Biology subjects

Hobolth, A.

Publications and source records attributed to Hobolth, A..

13 recordsLinked to original sources

bioRxiv2025

PhaseGen: Exact Solutions for Time-Inhomogenous Multivariate Coalescent Distributions under Diverse Demographies

Phase-type theory is emerging as a powerful framework for modeling coalescent processes, allowing for the exact computation of quantities of interest. This includes moments of tree height, total branch length, the site-frequency spectrum (SFS), and the full distribution of the time to the most recent common ancestor (TMRCA). However, prior applications have largely been limited to time-homogeneous settings, with constant population sizes and migration rates, restricting the range of demographic scenarios that can be modeled. In this study, we apply time-inhomogeneous phase-type theory to enable the exact computation of (cross-)moments of arbitrary order and reward structure under piecewise-constant demographies. This extension enables the modeling of significantly more complex demographic scenarios, including population expansions, contractions, bottlenecks, and splits. It furthermore supports the fitting of demo-graphic models to data through gradient-based optimization. To support these advancements, we introduce PhaseGen--a software package designed for the numerically stable computation of exact moments under diverse demographic scenarios, with support for gradient-based parameter estimation.

genetics↗

bioRxiv2025

Enhancement of hidden Markov model analyses for improved inference of archaic introgression in modern humans

Insights into the admixture history between modern and archaic humans require accurately inferred introgressed fragments within modern genomes. Here, we introduce two enhancements to hidden Markov models (HMMs) implemented in hmmix. First, we develop a method for sampling hidden state sequences conditional on observed genomic data, enabling robust estimation of admixture summary statistics--such as admixture proportion and fragment length distributions. This represents an improvement compared to relying solely on point estimates as provided by classical decoding methods. Additionally, we integrate the Finite Markov Chain Imbedding (FMCI) framework, allowing exact analytical calculation of these admixture statistics, tailored to large scale human genomes. Second, we implement a novel hybrid decoding method which combines the strengths of Viterbi and Posterior decoding methods, substantially improving the reliability of archaic fragments identified. We validate these improvements on data from the 1000 Genomes Project and demonstrate that our sampling method yield more accurate admixture estimates from single individuals compared to existing approaches requiring extensive population-level datasets. Moreover we show how hybrid decoding can be instrumental in resolving the inference of local archaic haplotype structure in modern human genomes. These methodological advancements broadly enhance HMM-based analyses and will provide deeper insight into the complex history of genetic interactions between archaic and modern human populations.

evolutionary biology↗

bioRxiv2024

Estimating gene conversion tract length and rate from PacBio HiFi data

Gene conversions are broadly defined as the transfer of genetic material from a donor to an acceptor sequence and can happen both in meiosis and mitosis. They are a subset of non-crossover events and, like crossover events, gene conversion can generate new combinations of alleles and counteract mutation load by reverting germline mutations through GC-biased gene conversion. Estimating gene conversion rate and the distribution of gene conversion tract lengths remains challenging. We present a new method for estimating tract length, rate and detection probability of non-crossover events directly in HiFi PacBio long read data. The method can be used to make inference from sequencing of gametes from a single individual. The method is unbiased even under low single nucleotide variant (SNV) densities and does not necessitate any demographic or evolutionary assumptions. We test the accuracy and robustness of our method using simulated datasets where we vary length of tracts, number of tracts, the genomic SNV density and levels of correlation between SNV density and NCO event position. Our simulations show that under low SNV densities, like those found in humans, only a minute fraction ([~]2%) of NCO events are expected to become visible as gene conversions by moving at least one SNV. We finally illustrate our method by applying it to PacBio sequencing data from human sperm.

genomics↗

bioRxiv2024

Insights into gene conversion and crossing-over processes from long-read sequencing of human, chimpanzee and gorilla testes and sperm

Homologous recombination rearranges genetic information during meiosis to generate new combinations of variants. Recombination also causes new mutations, affects the GC content of the genome and reduces selective interference. Here, we use HiFi long-read sequencing to directly detect crossover and gene conversion events from switches between the two haplotypes along single HiFi-reads from testis tissue of humans, chimpanzees and gorillas as well as human sperm samples. Furthermore, based on DNA methylation calls, we classify the cellular origin of reads to either somatic or germline cells in the testis tissue. We identify 1692 crossovers and 1032 gene conversions in nine samples and investigate their chromosomal distribution. Crossovers are more telomeric and correlate better with recombination maps than gene conversions. We show a strong concordance between a human double-strand break map and the human samples, but not for the other species, supporting different PRDM9-programmed double-strand break loci. We estimate the average gene conversion tract lengths to be similar and very short in all three species (means 40-100 bp, fitted well by a geometric distribution) and that 95-98% of non-crossover events do not involve tracts intersecting with polymorphism and are therefore not detectable. Finally, we detect a GC bias in the gene conversion of both single and multiple SNVs and show that the GC-biased gene conversion affects SNVs flanking crossover events. This implies that gene conversion events associated with crossover events are much longer (estimated above 500 bp) than those associated with non-crossover events. Highly accurate long-read sequencing combined with the classification of reads to specific cell types provides a new, powerful way to make individual, detailed maps of gene conversion and crossovers for any species.

evolutionary biology↗

bioRxiv2023

TRAILS: tree reconstruction of ancestry using incomplete lineage sorting

Genome-wide genealogies of multiple species carry detailed information about demographic and selection processes on individual branches of the phylogeny. Here, we introduce TRAILS, a hidden Markov model that accurately infers time-resolved population genetics parameters, such as ancestral effective population sizes and speciation times, for ancestral branches using a multi-species alignment of three species and an outgroup. TRAILS leverages the information contained in incomplete lineage sorting fragments by modelling genealogies along the genome as rooted three-leaved trees, each with a topology and two coalescent events happening in discretized time intervals within the phylogeny. Posterior decoding of the hidden Markov model can be used to infer the ancestral recombination graph for the alignment and details on demographic changes within a branch. Since TRAILS performs posterior decoding at the base-pair level, genome-wide scans based on the posterior probabilities can be devised to detect deviations from neutrality. Using TRAILS on a human-chimp-gorilla-orangutan alignment, we recover speciation parameters and extract information about the topology and coalescent times at high resolution. Author summaryDNA sequences can be compared to reconstruct the evolutionary history of different species. While the ancestral history is usually represented by a single phylogenetic tree, speciation is a more complex process, and, due to the effect of recombination, different parts of the genome might follow different genealogies. For example, even though humans are more closely related to chimps than to gorillas, around 15% of our genome is more similar to the gorilla genome than to the chimp one. Even for those parts of the genome that do follow the same human-chimp topology, we might encounter a last common ancestor at different time points in the past for different genomic fragments. Here, we present TRAILS, a new framework that utilizes the information contained in all these genealogies to reconstruct the speciation process. TRAILS infers unbiased estimates of the speciation times and the ancestral effective population sizes, improving the accuracy when compared to previous methods. TRAILS also reconstructs the genealogy at the highest resolution, inferring, for example, when common ancestry was found for different parts of the genome. This information can also be used to detect deviations from neutrality, effectively inferring natural selection that happened millions of years ago. We validate the method using extensive simulations, and we apply TRAILS to a human-chimp-gorilla multiple genome alignment, from where we recover speciation parameters that are in good agreement with previous estimates.

evolutionary biology↗

bioRxiv2022

PhaseTypeR: phase-type distributions in R with reward transformations and a view towards population genetics

Phase-type distributions are a general class of models that are traditionally used in actuarial sciences and queuing theory, and more recently in population genetics. A phase-type distributed random variable is the time to absorption in a discrete or continuous time Markov chain on a finite state space with an absorbing state. The R package PhaseTypeR contains all the key functions--mean, (co)variance, probability density function, cumulative distribution function, quantile function, random sampling and reward transformations--for both continuous (PH) and discrete (DPH) phase-type distributions. Additionally, we have also implemented the multivariate continuous case (MPH) and the multivariate discrete case (MDPH). We illustrate the usage of PhaseTypeR in simple examples from population genetics (e.g. the time until the most recent common ancestor or the total number of mutations in an alignment of homologous DNA sequences), and we demonstrate the power of PhaseTypeR in more involved applications from population genetics, such as the coalescent with recombination and the structured coalescent. The multivariate distributions and ability to reward-transform are particularly important in population genetics, and a unique feature of PhaseTypeR.

bioinformatics↗

bioRxiv2022

Graph-based algorithms for phase-type distributions

Phase-type distributions model the time until absorption in continuous or discrete-time Markov chains on a finite state space. The multivariate phase-type distributions have diverse and important applications by modeling rewards accumulated at visited states. However, even moderately sized state spaces make the traditional matrix-based equations computationally infeasible. State spaces of phase-type distributions are often large but sparse, with only a few transitions from a state. This sparseness makes a graph-based representation of the phase-type distribution more natural and efficient than the traditional matrix-based representation. In this paper, we develop graph-based algorithms for analyzing phase-type distributions. In addition to algorithms for state space construction, reward transformation, and moments calculation, we give algorithms for the marginal distribution functions of multivariate phase-type distributions and for the state probability vector of the underlying Markov chains of both time-homogeneous and time-inhomogeneous phase-type distributions. The algorithms are available as a numerically stable and memory-efficient open source software package written in C named ptdalgorithms. This library exposes all methods in the programming languages C and R. We compare the running time of ptdalgorithms to the fastest tools using a traditional matrix-based formulation. This comparison includes the computation of the probability distribution, which is usually computed by exponentiation of the sub-intensity or sub-transition matrix. We also compare time spent calculating the moments of (multivariate) phase-type distributions usually defined by inversion of the same matrices. The numerical results of our graph-based and traditional matrix-based methods are identical, and our graph-based algorithms are often orders of magnitudes faster. Finally, we demonstrate with a classic problem from population genetics how ptdalgorithms serves as a much faster, simpler, and completely general modeling alternative.

bioinformatics↗

bioRxiv2020

Studying models of balancing selection usingphase-type theory

Balancing selection (BLS) is the evolutionary force that maintains high levels of genetic variability in many important genes. To further our understanding of its evolutionary significance, we analyse models with BLS acting on a biallelic locus: an equilibrium model with long-term BLS, a model with long-term BLS and recent changes in population size, and a model of recent BLS. Using phase-type theory, a mathematical tool for analysing continuous time Markov chains with an absorbing state, we examine how BLS affects polymorphism patterns in linked neutral regions, as summarised by nucleotide diversity, the expected number of segregating sites, the site frequency spectrum, and the level of linkage disequilibrium (LD). Long-term BLS affects polymorphism patterns in a relatively small genomic neighbourhood, and such selection targets are easier to detect when the equilibrium frequencies of the selected variants are close to 50%, or when there has been a population size reduction. For a new mutation subject to BLS, its initial increase in frequency in the population causes linked neutral regions to have reduced diversity, an excess of both high and low frequency derived variants, and elevated LD with the selected locus. These patterns are similar to those produced by selective sweeps, but the effects of recent BLS are weaker. Nonetheless, compared to selective sweeps, non-equilibrium polymorphism and LD patterns persist for a much longer period under recent BLS, which may increase the chance of detecting such selection targets. An R package for analysing these models, among others (e.g., isolation with migration), is available.

evolutionary biology↗

bioRxiv2018

Phase-type distributions in population genetics

Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in terms of the so-called phase-type theory, where complicated and tedious calculations are circumvented by the use of matrices. The application of phase-type theory consists of describing the stochastic model as a Markov model by appropriately setting up a state space and calculating the corresponding intensity and reward matrices. Formulae of interest are then expressed in terms of these aforementioned matrices. We illustrate this by a few examples calculating the mean, variance and even higher order moments of the site frequency spectrum in the multiple merger coalescent models, and by analysing the mean and variance for the number of segregating sites for multiple samples in the two-locus ancestral recombination graph. We believe that phase-type theory has great potential as a tool for analysing probability models in population genetics. The compact matrix notation is useful for clarification of current models, in particular their formal manipulation (calculation), but also for further development or extensions.

bioinformatics↗

bioRxiv2018

Detecting archaic introgression without archaic reference genomes

Human populations out of Africa have experienced at least two bouts of introgression from archaic humans, from Neanderthals and Denisovans. In Papuans there is prior evidence of both these introgressions. Here we present a new approach to detect segments of individual genomes of archaic origin without using an archaic reference genome. The approach is based on a hidden Markov model that identifies genomic regions with a high density of single nucleotide variants (SNVs) not seen in unadmixed populations. We show using simulations that this provides a powerful approach to identifying segments of archaic introgression with a small rate of false detection. Furthermore our approach is able to accurately infer admixture proportions and divergence time of human and archaic populations.\n\nWe apply the model to detect archaic introgression in 89 Papuans and show how the identified segments can be assigned to likely Neanderthal or Denisovan origin. We report more Denisovan admixture than previous studies and directly find a shift in size distribution of fragments of Neanderthal and Denisovan origin that is compatible with a difference in admixture time. Furthermore, we identify small amounts of Denisova ancestry in West Eurasians, South East Asians and South Asians.

evolutionary biology↗

bioRxiv2017

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Non-coding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g. TERT and CDH10) and survival (e.g. CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217 and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

cancer biology↗

bioRxiv2017

Non-Parametric Estimation Of Population Size Changes From The Site Frequency Spectrum

The variability in population size is a key quantity for understanding the evolutionary history of a species. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from the site frequency spectrum. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the variability in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on data from nine different human populations.

bioinformatics↗

bioRxiv2017

A Site Specific Model And Analysis Of The Neutral Somatic Mutation Rate In Whole-Genome Cancer Data

BackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.\n\nResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.\n\nConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

bioinformatics↗