arXiv subjects

Marcus W. Feldman

Publications and source records attributed to Marcus W. Feldman.

8 recordsLinked to original sources

arXiv2026

How recombination rates affect escape from low-fitness states

Adaptation often requires the assembly of favorable combinations of mutations that are individually deleterious. As a result, populations may remain trapped in low-fitness genetic states even when higher-fitness genotypes exist. Recombination plays a dual role in this process because it can both generate and disrupt advantageous multilocus combinations. Previous work showed that the balance between selection and recombination determines whether populations cross fitness valleys or persist in low-fitness states associated with demographic decline. We study this problem in a three-locus model consisting of two selected loci and a recombination modifier locus. The modifier has no direct effect on fitness but alters the recombination rate between the selected loci, allowing recombination itself to evolve. We characterize the fixation states of the system and derive explicit conditions for the local stability of the low-fitness fixation set. Stability depends on selection strength, recombination among selected loci, recombination between the modifier and selected loci, and modifier composition. In the classical two-locus model, stability depends on a single recombination parameter. By contrast, the modifier model generates a continuum of fixation states whose stability varies with modifier frequency. Populations with identical selected-haplotype frequencies can therefore differ in stability solely because they differ in modifier composition. We further show that modifier polymorphism can either stabilize or destabilize the low-fitness state, depending on the relative magnitudes of modifier-dependent recombination rates. These results demonstrate that genetic variation affecting recombination alters evolutionary outcomes not only by changing the formation of favorable multilocus combinations but also by changing the stability of alternative evolutionary states.

q-bio.PE↗

arXiv2026

Recombination Rate Modifiers under Stochastic Transmission

The Reduction Principle states that, near a stable equilibrium under fixed viability selection, a selectively neutral modifier allele that reduces recombination rate among selected loci is favored, whereas one that increases recombination rate is eliminated. This result assumes constant transmission parameters across generations, so that invasion is determined by the dominant eigenvalue of a single transmission-selection matrix. Here we analyze a minimal departure from this framework. In a diploid model, two loci experience symmetric multiplicative viability selection and a third, neutral locus modifies their recombination rate. All parameters are fixed except that recombination in modifier heterozygotes varies randomly across generations according to a stochastic process. When the recombination rate in modifier heterozygotes is constant, the Reduction Principle holds exactly: invasion occurs if the rare modifier allele reduces recombination relative to the resident rate. When recombination varies randomly across generations, invasion is governed by the top Lyapunov exponent of a product of random matrices. We show that temporal variation in recombination rate alone, in the absence of fluctuating viability selection, can reverse the direction of selection on the modifier locus predicted by the deterministic model. The mean recombination rate is insufficient to determine invasion of $M_2$; instead, outcomes depend on the full distribution of recombination rates and their ordered accumulation across generations. Parameters that affect only the magnitude of selection under constant transmission - including resident recombination, selection strength, and background linkage - can alter its sign under stochastic transmission. These results demonstrate that temporal variability in transmission constitutes an independent and qualitatively distinct force in the evolution of recombination rates.

q-bio.PE↗

arXiv2026

Mutation Rate Variation Across Genomic Regions in \textit{Arabidopsis thaliana}

In population genetics, mutation rate is often treated as a homogeneous parameter across the genome. Empirical evidence, however, shows systematic variation across genomic contexts associated with chromatin organization and epigenomic features. Using gene-level de novo mutation data from Arabidopsis thaliana, we test whether chromatin features predict not only the mean per-base mutation rate but also its variability across genes. To reduce heterogeneity in selective regime, we restrict analysis to essential and lethal loci subject to strong purifying selection. Across complementary multivariable models including heteroskedasticity-robust linear regression, length-weighted regression, and Poisson generalized linear models with exposure offsets, histone marks associated with active transcription (H3K4me1, H3K4me3, H3K36ac) are consistently associated with lower mean mutation rates and substantially reduced between-gene variance. GC content shows little association with the mean once chromatin predictors are controlled but is positively associated with mutation-rate variability. Estimates of skewness and kurtosis reveal no significant higher-order structure attributable to epigenomic predictors. A standardized Tajima's $D$ statistic yields directionally consistent but statistically underpowered associations with both the mean and variance of gene-level mutation rates. These results indicate that mutation rate is systematically structured by chromatin state within functionally constrained genes and suggest that evolutionary processes may act not only on expected mutation rate but also on its variability across loci.

q-bio.PE↗

arXiv2024

Bayesian estimation of the number of significant principal components for cultural data

Principal component analysis (PCA) is often used to analyze multivariate data together with cluster analysis, which depends on the number of principal components used. It is therefore important to determine the number of significant principal components (PCs) extracted from a data set. Here we use a variational Bayesian version of classical PCA, to develop a new method for estimating the number of significant PCs in contexts where the number of samples is of a similar to or greater than the number of features. This eliminates guesswork and potential bias in manually determining the number of principal components and avoids overestimation of variance by filtering noise. This framework can be applied to datasets of different shapes (number of rows and columns), different data types (binary, ordinal, categorical, continuous), and with noisy and missing data. Therefore, it is especially useful for data with arbitrary encodings and similar numbers of rows and columns, such as cultural, ecological, morphological, and behavioral datasets. We tested our method on both synthetic data and empirical datasets and found that it may underestimate but not overestimate the number of principal components for the synthetic data. A small number of components was found for each empirical dataset. These results suggest that it is broadly applicable across the life sciences.

stat.AP↗

arXiv2024

Conformity to continuous and discrete ordinal traits

Models of conformity and anti-conformity have typically focused on cultural traits with nominal (unordered) variants, such as baby names, strategies (cooperate/defect), or the presence/absence of an innovation. There have been fewer studies of conformity to "ordinal" cultural traits with ordered variants, such as level of cooperation (low to high) or fraction of time spent on a task (0 to 1). In these latter studies, conformity is conceptualized as a preference for the mean trait value in a population even if no members of the population have variants near this mean; e.g., 50% of the population has variant 0 and 50% has variant 1, producing a mean of 0.5. Here, we introduce models of conformity to ordinal traits, which can be either discrete or continuous and linear (with minimum and maximum values) or circular (without boundaries). In these models, conformists prefer to adopt more popular cultural variants, even if these variants are far from the population mean. To measure a variant's "popularity" in cases where no two individuals share precisely the same variant on a continuum, we introduce a metric called $k$-dispersal; this takes into account a variant's distance to its $k$ closest neighbors, with more "popular" variants having lower distances to their neighbors. We demonstrate through simulations that conformity to ordinal traits need not produce a homogeneous population, as has previously been claimed. Under some combinations of parameter values, conformity sustains substantial trait variation over many generations. Anti-conformist transmission may produce high levels of polarization.

q-bio.PE↗

arXiv2023

Cultural transmission of move choice in chess

The study of cultural evolution benefits from detailed analysis of cultural transmission in specific human domains. Chess provides a platform for understanding the transmission of knowledge due to its active community of players, precise behaviors, and long-term records of high-quality data. In this paper, we perform an analysis of chess in the context of cultural evolution, describing multiple cultural factors that affect move choice. We then build a population-level statistical model of move choice in chess, based on the Dirichlet-multinomial likelihood, to analyze cultural transmission over decades of recorded games played by leading players. For moves made in specific positions, we evaluate the relative effects of frequency-dependent bias, success bias, and prestige bias on the dynamics of move frequencies. We observe that negative frequency-dependent bias plays a role in the dynamics of certain moves, and that other moves are compatible with transmission under prestige bias or success bias. These apparent biases may reflect recent changes, namely the introduction of computer chess engines and online tournament broadcasts. Our analysis of chess provides insights into broader questions concerning how social learning biases affect cultural evolution.

physics.soc-ph↗

arXiv2022

Ancestry-specific analyses of genome-wide data confirm the settlement sequence of Polynesia

By demonstrating the role that historical population replacements and waves of admixture have played around the world, the genetics work of Reich and colleagues has provided a paradigm for understanding human history [Reich et al. 2009; Reich et al. 2012; Patterson et al. 2012]. Although we show in Ioannidis et al. [2021] that the peopling of Polynesia was a range expansion, and not, as suggested by Huang et al. [2022], yet another example of waves of admixture and large-scale gene flow between populations, we believe that our result in this recently settled oceanic expanse is the exception that proves the rule.

q-bio.PE↗

arXiv2010

A High-Resolution Human Contact Network for Infectious Disease Transmission

The most frequent infectious diseases in humans - and those with the highest potential for rapid pandemic spread - are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At a 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 meters among 788 individuals. The data revealed a high density network with typical small world properties and a relatively homogenous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.

physics.med-ph↗