disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data

Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .

Public perception of COVID-19 vaccines through analysis of Twitter content and users

BACKGROUND

With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter.

METHODS

This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period.

FINDINGS

We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time.

INTERPRETATION

This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.

Genomic signatures and evolutionary history of the endangered blue-crowned laughingthrush and other Garrulax species

Background

The blue-crowned laughingthrush (Garrulax courtoisi) is a critically endangered songbird endemic to Wuyuan, China, with population of ~323 individuals. It has attracted widespread attention, but the lack of a published genome has limited research and species protection.

Results

We report two laughingthrush genome assemblies and reveal the taxonomic status of laughingthrush species among 25 common avian species according to the comparative genomic analysis. The blue-crowned laughingthrush, black-throated laughingthrush, masked laughingthrush, white-browed laughingthrush, and rusty laughingthrush showed a close genetic relationship, and they diverged from a common ancestor between ~2.81 and 12.31 million years ago estimated by the population structure and divergence analysis using 66 whole-genome sequencing birds from eight laughingthrush species and one out group (Cyanopica cyanus). Population inference revealed that the laughingthrush species experienced a rapid population decline during the last ice age and a serious bottleneck caused by a cold wave during the Chinese Song Dynasty (960–1279 AD). The blue-crowned laughingthrush is still in a bottleneck, which may be the result of a cold wave together with human exploitation. Interestingly, the existing blue-crowned laughingthrush exhibits extremely rich genetic diversity compared to other laughingthrushes. These genetic characteristics and demographic inference patterns suggest a genetic heritage of population abundance in the blue-crowned laughingthrush. The results also suggest that fewer deleterious mutations in the blue-crowned laughingthrush genomes have allowed them to thrive even with a small population size. We believe that cooperative breeding behavior and a long reproduction period may enable the blue-crowned laughingthrush to maintain genetic diversity and avoid inbreeding depression. We identified 43 short tandem repeats that can be used as markers to identify the sex of the blue-crowned laughingthrush and aid in its genetic conservation.

Conclusions

This study supplies the missing reference genome of laughingthrush, provides insight into the genetic variability, evolutionary potential, and molecular ecology of laughingthrush and provides a genomic resource for future research and conservation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01390-4.

Subtle limits to connectivity revealed by outlier loci within two divergent metapopulations of the deep-sea hydrothermal gastropod Ifremeria nautilei

Hydrothermal vents form archipelagos of ephemeral deep-sea habitats that raise interesting questions about the evolution and dynamics of the associated endemic fauna, constantly subject to extinction-recolonization processes. These metal-rich environments are coveted for the mineral resources they harbor, thus raising recent conservation concerns. The evolutionary fate and demographic resilience of hydrothermal species strongly depend on the degree of connectivity among and within their fragmented metapopulations. In the deep sea, however, assessing connectivity is difficult and usually requires indirect genetic approaches. Improved detection of fine-scale genetic connectivity is now possible based on genome-wide screening for genetic differentiation. Here, we explored population connectivity in the hydrothermal vent snail Ifremeria nautilei across its species range encompassing five distinct back-arc basins in the Southwest Pacific. The global analysis, based on 10 570 single nucleotide polymorphism (SNP) markers derived from double digest restriction-site associated DNA sequencing (ddRAD-seq), depicted two semi-isolated and homogeneous genetic clusters. Demo-genetic modeling suggests that these two groups began to diverge about 70 000 generations ago, but continue to exhibit weak and slightly asymmetrical gene flow. Furthermore, a careful analysis of outlier loci showed subtle limitations to connectivity between neighboring basins within both groups. This finding indicates that migration is not strong enough to totally counterbalance drift or local selection, hence questioning the potential for demographic resilience at this latter geographical scale. These results illustrate the potential of large genomic datasets to understand fine-scale connectivity patterns in hydrothermal vents and the deep sea.

Recent demographic histories of temperate deciduous trees inferred from microsatellite markers

Background

Accurate inference of demographic histories for temperate tree species can aid our understanding of current climate change as a driver of evolution. Microsatellites are more suitable for inferring recent historical events due to their high mutation rates. However, most programs analyzing microsatellite data assume a strict stepwise mutation model (SMM), which could cause false detection of population shrinkage when microsatellite mutation does not follow SMM.

Results

This study aims to reconstruct the recent demographic histories of five cool-temperate tree species in Eastern Asia, Quercus mongolica, Q. liaotungensis, Juglans cathayensis, J. mandshurica and J. ailantifolia, by using 19 microsatellite markers with two methods considering generalized stepwise mutation model (GSM) (MIGRAINE and VarEff). Both programs revealed that all the five species experienced expansions after the Last Glacial Maximum (LGM). Within butternuts, J. cathayensis experienced a more serious bottleneck than the other species, and within oaks, Q. mongolica showed a moderate increase in population size and remained stable after the expansion. In addition, the point estimates of the multistep mutation proportion in the GSM model (p_GSM) for all five species were between 0.50 and 0.65, indicating that when inferring population demographic history of the cool-temperate forest species using microsatellite markers, it is better to assume a GSM rather than a SMM.

Conclusions

This study provides the first direct evidence that five cool-temperate tree species in East Asia have experienced expansions after the LGM with microsatellite data. Considering the mutation model of microsatellite has a vital influence on demographic inference, combining multiple programs such as MIGRAINE and VarEff can effectively reduce errors caused by inappropriate model selection and prior setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01805-w.

Genomic history of the Italian population recapitulates key evolutionary dynamics of both Continental and Southern Europeans

BACKGROUND

The cline of human genetic diversity observable across Europe is recapitulated at a micro-geographic scale by variation within the Italian population. Besides resulting from extensive gene flow, this might be ascribable also to local adaptations to diverse ecological contexts evolved by people who anciently spread along the Italian Peninsula. Dissecting the evolutionary history of the ancestors of present-day Italians may thus improve the understanding of demographic and biological processes that contributed to shape the gene pool of European populations. However, previous SNP array-based studies failed to investigate the full spectrum of Italian variation, generally neglecting low-frequency genetic variants and examining a limited set of small effect size alleles, which may represent important determinants of population structure and complex adaptive traits. To overcome these issues, we analyzed 38 high-coverage whole-genome sequences representative of population clusters at the opposite ends of the cline of Italian variation, along with a large panel of modern and ancient Euro-Mediterranean genomes.

RESULTS

We provided evidence for the early divergence of Italian groups dating back to the Late Glacial and for Neolithic and distinct Bronze Age migrations having further differentiated their gene pools. We inferred adaptive evolution at insulin-related loci in people from Italian regions with a temperate climate, while possible adaptations to pathogens and ultraviolet radiation were observed in Mediterranean Italians. Some of these adaptive events may also have secondarily modulated population disease or longevity predisposition.

CONCLUSIONS

We disentangled the contribution of multiple migratory and adaptive events in shaping the heterogeneous Italian genomic background, which exemplify population dynamics and gene-environment interactions that played significant roles also in the formation of the Continental and Southern European genomic landscapes.

MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent

The Multiple Sequentially Markovian Coalescent (MSMC) is a population genetic method and software for inferring demographic history and population structure through time from genome sequences. Here we describe the main program MSMC and its successor MSMC2. We go through all the necessary steps of processing genomic data from BAM files all the way to generating plots of inferred population size and separation histories. Some background on the methodology itself is provided, as well as bash scripts and python source code to run the necessary programs. The reader is also referred to community resources such as a mailing list and github repositories for further advice.

An empirical approach to demographic inference with genomic data

Inference with population genetic data usually treats the population pedigree as a nuisance parameter, the unobserved product of a past history of random mating. However, the history of genetic relationships in a given population is a fixed, unobserved object, and so an alternative approach is to treat this network of relationships as a complex object we wish to learn about, by observing how genomes have been noisily passed down through it. This paper explores this point of view, showing how to translate questions about population genetic data into calculations with a Poisson process of mutations on all ancestral genomes. This method is applied to give a robust interpretation to the f₄ statistic used to identify admixture, and to design a new statistic that measures covariances in mean times to most recent common ancestor between two pairs of sequences. The method more generally interprets population genetic statistics in terms of sums of specific functions over ancestral genomes, thereby providing concrete, broadly interpretable interpretations for these statistics. This provides a method for describing demographic history without simplified demographic models. More generally, it brings into focus the population pedigree, which is averaged over in model-based demographic inference.

Estimating Lactase Nonpersistence Distributions in the Multi-Ethnic Canadian Demographic: A Population-Based Study

Objectives

The lactase persistence/nonpersistence (LP/LNP) phenotypes follow a geographic pattern that is rooted in the gene-culture coevolution observed throughout the history of human migrations. The immense size and relatively open immigration policy have drawn migrants of diverse ethnicities to Canada. Among the multicultural demographic, two-thirds of the population are derived from the British Isles and northwestern France. A recent assessment of worldwide lactase distributions found Canada to have an LNP rate of 59% (confidence interval [CI] 44%-74%). This estimate is rather high compared with earlier reports that listed Canada as a country with a 10% LNP rate; the authors had also noted that biases were likely because their calculations were based largely on Aboriginal studies. We hereby present an alternate LNP prevalence estimate at the national, provincial and territorial level.

Methods

We applied the referenced LNP frequency distribution data to the 2016 population census to account for the current multi-ethnic distributions in Canada. Prevalence rates for Canada, the provinces and territories were calculated.

Results

The national LNP rate is estimated at 44% (CI 41%-47%) after accounting for the 254 ethnic groups, with the lowest rates found in the eastern provinces and the highest rates in the Northwest Territories (57%) and Nunavut (66%), respectively.

Conclusion

Despite the heterogeneous nature of the referenced data and the inference measures taken, evidently, the validity of our LNP estimate is anchored on the inclusion of multi-ethnic groups representing the current Canadian demographic.

On the decidability of population size histories from finite allele frequency spectra

Understanding the historical events that shaped current genomic diversity has applications in historical, biological, and medical research. However, the amount of historical information that can be inferred from genetic data is finite, which leads to an identifiability problem. For example, different historical processes can lead to identical distribution of allele frequencies. This identifiability issue casts a shadow of uncertainty over the results of any study which uses the frequency spectrum to infer past demography. It has been argued that imposing mild 'reasonableness' constraints on demographic histories can enable unique reconstruction, at least in an idealized setting where the length of the genome is nearly infinite. Here, we discuss this problem for finite sample size and genome length. Using the diffusion approximation, we obtain bounds on likelihood differences between similar demographic histories, and use them to construct pairs of very different reasonable histories that produce almost-identical frequency distributions. The finite-genome problem therefore remains poorly determined even among reasonable histories. Where fits to few-parameter models produce narrow parameter confidence intervals, large uncertainties lurk hidden by model assumption.

Resampling: An improvement of importance sampling in varying population size models

Sequential importance sampling algorithms have been defined to estimate likelihoods in models of ancestral population processes. However, these algorithms are based on features of the models with constant population size, and become inefficient when the population size varies in time, making likelihood-based inferences difficult in many demographic situations. In this work, we modify a previous sequential importance sampling algorithm to improve the efficiency of the likelihood estimation. Our procedure is still based on features of the model with constant size, but uses a resampling technique with a new resampling probability distribution depending on the pairwise composite likelihood. We tested our algorithm, called sequential importance sampling with resampling (SISR) on simulated data sets under different demographic cases. In most cases, we divided the computational cost by two for the same accuracy of inference, in some cases even by one hundred. This study provides the first assessment of the impact of such resampling techniques on parameter inference using sequential importance sampling, and extends the range of situations where likelihood inferences can be easily performed.

Incipient sympatric speciation in Midas cichlid fish from the youngest and one of the smallest crater lakes in Nicaragua due to differential use of the benthic and limnetic habitats?

Understanding how speciation can occur without geographic isolation remains a central objective in evolutionary biology. Generally, some form of disruptive selection and assortative mating are necessary for sympatric speciation to occur. Disruptive selection can arise from intraspecific competition for resources. If this competition leads to the differential use of habitats and variation in relevant traits is genetically determined, then assortative mating can be an automatic consequence (i.e., habitat isolation). In this study, we caught Midas cichlid fish from the limnetic (middle of the lake) and benthic (shore) habitats of Crater Lake Asososca Managua to test whether some of the necessary conditions for sympatric speciation due to intraspecific competition and habitat isolation are given. Lake As. Managua is very small (<900 m in diameter), extremely young (maximally 1245 years of age), and completely isolated. It is inhabited by, probably, only a single endemic species of Midas cichlids, Amphilophus tolteca. We found that fish from the limnetic habitat were more elongated than fish collected from the benthic habitat, as would be predicted from ecomorphological considerations. Stable isotope analyses confirmed that the former also exhibit a more limnetic lifestyle than the latter. Furthermore, split‐brood design experiments in the laboratory suggest that phenotypic plasticity is unlikely to explain much of the observed differences in body elongation that we observed in the field. Yet, neutral markers (microsatellites) did not reveal any genetic clustering in the population. Interestingly, demographic inferences based on RAD‐seq data suggest that the apparent lack of genetic differentiation at neutral markers could simply be due to a lack of time, as intraspecific competition may only have begun a few hundred generations ago.

Demographic inference under the coalescent in a spatial continuum

Understanding population dynamics from the analysis of molecular and spatial data requires sound statistical modeling. Current approaches assume that populations are naturally partitioned into discrete demes, thereby failing to be relevant in cases where individuals are scattered on a spatial continuum. Other models predict the formation of increasingly tight clusters of individuals in space, which, again, conflicts with biological evidence. Building on recent theoretical work, we introduce a new genealogy-based inference framework that alleviates these issues. This approach effectively implements a stochastic model in which the distribution of individuals is homogeneous and stationary, thereby providing a relevant null model for the fluctuation of genetic diversity in time and space. Importantly, the spatial density of individuals in a population and their range of dispersal during the course of evolution are two parameters that can be inferred separately with this method. The validity of the new inference framework is confirmed with extensive simulations and the analysis of influenza sequences collected over five seasons in the USA.

Ancestral population genomics using coalescence hidden Markov models and heuristic optimisation algorithms

With full genome data from several closely related species now readily available, we have the ultimate data for demographic inference. Exploiting these full genomes, however, requires models that can explicitly model recombination along alignments of full chromosomal length. Over the last decade a class of models, based on the sequential Markov coalescence model combined with hidden Markov models, has been developed and used to make inference in simple demographic scenarios. To move forward to more complex demographic modelling we need better and more automated ways of specifying these models and efficient optimisation algorithms for inferring the parameters in complex and often high-dimensional models. In this paper we present a framework for building such coalescence hidden Markov models for pairwise alignments and present results for using heuristic optimisation algorithms for parameter estimation. We show that we can build more complex demographic models than our previous frameworks and that we obtain more accurate parameter estimates using heuristic optimisation algorithms than when using our previous gradient based approaches. Our new framework provides a flexible way of constructing coalescence hidden Markov models almost automatically. While estimating parameters in more complex models is still challenging we show that using heuristic optimisation algorithms we still get a fairly good accuracy.