Biology subjects

Carlson, M. O.

Publications and source records attributed to Carlson, M. O..

6 recordsLinked to original sources

bioRxiv2026

Mutation choice biases the structure of empirical fitness landscapes

Evolution can be thought of as taking place in a fitness landscape which maps each genotype to a fitness value. This abstraction has motivated significant efforts to quantify the statistical properties of random fitness landscapes with different amounts and types of interactions among loci---broadly referred to as epistasis. New high-throughput experimental techniques measuring the phenotypes of thousands of genotypes promise to bridge this theory with data. Yet, while impressive, thousands is still small relative to the size of genotype space. Moreover, experiments often assay a highly non-random subset of the fitness landscape. For example, a common experimental approach is to generate all possible 2M sequence intermediates between ancestral and evolved sequences differing by M much less than L mutations. Here, we ask to what extent the statistics of an empirical landscape constructed under distinct ascertainment protocols recapitulate the statistics of a random subset of the global landscape in a few well-studied random landscape models. We show that, in most landscapes, selecting mutations which occurred along an adaptive trajectory inflates the variance explained by lower order epistasis, reduces the number of local maxima, and increases the number of accessible paths relative to a random fitness landscape. Selecting the most beneficial or deleterious mutations, on the other hand, results in distortions to these statistics which depend more critically on the amount of epistasis in the global landscape. We then take advantage of several large-scale mutagenesis datasets to simulate the effects of ascertainment in more realistic settings.

genetics↗

bioRxiv2025

Robust detection of specific epistasis using rank statistics

The phenotypic effect of a mutation may depend on the genetic background in which it occurs, a phenomenon referred to as epistasis. One source of epistasis in proteins is direct interactions between residues in close physical proximity to one another. However, epistasis may also occur in the absence of specific interactions between amino acids if the genotype-to-phenotype map is nonlinear. Disentangling the contributions of these two phenomena--specific and global epistasis--from noisy, high-throughput mutagenesis experiments is highly non-trivial: the form of the nonlinearity is generally not known and model misspecification may lead to over- or underestimation of specific epistasis. In contrast to previous approaches, we do not attempt to model the fitness measurements directly. Rather, we begin with the observation that global epistasis, under the assumption of monotonicity, imposes strong constraints on the rank statistics of a combinatorial mutagenesis experiment. Namely, the rank-order of mutant phenotypes should be preserved across genetic backgrounds. We exploit this constraint to devise a simple semi-parametric method to detect specific epistasis in the presence of global epistasis and measurement noise. We apply this method to three high-throughput mutagenesis experiments, uncovering known protein contacts with similar or higher accuracy than existing, more complicated procedures. Moreover, the principles underlying our framework may suggest new ways of understanding the mechanisms which generate epistasis and their consequences for protein evolution.

biophysics↗

bioRxiv2024

Examining motor evidence for the pause-then-cancel model of action-stopping: Insights from motor system physiology

Stopping initiated actions is fundamental to adaptive behavior. Longstanding, single-process accounts of action-stopping have been challenged by recent, two-process, pause-then-cancel models. These models propose that action-stopping involves two inhibitory processes: 1) a fast Pause process, which broadly suppresses the motor system as the result of detecting any salient event, and 2) a slower Cancel process, which involves motor suppression specific to the cancelled action. A purported signature of the Pause process is global suppression, or the reduced corticospinal excitability (CSE) of task-unrelated effectors early on in action-stopping. However, unlike the Pause process, few (if any) motor system signatures of a Cancel process have been identified. Here, we used single- and paired-pulse TMS methods to comprehensively measure the local physiological excitation and inhibition of both responding and task-unrelated motor effector systems during action-stopping. Specifically, we measured CSE, short-interval intracortical inhibition (SICI), and the duration of the cortical silent period (CSP). Consistent with key predictions from the pause-then-cancel model, CSE measurements at the responding effector indicated that additional suppression was necessary to counteract Go-related increases in CSE during-action-stopping, particularly at later timepoints. Increases in SICI on Stop-signal trials did not differ across responding and non-responding effectors, or across timepoints. This suggests SICI as a potential source of global suppression. Increases in CSP duration on Stop- signal trials were more prominent at later timepoints. SICI and CSP duration therefore appeared most consistent with the Pause and Cancel processes, respectively. Our study provides further evidence from motor system physiology that multiple inhibitory processes influence action-stopping.

neuroscience↗

bioRxiv2021

Polygenic score accuracy in ancient samples: quantifying the effects of allelic turnover

Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the correlation between an ancient individuals polygenic score and true phenotype, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.

genetics↗

bioRxiv2019

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.)

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

genetics↗

bioRxiv2016

Temporal Genetic Dynamics of an Experimental, Biparental Field Population of Phytophthora capsici

Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a growers field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; mixed generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

evolutionary biology↗