Detecting Positive Selection in Populations Using Genetic Data

Thrips dynamics in Allium crops: Unraveling the role of reproductive mode and weather variables in Thrips tabaci population development

Thrips tabaci is the main thrips species affecting onion and related species. It is a cryptic species complex comprising three phylogenetic groups characterized by different reproductive modes (thelytoky or arrhenotoky) and host plant specialization. Thrips tabaci populations vary widely in genetic diversity, raising questions about the factor(s) that drive this diversity. We investigated the genetic diversity, reproductive mode, and heteroplasmy frequency in T. tabaci populations from different Allium spp fields in six locations in the Netherlands in 2021 and at two locations from the North and South of Uruguay over three years to unravel how the diversity is structured in the populations and if changes can be associated with weather variables. The thrips populations from each location studied were characterized by sequencing 33 individuals per sampling using the cytochrome oxidase subunit I (COI) gene. The reproductive mode was determined using specific primers and a phylogenetic analysis. Daily data for the weather variables was obtained from agrometeorological experimental stations located in the Uruguayan sampled crop fields. The diversity, reproductive mode, and heteroplasmy of T. tabaci populations in onion crops exhibited significant year-to-year variation depending on the location. Changes in the relative frequency of each reproductive mode in populations are associated with weather variables: precipitation, temperature, number of frosts, and relative humidity. Heteroplasmy frequency was associated with the same weather variables except temperature. In Uruguay and the Netherlands, T. tabaci thelytokous haplotype H1 was the most common, showing different heteroplasmy levels within and between the populations. In the field populations, a high frequency of heteroplasmic H1 individuals was associated with low precipitation, while all H1 individuals were also associated with high temperature and high relative humidity. In Uruguay, heteroplasmy was associated with arrhenotokous T. tabaci haplotypes, which were highly common in the North, pointing at specific adaptations leading to a faster population build-up. All this information may be instrumental for designing more precise integrated pest management techniques in both conventional and organic production.

Repeated global adaptation across plant species

Global adaptation occurs when all populations of a species undergo selection toward a common optimum. This can occur by a hard selective sweep with the emergence of a new globally advantageous allele that spreads throughout a species' natural range until reaching fixation. This evolutionary process leaves a temporary trace in the region affected, which is detectable using population genomic methods. While selective sweeps have been identified in many species, there have been few comparative and systematic studies of the genes involved in global adaptation. Building upon recent findings showing repeated genetic basis of local adaptation across independent populations and species, we asked whether certain genes play a more significant role in driving global adaptation across plant species. To address this question, we scanned the genomes of 17 plant species to identify signals of repeated global selective sweeps. Despite the substantial evolutionary distance between the species analyzed, we identified several gene families with strong evidence of repeated positive selection. These gene families tend to be enriched for reduced pleiotropy, consistent with predictions from Fisher's evolutionary model and the cost of complexity hypothesis. We also found that genes with repeated sweeps exhibit elevated levels of gene duplication. Our findings contrast with recent observations of increased pleiotropy in genes driving local adaptation, consistent with predictions based on the theory of migration-selection balance.

Mutation-based mechanism and evolution of the potent multidrug efflux pump RE-CmeABC in Campylobacter

The resistance-nodulation-cell division (RND) superfamily of multidrug efflux systems are important players in mediating antibiotic resistance in gram-negative pathogens. Campylobacter jejuni, a major enteric pathogen, utilizes an RND-type transporter system, CmeABC, as the primary mechanism for extrusion of various antibiotics. Recently, a functionally potent variant of CmeABC (named RE-CmeABC) emerged in clinical Campylobacter isolates, conferring enhanced resistance to multiple antibiotic classes. Despite the clinical importance of RE-CmeABC, the molecular mechanisms for its functional gain and its evolutionary trajectory remain unknown. Here, we demonstrated that amino acid substitutions in RE-CmeB (inner membrane transporter), but not in RE-CmeA (periplasmic protein) and RE-CmeC (outer membrane protein), in conjunction with a nucleotide mutation in the promoter region of the efflux operon, are responsible for the functional gain of the multidrug efflux system. We also showed that RE-cmeABC is emerging globally and distributed in genetically diverse C. jejuni strains, suggesting its possible spread by horizontal gene transfer. Notably, many of RE-cmeABC harboring isolates were associated with the human host including strains from large disease outbreaks, indicating the clinical relevance and significance of RE-CmeABC. Evolutionary analysis indicated that RE-cmeB likely originated from Campylobacter coli, but its expansion mainly occurred in C. jejuni, possibly driven by antibiotic selection pressure. Additionally, RE-cmeB, but not RE-cmeA and RE-cmeC, experienced a selective sweep and was progressing to be fixed during evolution. Together, these results identify a mutation-based mechanism for functional gain in RE-CmeABC and reveal the key role of RE-CmeB in facilitating Campylobacter adaptation to antibiotic selection.

Pervasive selective sweeps across human gut microbiomes

The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many human gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across human gut microbiomes. Here, we develop a novel selection scan statistic named the integrated Linkage Disequilibrium Score (iLDS) that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant. Application of the statistic to ~30 of the most prevalent commensal gut species from 24 populations around the world revealed more than 300 selective sweeps across species. We find an enrichment for selective sweeps at loci involved in carbohydrate metabolism-potentially indicative of adaptation to features of host diet-and we find that the targets of selection significantly differ between Westernized and non-Westernized populations. Underscoring the potential role of diet in driving selection, we find a selective sweep absent from non-Westernized populations but ubiquitous in Westernized populations at a locus known to be involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome, and that targets of selection may be strongly impacted by host diet.

DeepGenomeScan of 15 Worldwide Bovine Populations Detects Spatially Varying Positive Selection Signals

Identifying genomic regions under selection is essential for understanding the genetic mechanisms driving species evolution and adaptation. Traditional methods often fall short in detecting complex, spatially varying selection signals. Recent advances in deep learning, however, present promising new approaches for uncovering subtle selection signals that traditional methods might miss. In this study, we utilized the deep learning framework DeepGenomeScan to detect spatially varying selection signatures across 15 bovine populations worldwide. Our analysis uncovered novel insights into selective sweep hotspots within the bovine genome, revealing key genes associated with physiological and adaptive traits that were previously undetected. We identified significant quantitative trait loci linked to milk protein and fat percentages. By comparing the selection signatures identified in this study with those reported in the Bovine Genome Variation Database, we discovered 38 novel genes under selection that were not identified through traditional methods. These genes are primarily associated with milk and meat yield and quality. Our findings enhance our understanding of spatially varying selection's impact on bovine genomic diversity, laying a foundation for future research in genetic improvement and conservation. This is the first deep learning-based study of selection signatures in cattle, offering new insights for evolutionary and livestock genomics research.

Mitochondrial DNA D-Loop Polymorphisms among the Galla Goats Reveals Multiple Maternal Origins with Implication on the Functional Diversity of the HSP70 Gene

Despite much attention given to the history of goat evolution in Kenya, information on the origin, demographic history, dispersal route, and genetic diversity of Galla goats remains unclear. Here, we examined the genetic background, diversity, demographic history, and population genetic variation of Galla goats using mtDNA D-loop and HSP70 single-nucleotide polymorphism markers. The results revealed 90 segregating sites and 68 haplotypes in a 600-bp mtDNA D-loop sequence. The overall mean mitochondrial haplotype diversity was 0.993. The haplotype diversities ranged between 0.8939 ± 0.0777 and 1.0000 ± 0.0221 in all populations supporting high genetic diversity. Mitochondrial phylogenetic analysis revealed three Galla goat haplogroups (A, G, and D), supporting multiple maternal ancestries, of which haplogroup A was the most predominant. Analysis of molecular variance (AMOVA) showed considerable variation within populations at 94.39%, evidence of high genetic diversity. Bimodal mismatch distribution patterns were observed while most populations recorded negative results for Tajima and Fu's Fs neutrality tests supporting population expansion. Genetic variation among populations was also confirmed using HSP70 gene fragment sequences, where six polymorphic sites which defined 21 haplotypes were discovered. Analysis of molecular variance revealed a significant FST index value of 0.134 and a high FIS index value of 0.746, an indication of inbreeding. This information will pave the way for conservation strategies and informed breeding to improve Galla or other goat breeds for climate-smart agriculture.

Landmarks in the history of selective sweeps

Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.

Deep convolutional and conditional neural networks for large-scale genomic data generation

Applications of generative models for genomic data have gained significant momentum in the past few years, with scopes ranging from data characterization to generation of genomic segments and functional sequences. In our previous study, we demonstrated that generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be used to create novel high-quality artificial genomes (AGs) which can preserve the complex characteristics of real genomes such as population structure, linkage disequilibrium and selection signals. However, a major drawback of these models is scalability, since the large feature space of genome-wide data increases computational complexity vastly. To address this issue, we implemented a novel convolutional Wasserstein GAN (WGAN) model along with a novel conditional RBM (CRBM) framework for generating AGs with high SNP number. These networks implicitly learn the varying landscape of haplotypic structure in order to capture complex correlation patterns along the genome and generate a wide diversity of plausible haplotypes. We performed comparative analyses to assess both the quality of these generated haplotypes and the amount of possible privacy leakage from the training data. As the importance of genetic privacy becomes more prevalent, the need for effective privacy protection measures for genomic data increases. We used generative neural networks to create large artificial genome segments which possess many characteristics of real genomes without substantial privacy leakage from the training dataset. In the near future, with further improvements in haplotype quality and privacy preservation, large-scale artificial genome databases can be assembled to provide easily accessible surrogates of real databases, allowing researchers to conduct studies with diverse genomic data within a safe ethical framework in terms of donor privacy.

Whole genome population structure of North Atlantic kelp confirms high-latitude glacial refugia

Coastal refugia during the Last Glacial Maximum (~21,000 years ago) have been hypothesized at high latitudes in the North Atlantic, suggesting marine populations persisted through cycles of glaciation and are potentially adapted to local environments. Here, whole-genome sequencing was used to test whether North Atlantic marine coastal populations of the kelp Alaria esculenta survived in the area of southwestern Greenland during the Last Glacial Maximum. We present the first annotated genome for A. esculenta and call variant positions in 54 individuals from populations in Atlantic Canada, Greenland, Faroe Islands, Norway and Ireland. Differentiation across populations was reflected in ~1.9 million single nucleotide polymorphisms, which further revealed mixed ancestry in the Faroe Islands individuals between putative Greenlandic and European lineages. Time-calibrated organellar phylogenies suggested Greenlandic populations were established during the last interglacial period more than 100,000 years ago, and that the Faroe Islands population was probably established following the Last Glacial Maximum. Patterns in population statistics, including nucleotide diversity, minor allele frequencies, heterozygosity and linkage disequilibrium decay, nonetheless suggested glaciation reduced Canadian Atlantic and Greenlandic populations to small effective sizes during the most recent glaciation. Functional differentiation was further reflected in exon read coverage, which revealed expansions unique to Greenland in 337 exons representing 162 genes, and a modest degree of exon loss (103 exons from 56 genes). Altogether, our genomic results provide strong evidence that A. esculenta populations were resilient to past climatic fluctuations related to glaciations and that high-latitude populations are potentially already adapted to local conditions as a result.

The Impact of Genetic Surfing on Neutral Genomic Diversity

Range expansions have been common in the history of most species. Serial founder effects and subsequent population growth at expansion fronts typically lead to a loss of genomic diversity along the expansion axis. A frequent consequence is the phenomenon of "gene surfing," where variants located near the expanding front can reach high frequencies or even fix in newly colonized territories. Although gene surfing events have been characterized thoroughly for a specific locus, their effects on linked genomic regions and the overall patterns of genomic diversity have been little investigated. In this study, we simulated the evolution of whole genomes during several types of 1D and 2D range expansions differing by the extent of migration, founder events, and recombination rates. We focused on the characterization of local dips of diversity, or "troughs," taken as a proxy for surfing events. We find that, for a given recombination rate, once we consider the amount of diversity lost since the beginning of the expansion, it is possible to predict the initial evolution of trough density and their average width irrespective of the expansion condition. Furthermore, when recombination rates vary across the genome, we find that troughs are over-represented in regions of low recombination. Therefore, range expansions can leave local and global genomic signatures often interpreted as evidence of past selective events. Given the generality of our results, they could be used as a null model for species having gone through recent expansions, and thus be helpful to correctly interpret many evolutionary biology studies.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Genetic diversity and selective sweeps in historical and modern Canadian spring wheat cultivars using the 90K SNP array

Previous molecular characterization studies conducted in Canadian wheat cultivars shed some light on the impact of plant breeding on genetic diversity, but the number of varieties and markers used was small. Here, we used 28,798 markers of the wheat 90K single nucleotide polymorphisms to (a) assess the extent of genetic diversity, relationship, population structure, and divergence among 174 historical and modern Canadian spring wheat varieties registered from 1905 to 2018 and 22 unregistered lines (hereinafter referred to as cultivars), and (b) identify genomic regions that had undergone selection. About 91% of the pairs of cultivars differed by 20-40% of the scored alleles, but only 7% of the pairs had kinship coefficients of < 0.250, suggesting the presence of a high proportion of redundancy in allelic composition. Although the 196 cultivars represented eight wheat classes, our results from phylogenetic, principal component, and the model-based population structure analyses revealed three groups, with no clear structure among most wheat classes, breeding programs, and breeding periods. F_ST statistics computed among different categorical variables showed little genetic differentiation (< 0.05) among breeding periods and breeding programs, but a diverse level of genetic differentiation among wheat classes and predicted groups. Diversity indices were the highest and lowest among cultivars registered from 1970 to 1980 and from 2011 to 2018, respectively. Using two outlier detection methods, we identified from 524 to 2314 SNPs and 41 selective sweeps of which some are close to genes with known phenotype, including plant height, photoperiodism, vernalization, gluten strength, and disease resistance.

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.

Benchmarking the performance of Pool-seq SNP callers using simulated and real sequencing data

Population genomics is a fast-developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome-wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool-seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE-pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large-scale population studies but also in cases where the Pool-seq strategy is the only option, such as in metagenomic or polyploid studies.

Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach

Marker assisted breeding, facilitated by reference genome assemblies, can help to produce cultivars adapted to changing environmental conditions. However, anomalous linkage disequilibrium (LD), where single markers show high LD with markers on other chromosomes but low LD with adjacent markers, is a serious impediment for genetic studies. We used a LD-correction approach to overcome these drawbacks, correcting the physical position of markers derived from 15 and 135 K arrays in a diversity panel of bread wheat representing 50 years of breeding history. We detected putative mismapping of 11.7% markers and improved the physical alignment of 5.4% markers. Population analysis indicated reduced genetic diversity over time as a result of breeding efforts. By analysis of outlier loci and allele frequency change over time we traced back the 2NS/2AS translocation of Aegilops ventricosa to one cultivar, "Cardos" (registered in 1998) which was the first among the panel to contain this translocation. A "selective sweep" for this important translocation region on chromosome 2AS was found, putatively linked to plant response to biotic stress factors. Our approach helps in overcoming the drawbacks of incorrectly anchored markers on the wheat reference assembly and facilitates detection of selective sweeps for important agronomic traits.

A joint analysis strategy reveals genetic changes associated with artificial selection between egg-type and meat-type ducks

Egg-type ducks and meat-type ducks are predominantly commercial or indigenous and have been subjected to artificial directional selection. These two duck types differ substantially in body shape, production performance and reproductivity. However, the genetic changes associated with phenotypic differences remain unclear. Here, we compared the two duck types at the genomic and transcriptomic levels. We identified a large number of SNPs and genes in genomic divergent regions in terms of F_ST and θπ values. The corresponding genes were mainly enriched in embryonic development function and metabolic pathway. RNA-seq analysis also revealed differential gene expression in the liver and gonads. The differentially expressed genes were functionally associated with signal transmission and substance metabolism respectively. Furthermore, we found that seven genes were related to differentiation between the two types by both g genome and transcriptome analysis and were plausible candidate genes. These genes were annotated to GO categories of cell development and disease immunity. These findings will enable a better understanding of the artificial selection history of meat and egg ducks and provide a valuable resource for future research on the breeding of these two lineages.