The genetic population structure of Thunnus thynnus (Linnaeus, 1758) in the Mediterranean Sea,a controversial issue

The Atlantic bluefin tuna (ABFT), Thunnus thynnus (Linnaeus, 1758), is an important commercial species managed as two different stocks, western and eastern Atlantic, with their spawning grounds in the Gulf of Mexico and in the Mediterranean Sea, respectively. The eastern Atlantic stock has been overexploited in the last decades, leading to the application of specific management measures introduced by the International Commission for the Atlantic Tuna (ICCAT). A clear understanding of the genetic structure of ABFT Mediterranean population should be pursued in order to support management decisions. To date the genetic studies on the Mediterranean ABFT, carried out with different molecular markers and sampling procedures, have produced unclear results. Here, we analysed ABFT samples from central and western Mediterranean Sea with mitochondrial sequences and 11 microsatellite loci to investigate, among the others, the area of the Strait of Messina, where environmental conditions seem to support a resident population of ABFT. Furthermore, genetic analyses of mitochondrial sequences were carried out including nucleotide sequences of Adriatic ABFT wild larvae retrieved from GenBank. Among the investigated areas a genetic differentiation was detected between the Strait of Messina and the Tyrrhenian Sea with microsatellite loci according to the exact G test, but not to the Bayesian analyses carried out with STRUCTURE. The analyses with mitochondrial sequences do not reveal any differentiation among sampled areas, however, a highly significant genetic divergence was observed between the Adriatic mitochondrial sequences retrieved from GenBank and the central‐western Mediterranean sequences obtained in the present work. Our results provide some evidence of population structure of Mediterranean ABFT adding pieces to a still unclear picture.     

Unexpected cryptic species diversity in the widespread coral Seriatopora hystrix masks spatial‐genetic patterns of connectivity

Mounting evidence of cryptic species in a wide range of taxa highlights the need for careful analyses of population genetic data sets to unravel within‐species diversity from potential interspecies relationships. Here, we use microsatellite loci and hierarchical clustering analysis to investigate cryptic diversity in sympatric and allopatric (separated by 450 km) populations of the widespread coral Seriatopora hystrix on the Great Barrier Reef. Structure analyses delimited unique genetic clusters that were confirmed by phylogenetic and extensive population‐level analyses. Each of four sympatric yet distinct genetic clusters detected within S. hystrix demonstrated greater genetic cohesion across regional scales than between genetic clusters within regions (<10 km). Moreover, the magnitude of genetic differentiation between different clusters (>0.620 G”_ST) was similar to the difference between S. hystrix clusters and the congener S. caliendrum (mean G”_ST 0.720). Multiple lines of evidence, including differences in habitat specificity, mitochondrial identity, Symbiodinium associations and morphology, corroborate the nuclear genetic evidence that these distinct clusters constitute different species. Hierarchical clustering analysis combined with more traditional population genetic methods provides a powerful approach for delimiting species and should be regularly applied to ensure that ecological and evolutionary patterns interpreted for single species are not confounded by the presence of cryptic species.     

Analyses of genetic ancestry enable key insights for molecular ecology

Gene flow and recombination in admixed populations produce genomes that are mosaic combinations of chromosome segments inherited from different source populations, that is, chromosome segments with different genetic ancestries. The statistical problem of estimating genetic ancestry from DNA sequence data has been widely studied, and analyses of genetic ancestry have facilitated research in molecular ecology and ecological genetics. In this review, we describe and compare different model‐based statistical methods used to infer genetic ancestry. We describe the conceptual and mathematical structure of these models and highlight some of their key differences and shared features. We then discuss recent empirical studies that use estimates of genetic ancestry to analyse population histories, the nature and genetic basis of species boundaries, and the genetic architecture of traits. These diverse studies demonstrate the breadth of applications that rely on genetic ancestry estimates and typify the genomics‐enabled research that is becoming increasingly common in molecular ecology. We conclude by identifying key research areas where future studies might further advance this field.     

A genetic analysis of dragonfly population structure

Dragonflies reside in both aquatic and terrestrial environments, depending on their life stage, necessitating the conservation of drastically different habitats; however, little is understood about how nymph and adult dragonflies function as metapopulations within connected habitat. We used genetic techniques to examine nymphs and adults within a single metapopulation both spatially and temporally to better understand metapopulation structure and the processes that might influence said structure. We sampled 97 nymphs and 149 adult Sympetrum obtrusum from eight locations, four aquatic, and four terrestrial, at the Pierce Cedar Creek Institute in Southwest Michigan over two summers. We performed AFLP genetic analysis and used the Bayesian analysis program STRUCTURE to detect genetic clusters from sampled individuals. STRUCTURE detected k = u4 populations, in which nymphs and adults from the same locations collected in different years did not necessarily fall into the same clusters. We also evaluated grouping using the statistical clustering analyses NMDS and MRPP. The results of these confirmed findings from STRUCTURE and emphasized differences between adults collected in 2012 and all other generations. These results suggest that both dispersal and a temporal cycle of emergence of nymphs from unique clusters every other year could be influential in structuring dragonfly populations, although our methods were not able to fully distinguish the influences of either force. This study provides a better understanding of local dragonfly metapopulation structure and provides a starting point for future studies to investigate the spatial and temporal mechanisms controlling metapopulation structure. The results of the study should prove informative for managers working to preserve genetic diversity in connected dragonfly metapopulations, especially in the face of increasing anthropogenic landscape changes.     

Minor allele frequency thresholds strongly affect population structure inference with genomic data sets

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure—often the first step in analysis of population genomic data. We find that model‐based inference of population structure is confounded when singletons are included in the alignment, and that both model‐based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.     

Phenotypic and genetic diversity in aposematic Malagasy poison frogs (genus Mantella)

Intraspecific color variation has long fascinated evolutionary biologists. In species with bright warning coloration, phenotypic diversity is particularly compelling because many factors, including natural and sexual selection, contribute to intraspecific variation. To better understand the causes of dramatic phenotypic variation in Malagasy poison frogs, we quantified genetic structure and color and pattern variation across three closely related species, Mantella aurantiaca, Mantella crocea, and Mantella milotympanum. Although our restriction site‐associated DNA (RAD) sequencing approach identified clear genetic clusters, they do not align with current species designations, which has important conservation implications for these imperiled frogs. Moreover, our results suggest that levels of intraspecific color variation within this group have been overestimated, while species diversity has been underestimated. Within major genetic clusters, we observed distinct patterns of variation including: populations that are phenotypically similar yet genetically distinct, populations where phenotypic and genetic breaks coincide, and populations that are genetically similar but have high levels of within‐population phenotypic variation. We also detected admixture between two of the major genetic clusters. Our study suggests that several mechanisms—including hybridization, selection, and drift—are contributing to phenotypic diversity. Ultimately, our work underscores the need for a reevaluation of how polymorphic and polytypic populations and species are classified, especially in aposematic organisms.     

Population genetic structure of two ecologically distinct Amazonian spiny rats: separating history and current ecology

Abstract. Population history and current demographic and ecological factors determine the amount of genetic variation within and the degree of differentiation among populations. Differences in the life history and ecology of codistributed species may lead to differences in hierarchical population genetic structure. Here, we compare patterns of genetic diversity and structure of two species of spiny rats in the genus Proechimys from the Rio Jurua of western Amazonian Brazil. Based on the ecological and life-history differences between the two species, we make predictions as to how they might differ in patterns of genetic diversity and structure. We use mitochondrial sequence data from the cytochrome b gene to test these predictions. Although both species maintain nearly the same number of mitochondrial haplotypes across the sampled range, they differ in levels of genetic diversity and geographic structure. Patterns of gene flow are also different between the two species with average M-values of nearly three in P. steerei and less than one in P. simonsi . Our initial predictions are largely upheld by the genetic data and where conflicting hypotheses arise, we suggest further studies that may allow us to distinguish among evolutionary scenarios. Separating the effects of history and ongoing demography on patterns of genetic diversity is challenging. Combining genetic analyses with field studies remains essential to disentangling these complex processes.     

Identifying the number of population clusters with structure: problems and solutions

The program structure has been used extensively to understand and visualize population genetic structure. It is one of the most commonly used clustering algorithms, cited over 11 500 times in Web of Science since its introduction in 2000. The method estimates ancestry proportions to assign individuals to clusters, and post hoc analyses of results may indicate the most likely number of clusters, or populations, on the landscape. However, as has been shown in this issue of Molecular Ecology Resources by Puechmaille ( 2016 ), when sampling is uneven across populations or across hierarchical levels of population structure, these post hoc analyses can be inaccurate and identify an incorrect number of population clusters. To solve this problem, Puechmaille ( 2016 ) presents strategies for subsampling and new analysis methods that are robust to uneven sampling to improve inferences of the number of population clusters.     

Testing for Hardy–Weinberg equilibrium in structured populations using genotype or low‐depth next generation sequencing data

Testing for deviations from Hardy–Weinberg equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of particular evolutionary interest. Large‐scale genetic studies based on next‐generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large‐scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analysing these data set, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low‐depth NGS data, as well as in genotype data, for both simulated and real data set, where the use of genotype likelihoods enables us to model the uncertainty.     

Symbiodinium population genetics: testing for species boundaries and analysing samples with mixed genotypes

Population genetic markers are increasingly being used to study the diversity, ecology and evolution of Symbiodinium, a group of eukaryotic microbes that are often mutualistic with reef‐building corals. Population genetic markers can resolve individual clones, or strains, from samples of host tissue; however, samples may comprise different species that may confound interpretations of gene flow and genetic structure. Here, we propose a method for resolving species from population genetic data using tests for genetic recombination. Assigning individuals to genetically recombining populations prior to further analyses avoids critical errors in the interpretation of gene flow and dispersal. To demonstrate the effectiveness of the approach, we first apply this method to a simulated data set. We then use the method to resolve two species of host generalist Symbiodinium that commonly co‐occur in reef‐building corals collected from Indo‐West Pacific reefs. We demonstrate that the method is robust even when some hosts contain genotypes from two distinct species. Finally, we examine population genetic data sets from two recently published papers in Molecular Ecology. We show that each strongly supports a two species interpretation, which significantly changes the original conclusions presented in these studies. When combined with available phylogenetic and ecological evidence, the use of population genetic data offers a robust method for unambiguously delimiting morphologically cryptic species.     

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever‐widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F_ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity‐based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration‐specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context‐dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non‐human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high‐throughput technologies in population genetics.     

Population genetic structure of wolves in the northwestern Dinaric‐Balkan region

The Balkan Peninsula and the Dinaric Mountains possess extraordinary biodiversity and support one of the largest and most diverse wolf (Canis lupus) populations in Europe. Results obtained with diverse genetic markers show west‐east substructure, also seen in various other species, despite the absence of obvious barriers to movement. However, the spatial extent of the genetic clusters remains unresolved, and our aim was to combine fine‐scale sampling with population and spatial genetic analyses to improve resolution of wolf genetic clusters. We analyzed 16 autosomal microsatellites from 255 wolves sampled in Slovenia, Croatia, Bosnia and Herzegovina (BIH), and Serbia and documented three genetic clusters. These comprised (1) Slovenia and the regions of Gorski kotar and Lika in Croatia, (2) the region of Dalmatia in southern Croatia and BIH, and (3) Serbia. When we mapped the clusters geographically, we observed west‐east genetic structure across the study area, together with some specific structure in BIH–Dalmatia. We observed that cluster 1 had a smaller effective population size, consistent with earlier reports of population recovery since the 1980s. Our results provide foundation for future genomic studies that would further resolve the observed west‐east population structure and its evolutionary history in wolves and other taxa in the region and identify focal areas for habitat conservation. They also have immediate importance for conservation planning for the wolves in one of the most important parts of the species’ European range.     

Influence of life-history variation on the genetic structure of two sympatric salamander taxa

Life-history characteristics are an important determinant of a species' dispersal abilities. We predict that variation in life history can influence population-level genetic patterns. To test this prediction, we estimate population-level genetic structure for two sympatric species of stream-breeding salamander. The Cope's giant salamander ( Dicamptodon copei ) rarely metamorphoses into a terrestrial adult, thereby limiting overland dispersal and potentially gene flow. In contrast, the Pacific giant salamander ( D. tenebrosus ) commonly metamorphoses, which is expected to facilitate overland dispersal and gene flow. Three sets of analyses based on microsatellite data support these hypotheses, showing that D . tenebrosus displays minimal population-level genetic structuring and no pattern of isolation by distance, whereas D . copei displays a high degree of population-level genetic structure and significant isolation by distance. Specifically, nearly all pairwise F _ST values were significantly different from 0 between populations of D. copei , with fewer than half the pairwise F _ST values significant from 0 in D. tenebrosus . Additionally, S tructure analyses indicated eight genetic clusters for D. copei but only one genetic cluster for D. tenebrosus . Finally, Mantel tests showed significant correlations between stream and overland distance with genetic distance for D. copei but no significant correlations of either landscape feature for D. tenebrosus at the scale of the study. These results provide a case study of the link between life-history variation and population genetic patterns while controlling for phylogeny and environmental variation.     

Global patterns of population genetic differentiation in seed plants

Evaluating the factors that drive patterns of population differentiation in plants is critical for understanding several biological processes such as local adaptation and incipient speciation. Previous studies have given conflicting results regarding the significance of pollination mode, seed dispersal mode, mating system, growth form and latitudinal region in shaping patterns of genetic structure, as estimated by F_ST values, and no study to date has tested their relative importance together across a broad scale. Here, we assembled a 337‐species data set for seed plants from publications with data on F_ST from nuclear markers and species traits, including variables pertaining to the sampling scheme of each study. We used species traits, while accounting for sampling variables, to perform phylogenetic multiple regressions. Results demonstrated that F_ST values were higher for tropical, mixed‐mating, non‐woody species pollinated by small insects, indicating greater population differentiation, and lower for temperate, outcrossing trees pollinated by wind. Among the factors we tested, latitudinal region explained the largest portion of variance, followed by pollination mode, mating system and growth form, while seed dispersal mode did not significantly relate to F_ST. Our analyses provide the most robust and comprehensive evaluation to date of the main ecological factors predicted to drive population differentiation in seed plants, with important implications for understanding the basis of their genetic divergence. Our study supports previous findings showing greater population differentiation in tropical regions and is the first that we are aware of to robustly demonstrate greater population differentiation in species pollinated by small insects.     

Number of individuals and molecular markers to use in genetic differentiation studies

Molecular markers are frequently used to study genetic variation among individuals within or between populations. Differences in marker banding patterns can be used to verify if individuals do, or do not, represent distinct groups or populations. Only in 2005, more than 500 studies used molecular markers to group individuals in clusters. Such studies make use of an arbitrary number of molecular markers from each of an arbitrary number of individuals presumed to represent distinct genotypes. However, the greater the genetic variation, the more likely a larger number of individuals and markers will be needed to capture a population's genetic signature. The numbers of both, markers and individuals included thus affect the way in which individuals are organized through cluster analyses, thereby affecting the conclusions drawn. Here we present a method that provides statistical criteria to verify that individual and marker sample sizes are sufficient to accurately depict genetic differentiation among different populations. Our method uses a resampling technique to assess the reproducibility of obtaining a particular grouping pattern for specific data sets. It thus, allows to estimate the robustness of the results obtained without including additional individuals, or markers.     

Evidence for the Selective Reporting of Analyses and Discrepancies in Clinical Trials: A Systematic Review of Cohort Studies of Clinical Trials

Background

Most publications about selective reporting in clinical trials have focussed on outcomes. However, selective reporting of analyses for a given outcome may also affect the validity of findings. If analyses are selected on the basis of the results, reporting bias may occur. The aims of this study were to review and summarise the evidence from empirical cohort studies that assessed discrepant or selective reporting of analyses in randomised controlled trials (RCTs).

Methods and Findings

A systematic review was conducted and included cohort studies that assessed any aspect of the reporting of analyses of RCTs by comparing different trial documents, e.g., protocol compared to trial report, or different sections within a trial publication. The Cochrane Methodology Register, Medline (Ovid), PsycInfo (Ovid), and PubMed were searched on 5 February 2014. Two authors independently selected studies, performed data extraction, and assessed the methodological quality of the eligible studies. Twenty-two studies (containing 3,140 RCTs) published between 2000 and 2013 were included. Twenty-two studies reported on discrepancies between information given in different sources. Discrepancies were found in statistical analyses (eight studies), composite outcomes (one study), the handling of missing data (three studies), unadjusted versus adjusted analyses (three studies), handling of continuous data (three studies), and subgroup analyses (12 studies). Discrepancy rates varied, ranging from 7% (3/42) to 88% (7/8) in statistical analyses, 46% (36/79) to 82% (23/28) in adjusted versus unadjusted analyses, and 61% (11/18) to 100% (25/25) in subgroup analyses. This review is limited in that none of the included studies investigated the evidence for bias resulting from selective reporting of analyses. It was not possible to combine studies to provide overall summary estimates, and so the results of studies are discussed narratively.

Conclusions

Discrepancies in analyses between publications and other study documentation were common, but reasons for these discrepancies were not discussed in the trial reports. To ensure transparency, protocols and statistical analysis plans need to be published, and investigators should adhere to these or explain discrepancies. Please see later in the article for the Editors'' Summary     

Analysis of twin data using SAS

Summary .  Twin studies are essential for assessing disease inheritance. Data generated from twin studies are traditionally analyzed using specialized computational programs. For many researchers, especially those who are new to twin studies, understanding and using those specialized computational programs can be a daunting task. Given that SAS (Statistical Analysis Software) is the most popular software for statistical analysis, we suggest that the use of SAS procedures for twin data may be a helpful alternative and demonstrate that we can obtain similar results from SAS to those produced by specialized computational programs. This numerical validation is practically useful, because a natural concern with general statistical software is whether it can deal with data that are generated from special study designs such as twin studies and if it can test a particular hypothesis. We concluded through our extensive simulation that SAS procedures can be used easily as a very convenient alternative to specialized programs for twin data analysis.     

Quality and quantity of genetic relatedness data affect the analysis of social structure

Kinship plays a fundamental role in the evolution of social systems and is considered a key driver of group living. To understand the role of kinship in the formation and maintenance of social bonds, accurate measures of genetic relatedness are critical. Genotype‐by‐sequencing technologies are rapidly advancing the accuracy and precision of genetic relatedness estimates for wild populations. The ability to assign kinship from genetic data varies depending on a species’ or population's mating system and pattern of dispersal, and empirical data from longitudinal studies are crucial to validate these methods. We use data from a long‐term behavioural study of a polygynandrous, bisexually philopatric marine mammal to measure accuracy and precision of parentage and genetic relatedness estimation against a known partial pedigree. We show that with moderate but obtainable sample sizes of approximately 4,235 SNPs and 272 individuals, highly accurate parentage assignments and genetic relatedness coefficients can be obtained. Additionally, we subsample our data to quantify how data availability affects relatedness estimation and kinship assignment. Lastly, we conduct a social network analysis to investigate the extent to which accuracy and precision of relatedness estimation improve statistical power to detect an effect of relatedness on social structure. Our results provide practical guidance for minimum sample sizes and sequencing depth for future studies, as well as thresholds for post hoc interpretation of previous analyses.