Limits and Convergence properties of the Sequentially Markovian Coalescent

AbstractMany methods based on the Sequentially Markovian Coalescent (SMC) have been and are being developed. These methods make use of genome sequence data to uncover population demographic history. More recently, new methods have extended the original theoretical framework, allowing the simultaneous estimation of the demographic history and other biological variables. These methods can be applied to many different species, under different model assumptions, in hopes of unlocking the population/species evolutionary history. Although convergence proofs in particular cases have been given using simulated data, a clear outline of the performance limits of these methods is lacking. We here explore the limits of this methodology, as well as present a tool that can be used to help users quantify what information can be confidently retrieved from given datasets. In addition, we study the consequences for inference accuracy violating the hypotheses and the assumptions of SMC approaches, such as the presence of transposable elements, variable recombination and mutation rates along the sequence and SNP call errors. We also provide a new interpretation of the SMC through the use of the estimated transition matrix and offer recommendations for the most efficient use of these methods under budget constraints, notably through the building of data sets that would be better adapted for the biological question at hand.

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The power of microbiome studies: some considerations on which alpha and beta metrics to use and how to report analysis the results

10.21203/rs.3.rs-698991/v1 ◽

2021 ◽

Author(s):

Jannigje Gerdien Kers ◽

Edoardo Saccenti

Keyword(s):

Sample Size ◽

Beta Diversity ◽

Sequence Data ◽

Animal Experiments ◽

Simulated Data ◽

Alpha Diversity ◽

Data Sets ◽

Power Calculations ◽

Alpha And Beta Diversity ◽

Diversity Metrics

Abstract Since sequencing techniques become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observed dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data was used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota data sets and the effect on the sample size. Our data showed that beta diversity metrics are most sensitive to observe differences compared to alpha diversity metrics. The structure of the data influenced which alpha metrics are most sensitive. Regarding beta diversity, the Bray-Curtis metric is in general most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias. We recommend to perform power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: on the basis of this observation, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e. p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish, and we stress here the word publish, a statistical plan before experiments are initiated: this practice is customary for clinical trials where a statistical plan describing the endpoints and the corresponding statistical analyses must be disclosed before the start of the study.

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Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data

10.1101/701185 ◽

2019 ◽

Author(s):

Thibaut Sellinger ◽

Diala Abu Awad ◽

Markus Möst ◽

Aurélien Tellier

Keyword(s):

Life History ◽

Genome Sequence ◽

Life History Traits ◽

Sequence Data ◽

Demographic History ◽

Full Genome Sequence ◽

Egg Bank ◽

Genome Sequence Data ◽

Self Fertilization ◽

Population Demographic

AbstractSeveral methods based on the Sequential Markovian Coalescent (SMC) have been developed to use full genome sequence data to uncover population demographic history, which is of interest in its own right and a key requirement to generate a null model for selection tests. While these methods can be applied to all possible species, the underlying assumptions are sexual reproduction at each generation and no overlap of generations. However, in many plant, invertebrate, fungi and other species, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates of seed/egg-bank and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.8 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations on the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

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Y-Chromosome Variation in Southern African Khoe-San Populations Based on Whole-Genome Sequences

Genome Biology and Evolution ◽

10.1093/gbe/evaa098 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1031-1039 ◽

Cited By ~ 1

Author(s):

Thijessen Naidoo ◽

Jingzi Xu ◽

Mário Vicente ◽

Helena Malmström ◽

Himla Soodyall ◽

...

Keyword(s):

Y Chromosome ◽

Sequence Data ◽

Demographic History ◽

Data Sets ◽

High Coverage ◽

Hunter Gatherers ◽

Data Set ◽

Chromosome Sequence ◽

Shared Ancestry ◽

Chromosome Variant

Abstract Although the human Y chromosome has effectively shown utility in uncovering facets of human evolution and population histories, the ascertainment bias present in early Y-chromosome variant data sets limited the accuracy of diversity and TMRCA estimates obtained from them. The advent of next-generation sequencing, however, has removed this bias and allowed for the discovery of thousands of new variants for use in improving the Y-chromosome phylogeny and computing estimates that are more accurate. Here, we describe the high-coverage sequencing of the whole Y chromosome in a data set of 19 male Khoe-San individuals in comparison with existing whole Y-chromosome sequence data. Due to the increased resolution, we potentially resolve the source of haplogroup B-P70 in the Khoe-San, and reconcile recently published haplogroup A-M51 data with the most recent version of the ISOGG Y-chromosome phylogeny. Our results also improve the positioning of tentatively placed new branches of the ISOGG Y-chromosome phylogeny. The distribution of major Y-chromosome haplogroups in the Khoe-San and other African groups coincide with the emerging picture of African demographic history; with E-M2 linked to the agriculturalist Bantu expansion, E-M35 linked to pastoralist eastern African migrations, B-M112 linked to earlier east-south gene flow, A-M14 linked to shared ancestry with central African rainforest hunter-gatherers, and A-M51 potentially unique to the Khoe-San.

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Mean reversion in corporate leverage: evidence from India

Managerial Finance ◽

10.1108/mf-09-2018-0425 ◽

2019 ◽

Vol 45 (9) ◽

pp. 1183-1198

Author(s):

Gaurav S. Chauhan ◽

Pradip Banerjee

Keyword(s):

Capital Structure ◽

Emerging Market ◽

Simulated Data ◽

Mean Reversion ◽

Developed Countries ◽

Data Sets ◽

Debt Ratio ◽

Testing Strategy ◽

Content Type ◽

Financing Behavior

Purpose Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure. Design/methodology/approach The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form. Findings Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly. Research limitations/implications The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms. Practical implications The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate. Originality/value This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

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mtDNAcombine: tools to combine sequences from multiple studies

BMC Bioinformatics ◽

10.1186/s12859-021-04048-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Eleanor F. Miller ◽

Andrea Manica

Keyword(s):

Sequence Data ◽

Data Extraction ◽

Bayesian Skyline Plot ◽

Model Organisms ◽

Data Sets ◽

Data Handling ◽

Online Database ◽

Genetic Studies ◽

Wide Range ◽

Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

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An Integrated Framework for the Inference of Viral Population History From Reconstructed Genealogies

Genetics ◽

10.1093/genetics/155.3.1429 ◽

2000 ◽

Vol 155 (3) ◽

pp. 1429-1437

Author(s):

Oliver G Pybus ◽

Andrew Rambaut ◽

Paul H Harvey

Keyword(s):

Maximum Likelihood ◽

Sequence Data ◽

Demographic History ◽

Population History ◽

Maximum Likelihood Estimates ◽

Viral Population ◽

True Parameter ◽

Subtype B ◽

Exponential Growth Model ◽

Parameter Values

Abstract We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.

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Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽

10.3390/e22090949 ◽

2020 ◽

Vol 22 (9) ◽

pp. 949

Author(s):

Jiangyi Wang ◽

Min Liu ◽

Xinwu Zeng ◽

Xiaoqiang Hua

Keyword(s):

Neural Network ◽

Signal Detection ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection Method ◽

Learning Algorithm ◽

Simulated Data ◽

Data Sets ◽

Feature Maps ◽

Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2015/693450 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13

Author(s):

Jianwei Ding ◽

Yingbo Liu ◽

Li Zhang ◽

Jianmin Wang

Keyword(s):

Working Condition ◽

Sequence Data ◽

A Priori ◽

Real Data ◽

Data Sets ◽

Main Task ◽

Event Sequence ◽

Telemetry Data ◽

Condition Monitoring Systems ◽

Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

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Ecological opportunities and specializations shaped genetic divergence in a highly mobile marine top predator

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2014.1558 ◽

2014 ◽

Vol 281 (1795) ◽

pp. 20141558 ◽

Cited By ~ 34

Author(s):

Marie Louis ◽

Michael C. Fontaine ◽

Jérôme Spitz ◽

Erika Schlund ◽

Willy Dabin ◽

...

Keyword(s):

Population Structure ◽

Mediterranean Sea ◽

Environmental Changes ◽

Demographic History ◽

World Ocean ◽

Ecological Niches ◽

Morphological Divergence ◽

Evolutionary Diversification ◽

The Mediterranean ◽

Population Demographic

Environmental conditions can shape genetic and morphological divergence. Release of new habitats during historical environmental changes was a major driver of evolutionary diversification. Here, forces shaping population structure and ecotype differentiation (‘pelagic’ and ‘coastal’) of bottlenose dolphins in the North-east Atlantic were investigated using complementary evolutionary and ecological approaches. Inference of population demographic history using approximate Bayesian computation indicated that coastal populations were likely founded by the Atlantic pelagic population after the Last Glacial Maxima probably as a result of newly available coastal ecological niches. Pelagic dolphins from the Atlantic and the Mediterranean Sea likely diverged during a period of high productivity in the Mediterranean Sea. Genetic differentiation between coastal and pelagic ecotypes may be maintained by niche specializations, as indicated by stable isotope and stomach content analyses, and social behaviour. The two ecotypes were only weakly morphologically segregated in contrast to other parts of the World Ocean. This may be linked to weak contrasts between coastal and pelagic habitats and/or a relatively recent divergence. We suggest that ecological opportunity to specialize is a major driver of genetic and morphological divergence. Combining genetic, ecological and morphological approaches is essential to understanding the population structure of mobile and cryptic species.

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Reef development and Sea level changes drive Acanthaster Population Expansion in the Indo-Pacific region

10.1101/2020.11.18.388207 ◽

2020 ◽

Author(s):

P.C. Pretorius ◽

T.B. Hoareau

Keyword(s):

Population Size ◽

Sea Level ◽

Environmental Changes ◽

Sequence Data ◽

Demographic History ◽

Population Expansion ◽

Population Level ◽

Sea Level Changes ◽

Reef Development ◽

Curve Fitting Method

AbstractMolecular clock calibration is central in population genetics as it provides an accurate inference of demographic history, whereby helping with the identification of driving factors of population changes in an ecosystem. This is particularly important for coral reef species that are seriously threatened globally and in need of conservation. Biogeographic events and fossils are the main source of calibration, but these are known to overestimate timing and parameters at population level, which leads to a disconnection between environmental changes and inferred reconstructions. Here, we propose the Last Glacial Maximum (LGM) calibration that is based on the assumptions that reef species went through a bottleneck during the LGM, which was followed by an early yet marginal increase in population size. We validated the LGM calibration using simulations and genetic inferences based on Extended Bayesian Skyline Plots. Applying it to mitochondrial sequence data of crown-of-thorns starfish Acanthaster spp., we obtained mutation rates that were higher than phylogenetically based calibrations and varied among populations. The timing of the greatest increase in population size differed slightly among populations, but all started between 10 and 20 kya. Using a curve-fitting method, we showed that Acanthaster populations were more influenced by sea-level changes in the Indian Ocean and by reef development in the Pacific Ocean. Our results illustrate that the LGM calibration is robust and can probably provide accurate demographic inferences in many reef species. Application of this calibration has the potential to help identify population drivers that are central for the conservation and management of these threatened ecosystems.

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