Luminance and Brightness: Application to Lanthanide-Based Coordination Polymers.

Measuring the luminance of lanthanide-based coordination polymers under UV excitation is of prime importance for many technological applications. This study highlights that the quantum yield gives no information about the luminescence intensity of a solid-state compound. Indeed, compounds with high quantum yield can actually be poorly luminescent. Therefore, a brightness calculation or a luminance measurement are mandatory for a quantitative estimation of the luminescence intensity. The calculated brightness appears to be a convenient quantitative parameter for the estimation of the luminescence intensity in the infrared domain, in which luminance is senseless. It is also a useful parameter in the visible domain, but one must keep in mind that only compounds with similar colorimetric coordinates can be compared. For comparing the luminescence intensities of compounds that exhibit different emission colors, the luminance measurement seems to be the most efficient method. A home-made setup that allows this measurement with high reproducibility is described in detail. The luminance of several lanthanide-based coordination polymers with benzene-poly-carboxylate ligands is measured, and the results are compared with brightness and quantum yield measurements. A standard is suggested for calibration.

Climate-associated genetic variation in Fagus sylvatica and potential responses to climate change in the French Alps.

Local adaptation patterns have been found in many plants and animals, highlighting the genetic heterogeneity of species along their range of distribution. In the next decades, global warming is predicted to induce a change in the selective pressures that drive this adaptive variation, forcing a reshuffling of the underlying adaptive allele distributions. For species with low dispersion capacity and long generation time such as trees, the rapidity of the change could impede the migration of beneficial alleles and lower their capacity to track the changing environment. Identifying the main selective pressures driving the adaptive genetic variation is thus necessary when investigating species capacity to respond to global warming. In this study, we investigate the adaptive landscape of Fagus sylvatica along a gradient of populations in the French Alps. Using a double-digest restriction-site-associated DNA (ddRAD) sequencing approach, we identified 7,000 SNPs from 570 individuals across 36 different sites. A redundancy analysis (RDA)-derived method allowed us to identify several SNPs that were strongly associated with climatic gradients; moreover, we defined the primary selective gradients along the natural populations of F. sylvatica in the Alps. Strong effects of elevation and humidity, which contrast north-western and south-eastern site, were found and were believed to be important drivers of genetic adaptation. Finally, simulations of future genetic landscapes that used these findings allowed identifying populations at risk for F. sylvatica in the Alps, which could be helpful for future management plans.

Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult.

Changes in selective pressures associated with human population expansion may explain metabolic and immune related pathways enriched for signatures of positive selection.

BACKGROUND: The study of local adaptation processes is a very important research topic in the field of population genomics. There is a particular interest in the study of human populations because they underwent a process of rapid spatial expansion and faced important environmental changes that translated into changes in selective pressures. New mutations may have been selected for in the new environment and previously existing genetic variants may have become detrimental. Immune related genes may have been released from the selective pressure exerted by pathogens in the ancestral environment and new variants may have been positively selected due to pathogens present in the newly colonized habitat. Also, variants that had a selective advantage in past environments may have become deleterious in the modern world due to external stimuli including climatic, dietary and behavioral changes, which could explain the high prevalence of some polygenic diseases such as diabetes and obesity. RESULTS: We performed an enrichment analysis to identify gene sets enriched for signals of positive selection in humans. We used two genome scan methods, XPCLR and iHS to detect selection using a dense coverage of SNP markers combined with two gene set enrichment approaches. We identified immune related gene sets that could be involved in the protection against pathogens especially in the African population. We also identified the glycolysis & gluconeogenesis gene set, related to metabolism, which supports the thrifty genotype hypothesis invoked to explain the current high prevalence of diseases such as diabetes and obesity. Extending our analysis to the gene level, we found signals for 23 candidate genes linked to metabolic syndrome, 13 of which are new candidates for positive selection. CONCLUSIONS: Our study provides a list of genes and gene sets associated with immunity and metabolic syndrome that are enriched for signals of positive selection in three human populations (Europeans, Africans and Asians). Our results highlight differences in the relative importance of pathogens as drivers of local adaptation in different continents and provide new insights into the evolution and high incidence of metabolic syndrome in modern human populations.

Detection of selective sweeps in structured populations: a comparison of recent methods.

Identifying genomic regions targeted by positive selection has been a long-standing interest of evolutionary biologists. This objective was difficult to achieve until the recent emergence of next-generation sequencing, which is fostering the development of large-scale catalogues of genetic variation for increasing number of species. Several statistical methods have been recently developed to analyse these rich data sets, but there is still a poor understanding of the conditions under which these methods produce reliable results. This study aims at filling this gap by assessing the performance of genome-scan methods that consider explicitly the physical linkage among SNPs surrounding a selected variant. Our study compares the performance of seven recent methods for the detection of selective sweeps (iHS, nSL, EHHST, xp-EHH, XP-EHHST, XPCLR and hapFLK). We use an individual-based simulation approach to investigate the power and accuracy of these methods under a wide range of population models under both hard and soft sweeps. Our results indicate that XPCLR and hapFLK perform best and can detect soft sweeps under simple population structure scenarios if migration rate is low. All methods perform poorly with moderate-to-high migration rates, or with weak selection and very poorly under a hierarchical population structure. Finally, no single method is able to detect both starting and nearly completed selective sweeps. However, combining several methods (XPCLR or hapFLK with iHS or nSL) can greatly increase the power to pinpoint the selected region.

Genome scans for detecting footprints of local adaptation using a Bayesian factor model.

There is a considerable impetus in population genomics to pinpoint loci involved in local adaptation. A powerful approach to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. One of the most common approaches for selection scans is based on statistics that measure population differentiation such as FST. However, there are important caveats with approaches related to FST because they require grouping individuals into populations and they additionally assume a particular model of population structure. Here, we implement a more flexible individual-based approach based on Bayesian factor models. Factor models capture population structure with latent variables called factors, which can describe clustering of individuals into populations or isolation-by-distance patterns. Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. In order to identify outlier loci, the hierarchical factor model searches for loci that are atypically related to population structure as measured by the latent factors. In a model of population divergence, we show that it can achieve a 2-fold or more reduction of false discovery rate compared with the software BayeScan or with an FST approach. We show that our software can handle large data sets by analyzing the single nucleotide polymorphisms of the Human Genome Diversity Project. The Bayesian factor model is implemented in the open-source PCAdapt software.

Genome scan methods against more complex models: when and how much should we trust them?

The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.

Cytonuclear discordance among Southeast Asian black rats (Rattus rattus complex).

Black rats are major invasive vertebrate pests with severe ecological, economic and health impacts. Remarkably, their evolutionary history has received little attention, and there is no firm agreement on how many species should be recognized within the black rat complex. This species complex is native to India and Southeast Asia. According to current taxonomic classification, there are three taxa living in sympatry in several parts of Thailand, Cambodia and Lao People's Democratic Republic, where this study was conducted: two accepted species (Rattus tanezumi, Rattus sakeratensis) and an additional mitochondrial lineage of unclear taxonomic status referred to here as 'Rattus R3'. We used extensive sampling, morphological data and diverse genetic markers differing in rates of evolution and parental inheritance (two mitochondrial DNA genes, one nuclear gene and eight microsatellite loci) to assess the reproductive isolation of these three taxa. Two close Asian relatives, Rattus argentiventer and Rattus exulans, were also included in the genetic analyses. Genetic analyses revealed discordance between the mitochondrial and nuclear data. Mitochondrial phylogeny studies identified three reciprocally monophyletic clades in the black rat complex. However, studies of the phylogeny of the nuclear exon interphotoreceptor retinoid-binding protein gene and clustering and assignation analyses with eight microsatellites failed to separate R. tanezumi and R3. Morphometric analyses were consistent with nuclear data. The incongruence between mitochondrial and nuclear (and morphological) data rendered R. tanezumi/R3 paraphyletic for mitochondrial lineages with respect to R. sakeratensis. Various evolutionary processes, such as shared ancestral polymorphism and incomplete lineage sorting or hybridization with massive mitochondrial introgression between species, may account for this unusual genetic pattern in mammals.

Sex at the origin: an Asian population of the rice blast fungus Magnaporthe oryzae reproduces sexually.

Sexual reproduction may be cryptic or facultative in fungi and therefore difficult to detect. Magnaporthe oryzae, which causes blast, the most damaging fungal disease of rice, is thought to originate from southeast Asia. It reproduces asexually in all rice-growing regions. Sexual reproduction has been suspected in limited areas of southeast Asia, but has never been demonstrated in contemporary populations. We characterized several M. oryzae populations worldwide both biologically and genetically, to identify candidate populations for sexual reproduction. The sexual cycle of M. oryzae requires two strains of opposite mating types, at least one of which is female-fertile, to come into contact. In one Chinese population, the two mating types were found to be present at similar frequencies and almost all strains were female-fertile. Compatible strains from this population completed the sexual cycle in vitro and produced viable progenies. Genotypic richness and linkage disequilibrium data also supported the existence of sexual reproduction in this population. We resampled this population the following year, and the data obtained confirmed the presence of all the biological and genetic characteristics of sexual reproduction. In particular, a considerable genetic reshuffling of alleles was observed between the 2 years. Computer simulations confirmed that the observed genetic characteristics were unlikely to have arisen in the absence of recombination. We therefore concluded that a contemporary population of M. oryzae, pathogenic on rice, reproduces sexually in natura in southeast Asia. Our findings provide evidence for the loss of sexual reproduction by a fungal plant pathogen outside its centre of origin.

Determination of mitochondrial genetic diversity in mammals.

Mitochondrial DNA (mtDNA) is one of the most popular population genetic markers. Its relevance as an indicator of population size and history has recently been questioned by several large-scale studies in animals reporting evidence for recurrent adaptive evolution, at least in invertebrates. Here we focus on mammals, a more restricted taxonomic group for which the issue of mtDNA near neutrality is crucial. By analyzing the distribution of mtDNA diversity across species and relating it to allozyme diversity, life-history traits, and taxonomy, we show that (i) mtDNA in mammals does not reject the nearly neutral model; (ii) mtDNA diversity, however, is unrelated to any of the 14 life-history and ecological variables that we analyzed, including body mass, geographic range, and The World Conservation Union (IUCN) categorization; (iii) mtDNA diversity is highly variable between mammalian orders and families; (iv) this taxonomic effect is most likely explained by variations of mutation rate between lineages. These results are indicative of a strong stochasticity of effective population size in mammalian species. They suggest that, even in the absence of selection, mtDNA genetic diversity is essentially unpredictable, knowing species biology, and probably uncorrelated to species abundance.

Increasing genomic information in bivalves through new EST collections in four species: development of new genetic markers for environmental studies and genome evolution.

The generation of EST information is an essential step in the genomic characterisation of species. In the context of the European Network Marine Genomics, a common goal was to significantly increase the amount of ESTs in commercial marine mollusk species and more specifically in the less studied but ecologically and commercially important groups, such as mussel and clam genera. Normalized cDNA libraries were constructed for four different relevant bivalves species (Crassostrea gigas, Mytilus edulis, Ruditapes decussatus and Bathymodiolus azoricus), using numerous tissues and physiological conditions. In this paper, we present the analysis of the 13,013 expressed sequence tags (ESTs) generated. Each EST library was independently assembled and 1300-3000 unique sequences were identified in each species. For the different species, functional categories could be assigned to only about 16 to 27% of ESTs using the GO annotation tool. All sequences have been incorporated into a publicly available database and form the basis for subsequent microarray design, SNP detection and polymorphism analysis, and the placement of novel markers on genetic linkage maps.

Evaluation of redundancy analysis to identify signatures of local adaptation.

Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This study aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. In addition, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., ). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the north-western American coast.

GC-biased segregation of noncoding polymorphisms in Drosophila.

The study of base composition evolution in Drosophila has been achieved mostly through the analysis of coding sequences. Third codon position GC content, however, is influenced by both neutral forces (e.g., mutation bias) and natural selection for codon usage optimization. In this article, large data sets of noncoding DNA sequence polymorphism in D. melanogaster and D. simulans were gathered from public databases to try to disentangle these two factors-noncoding sequences are not affected by selection for codon usage. Allele frequency analyses revealed an asymmetric pattern of AT vs. GC noncoding polymorphisms: AT --> GC mutations are less numerous, and tend to segregate at a higher frequency, than GC --> AT ones, especially at GC-rich loci. This is indicative of nonstationary evolution of base composition and/or of GC-biased allele transmission. Fitting population genetics models to the allele frequency spectra confirmed this result and favored the hypothesis of a biased transmission. These results, together with previous reports, suggest that GC-biased gene conversion has influenced base composition evolution in Drosophila and explain the correlation between intron and exon GC content.

Polymorphix: a sequence polymorphism database.

Within-species sequence variation data are of special interest since they contain information about recent population/species history, and the molecular evolutionary forces currently in action in natural populations. These data, however, are presently dispersed within generalist databases, and are difficult to access. To solve this problem, we have developed Polymorphix, a database dedicated to sequence polymorphism. It contains within-species homologous sequence families built using EMBL/GenBank under suitable similarity and bibliographic criteria. Polymorphix is an ACNUC structured database allowing both simple and complex queries for population genomic studies. Alignments within families as well as phylogenetic trees can be download. When available, outgroups are included in the alignment. Polymorphix contains sequences from the nuclear, mitochondrial and chloroplastic genomes of every eukaryote species represented in EMBL. It can be accessed by a web interface (http://pbil.univ-lyon1.fr/polymorphix/query.php).

pcadapt: an R package to perform genome scans for selection based on principal component analysis.

The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

BACKGROUND: A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. RESULTS: We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. CONCLUSION: Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Impact of mating systems on patterns of sequence polymorphism in flowering plants.

A fundamental challenge in population genetics and molecular evolution is to understand the forces shaping the patterns of genetic diversity within and among species. Among them, mating systems are thought to have important influences on molecular diversity and genome evolution. Selfing is expected to reduce effective population size, Ne, and effective recombination rates, directly leading to reduced polymorphism and increased linkage disequilibrium compared with outcrossing. Increased isolation between populations also results directly from selfing or indirectly from evolutionary changes, such as small flowers and low pollen output, leading to greater differentiation of molecular markers than under outcrossing. The lower effective recombination rate increases the likelihood of hitch-hiking, further reducing within-deme diversity of selfers and thus increasing their genetic differentiation. There are also indirect effects on molecular evolutionary processes. Low Ne reduces the efficacy of selection; in selfers, selection should thus be less efficient in removing deleterious mutations. The rarity of heterozygous sites in selfers leads to infrequent action of biased conversion towards GC, which tends to increase sequences' GC content in the most highly recombining genome regions of outcrossers. To test these predictions in plants, we used a newly developed sequence polymorphism database to investigate the effects of mating system differences on sequence polymorphism and genome evolution in a wide set of plant species. We also took into account other life-history traits, including life form (whether annual or perennial herbs, and woody perennial) and the modes of pollination and seed dispersal, which are known to affect enzyme and DNA marker polymorphism. We show that among various life-history traits, mating systems have the greatest influence on patterns of polymorphism.

Likelihood-free inference of population structure and local adaptation in a Bayesian hierarchical model.

We address the problem of finding evidence of natural selection from genetic data, accounting for the confounding effects of demographic history. In the absence of natural selection, gene genealogies should all be sampled from the same underlying distribution, often approximated by a coalescent model. Selection at a particular locus will lead to a modified genealogy, and this motivates a number of recent approaches for detecting the effects of natural selection in the genome as "outliers" under some models. The demographic history of a population affects the sampling distribution of genealogies, and therefore the observed genotypes and the classification of outliers. Since we cannot see genealogies directly, we have to infer them from the observed data under some model of mutation and demography. Thus the accuracy of an outlier-based approach depends to a greater or a lesser extent on the uncertainty about the demographic and mutational model. A natural modeling framework for this type of problem is provided by Bayesian hierarchical models, in which parameters, such as mutation rates and selection coefficients, are allowed to vary across loci. It has proved quite difficult computationally to implement fully probabilistic genealogical models with complex demographies, and this has motivated the development of approximations such as approximate Bayesian computation (ABC). In ABC the data are compressed into summary statistics, and computation of the likelihood function is replaced by simulation of data under the model. In a hierarchical setting one may be interested both in hyperparameters and parameters, and there may be very many of the latter--for example, in a genetic model, these may be parameters describing each of many loci or populations. This poses a problem for ABC in that one then requires summary statistics for each locus, which, if used naively, leads to a consequent difficulty in conditional density estimation. We develop a general method for applying ABC to Bayesian hierarchical models, and we apply it to detect microsatellite loci influenced by local selection. We demonstrate using receiver operating characteristic (ROC) analysis that this approach has comparable performance to a full-likelihood method and outperforms it when mutation rates are variable across loci.

Population size does not influence mitochondrial genetic diversity in animals.

Within-species genetic diversity is thought to reflect population size, history, ecology, and ability to adapt. Using a comprehensive collection of polymorphism data sets covering approximately 3000 animal species, we show that the widely used mitochondrial DNA (mtDNA) marker does not reflect species abundance or ecology: mtDNA diversity is not higher in invertebrates than in vertebrates, in marine than in terrestrial species, or in small than in large organisms. Nuclear loci, in contrast, fit these intuitive expectations. The unexpected mitochondrial diversity distribution is explained by recurrent adaptive evolution, challenging the neutral theory of molecular evolution and questioning the relevance of mtDNA in biodiversity and conservation studies.

Mutation hot spots in mammalian mitochondrial DNA.

Animal mitochondrial DNA is characterized by a remarkably high level of within-species homoplasy, that is, phylogenetic incongruence between sites of the molecule. Several investigators have invoked recombination to explain it, challenging the dogma of maternal, clonal mitochondrial inheritance in animals. Alternatively, a high level of homoplasy could be explained by the existence of mutation hot spots. By using an exhaustive mammalian data set, we test the hot spot hypothesis by comparing patterns of site-specific polymorphism and divergence in several groups of closely related species, including hominids. We detect significant co-occurrence of synonymous polymorphisms among closely related species in various mammalian groups, and a correlation between the site-specific levels of variability within humans (on one hand) and between Hominoidea species (on the other hand), indicating that mutation hot spots actually exist in mammalian mitochondrial coding regions. The whole data, however, cannot be explained by a simple mutation hot spots model. Rather, we show that the site-specific mutation rate quickly varies in time, so that the same sites are not hypermutable in distinct lineages. This study provides a plausible mutation model that potentially accounts for the peculiar distribution of mitochondrial sequence variation in mammals without the need for invoking recombination. It also gives hints about the proximal causes of mitochondrial site-specific hypermutability in humans.