Biology subjects

Koskela, J.

Publications and source records attributed to Koskela, J..

10 recordsLinked to original sources

bioRxiv2025

EWF 2.0: Exact sampling from the Wright-Fisher diffusion with time-varying demography

Accurate modelling of allele frequency trajectories requires incorporation of both genetic mechanisms such as selection and mutation, as well as realistic population demography. Accounting for a non-constant demography within a Wright-Fisher diffusion framework induces a time-inhomogenous drift coefficient, a regime falling outside the scope of existing exact simulation routines. To address this gap, we introduce EWF 2.0, an exact simulation algorithm that accommodates time-varying demography within Wright-Fisher diffusions whilst retaining all the functionality of previous EWF versions. We validate correctness using distributional tests (Kolmogorov-Smirnov, QQ plots), confirming agreement with theoretical expectations. In spite of its greater generality, EWF 2.0 retains the same runtime as in previous versions, ensuring computational efficiency and scalability. All software is available at https://github.com/JaroSant/EWF. EWF 2.0 is particularly valuable for bridge simulation, where existing methods cannot handle time-varying mutation and selection rates. For a specified demographic history, mutation parameters, selection function and sampling times, EWF 2.0 generates exact draws from the law of the corresponding Wright-Fisher diffusion or diffusion bridge.

genetics↗

bioRxiv2024

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing comparable software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

evolutionary biology↗

bioRxiv2023

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

genetics↗

bioRxiv2023

Inference of multiple mergers while dating a pathogen phylogeny

The vast majority of pathogen phylogenetic studies do not consider the possibility of multiple merger events being present, where a single node of the tree leads to more than two descendent branches. These events are however likely to occur when studying a relatively small population or if there is high variability in the reproductive chances. Here we consider the problem of detecting the presence of multiple mergers in the context of dating a phylogeny, that is determining the date of each of the nodes. We use the Lambda-coalescent theory as a modelling framework and show how Bayesian inference can be efficiently performed using a Billera-Holmes-Vogtmann space embedding and a customised Markov Chain Monte Carlo sampling scheme. We applied this new analysis methodology to a large number of simulated datasets to show that it is possible to infer if and when multiple merger events occurred, and that the phylogenetic dating is improved as a result of taking this information into account. We also analysed real datasets of Vibrio cholerae and Mycobacterium tuberculosis to demonstrate the relevance of our approach to real pathogen evolutionary epidemiology. We have implemented our new methodology in a R package which is freely available at https://github.com/dhelekal/MMCTime.

evolutionary biology↗

bioRxiv2023

The distribution of branch duration and detection of inversions in ancestral recombination graphs

Recent breakthroughs have enabled the accurate inference of large-scale genealogies. Through modelling the impact of recombination on the correlation structure between genealogical local trees, we evaluate how this structure is reconstructed by leading approaches. Despite identifying pervasive biases, we show that applying a simple correction recovers the desired distributions for one algorithm, Relate. We develop a statistical test to identify clades spanning unexpectedly long genomic regions, likely reflecting regional suppression of recombination in some individuals. Our approach allows a systematic scan for inter-individual recombination rate variation at an intermediate scale, between genome-wide differences and individual hotspots. Using genealogies reconstructed with Relate for 2 504 human genomes, we identify 50 regions possessing clades with unexpectedly long genomic spans (p < 1 {middle dot} 10-12). The strongest signal corresponds to a known inversion on chromosome 17. The second strongest uncovers a novel 760kb inversion on chromosome 10, common (21%) in S. Asians and correlated with GWAS hits for a range of phenotypes. Other regions indicate additional genomic rearrangements: inversions (8), copy number changes (2), or other variants (12). The remaining regions appear to reflect recombination suppression by previously unevidenced mechanisms. They are enriched for precisely spanning single genes (p = 5 10-10), specifically those expressed in male gametogenesis, and for eQTLs (p = 2 {middle dot}10-3). This suggests an extension of previously hypothesised crossover suppression within meiotic genes, towards a model of suppression varying across individuals with different expression levels. Our methods can be readily applied to other species, showing that genealogies offer previously un-tapped potential to study structural variation and other phenomena impacting evolution.

evolutionary biology↗

bioRxiv2022

Sweepstakes reproductive success via pervasive and recurrent selective sweeps

Highly fecund natural populations characterized by high early mortality abound, yet our knowledge about their recruitment dynamics is somewhat rudimentary. This knowledge gap has implications for our understanding of genetic variation, population connectivity, local adaptation, and the resilience of highly fecund populations. The concept of sweepstakes reproductive success, which posits a considerable variance and skew in individual reproductive output, is key to understanding the distribution of individual reproductive success. However, it still needs to be determined whether highly fecund organisms reproduce through sweepstakes and, if they do, the relative roles of neutral and selective sweepstakes. Here we use coalescent-based statistical analysis of population genomic data to show that selective sweepstakes likely explain recruitment dynamics in the highly fecund Atlantic cod. We show that the Kingman coalescent (modeling no sweepstakes) and the Xi-Beta coalescent (modeling random sweepstakes), including complex demography and background selection, do not provide an adequate fit for the data. The Durrett-Schweinsberg coalescent, in which selective sweepstakes result from recurrent and pervasive selective sweeps of new mutations, offers greater explanatory power. Our results show that models of sweepstakes reproduction and multiple-merger coalescents are relevant and necessary for understanding genetic diversity in highly fecund natural populations. These findings have fundamental implications for understanding the recruitment variation of fish stocks and general evolutionary genomics of high-fecundity organisms.

evolutionary biology↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2019

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 putative causal variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1x10-4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the putative causal variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

genetics↗

bioRxiv2019

Polygenic risk score of alcohol consumption predicts alcohol-related morbidity and all-cause mortality

ObjectiveTo develop a highly polygenic risk score (PRS) for alcohol consumption and study whether it predicts alcohol-related morbidity and all-cause mortality.\n\nDesignBiobank-based prospective cohort study\n\nSettingFinnGen Study (Finland)\n\nParticipants96,499 genotyped participants from the nationwide prospective FinnGen study and 36,499 participants from prospective cohorts (Health 2000, FINRISK, Twin Cohort) with detailed baseline data and up to 25 years of follow-up time.\n\nMain outcome measuresIncident alcohol-related morbidity and alcohol-related or all-cause mortality, based on hospitalizations, outpatient specialist care, drug purchases, and death reports.\n\nResultsIn 96,499 FinnGen participants there were in total 4,785 first-observed incident alcohol-related health events. The PRS of alcohol consumption was associated with alcohol-related morbidity and the risk estimate (hazard ratio, HR) between the highest and lowest quintiles of the PRS was 1.67 [ 95 % confidence interval: 1.52-1.84], p=3.2*10-27). In 28,639 participants with comprehensive baseline data from prospective Health 2000 and FINRISK cohorts, 911 incident first alcohol-related events were observed. When adjusted for self-reported alcohol consumption, education, marital status, and gamma-glutamyl transferase blood levels, the risk estimate between the highest and lowest quintiles of the PRS was 1.58 (CI=[1.26-1.99], p=8.2*10-5). The PRS was also associated with all-cause mortality with a risk estimate of 1.33 between the highest and lowest quintiles (CI=[1.2-1.47], p=4.5e-08) in the adjusted model. In all 39,695 participants with self-reported alcohol consumption available, a 1 SD increase in the PRS was associated with 11.2 g (=0.93 drinks) higher weekly alcohol consumption ({beta}=11.2 [9.85-12.58 g], p = 2.3*10-58).\n\nConclusionsThe PRS for alcohol consumption associates for both alcohol-related morbidity and all-cause mortality. These findings underline the importance of heritable factors in alcohol-related behavior and the related health burden. The results highlight how measured genetic risk for an important behavioral risk factor can be used to predict related health outcomes.

genomics↗

bioRxiv2018

Geographic variation and bias in polygenic scores of complex diseases and traits in Finland

Polygenic scores (PS) are becoming a useful tool to identify individuals with high genetic risk for complex diseases and several projects are currently testing their utility for translational applications. It is also tempting to use PS to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how population genetic properties of the training and target samples affect the geographic distribution of PS. Here, we evaluate geographic differences, and related biases, of PS in Finland with geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PS for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waits-hip ratio (WHR), body-mass index (BMI) and height, but not for Crohns disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PS and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulation of geographic differences for CAD, WHR, BMI and height, suggesting bias arising from population genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PS are for small biases even within relatively homogenous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PS is essential for translational applications of PS.

genetics↗