Properties of Markov Chain Monte Carlo Performance across Many Empirical Alignments

How Trustworthy Is Your Tree? Bayesian Phylogenetic Effective Sample Size Through the Lens of Monte Carlo Error

Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as "split") probabilities and tree probabilities, and variability in the estimated summary tree. Specifically, we ask if there is any measure of effective sample size (ESS) applicable to phylogenetic trees which is capable of capturing the Monte Carlo error of these three summary measures. We find that there are some ESS measures capable of capturing the error inherent in using MCMC samples to approximate the posterior distributions on phylogenies. We term these tree ESS measures, and identify a set of three which are useful in practice for assessing the Monte Carlo error. Lastly, we present visualization tools that can improve comparisons between multiple independent MCMC runs by accounting for the Monte Carlo error present in each chain. Our results indicate that common post-MCMC workflows are insufficient to capture the inherent Monte Carlo error of the tree, and highlight the need for both within-chain mixing and between-chain convergence assessments.

Global burden and prediction study of cutaneous squamous cell carcinoma from 1990 to 2030: A systematic analysis and comparison with China

Background

China has the highest number of new cancer cases and deaths globally. Due to particularly low scores in health care quality for cutaneous squamous cell carcinoma (cSCC), the country's cSCC burden requires greater awareness. Consequently, we aimed to evaluate and predict the trend of the cSCC burden globally and in China from 1990 to 2030.

Methods

We retrieved data from the Global Burden of Disease 2019 study, which provided estimates of the incidence, mortality, prevalence, and disability-adjusted life years (DALYs) of cSCC from 1990 to 2019. We set up joint-point analyses and Bayesian age-period-cohort (BAPC) models to predict the disease burden of cSCC up to 2030.

Results

In 2019, China reported age-standardised rates of cSCC prevalence, incidence, mortality, and DALYs of 2.54, 2.12, 0.88, and 16.76 per 100 000 population, respectively. The country's prevalence and incidence rates from 1990 to 2019 were lower than the global levels, but its mortality and DALY rates were higher. The age-standardised rates were higher for males, and the disease burden increased with each age group globally and in China. Moreover, the average annual percentage change showed all indicators were growing faster than the global levels. According to the BAPC model, there will be an upward trend in the prevalence and incidence globally and in China between 2020 and 2030, with a decrease in mortality and DALYs.

Conclusions

We observed an upward trend in the cSCC burden over the past 30 years in China. Prevalence and incidence are expected to continue at a higher rate than the global average in the next decade, while mortality and DALYs are predicted to decrease. As the Chinese population ages, efforts toward managing and preventing cSCC should be targeted towards the elderly population.

Rediscovering the unusual, solitary bryozoan Monobryozoon ambulans Remane, 1936: first molecular and new morphological data clarify its phylogenetic position

BACKGROUND

One of the most peculiar groups of the mostly colonial phylum Bryozoa is the taxon Monobryozoon, whose name already implies non-colonial members of the phylum. Its peculiarity and highly unusual lifestyle as a meiobenthic clade living on sand grains has fascinated many biologists. In particular its systematic relationship to other bryozoans remains a mystery. Despite numerous searches for M. ambulans in its type locality Helgoland, a locality with a long-lasting marine station and tradition of numerous courses and workshops, it has never been reencountered until today. Here we report the first observations of this almost mythical species, Monobryozoon ambulans.

RESULTS

For the first time since 1938, we present new modern, morphological analyses of this species as well as the first ever molecular data. Our detailed morphological analysis confirms most previous descriptions, but also ascertains the presence of special ambulatory polymorphic zooids. We consider these as bud anlagen that ultimately consecutively separate from the animal rendering it pseudo-colonial. The remaining morphological data show strong ties to alcyonidioidean ctenostome bryozoans. Our morphological data is in accordance with the phylogenomic analysis, which clusters it with species of Alcyonidium as a sister group to multiporate ctenostomes. Divergence time estimation and ancestral state reconstruction recover the solitary state of M. ambulans as a derived character that probably evolved in the Late Cretaceous. In this study, we also provide the entire mitogenome of M. ambulans, which-despite the momentary lack of comparable data-provides important data of a unique and rare species for comparative aspects in the future.

CONCLUSIONS

We were able to provide first sequence data and modern morphological data for the unique bryozoan, M. ambulans, which are both supporting an alcyonidioidean relationship within ctenostome bryozoans.

The Impact of Phenological Gaps on Leaf Characteristics and Foliage Dynamics of an Understory Dwarf Bamboo, Sasa kurilensis

Phenological gaps exert a significant influence on the growth of dwarf bamboos. However, how dwarf bamboos respond to and exploit these phenological gaps remain enigmatic. The light environment, soil nutrients, leaf morphology, maximum photosynthetic rate, foliage dynamics, and branching characteristics of Sasa kurilensis were examined under the canopies of Fagus crenata and Magnolia obovata. The goal was to elucidate the adaptive responses of S. kurilensis to phenological gaps in the forest understory. The findings suggest that phenological gaps under an M. obovata canopy augment the available biomass of S. kurilensis, enhancing leaf area, leaf thickness, and carbon content per unit area. However, these gaps do not appreciably influence the maximum photosynthetic rate, total leaf number, leaf lifespan, branch number, and average branch length. These findings underscore the significant impact of annually recurring phenological gaps on various aspects of S. kurilensis growth, such as its aboveground biomass, leaf morphology, and leaf biochemical characteristics. It appears that leaf morphology is a pivotal trait in the response of S. kurilensis to phenological gaps. Given the potential ubiquity of the influence of phenological gaps on dwarf bamboos across most deciduous broadleaf forests, this canopy phenomenon should not be overlooked.

Plasticity in the Morphology of Growing Bamboo: A Bayesian Analysis of Exogenous Treatment Effects on Plant Height, Internode Length, and Internode Numbers

Sucrose (Suc) and gibberellin (GA) can promote the elongation of certain internodes in bamboo. However, there is a lack of field studies to support these findings and no evidence concerning how Suc and GA promote the plant height of bamboo by regulating the internode elongation and number. We investigated the plant height, the length of each internode, and the total number of internodes of Moso bamboo (Phyllostachys edulis) under exogenous Suc, GA, and control group (CTRL) treatments in the field and analyzed how Suc and GA affected the height of Moso bamboo by promoting the internode length and number. The lengths of the 10th-50th internodes were significantly increased under the exogenous Suc and GA treatments, and the number of internodes was significantly increased by the exogenous Suc treatment. The increased effect of Suc and GA exogenous treatment on the proportion of longer internodes showed a weakening trend near the plant height of 15-16 m compared with the CTRL, suggesting that these exogenous treatments may be more effective in regions where bamboo growth is suboptimal. This study demonstrated that both the exogenous Suc and GA treatments could promote internode elongation of Moso bamboo in the field. The exogenous GA treatment had a stronger effect on internode elongation, and the exogenous Suc treatment had a stronger effect on increasing the internode numbers. The increase in plant height by the exogenous Suc and GA treatments was promoted by the co-elongation of most internodes or the increase in the proportion of longer internodes.

Fidelity of hyperbolic space for Bayesian phylogenetic inference

Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees. Fortunately, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences as points in hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference in this space. The posterior probability of an embedding is computed by decoding a neighbour-joining tree from the embedding locations of the sequences. We empirically demonstrate the fidelity of this method on eight data sets. We systematically investigated the effect of embedding dimension and hyperbolic curvature on the performance in these data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree over a range of curvatures and dimensions. We systematically investigated the effects of the embedding space's curvature and dimension on the Markov Chain's performance, demonstrating the suitability of hyperbolic space for phylogenetic inference.

A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software

Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.

Trends and predictions of lung cancer incidence in Jiangsu Province, China, 2009-2030: a bayesian age-period-cohort modelling study

BACKGROUND

Lung cancer is currently the most frequent cancer in Jiangsu Province, China, and the features of cancer distribution have changed continuously in the last decade. The aim of this study was to analyse the trend of the incidence of lung cancer in Jiangsu from 2009 to 2018 and predict the incidence from 2019 to 2030.

METHODS

Data on lung cancer incidence in Jiangsu from 2009 to 2018 were retrieved from the Jiangsu Cancer Registry. The average annual percentage change (AAPC) was used to quantify the trend of the lung cancer age-standardized rate (ASR) using Joinpoint software. Bayesian age-period-cohort models were used to predict lung cancer incidence up to 2030.

RESULTS

In Jiangsu, the lung cancer crude rate increased from 45.73 per 100,000 in 2009 to 69.93 per 100,000 in 2018. The lung cancer ASR increased from 29.03 per 100,000 to 34.22 per 100,000 during the same period (AAPC = 2.17%, 95% confidence interval [CI], 1.54%, 2.80%). Between 2019 and 2030, the lung cancer ASR is predicted to decrease slightly to 32.14 per 100,000 (95% highest density interval [HDI], 24.99, 40.22). Meanwhile, the ASR showed a downward trend in males and rural regions while remaining stable in females and urban regions.

CONCLUSION

We predict that the incidence of lung cancer in Jiangsu will decrease in the next 12 years, mainly due to the decrease in males and rural areas. Therefore, future lung cancer prevention and control efforts should be focused on females and urban regions.

Data integration in Bayesian phylogenetics

Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.