Biology subjects

Johri, P.

Publications and source records attributed to Johri, P..

At least 19 recordsLinked to original sources

bioRxiv2026

A model for within- and between-host evolution in pathogens

Many pathogenic and microbial species undergo complex life cycles wherein they experience drastic bottlenecks during invasion into a host and rapid growth within hosts. The effects of such a complex population history on patterns of genetic variation is not currently understood. We model a pathogen population as a metapopulation where each deme represents a host and incorporate transmission dynamics as well as the effects of transmission bottlenecks. We employ a coalescent framework, obtaining analytical expressions for pairwise times to coalescence and genetic differentiation within and between hosts. We find that recurrent bottlenecks rescale the coalescent process within hosts to the size of the bottleneck, reducing within-host variation. In addition, the pairwise distribution of time to coalescence under our model deviates substantially from the Wright-Fisher process when the number of hosts (d) is of similar order to the size of the bottleneck (k), i.e., k [~] d and when m <<1/(2k) +1/(2d), where m is the migration rate. Otherwise, the pairwise process under our model converges in distribution to the Wright-Fisher process. Coalescent simulations show that under the same conditions the site frequency spectrum (SFS) under our model deviates from that under the Kingman coalescent and is drastically skewed towards singletons. Our results suggest that the inference of selection or demographic history from pathogen population genetics data may be systematically biased by the mechanics of pathogen reproduction, indicating a need for further theoretical work that can inform inference.

evolutionary biology↗

bioRxiv2026

Maintenance of quantitative genetic variation in regions of restricted recombination via associative overdominance

Mutation-selection balance is the most plausible general mechanism for the maintenance of additive genetic variance (VA) for quantitative traits in natural populations. It is well established that the genetic variants affecting traits (QTLs) are widely distributed across species' genomes. At the same time, unconditionally deleterious mutations are also expected to be common, with those with the largest fitness effects tending to be strongly recessive. Many genomes possess regions of restricted recombination that could result in tight linkage between largely recessive deleterious alleles, conditions which can lead to elevated heterozygosity via associative overdominance (AOD). Here we show that weakly selected QTLs linked to genomic regions exhibiting AOD can elevate VA above what is expected in the absence of unconditionally deleterious mutations. Selection on traits when VA is maintained by AOD, however, may expose recessive deleterious alleles as homozygotes, reducing fitness. Thus, while AOD may contribute to the maintenance of VA, variation maintained in this way may not be readily accessible by natural or artificial selection.

evolutionary biology↗

bioRxiv2026

Sea-level and climate changes drive lineage diversification in the imperiled Venus flytrap (Dionaea muscipula J. Ellis, Droseraceae)

Venus flytrap (Dionaea muscipula) is an insectivorous plant in the monotypic genus Dionaea, endemic to the Southeastern United States, and found in a small geographic range in North and South Carolina. Its unique morphology and natural history have intrigued biologists for centuries, yet the evolution of populations has not been adequately characterized. Population decline, driven primarily by disruptive land conversion, fire suppression, and illegal harvesting/poaching underscores the urgency of defining the genetic diversity in the present population to guide species conservation. We applied a genome-wide SNP dataset to analyze admixture and population structure and used coalescent modeling to trace the ancestry and migration of geographically distinct population clusters. Our population dynamics model supports four lineages, with the North Cape Fear Arch lineage as the ancestor of all lineages and two genetically distinct Sandhills lineages derived from the South Cape Fear Arch lineage independently at [~]13 Ma and [~]3 Ma, respectively, during peak climate optima periods and maximal sea level rise. Severe population decline events occurred among all lineages, with two ancient instant bottleneck population decline events in Sandhills lineages and two recent ones in the two Cape Fear Arch lineages, which contributed to the current genetic structure. Shorter peduncle lengths in Sandhills populations correspond with suspected local adaptation to Sandhills habitats.

evolutionary biology↗

bioRxiv2026

The B-value calculator: expected diversity under background selection

Background selection (BGS), the indirect effect of purifying selection at linked and unlinked sites, is a key evolutionary process shaping genomic patterns of variation. Calculating the expected diversity at neutral sites experiencing BGS relative to that under strict neutrality (referred to as B-value or simply B) is important for developing null models when performing population genomic inference, in particular, demographic inference and detection of selective sweeps. We extend and integrate previous theory to estimate B-values analytically, assuming no selective interference, with novel expressions to account for gene conversion between proximal sites. Here, we present the B-value calculator, Bvalcalc, an easy-to-use command-line interface written in Python for efficient analytical calculation of expected genome-wide B at single base-pair resolution. Bvalcalc has several modules for calculating diversity as a function of distance from a single selected element or considering the multiplicative effects of all selected elements across the genome, accounting for recombination maps, gene conversion, self-fertilization, single population size changes, and unlinked effects from other chromosomes. We validated the effectiveness of Bvalcalc with comparisons against simulated results, and generated B-maps for the model species Homo sapiens, Drosophila melanogaster and Arabidopsis thaliana as a proof of concept using public data. Bvalcalc is available with documentation at johrilab.github.io/Bvalcalc/.

evolutionary biology↗

bioRxiv2026

The distribution of fitness effects of new mutations in regulatory regions of the D. melanogaster genome

Although non-coding regions play important roles in gene regulation and contribute to individual fitness, the precise distribution of fitness effects (DFE) of new mutations in these regions remains poorly understood. Here, we carefully compile experimentally validated regulatory regions in non-coding regions in the Drosophila melanogaster genome and identify putatively neutral sites near them. Incorporating a realistic genomic architecture that mimics the placement of the regulatory and coding regions, as well as a realistic heterogeneity in recombination and mutation rates across the genome, we use forward-in-time simulations to assess the power and accuracy of population genetics approaches that infer the DFE of new mutations in these regions. While the parameters of DFEs primarily comprising moderately and strongly deleterious mutations are estimated accurately, those of a DFE comprising mostly mildly deleterious mutations are misinferred. Applying these insights to three African D. melanogaster populations, we find that a large fraction of new mutations in functionally important non-coding regions are moderately deleterious, as opposed to strongly deleterious in coding regions. While the fraction of beneficial substitutions in regulatory regions (0.25-0.45) was also lower in coding regions ([~]0.5), our results suggest that non-coding regions contribute a majority of new deleterious mutations and beneficial substitutions in D. melanogaster populations. By incorporating both the genomic distribution and the inferred DFE of non-coding regions, we demonstrate that the effects of background selection across the genome are more accurately captured than with coding regions alone, highlighting the importance of considering selection on non-coding regions when interpreting patterns of genomic variation.

evolutionary biology↗

bioRxiv2026

Malaria control and the unexpected spread of diagnostic-resistant Plasmodium falciparum in Peru

Plasmodium falciparum parasites with deletions of the histidine-rich protein 2 and 3 (hrp2 and hrp3) genes evade detection by common rapid diagnostic tests (RDTs) and pose a growing threat to malaria control. While these deletions have emerged in multiple regions globally, the evolutionary forces driving their spread remain unclear. Here, we analyze 1,215 P. falciparum samples collected between 2003 and 2018 in Loreto, Peru. This region experienced a major decline in malaria transmission following the Project for Malaria Control in Andean Border Areas (PAMAFRO) and now harbors a high proportion of hrp2/3 deleted parasites despite limited RDT use. Using molecular inversion probe (MIP) sequencing across > 2,000 genome-wide loci, we observed a marked reduction in genetic diversity, increased clonality, and fixation of parasites with deletions of both hrp2 and hrp3 genes (hrp2-/3-) over time. Identity-by-descent (IBD) analysis revealed rapid expansion of a single hrp2-/3- dominant lineage in the post-PAMAFRO period, consistent with clonal replacement after intense malaria control. Targeted sequencing of the hrp2/3 regions showed conserved deletion breakpoints across three different lineages, indicative of recombination of a common haplotype into distinct genetic backgrounds. To investigate the evolutionary forces driving the fixation of hrp2-/3- in Loreto, we simulated allele frequency trajectories under different selection coefficients. We found that fixation of hrp2-/3- due solely to genetic drift (selection coefficient s = 0) is unlikely; a selection coefficient of s [≥] 0.03 was required for fixation to occur consistently. However, our simulations also indicate that a genetic bottleneck caused by PAMAFRO increased the likelihood of fixation through drift by 4.5- to 17-fold depending on the population. These findings suggest that hrp2-/3- fixation was likely driven by a combination of demographic changes resulting from PAMAFRO and selective advantage unrelated to RDT use. Our results demonstrate how intensive malaria control efforts can reshape parasite populations and underscore the value of expanded genomic surveillance as countries move toward malaria elimination.

genetics↗

bioRxiv2025

Towards an evolutionary baseline model of Plasmodium falciparum for population-genomic inference

Malaria has caused over 15.7 million deaths in the 21st century and was responsible for [~]600 thousand deaths globally in 2023 alone. Although many effective antimalarial drugs have been developed and widely adopted to reduce the occurrence and severity of the disease, recurrent resistance to the frontline treatment has been of major concern. Multiple drug resistance alleles at intermediate and high allele frequency have been identified in specific Asian and African populations of P. falciparum, the deadliest malaria parasite. With the improvement in throughput of sequencing technologies and global efforts such as the MalariaGEN project to build genomic surveillance, we now have access to tens of thousands of genomes of P. falciparum from across the world. With this data, it is becoming increasingly possible to employ powerful population genetics approaches to understand the selective pressures and demographic history of the parasite. While several empirically motivated outlier-based approaches have been employed to identify targets of drug resistance, there is a lack of a framework that jointly accounts for the multiple concurrent processes occurring in natural populations of P. falciparum. We argue that a baseline evolutionary model that accounts for simultaneously acting evolutionary processes is needed to understand patterns of genomic variation in P. falciparum populations. Here, we identify key components essential for building such a baseline model for the malaria-causing pathogen. The development of an appropriate null model will be important to test evolutionary hypotheses using genomic datasets, will provide a path forward to improve the accuracy of inference of evolutionary parameters, and will help identify new gene candidates involved in drug resistance.

evolutionary biology↗

bioRxiv2025

Effects of rescaling forward-in-time population genetic simulations

Forward-in-time population genetic simulations enable modelling of a wide array of complex evolutionary scenarios. Simulating small genomic regions, and rescaling by reducing population size by a scaling factor while maintaining population-scaled parameters, are common approaches for improving computational tractability. However, simulating whole chromosomes in large populations remains computationally prohibitive and its unclear what scaling factors produce consistent evolutionary dynamics between rescaled and unscaled populations. Here, we thoroughly test the effects of rescaling in populations experiencing direct and linked effects of selection for various scaling factors and genomic lengths in D. melanogaster-like populations by comparing them with theoretical expectations. We find that while rescaling has minimal effects with most length and scaling factor combinations, simulating long regions with large scaling factors can introduce biases in summary statistics including neutral diversity, allele frequencies, linkage disequilibrium, and non-neutral divergence. These deviations occur particularly when the crossover rate exceeds 0.44 per individual/generation because substantial multiple-crossover events are likely to occur within individuals, reducing the effective rate of genetic recombination. In addition, when the genome-wide deleterious mutation rate is high (U >> 1) in highly rescaled long regions, we observe increased Hill-Robertson interference effects and progeny skew, the extent of which was strongly dependent on the fitness effects of selected mutations. We find that hitchhiking effects near functional regions are relatively unaffected by rescaling the population, even with large scaling factors. Our findings expose potential pitfalls when simulating long regions with rescaling and highlight parameter spaces within which expected evolutionary dynamics are conserved.

genetics↗

bioRxiv2025

A gene-based model of fitness and its implications for genetic variation: Genetic and inbreeding loads

In the companion paper to this, we examined the consequences for patterns of linkage disequilibrium of the "gene" model of fitness, which postulates that the effects of recessive or partially recessive deleterious mutations located at different sites within a gene fail to complement each other. Here, we examine the consequences of the gene model for the genetic and inbreeding loads, using both analytical and simulation methods, and contrast it with the frequently used "sites" model that allows allelic complementation. We show that the gene model results in a slightly lower genetic load, but a much smaller inbreeding load, than the sites model, implying that standard predictions of mutational contributions to inbreeding depression may be overestimates. Synergistic epistasis between pairs of mutations was also modeled, and shown to considerably reduce the inbreeding load for both the gene and sites models. The theoretical results are discussed in relation to data on inbreeding load in Drosophila melanogaster. The widespread assumption that inbreeding depression is largely due to deleterious mutations should be re-examined in the light of our findings.

evolutionary biology↗

bioRxiv2024

Genetic diversity during selective sweeps in non-recombining populations

Selective sweeps, resulting from the spread of beneficial, neutral, or deleterious mutations through a population, shape patterns of genetic variation at linked neutral sites. While many theoretical, computational, and statistical advances have been made in understanding the genomic signatures of selective sweeps in recombining populations, relatively less is understood in populations with little/no recombination, and arbitrary dominance and inbreeding. Using diffusion theory, we obtain the full expression for the expected site frequency spectrum (SFS) at linked neutral sites immediately post and during the fixation of moderately or strongly beneficial mutations. When a single hard sweep occurs, the SFS decays as 1/x for low derived allele frequencies (x), similar to the neutral SFS at equilibrium, whereas at higher derived allele frequencies, it follows a 1/x2 power law as also seen in a rapidly expanding neutral population. We show that these power laws are universal in the sense that they are independent of the dominance and inbreeding coefficients, and also characterize the SFS during the sweep. Additionally, we find that the derived allele frequency where the SFS shifts from the 1/x to 1/x2 power law is inversely proportional to the selection strength; thus under strong selection, the SFS follows the 1/x2 dependence for most allele frequencies. When clonal interference is pervasive, the SFS immediately post-fixation becomes U-shaped and can be approximated by the equilibrium SFS of selected sites. Our results will be important in developing statistical methods to infer the timing and strength of recent selective sweeps in asexual populations, genomic regions that lack recombination, and clonally propagating tumor populations.

genetics↗

bioRxiv2024

A gene-based model of fitness and its implications for genetic variation

A widely used model of the effects of mutations on fitness (the "sites" model) assumes that heterozygous recessive or partially recessive deleterious mutations at different sites in a gene complement each other, similarly to mutations in different genes. However, the general lack of complementation between major effect allelic mutations suggests an alternative possibility, which we term the "gene" model. This assumes that a pair of heterozygous deleterious mutations in trans behave effectively as homozygotes, so that the fitnesses of trans heterozygotes are lower than those of cis heterozygotes. We examine the properties of the two different models, using both analytical and simulation methods. We show that the gene model predicts positive linkage disequilibrium (LD) between deleterious variants within the coding sequence, under conditions when the sites model predicts zero or slightly negative LD. We also show that focussing on rare variants when examining patterns of LD, especially with Lewontins D measure, is likely to produce misleading results with respect to inferences concerning the causes of the sign of LD. Synergistic epistasis between pairs of mutations was also modeled; it is less likely to produce negative LD under the gene model than the sites model. The theoretical results are discussed in relation to patterns of LD in natural populations of several species.

evolutionary biology↗

bioRxiv2024

Biases in ARG-based inference of historical population size in populations experiencing selection

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.

genetics↗

bioRxiv2024

Limitations of the inference of the distribution of fitness effects of new mutations in partially selfing populations with linkage

The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill- Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.

genetics↗

bioRxiv2023

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser TextOutlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.

genomics↗

bioRxiv2023

Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history

Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome (i.e., purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.

evolutionary biology↗

bioRxiv2021

Statistical inference in population genomics

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population-genetic insights out-paced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous non-adaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model-fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

evolutionary biology↗

bioRxiv2020

Revisiting the notion of deleterious sweeps

It has previously been shown that, conditional on its fixation, the time to fixation of a semi-dominant deleterious autosomal mutation in a randomly mating population is the same as that of an advantageous mutation. This result implies that deleterious mutations could generate selective sweep-like effects. Although their fixation probabilities greatly differ, the much larger input of deleterious relative to beneficial mutations suggests that this phenomenon could be important. We here examine how the fixation of mildly deleterious mutations affects levels and patterns of polymorphism at linked sites - both in the presence and absence of interference amongst deleterious mutations - and how this class of sites may contribute to divergence between-populations and species. We find that, while deleterious fixations are unlikely to represent a significant proportion of outliers in polymorphism-based genomic scans within populations, minor shifts in the frequencies of deleterious mutations can influence the proportions of private variants and the value of FST after a recent population split. As sites subject to deleterious mutations are necessarily found in functional genomic regions, interpretations in terms of recurrent positive selection may require reconsideration.

evolutionary biology↗

bioRxiv2020

The impact of purifying and background selection on the inference of population history: problems and prospects

Current procedures for inferring population history generally assume complete neutrality - that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects (DFE) and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the DFE as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.

evolutionary biology↗