Biology subjects

Steinruecken, M.

Publications and source records attributed to Steinruecken, M..

9 recordsLinked to original sources

bioRxiv2026

Microevolutionary cophylogeny reflects host-symbiont population dynamics and human mitonuclear interactions

Cophylogeny, the study of phylogenetic similarity between interacting organisms, provides insights into the specificity and shared evolutionary history of symbiosis. While the ecological drivers of cophylogeny have been investigated at the macroevolutionary scale, the influence of these processes on microevolution remains unclear. This is due, in part, to the fact that the ancestral relations between individuals within a sexually reproducing eukaryotic host species cannot be well represented with a single phylogenetic tree, since genetic distances between individuals change substantially across the genome due to meiotic recombination. This heterogeneity can be captured and utilized through the inference of an ancestral recombination graph (ARG) built from the genomic data of the host. Here, we propose to measure microevolutionary cophylogeny by comparing a symbiont evolutionary tree to a host ARG. This approach simultaneously measures genome-wide cophylogeny, as well as locus-specific signals. Through simulations, we investigate the effects of transmission mode, population structure, admixture, and allelic incompatibility on microevolutionary cophylogeny. In contrast to macroevolutionary patterns, we find a limited relationship between cophylogeny and vertical transmission, with vertically transmitted host-symbiont systems displaying no cophylogeny in large panmictic populations. We apply our approach to mitochondrial and nuclear genomes within the 1000 Genomes Project--a host-symbiont system with strict maternal transmission--and observe substantial variation in mitochondrial-nuclear (mitonuclear) cophylogeny across human populations. Finally, we investigate locus-specific signals of cophylogeny and observe limited evidence of mitonuclear incompatibility.

evolutionary biology↗

bioRxiv2026

General moment closure for the neutral two-locus Wright-Fisher dynamics

The Wright-Fisher diffusion and its dual, the coalescent process, are at the core of many results and methods in population genetics. Approaches have been developed to study the dynamics of its moments under genetic drift, mutation, and recombination using ordinary differential equations. The dynamics of these moments can be used to study population genetic processes and are key building blocks of efficient methods to infer population genetic parameters, like demographic histories or fine-scale recombination rates. However, the system of equations does not close under recombination; that is, computing moments of a certain order requires knowledge of moments of higher order. By applying a coordinate transformation to the diffusion generator, we show that the canonical moments in these alternative coordinates yield a closed system, enabling more accurate numerical computations. Compared to previous approaches in the literature, we believe that this approach can be more readily extended to general scenarios. Through simulations, we verify that the derived system of differential equations can accurately capture the dynamics of the moments, and can be used to efficiently compute expected diversity and linkage statistics in population genetic samples.

genetics↗

bioRxiv2024

The TMRCA of general genealogies in populations of variable size

We study the time to the most recent common ancestor (TMRCA) of a sample of finite size in a wide class of genealogical models for populations with deterministically varying size. This is made possible by recently developed results on inhomogeneous phase-type random variables, allowing us to obtain the density and the moments of the TMRCA of time-dependent coalescent processes in terms of matrix formulas. We also provide matrix simplifications permitting a more straightforward calculation. With these results, the TMRCA provides an explanatory variable to distinguish different evolutionary scenarios, and to infer model parameters.

evolutionary biology↗

bioRxiv2024

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

Detecting and quantifying the strength of selection is a main objective in population genetics. Since selection acts over multiple generations, many approaches have been developed to detect and quantify selection using genetic data sampled at multiple points in time. Such time series genetic data is commonly analyzed using Hidden Markov Models, but in most cases, under the assumption of additive selection. However, many examples of genetic variation exhibiting non-additive mechanisms exist, making it critical to develop methods that can characterize selection in more general scenarios. Here, we extend a previously introduced expectation-maximization algorithm for the inference of additive selection coefficients to the case of general diploid selection, in which the heterozygote and homozygote fitness are parameterized independently. We furthermore introduce a framework to identify bespoke modes of diploid selection from given data, a heuristic to account for variable population size, and a procedure for aggregating data across linked loci to increase power and robustness. Using extensive simulation studies, we find that our method accurately and efficiently estimates selection coefficients for different modes of diploid selection across a wide range of scenarios; however, power to classify the mode of selection is low unless selection is very strong. We apply our method to ancient DNA samples from Great Britain in the last 4,450 years, and detect evidence for selection in six genomic regions, including the well-characterized LCT locus. Our work is the first genome-wide scan characterizing signals of general diploid selection. Author SummaryNatural selection increases the likelihood that beneficial genetic variants are passed from parent to offspring and thus forms the basis of genetic adaptation to novel environments. Genomic data sampled at multiple timepoints, such as genetic material extracted from ancient remains (ancient DNA) or data from evolve and resequence experiments, can enable more precise identification of genetic variants subject to selective pressure than contemporary samples alone. However, most methods for identifying genetic variation under selection focus on additive selection, where the fitness of the heterozygote is exactly intermediate between the homozygotes. Leveraging genetic data at multiple timepoints, we develop a method to detect additive and non-additive selection as well as to infer the most likely dominance mechanism. We apply our methods to a dataset of ancient DNA from Great Britain dated less than 4,450 years before present and identify six regions with signals of recent selection, including one at the TFR2 locus that has not been previously reported as a target of selection. Our work enables more accurate quantification of non-additive selection dynamics and can be used to test more complex models of selection.

genetics↗

bioRxiv2023

diplo-locus: A lightweight toolkit for inference and simulation of time-series genetic data under general diploid selection

Whole-genome time-series allele frequency data are becoming more prevalent as ancient DNA (aDNA) sequences and data from evolve-and-resequence (E&R) experiments are generated at a rapid pace. Such data presents unprecedented opportunities to elucidate the dynamics of genetic variation under selection. However, despite many methods to infer parameters of selection models from allele frequency trajectories available in the literature, few provide user-friendly implementations for large-scale empirical applications. Here, we present diplo-locus, an open-source Python package that provides functionality to simulate and perform inference from time-series data under the Wright-Fisher diffusion with general diploid selection. The package includes Python modules as well as command-line tools and is available at: https://github.com/steinrue/diplo_locus.

genetics↗

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↗

bioRxiv2021

Inferring Population Size Histories using Coalescent Hidden Markov Models with TMRCA and Total Branch Length as Hidden States

Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest, but is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes from genomic sequence data are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve along the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly. Here, we present our method CHIMP (CHMM History-Inference Maximum-Likelihood Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes. We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data. Author SummaryThe demograpic history of natural populations shapes their genetic variation. The genomes of contemporary individuals can thus be used to unravel past migration events and population size changes, which is of anthropological interest. However, it is also important to uncover these past events for studies investigating disease related genetic variation, since past demographic events can confound such analyses. Here we present a novel method for inferring the size history of a given population from full-genome sequencing data of contemporary individuals. Our method is based on a Coalescent Hidden Markov model framework, a model frequently applied to this type of inference. A key component of the model is the representation of unobserved local genealogical relationships among the sampled individuals as latent states. This is achieved by numerically solving certain differential equations that describe the distributions of these quantities and ultimately enables inference of past population size changes. Other methods performing similar inference rely on availability of high quality genomic data, whereas we demonstrate that our method can be applied in situations with limited data quality.

genetics↗

bioRxiv2021

A numerical framework for genetic hitchhiking in populations of variable size

Natural selection on beneficial or deleterious alleles results in an increase or decrease, respectively, of their frequency within the population. Due to chromosomal linkage, the dynamics of the selected site affect the genetic variation at nearby neutral loci in a process commonly referred to as genetic hitchhiking. Changes in population size, however, can yield patterns in genomic data that mimic the effects of selection. Accurately modeling these dynamics is thus crucial to understanding how selection and past population size changes impact observed patterns of genetic variation. Here, we model the evolution of haplotype frequencies with the Wright-Fisher diffusion to study the impact of selection on linked neutral variation. Explicit solutions are not known for the dynamics of this diffusion when selection and recombination act simultaneously. Thus, we present a method for numerically evaluating the Wright-Fisher diffusion dynamics of two linked loci separated by a certain recombination distance when selection is acting. We can account for arbitrary population size histories explicitly using this approach. A key step in the method is to express the moments of the associated transition density, or sampling probabilities, as solutions to ordinary differential equations. Numerically solving these differential equations relies on a novel accurate and numerically efficient technique to estimate higher order moments from lower order moments. We demonstrate how this numerical framework can be used to quantify the reduction and recovery of genetic diversity around a selected locus over time and elucidate distortions in the site-frequency-spectra of neutral variation linked to loci under selection in various demographic settings. The method can be readily extended to more general modes of selection and applied in likelihood frameworks to detect loci under selection and infer the strength of the selective pressure.

genetics↗

bioRxiv2020

Detecting runs of homozygosity from low-coverage ancient DNA

At present day, human parental relatedness varies substantially across the globe, but little is known about the past. Here we use ancient DNA to provide new insights, leveraging that parental relatedness leaves traces in the offsprings genome in the form of runs of homozygosity. We present a method to identify such runs in low-coverage ancient DNA data using linkage information from a reference panel of modern haplotypes. As a result, the method facilitates analysis of a much larger fraction of the global ancient DNA record than previously possible. Simulation and experiments show that this new method has power to detect runs of homozygosity longer than 4 centimorgan for ancient individuals with at least 0.3x coverage. We used this new method to analyze sequence data from 1,785 humans from the last 45,000 years. Generally, we detect very low rates of first cousin or closer unions across most ancient populations. Moreover, our results evidence a substantial impact of the adoption of agricultural lifestyles: We find a marked decay in background parental relatedness, co-occurring with or shortly after the advent of sedentary agriculture. We observe this signal, likely linked to increasing local population sizes, across several geographic regions worldwide.

genetics↗