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

In situ high-resolution insights into the dynamics of arsenic (As) species and heavy metals across the sediment-water interface in a deep karst reservoir

Arsenic (As) and heavy metal contamination in aquatic systems pose critical environmental challenges, particularly in reservoirs. This study utilized dual-sided high-resolution diffusive gradients in thin films (DGT) probes on-site to investigate the spatial distribution and mobility of As species and heavy metals (Cd, Cr, Cu, Ni, Pb, Sb, and Zn) in the Hongfeng Reservoir, a deep karst reservoir in southwest China. Results revealed that As mobility was primarily governed by redox-sensitive processes, including the reduction of As(V) to As(III) and the reductive dissolution of Fe/Mn oxides. As(III) dominated porewater under reducing conditions, while As(V) was prevalent in overlying water under oxidative environments. Sulfate reduction significantly influenced As mobility, and competitive adsorption with P enhanced As release in eutrophic conditions. Heavy metals exhibited distinct spatial profiles and inter-element correlations, shaped by redox variability. Flux analysis identified sediments as sources for As, Fe, Mn, P, and S, and as sinks for most heavy metals. As(III) fluxes in the North Central reflected strong reducing conditions, while As(V) fluxes in the South Central highlighted localized oxidative processes. These findings offer valuable insights into geochemical processes in karst reservoirs, aiding in the understanding of contaminant dynamics and providing guidance for managing sediment pollution and protecting water quality.

Strong Environmental and Genome-Wide Population Differentiation Underpins Adaptation and High Genomic Vulnerability in the Dominant Australian Kelp (Ecklonia radiata)

Ongoing and predicted range loss of kelp forests in response to climatic stressors is pressing marine managers to look into the adaptive capacity of populations to inform conservation strategies. Characterising how adaptive genetic diversity and structure relate to present and future environmental variation represents an emerging approach to quantifying kelp vulnerability to environmental change and identifying populations with genotypes that potentially confer an adaptive advantage in future ocean conditions. The dominant Australian kelp, Ecklonia radiata, was genotyped from 10 locations spanning 2000 km of coastline and a 9.5°C average temperature gradient along the east coast of Australia, a global warming hotspot. ddRAD sequencing generated 10,700 high-quality single nucleotide polymorphisms (SNPs) and characterized levels of neutral and adaptive genomic diversity and structure. The adaptive dataset, reflecting portions of the genome putatively under selection, was used to infer genomic vulnerability by 2050 under the RCP 8.5 scenario. There was strong neutral genetic differentiation between Australia mainland and Tasmanian populations, but only weak genetic structure among mainland populations within the main path of the East Australian Current. Genetic diversity was highest in the center of the range and lowest in the warm-edge population. The adaptive SNP candidates revealed similar genetic structure patterns, with a spread of adaptive alleles across most warm (northern) populations. The lowest, but most unique, adaptive genetic diversity values were found in both warm and cool population edges, suggesting local adaptation but low evolutionary potential. Critically, genomic vulnerability modeling identified high levels of vulnerability to future environmental conditions in Tasmanian populations. Populations of kelp at range edges are unlikely to adapt and keep pace with predicted climate change. Ensuring the persistence of these kelp forests, by boosting resilience to climate change, may require active management strategies with assisted adaptation in warm-edge (northern) populations and assisted gene flow in cool-edge (Tasmania) populations.

Targeted genotyping (90K-SPET) facilitates genome-wide association studies and the prediction of yield-related traits in faba bean (Vicia faba L.)

BACKGROUND

Establishing faba bean (Vicia faba L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These traits can be improved by exploiting the extensive but largely uncharacterized faba bean germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per pod (SP), and single seed weight (SSW) in 245 faba bean accessions (CGN panel) via a Genome-Wide Association Study (GWAS). The accessions were grown in 2021 and 2022 in the Netherlands. Additionally, we developed genomic selection (GS) models to predict the genetic merit within large germplasm collections for the mentioned traits, as well as yield (YLD).

RESULTS

The CGN panel was an optimal panel for performing high-resolution GWAS, showing large phenotypic variation, high narrow-sense heritability for all traits, and minimal genetic relatedness among accessions. Population structure analysis revealed the presence of four genetic groups. GWAS uncovered 33 SNP-trait associations in 2021 and 17 in 2022. We identified one stable QTL for FT and four for SSW over the two years, representing key molecular markers for testing in breeding applications. Short linkage disequilibrium decay (~ 268 Kbp) facilitated the identification of several important candidate genes with interesting homologs in other crops. Ten SNPs in 2021 and five in 2022 were predicted to be intra-genic missense variants, potentially altering protein function. Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. Predictive ability ranged from 0.58 to 0.81 in 2021, and 0.47 to 0.85 in 2022 for different traits. Additionally, across-year predictions showed stable PA.

CONCLUSION

GWAS revealed promising QTLs for use in molecular breeding and highlighted new candidate genes. Interestingly, the prediction of intra-genic SNPs categorized 15 SNPs as putatively affecting protein function. Moreover, we demonstrated for the first time in faba bean that GS has the potential to unlock untapped diversity in genebank collections and accelerate trait integration into faba bean breeding programs.

Prognostic value and immune landscapes of disulfidptosis‑related lncRNAs in bladder cancer

Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored. BLCA sample data were sourced from The Cancer Genome Atlas. A predictive signature comprising DRlncRNAs was formulated and subsequently validated. The combination of this signature with clinical characteristics facilitated the development of a nomogram with practical clinical utility. Additionally, enrichment analysis was conducted, the tumor microenvironment (TME) was assessed, the tumor mutational burden (TMB) was analyzed, and drug sensitivity was explored. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to quantify lncRNA expression. The results revealed an eight-gene signature based on DRlncRNAs was established, and the predictive accuracy of the nomogram that incorporated the risk score [area under the curve (AUC)=0.733] outperformed the nomogram without it (AUC=0.703). High-risk groups were associated with pathways such as WNT signaling, focal adhesion and cell cycle pathways. The TME study revealed that high-risk patients had increased immune infiltration, whereas the TMB and tumor immune dysfunction and exclusion scores in low-risk patients indicated a potentially robust immune response. Drug sensitivity analysis identified appropriate antitumor drugs for each group. RT-qPCR experiments validated significant differences in DRlncRNAs expression between normal and BLCA cell lines. In conclusion, the prognostic risk signature, which includes the eight identified DRlncRNAs, demonstrates promise for predicting prognosis of patients with BLCA and guiding the selection of suitable immunotherapy and chemotherapy strategies.

Epigenetic variation in light of population genetic practice

The evolutionary impact of epigenetic variation depends on its transgenerational stability and source - whether genetically determined, environmentally induced, or due to spontaneous, genotype-independent mutations. Here, we evaluate current approaches for investigating an independent role of epigenetics in evolution, pinpointing methodological challenges. We further identify opportunities arising from integrating epigenetic data with population genetic analyses in natural populations. Efforts to advance data quality, study design, and statistical treatment are encouraged to consolidate our understanding of the source of heritable epigenetic variation, quantify its autonomous potential for evolution, and enrich population genetic analyses with an additional layer of information.

Seascape Genomics of the Smooth Hammerhead Shark Sphyrna zygaena Reveals Regional Adaptive Clinal Variation

Globally, hammerhead sharks have experienced severe declines owing to continued overexploitation and anthropogenic change. The smooth hammerhead shark Sphyrna zygaena remains understudied compared to other members of the family Sphyrnidae. Despite its vulnerable status, a comprehensive understanding of its genetic landscape remains lacking in many regions worldwide. The present study aimed to conduct a fine-scale genomic assessment of Sphyrna zygaena within the highly dynamic marine environment of South Africa's coastline, using thousands of single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (3RAD). A combination of differentiation-based outlier detection methods and genotype-environment association (GEA) analysis was employed in Sphyrna zygaena. Subsequent assessments of putatively adaptive loci revealed a distinctive south to east genetic cline. Among these, notable correlations between adaptive variation and sea-surface dissolved oxygen and salinity were evident. Conversely, analysis of 111,243 neutral SNP markers revealed a lack of regional population differentiation, a finding that remained consistent across various analytical approaches. These results provide evidence for the presence of differential selection pressures within a limited spatial range, despite high gene flow implied by the selectively neutral dataset. This study offers notable insights regarding the potential impacts of genomic variation in response to fluctuating environmental conditions in the circumglobally distributed Sphyrna zygaena.

Combined Use of Univariate and Multivariate Approaches to Detect Selection Signatures Associated with Milk or Meat Production in Cattle

OBJECTIVES

The aim of this study was to investigate the genomic structure of the cattle breeds selected for meat and milk production and to identify selection signatures between them.

METHODS

A total of 391 animals genotyped at 41,258 SNPs and belonging to nine breeds were considered: Angus (N = 62), Charolais (46), Hereford (31), Limousin (44), and Piedmontese (24), clustered in the Meat group, and Brown Swiss (42), Holstein (63), Jersey (49), and Montbéliarde (30), clustered in the Milk group. The population stratification was analyzed by principal component analysis (PCA), whereas selection signatures were identified by univariate (Wright fixation index, FST) and multivariate (canonical discriminant analysis, CDA) approaches. Markers with FST values larger than three standard deviations from the chromosomal mean were considered interesting. Attention was focused on markers selected by both techniques.

RESULTS

A total of 10 SNPs located on seven different chromosomes (7, 10, 14, 16, 17, 18, and 24) were identified. Close to these SNPs (±250 kb), 165 QTL and 51 genes were found. The QTL were grouped in 45 different terms, of which three were significant (Bonferroni correction < 0.05): milk fat content, tenderness score, and length of productive life. Moreover, genes mainly associated with milk production, immunity and environmental adaptation, and reproduction were mapped close to the common SNPs.

CONCLUSIONS

The results of the present study suggest that the combined use of univariate and multivariate approaches can help to better identify selection signatures due to directional selection.

GenoSiS: A Biobank-Scale Genotype Similarity Search Architecture for Creating Dynamic Patient-Match Cohorts

Many patients do not experience optimal benefits from medical advances because clinical research does not adequately represent them. While the diversity of biomedical research cohorts is improving, ensuring that individual patients are adequately represented remains challenging. We propose a new approach, GenoSiS, which leverages machine learning-based similarity search to dynamically find patient-matched cohorts across different populations quickly. These cohorts could serve as reference cohorts to improve a range of clinical analyses, including disease risk score calculations and dosage decisions. While GenoSiS focuses on finding genetic similarity within a biobank, our similarity search architecture can be extended to represent other medically relevant patient characteristics and search other biobanks.

Linking the spatial and genomic structure of adaptive potential for conservation management: a review

We unified the recent literature with the goal to contribute to the discussion on how genetic diversity might best be conserved. We argue that this decision will be guided by how genomic variation is distributed among manageable populations (i.e., its spatial structure), the degree to which adaptive potential is best predicted by variation across the entire genome or the subset of that variation that is identified as putatively adaptive (i.e., its genomic structure), and whether we are managing species as single entities or as collections of diversifying lineages. The distribution of genetic variation and our ultimate goal will have practical implications for on-the-ground management. If adaptive variation is largely polygenic or responsive to change, its spatial structure might be broadly governed by the forces determining genome-wide variation (linked selection, drift, and gene flow), making measurement and prioritization straightforward. If we are managing species as single entities, then population-level prioritization schemes are possible so as to maximize future pooled genetic variation. We outline one such scheme based on the popular Shapley value from cooperative game theory that considers the relative genetic contribution of a population to an unknown future collection of populations.

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

Estimating scale-specific and localized spatial patterns in allele frequency

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

Ancient genomes reveal insights into ritual life at Chichén Itzá

The ancient city of Chichén Itzá in Yucatán, Mexico, was one of the largest and most influential Maya settlements during the Late and Terminal Classic periods (AD 600-1000) and it remains one of the most intensively studied archaeological sites in Mesoamerica1-4. However, many questions about the social and cultural use of its ceremonial spaces, as well as its population's genetic ties to other Mesoamerican groups, remain unanswered2. Here we present genome-wide data obtained from 64 subadult individuals dating to around AD 500-900 that were found in a subterranean mass burial near the Sacred Cenote (sinkhole) in the ceremonial centre of Chichén Itzá. Genetic analyses showed that all analysed individuals were male and several individuals were closely related, including two pairs of monozygotic twins. Twins feature prominently in Mayan and broader Mesoamerican mythology, where they embody qualities of duality among deities and heroes5, but until now they had not been identified in ancient Mayan mortuary contexts. Genetic comparison to present-day people in the region shows genetic continuity with the ancient inhabitants of Chichén Itzá, except at certain genetic loci related to human immunity, including the human leukocyte antigen complex, suggesting signals of adaptation due to infectious diseases introduced to the region during the colonial period.

Variable parallelism in the genomic basis of age at maturity across spatial scales in Atlantic Salmon

Complex traits often exhibit complex underlying genetic architectures resulting from a combination of evolution from standing variation, hard and soft sweeps, and alleles of varying effect size. Increasingly, studies implicate both large-effect loci and polygenic patterns underpinning adaptation, but the extent that common genetic architectures are utilized during repeated adaptation is not well understood. Sea age or age at maturation represents a significant life history trait in Atlantic Salmon (Salmo salar), the genetic basis of which has been studied extensively in European Atlantic populations, with repeated identification of large-effect loci. However, the genetic basis of sea age within North American Atlantic Salmon populations remains unclear, as does the potential for a parallel trans-Atlantic genomic basis to sea age. Here, we used a large single-nucleotide polymorphism (SNP) array and low-coverage whole-genome resequencing to explore the genomic basis of sea age variation in North American Atlantic Salmon. We found significant associations at the gene and SNP level with a large-effect locus (vgll3) previously identified in European populations, indicating genetic parallelism, but found that this pattern varied based on both sex and geographic region. We also identified nonrepeated sets of highly predictive loci associated with sea age among populations and sexes within North America, indicating polygenicity and low rates of genomic parallelism. Despite low genome-wide parallelism, we uncovered a set of conserved molecular pathways associated with sea age that were consistently enriched among comparisons, including calcium signaling, MapK signaling, focal adhesion, and phosphatidylinositol signaling. Together, our results indicate parallelism of the molecular basis of sea age in North American Atlantic Salmon across large-effect genes and molecular pathways despite population-specific patterns of polygenicity. These findings reveal roles for both contingency and repeated adaptation at the molecular level in the evolution of life history variation.

Genomics and conservation: Guidance from training to analyses and applications

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT-UMAP combination stands out in the evaluation metrics.

Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.

Individual-based landscape genomics for conservation: An analysis pipeline

Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non-model systems has also enabled a shift away from population-based sampling to individual-based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual-based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population-based sampling to individual-based sampling schemes. Here, we discuss the benefits of individual-based sampling for conservation and describe how landscape genomic methods, paired with individual-based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user-friendly, open-source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.

Prognostic value and immune landscapes of immunogenic cell death-related lncRNAs in hepatocellular carcinoma

BACKGROUND

Both immunogenic cell death (ICD) and long noncoding RNAs (lncRNAs) are strongly associated with tumor development, but the mechanism of action of ICD-associated lncRNAs in hepatocellular carcinoma (HCC) remains unclear.

METHODS

We collected data from 365 HCC patients from The Cancer Genome Atlas (TCGA) database. We formulated a prognostic signature of ICD-associated lncRNAs and a nomogram to predict prognosis. To explore the potential mechanisms and provide clinical guidance, survival analysis, enrichment analysis, tumor microenvironment analysis, tumor mutation burden (TMB), and drug sensitivity prediction were conducted based on the subgroups obtained from the risk score.

RESULTS

A prognostic signature of seven ICD-associated lncRNAs was constructed. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients. The nomogram had a higher predictive value than the nomogram constructed without the risk model. Enrichment analysis confirmed that risk lncRNAs were closely associated with cell proliferation and mitosis. Most of the immune checkpoints currently used in therapy (e.g., PDCD1 and CTLA4) appeared to be elevated in high-risk patients. Tumor microenvironment analysis showed differential expression of lymphocytes (including natural killer cells, regulatory T cells, etc.) in the high-risk group. TMB had a higher incidence of mutations in the high-risk group (P=0.004). Chemotherapy drug sensitivity prediction provides effective guidelines for individual therapy. RT-qPCR of human HCC tissues verified the accuracy of the model.

CONCLUSION

We constructed an effective prognostic signature for patients with HCC using seven ICD-lncRNAs, which provides guidance for the prognostic assessment and personalized treatment of patients.

Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy

Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.

Investigating difficulties and enhancing understanding in linear algebra: Leveraging SageMath and ChatGPT for (orthogonal) diagonalization and singular value decomposition

We explored some common challenges faced by undergraduate students when studying linear algebra, particularly when dealing with algorithmic thinking skills required for topics such as matrix factorization, focusing on (orthogonal) diagonalization and singular value decomposition (SVD). To address these challenges, we introduced SageMath, a Python-based open-source computer algebra system, as a supportive tool for students performing computational tasks despite its static output nature. We further examined the potential of dynamic ChatGPT, an AI-based chatbot, by requesting examples or problem-solving assistance related to (orthogonal) diagonalization or the SVD of a specific matrix. By reinforcing essential concepts in linear algebra and enhancing computational skills through effective practice, mastering these topics can become more accessible while minimizing mistakes. Although static in nature, SageMath proved valuable for confirming calculations and handling tedious computations because of its easy-to-understand syntax and accurate solutions. However, although dynamic ChatGPT may not be fully reliable for solving linear algebra problems, the errors it produces can serve as a valuable resource for improving critical thinking skills.

Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.)

The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.

Transcriptome-based analyses of adaptive divergence between two closely related spruce species on the Qinghai-Tibet plateau and adjacent regions

Speciation among populations connected by gene flow is driven by adaptation to different environments, but underlying gene-environment associations remain largely unknown. Here, 162 individuals from 32 populations were sampled to obtain 191,648 independent single nucleotide polymorphisms (SNPs) across the genomes of two closely related spruce species, Picea asperata and Picea crassifolia, which occur on the Qinghai-Tibet Plateau and in surrounding regions. Using the SNP data set, genotype-environment associations and demographic modelling were used to examine local adaptation and genetic divergence between these two species. While morphologically similar, the two Picea species were genetically differentiated in multiple analyses. These species diverged despite continuous gene flow, and their initial divergence was dated back to the late Quaternary. The effective population sizes of both species have expanded since their divergence, as confirmed by niche distribution simulations. A total of 6365 genes were associated with the tested environmental variables; of these, 41 were positively selected in P. asperata and were mainly associated with temperature, while 83 were positively selected in P. crassifolia and were primarily associated with precipitation. These results deepen our understanding of the adaptive divergence and demographic histories of these two spruce species and highlight the importance of genomic data in deciphering the environmental selection underlying Quaternary interspecific divergence.

Impact of in Situ Simulated Climate Change on Communities and Non-Indigenous Species: Two Climates, Two Responses

Climate change constitutes a major challenge for marine urban ecosystems and ocean warming will likely strongly affect local communities. Non-Indigenous Species (NIS) have been shown to often have higher heat resistance than natives, but studies investigating how forthcoming global warming might affect them in marine urban environments remain scarce, especially in Situ studies. Here we used an in Situ warming experiment in a NW Mediterranean (warm temperate) and a NE Atlantic (cold temperate) marina to see how global warming might affect recruited communities in the near future. In both marinas, warming resulted in significantly different community structure, lower biomass, and more empty space compared to control. However, while in the warm temperate marina, NIS showed an increased surface cover, it was reduced in the cold temperate one. Metabolomic analyses on Bugula neritina in the Atlantic marina revealed potential heat stress experienced by this introduced bryozoan and a potential link between heat stress and the expression of a halogenated alkaloid, Caelestine A. The present results might indicate that the effects of global warming on the prevalence of NIS may differ between geographical provinces, which could be investigated by larger scale studies.

Deciphering signatures of natural selection via deep learning

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning-based framework, DeepGenomeScan, which can detect signatures of spatially varying selection. We demonstrate that DeepGenomeScan outperformed principal component analysis- and redundancy analysis-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under nonlinear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified by SPA, iHS, Fst and Bayenv when applied to the same dataset.

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.

Ecological and genomic vulnerability to climate change across native populations of Robusta coffee (Coffea canephora)

The assessment of population vulnerability under climate change is crucial for planning conservation as well as for ensuring food security. Coffea canephora is, in its native habitat, an understorey tree that is mainly distributed in the lowland rainforests of tropical Africa. Also known as Robusta, its commercial value constitutes a significant revenue for many human populations in tropical countries. Comparing ecological and genomic vulnerabilities within the species' native range can provide valuable insights about habitat loss and the species' adaptive potential, allowing to identify genotypes that may act as a resource for varietal improvement. By applying species distribution models, we assessed ecological vulnerability as the decrease in climatic suitability under future climatic conditions from 492 occurrences. We then quantified genomic vulnerability (or risk of maladaptation) as the allelic composition change required to keep pace with predicted climate change. Genomic vulnerability was estimated from genomic environmental correlations throughout the native range. Suitable habitat was predicted to diminish to half its size by 2050, with populations near coastlines and around the Congo River being the most vulnerable. Whole-genome sequencing revealed 165 candidate SNPs associated with climatic adaptation in C. canephora, which were located in genes involved in plant response to biotic and abiotic stressors. Genomic vulnerability was higher for populations in West Africa and in the region at the border between DRC and Uganda. Despite an overall low correlation between genomic and ecological vulnerability at broad scale, these two components of vulnerability overlap spatially in ways that may become damaging. Genomic vulnerability was estimated to be 23% higher in populations where habitat will be lost in 2050 compared to regions where habitat will remain suitable. These results highlight how ecological and genomic vulnerabilities are relevant when planning on how to cope with climate change regarding an economically important species.

A novel pyroptosis-related lncRNA prognostic signature associated with the immune microenvironment in lung squamous cell carcinoma

Background

A growing body of evidence suggests that pyroptosis-related lncRNAs (PRncRNAs) are associated with the prognoses of tumor patients and their tumor immune microenvironments. However, the function of PRlncRNAs in lung squamous cell carcinoma (LUSC) remains unclear.

Methods

We downloaded the transcriptome and clinical information of 551 LUSC samples from the The Cancer Genome Atlas (TCGA) database and randomly separated patients with complete information into two cohorts. Based on the training cohort, we developed a pyroptosis-related signature. We then examined the signature in the test cohort and all retained patients. We also clustered two risk groups in each cohort according to the signature and performed survival analysis, functional analysis, tumor immune microenvironment analysis and drug sensitivity analysis.

Results

A prognostic signature containing five PRlncRNAs (AP001189.1, PICART1, LINC02555, AC010422.4, and AL606469.1) was developed. A principal component analysis (PCA) indicated better differentiation between patients with different risk scores. Kaplan–Meier (K–M) analysis demonstrated poorer survival among patients with higher risk scores (P < 0.001). A receiver operating characteristic (ROC) curve analysis provided evidence confirming the accuracy of the signature, and univariate (p = 0.005) and multivariate (p = 0.008) Cox regression analyses confirmed the independent value of the risk score in prognoses. Clinical subgroup validation indicated that the signature was more suitable for patients with early-stage LUSC. We also created a nomogram to increase the accuracy of the prediction. Moreover, functional analysis revealed that pathways related to tumor development and pyroptosis were enriched in the high-risk group. Furthermore, the prognostic signature was proven to be a predictor of sensitivity to immunotherapy and chemotherapy.

Conclusions

We developed a novel pyroptosis-associated signature with independent value for the prognosis of LUSC patients. PRlncRNAs are closely associated with the tumor immune microenvironment in LUSC and might offer new directions for immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09790-z.

Unraveling coevolutionary dynamics using ecological genomics

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Genome Instability-Associated Long Non-Coding RNAs Reveal Biomarkers for Glioma Immunotherapy and Prognosis

Genome instability is a hallmark of tumors and is involved in proliferation, invasion, migration, and treatment resistance of many tumors. However, the relationship of genome instability with gliomas remains unclear. Here, we constructed genome instability-derived long non-coding RNA (lncRNA)-based gene signatures (GILncSig) using genome instability-related lncRNAs derived from somatic mutations. Multiple platforms were used to confirm that the GILncSig were closely related to patient prognosis and clinical characteristics. We found that GILncSig, the glioma microenvironment, and glioma cell DNA methylation-based stemness index (mDNAsi) interacted with each other to form a complex regulatory network. In summary, this study confirmed that GILncSig was an independent prognostic indicator for patients, distinguished high-risk and low-risk groups, and affected immune-cell infiltration and tumor-cell stemness indicators (mDNAsi) in the tumor microenvironment, resulting in tumor heterogeneity and immunotherapy resistance. GILncSig are expected to provide new molecular targets for the clinical treatment of patients with gliomas.

A geometric relationship of F 2 , F 3 and F 4 -statistics with principal component analysis

Principal component analysis (PCA) and F -statistics sensu Patterson are two of the most widely used population genetic tools to study human genetic variation. Here, I derive explicit connections between the two approaches and show that these two methods are closely related. F -statistics have a simple geometrical interpretation in the context of PCA, and orthogonal projections are a key concept to establish this link. I show that for any pair of populations, any population that is admixed as determined by an F 3 -statistic will lie inside a circle on a PCA plot. Furthermore, the F 4 -statistic is closely related to an angle measurement, and will be zero if the differences between pairs of populations intersect at a right angle in PCA space. I illustrate my results on two examples, one of Western Eurasian, and one of global human diversity. In both examples, I find that the first few PCs are sufficient to approximate most F -statistics, and that PCA plots are effective at predicting F -statistics. Thus, while F -statistics are commonly understood in terms of discrete populations, the geometric perspective illustrates that they can be viewed in a framework of populations that vary in a more continuous manner.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Phylogeny and disparate selection signatures suggest two genetically independent domestication events in pea (Pisum L.)

Domestication is considered a model of adaptation that can be used to draw conclusions about the modus operandi of selection in natural systems. Investigating domestication may give insights into how plants react to different intensities of human manipulation, which has direct implication for the continuing efforts of crop improvement. Therefore, scientists of various disciplines study domestication-related questions to understand the biological and cultural bases of the domestication process. We employed restriction site-associated DNA sequencing (RAD-seq) of 494 Pisum sativum (pea) samples from all wild and domesticated groups to analyze the genetic structure of the collection. Patterns of ancient admixture were investigated by analysis of admixture graphs. We used two complementary approaches, one diversity based and one based on differentiation, to detect the selection signatures putatively associated with domestication. An analysis of the subpopulation structure of wild P. sativum revealed five distinct groups with a notable geographic pattern. Pisum abyssinicum clustered unequivocally within the P. sativum complex, without any indication of hybrid origin. We detected 32 genomic regions putatively subjected to selection: 29 in P. sativum ssp. sativum and three in P. abyssinicum. The two domesticated groups did not share regions under selection and did not display similar haplotype patterns within those regions. Wild P. sativum is structured into well-diverged subgroups. Although Pisum sativum ssp. humile is not supported as a taxonomic entity, the so-called 'southern humile' is a genuine wild group. Introgression did not shape the variation observed within the sampled germplasm. The two domesticated pea groups display distinct genetic bases of domestication, suggesting two genetically independent domestication events.

Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.

Genome resequencing reveals rapid, repeated evolution in the Colorado potato beetle

Insecticide resistance and rapid pest evolution threatens food security and the development of sustainable agricultural practices, yet the evolutionary mechanisms that allow pests to rapidly adapt to control tactics remains unclear. Here we examine how a global super-pest, the Colorado potato beetle (CPB), Leptinotarsa decemlineata, rapidly evolves resistance to insecticides. Using whole genome resequencing and transcriptomic data focused on its ancestral and pest range in North America, we assess evidence for three, non-mutually exclusive models of rapid evolution: pervasive selection on novel mutations, rapid regulatory evolution, and repeated selection on standing genetic variation. Population genomic analysis demonstrates that CPB is geographically structured, even among recently established pest populations. Pest populations exhibit similar levels of nucleotide diversity, relative to non-pest populations, and show evidence of recent expansion. Genome scans provide clear signatures of repeated adaptation across CPB populations, with especially strong evidence of selection on insecticide resistance genes in different populations. Analyses of gene expression show that constitutive upregulation of candidate insecticide resistance genes drives distinctive population patterns. CPB evolves insecticide resistance repeatedly across agricultural regions, leveraging similar genetic pathways but different genes, demonstrating a polygenic trait architecture for insecticide resistance that can evolve from standing genetic variation. Despite expectations, we do not find support for strong selection on novel mutations, or rapid evolution from selection on regulatory genes. These results suggest that integrated pest management practices must mitigate the evolution of polygenic resistance phenotypes among local pest populations, in order to maintain the efficacy and sustainability of novel control techniques.

Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.

Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations

Principal Component Analysis (PCA) projects high-dimensional genotype data into a few components that discern populations. Ancestry Informative Markers (AIMs) are a small subset of SNPs capable of distinguishing populations. We integrate these two approaches by proposing an algorithm to identify necessary informative loci whose removal from the data deteriorates the PCA structure. Unlike classical AIMs, necessary informative loci densely cover the genome, hence can illuminate the evolution and mixing history of populations. We conduct a comprehensive analysis to the genotype data of the 1000 Genomes Project using necessary informative loci. Projections along the top seven principal components demarcate populations at distinct geographic levels. Millions of necessary informative loci along each PC are identified. Population identities along each PC are approximately determined by weighted sums of minor (or major) alleles over the informative loci. Variations of allele frequencies are aligned with the history and direction of population evolution. The population distribution of projections along the top three PCs is recapitulated by a simple demographic model based on several waves of founder population separation and mixing. Informative loci possess locational concentration in the genome and functional enrichment. Genes at two hot spots encompassing dense PC 7 informative loci exhibit differential expressions among European populations. The mosaic of local ancestry in the genome of a mixed descendant from multiple populations can be inferred from partial PCA projections of informative loci. Finally, informative loci derived from the 1000 Genomes data well predict the projections of an independent genotype data of South Asians. These results demonstrate the utility and relevance of informative loci to investigate human evolution.

A spectral theory for Wright's inbreeding coefficients and related quantities

Wright’s inbreeding coefficient, F_ST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright’s inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of F_ST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright’s F_ST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of F_ST approximates the proportion of genetic variation explained by the first (K − 1) principal components accurately. The new definition of F_ST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.

Principal component analysis (PCA) is the most-frequently used approach to describe population genetic structure from large population genomic data sets. In this study, we show that PCA not only estimates ancestries of sampled individuals, but also computes the average value of Wright’s inbreeding coefficient over the loci included in the genotype matrix. Our result shows that inbreeding coefficients and PCA eigenvalues provide equivalent descriptions of population structure. As a consequence, PCA extends the definition of those coefficients beyond the framework of allelic frequencies. We give examples on how F_ST can be computed from ancient DNA samples for which genotypes are corrected for coverage, and in an ecological genomic example where a proportion of genetic variation is explained by environmental variables.

An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes

Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.

The genomes of ancient date palms germinated from 2,000 y old seeds

Resurrection genomics is an alternative to ancient DNA approaches in studying the genetics and evolution of past and possibly extinct populations. By reviving biological material such as germinating ancient seeds from archaeological and paleontological sites, or historical collections, one can study genomes of lost populations. We applied this approach by sequencing the genomes of seven Judean date palms (Phoenix dactylifera) that were germinated from ∼2,000 y old seeds recovered in the Southern Levant. Using this genomic data, we were able to document that introgressive hybridization of the wild Cretan palm Phoenix theophrasti into date palms had occurred in the Eastern Mediterranean by ∼2,200 y ago and examine the evolution of date palm populations in this pivotal region two millennia ago.

Seven date palm seeds (Phoenix dactylifera L.), radiocarbon dated from the fourth century BCE to the second century CE, were recovered from archaeological sites in the Southern Levant and germinated to yield viable plants. We conducted whole-genome sequencing of these germinated ancient samples and used single-nucleotide polymorphism data to examine the genetics of these previously extinct Judean date palms. We find that the oldest seeds from the fourth to first century BCE are related to modern West Asian date varieties, but later material from the second century BCE to second century CE showed increasing genetic affinities to present-day North African date palms. Population genomic analysis reveals that by ∼2,400 to 2,000 y ago, the P. dactylifera gene pool in the Eastern Mediterranean already contained introgressed segments from the Cretan palm Phoenix theophrasti, a crucial genetic feature of the modern North African date palm populations. The P. theophrasti introgression fraction content is generally higher in the later samples, while introgression tracts are longer in these ancient germinated date palms compared to modern North African varieties. These results provide insights into crop evolution arising from an analysis of plants originating from ancient germinated seeds and demonstrate what can be accomplished with the application of a resurrection genomics approach.

The larval environment strongly influences the bacterial communities of Aedes triseriatus and Aedes japonicus (Diptera: Culicidae)

Mosquito bacterial communities are essential in mosquito biology, and knowing the factors shaping these bacterial communities is critical to their application in mosquito-borne disease control. This study investigated how the larval environment influences the bacterial communities of larval stages of two container-dwelling mosquito species, Aedes triseriatus, and Aedes japonicus. Larval and water samples were collected from tree holes and used tires at two study sites, and their bacteria characterized through MiSeq sequencing of the 16S rRNA gene. Bacterial richness was highest in Ae. japonicus, intermediate in Ae. triseriatus, and lowest in water samples. Dysgonomonas was the dominant bacterial taxa in Ae. triseriatus larvae; the unclassified Comamonadaceae was dominant in water samples from waste tires, while Mycobacterium and Carnobacterium, dominated Ae. japonicus. The two mosquito species harbored distinct bacterial communities that were different from those of the water samples. The bacterial communities also clustered by habitat type (used tires vs. tree holes) and study site. These findings demonstrate that host species, and the larval sampling environment are important determinants of a significant component of bacterial community composition and diversity in mosquito larvae and that the mosquito body may select for microbes that are generally rare in the larval environment.

EigenGWAS: An online visualizing and interactive application for detecting genomic signatures of natural selection

Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology and breeding programmes. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to F ST but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, and here we extend its use to inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is a better option for this adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts plink data in a standard binary format that can be easily converted from the original sequencing data, provides the users with graphical results via the R-Shiny user-friendly interface. We applied the proposed method and tool to various data sets, and biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. The EigenGWAS online platform is available at www.eigengwas.com, and can be localized and scaled up via R (recommended) or docker.

LEA 3: Factor models in population genetics and ecological genomics with R

A major objective of evolutionary biology is to understand the processes by which organisms have adapted to various environments, and to predict the response of organisms to new or future conditions. The availability of large genomic and environmental data sets provides an opportunity to address those questions, and the R package LEA has been introduced to facilitate population and ecological genomic analyses in this context. By using latent factor models, the program computes ancestry coefficients from population genetic data and performs genotype-environment association analyses with correction for unobserved confounding variables. In this study, we present new functionalities of LEA, which include imputation of missing genotypes, fast algorithms for latent factor mixed models using multivariate predictors for genotype-environment association studies, population differentiation tests for admixed or continuous populations, and estimation of genetic offset based on climate models. The new functionalities are implemented in version 3.1 and higher releases of the package. Using simulated and real data sets, our study provides evaluations and examples of applications, outlining important practical considerations when analysing ecological genomic data in R.

Intraspecific niche partition without speciation: individual level web polymorphism within a single island spider population

Early in the process of adaptive radiation, allopatric disruption of gene flow followed by ecological specialization is key for speciation; but, do adaptive radiations occur on small islands without internal geographical barriers? Island populations sometimes harbour polymorphism in ecological specializations, but its significance remains unclear. On one hand, morphs may correspond to ‘cryptic’ species. Alternatively, they could result from population, developmental or behavioural plasticity. The spider Wendilgarda galapagensis (Araneae, Theridiosomatidae) is endemic to the small Isla del Coco and unique in spinning three different web types, each corresponding to a different microhabitat. We tested whether this variation is associated with ‘cryptic’ species or intraspecific behavioural plasticity. Despite analysing 36 803 loci across 142 individuals, we found no relationship between web type and population structure, which was only weakly geographically differentiated. The same pattern holds when looking within a sampling site or considering only F_st outliers. In line with genetic data, translocation experiments showed that web architecture is plastic within an individual. However, not all transitions between web types are equally probable, indicating the existence of individual preferences. Our data supports the idea that diversification on small islands might occur mainly at the behavioural level producing an intraspecific niche partition without speciation.

Exploring Population Structure with Admixture Models and Principal Component Analysis

Population structure is a commonplace feature of genetic variation data, and it has importance in numerous application areas, including evolutionary genetics, conservation genetics, and human genetics. Understanding the structure in a sample is necessary before more sophisticated analyses are undertaken. Here we provide a protocol for running principal component analysis (PCA) and admixture proportion inference-two of the most commonly used approaches in describing population structure. Along with hands-on examples with CEPH-Human Genome Diversity Panel and pragmatic caveats, readers will learn to analyze and visualize population structure on their own data.

Adaptive introgression from maize has facilitated the establishment of teosinte as a noxious weed in Europe

Global trade has considerably accelerated biological invasions. The annual tropical teosintes, the closest wild relatives of maize, were recently reported as new agricultural weeds in two European countries, Spain and France. Their prompt settlement under climatic conditions differing drastically from that of their native range indicates rapid genetic evolution. We performed a phenotypic comparison of French and Mexican teosintes under European conditions and showed that only the former could complete their life cycle during maize cropping season. To test the hypothesis that crop-to-wild introgression triggered such rapid adaptation, we used single nucleotide polymorphisms to characterize patterns of genetic variation in French, Spanish, and Mexican teosintes as well as in maize germplasm. We showed that both Spanish and French teosintes originated from Zea mays ssp. mexicana race "Chalco," a weedy teosinte from the Mexican highlands. However, introduced teosintes differed markedly from their Mexican source by elevated levels of genetic introgression from the high latitude Dent maize grown in Europe. We identified a clear signature of divergent selection in a region of chromosome 8 introgressed from maize and encompassing ZCN8, a major flowering time gene associated with adaptation to high latitudes. Moreover, herbicide assays and sequencing revealed that French teosintes have acquired herbicide resistance via the introgression of a mutant herbicide-target gene (ACC1) present in herbicide-resistant maize cultivars. Altogether, our results demonstrate that adaptive crop-to-wild introgression has triggered both rapid adaptation to a new climatic niche and acquisition of herbicide resistance, thereby fostering the establishment of an emerging noxious weed.

Skim-Sequencing Based Genotyping Reveals Genetic Divergence of the Wild and Domesticated Population of Black Tiger Shrimp (Penaeus monodon) in the Indo-Pacific Region

The domestication of a wild-caught aquatic animal is an evolutionary process, which results in genetic discrimination at the genomic level in response to strong artificial selection. Although black tiger shrimp (Penaeus monodon) is one of the most commercially important aquaculture species, a systematic assessment of genetic divergence and structure of wild-caught and domesticated broodstock populations of the species is yet to be documented. Therefore, we used skim sequencing (SkimSeq) based genotyping approach to investigate the genetic structure of 50 broodstock individuals of P. monodon species, collected from five sampling sites (n = 10 in each site) across their distribution in Indo-Pacific regions. The wild-caught P. monodon broodstock population were collected from Malaysia (MS) and Japan (MJ), while domesticated broodstock populations were collected from Madagascar (MMD), Hawaii, HI, USA (MMO), and Thailand (MT). After various filtering process, a total of 194,259 single nucleotide polymorphism (SNP) loci were identified, in which 4983 SNP loci were identified as putatively adaptive by the pcadapt approach. In both datasets, pairwise F_ST estimates high genetic divergence between wild and domesticated broodstock populations. Consistently, different spatial clustering analyses in both datasets categorized divergent genetic structure into two clusters: (1) wild-caught populations (MS and MJ), and (2) domesticated populations (MMD, MMO and MT). Among 4983 putatively adaptive SNP loci, only 50 loci were observed to be in the coding region. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that non-synonymous mutated genes might be associated with the energy production, metabolic functions, respiration regulation and developmental rates, which likely act to promote adaptation to the strong artificial selection during the domestication process. This study has demonstrated the applicability of SkimSeq in a highly duplicated genome of P. monodon specifically, across a range of genetic backgrounds and geographical distributions, and would be useful for future genetic improvement program of this species in aquaculture.

Genome scans for selection and introgression based on k-nearest neighbour techniques

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Most of these methods are either designed for one or the other case, which may impair the study of combined cases. Here, we introduce a series of versatile genome-scan methods applicable for both cases, the detection of selection and introgression. The proposed approaches are based on nonparametric k-nearest neighbour (kNN) techniques, while incorporating pairwise Fixation Index (F_ST ) and pairwise nucleotide differences (d_xy ) as features. We benchmark our methods using a wide range of simulation scenarios, with varying parameters, such as recombination rates, population background histories, selection strengths, the proportion of introgression and the time of gene flow. We find that kNN-based methods perform remarkably well compared with the state-of-the-art. Finally, we demonstrate how to perform kNN-based genome scans on real-world genomic data using the population genomics R-package popgenome.

Cytokine network analysis of immune responses before and after autologous dendritic cell and tumor cell vaccine immunotherapies in a randomized trial

Background

In a randomized phase II trial conducted in patients with metastatic melanoma, patient-specific autologous dendritic cell vaccines (DCV) were associated with longer survival than autologous tumor cell vaccines (TCV). Both vaccines presented antigens from cell-renewing autologous tumor cells. The current analysis was performed to better understand the immune responses induced by these vaccines, and their association with survival.

Methods

110 proteomic markers were measured at a week-0 baseline, 1 week before the first of 3 weekly vaccine injections, and at week-4, 1 week after the third injection. Data was presented as a deviation from normal controls. A two-component principal component (PC) statistical analysis and discriminant analysis were performed on this data set for all patients and for each treatment cohort.

Results

At baseline PC-1 contained 64.4% of the variance and included the majority of cytokines associated with Th1 and Th2 responses, which positively correlated with beta-2-microglobulin (B2M), programmed death protein-1 (PD-1) and transforming growth factor beta (TGFβ1). Results were similar at baseline for both treatment cohorts. After three injections, DCV-treated patients showed correlative grouping among Th1/Th17 cytokines on PC-1, with an inverse correlation with B2M, FAS, and IL-18, and correlations among immunoglobulins in PC-2. TCV-treated patients showed a positive correlation on PC-1 among most of the cytokines and tumor markers B2M and FAS receptor. There were also correlative changes of IL12p40 with both Th1 and Th2 cytokines and TGFβ1. Discriminant analysis provided additional evidence that DCV was associated with innate, Th1/Th17, and Th2 responses while TCV was only associated with innate and Th2 responses.

Conclusions

These analyses confirm that DCV induced a different immune response than that induced by TCV, and these immune responses were associated with improved survival.

Trial registration Clinical trials.gov NCT004936930 retrospectively registered 28 July 2009