by LAMP reaction were higher in CA-WT than in CA-RB-inoculated plants. In resistant tomato, CA-RB remained detectable by TSWV LAMP over 14 days, whereas the WT strain was undetectable. A two-step LAMP/Cas12a assay differentiated the two strains in 1 h. Our methods were validated with samples from TSWV-infected North Carolina fields. A phylogeny of NSm gene sequences that included North Carolina field samples revealed two independent origins of the North Carolina RB isolates. The LAMP/Cas12 assay showed excellent detection of the CA-C118Y mutation. The TSWV LAMP/Cas12a assay is adaptable for in-field applications on either a smart phone platform or heat block. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

The Physician Fee Schedule Was Not Built for High-Cost Supplies and Equipment

Cureus · 2024-08-27 · 2 citations

As reimbursement from the Medicare Physician Fee Schedule (PFS) continues to decline, cuts to practice expense relative value units disproportionately impact office-based interventionalists and private practices that rely on high-cost equipment. For 195 codes, specialties such as radiation oncology, vascular surgery, and interventional radiology are paid at rates less than their direct costs calculated by the Centers for Medicare and Medicaid Services itself. While reimbursement in the office-based setting continues to decline, high-cost hospital settings receive more payment for the same services. This disparity aligns with trends in care moving to the hospital setting and practice consolidation, resulting in increased costs to the healthcare system and decreased access to care. The current PFS is outdated, and the removal of high-cost supplies and equipment from the PFS is a critical step to reform.

Quantifying the genomic determinants of fitness in <i>E. coli</i> ST131 using phylodynamics

bioRxiv (Cold Spring Harbor Laboratory) · 2024-06-10 · 3 citations

Abstract Antimicrobial resistant pathogens such as Escherichia coli sequence type 131 (ST131) pose a serious threat to public health globally. In the United States, ST131 acquired multiple antimicrobial resistance (AMR) genes and rapidly grew to its current high prevalence in healthcare settings. Notably, this coincided with the introduction and widespread use of antibiotics such as fluoroquinolones, suggesting AMR as the major driver of ST131’s expansion. Yet, within ST131, there remains considerable diversity between strains in resistance profiles and their repertoires of virulence factors, stress factors, plasmids, and other accessory elements. Understanding which genomic features contribute to ST131’s competitive advantage and their relative effects on population-level fitness therefore poses a considerable challenge. Here we use phylodynamic birth-death models to estimate the relative fitness of different ST131 lineages from bacterial phylogenies. By extending these phylodynamic methods to allow multiple genomic features to shape bacterial fitness, we further quantify the relative contribution of individual AMR genes to ST131’s fitness. Our analysis indicates that while many genomic elements, including various AMR genes, virulence factors, and plasmids, have all contributed substantially to ST131’s rapid growth, major increases in ST131’s fitness are largely attributable to mutations in gyrase A that confer resistance to fluoroquinolones. Author summary ST131 is a pandemic lineage of E. coli that has spread globally and is now responsible for a large percentage of blood and urinary tract infections that cannot be treated with many common antibiotics. While antibiotic resistance has undoubtedly given ST131 a competitive edge, the relative importance of resistance compared with other factors shaping a pathogen’s growth or transmission potential (i.e. fitness) is often difficult to measure in natural settings. Here, we present a method that allows us to look at the entire spectrum of factors determining a pathogen’s fitness and estimate the individual contribution of each component to pathogen’s overall fitness. Our results suggest that resistance to fluoroquinolones, a widely used class of antibiotics, provides ST131 with a disproportionately large fitness advantage relative to many other factors with more moderate fitness effects. Understanding what determines the fitness of ST131 therefore provides insights that can be used to curb the spread of resistance and monitor for emerging lineages with high pandemic potential due to shared fitness enhancing attributes.

Exploring the Accuracy and Limits of Algorithms for Localizing Recombination Breakpoints

Molecular Biology and Evolution · 2024-06-23 · 2 citations

Phylogenetic methods are widely used to reconstruct the evolutionary relationships among species and individuals. However, recombination can obscure ancestral relationships as individuals may inherit different regions of their genome from different ancestors. It is, therefore, often necessary to detect recombination events, locate recombination breakpoints, and select recombination-free alignments prior to reconstructing phylogenetic trees. While many earlier studies have examined the power of different methods to detect recombination, very few have examined the ability of these methods to accurately locate recombination breakpoints. In this study, we simulated genome sequences based on ancestral recombination graphs and explored the accuracy of three popular recombination detection methods: MaxChi, 3SEQ, and Genetic Algorithm Recombination Detection. The accuracy of inferred breakpoint locations was evaluated along with the key factors contributing to variation in accuracy across datasets. While many different genomic features contribute to the variation in performance across methods, the number of informative sites consistent with the pattern of inheritance between parent and recombinant child sequences always has the greatest contribution to accuracy. While partitioning sequence alignments based on identified recombination breakpoints can greatly decrease phylogenetic error, the quality of phylogenetic reconstructions depends very little on how breakpoints are chosen to partition the alignment. Our work sheds light on how different features of recombinant genomes affect the performance of recombination detection methods and suggests best practices for reconstructing phylogenies based on recombination-free alignments.

Phylodynamics beyond neutrality: The impact of incomplete purifying selection on viral phylogenies and inference

bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-28

Abstract Viral phylodynamics focuses on using sequence data to make inferences about the population dynamics of viral infectious diseases. These inferences commonly include estimation of the viral growth rate, the reproduction number, and the time of most recent common ancestor. With few exceptions, existing phylodynamic inference approaches assume that all observed and ancestral viral genetic variation is fitness-neutral. This assumption is violated more often than not, with a large body of analyses indicating that fitness varies substantially among genotypes circulating viral populations. Here, we focus specifically on fitness variation arising from deleterious mutations, asking whether incomplete purifying selection of deleterious mutations has the potential to bias phylodynamic inference. We use simulations of an exponentially growing population to explore how incomplete purifying selection distorts tree shape as well as how it shifts the distribution of non-neutral mutations over trees. Consistent with previous results, we find that incomplete purifying selection strongly shapes the distribution of mutations while only weakly impacting tree shape. Despite incomplete purifying selection shifting the distribution of mutations, we find little discernible bias in estimates of the viral growth rate and times of the most recent common ancestor. Our results reassuringly indicate that existing phylodynamic inference approaches may not yield biased epidemiological parameter estimates in the face of incomplete purifying selection, although more work is needed to assess the generalizability of these findings.