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Tequila bats (genus Leptonycteris) have gained attention for their critical role in pollinating different plant species, especially Agave spp. and columnar cacti. Leptonycteris nivalis is the largest nectar-feeding bat in the Americas, and the females exhibit migratory behavior during the breeding season. Due to its relatively small and seemingly declining population sizes, this species is protected by government agencies in the United States and Mexico. We conducted population genetics and phylogeographic analyses to elucidate the genetic structure and demographic history of the species using two mitochondrial markers and a Y chromosome-associated gene, to describe both maternal and paternal lineages. We estimated high haplotypic diversity measures for the different markers (Dloop—Hd = 0.775; Cyt-b—Hd = 0.937; DBY -Hd = 0.946). We found that geographic genetic differentiation is very low, and there is high connectivity among localities. The estimated divergence time between L. nivalis and L. yerbabuenae, the other species in the genus found in Mexico, aligns with previous estimates for the genus (6.91–9.43 mya). A demographic expansion was detected approximately at 600 ka—700 ka (thousands of years ago). The historical demographic changes observed in L. nivalis appear to be associated with environmental shifts during the Pleistocene, which likely impacted the distribution range of the plants that these bats feed on, such as Agave species. [ABSTRACT FROM AUTHOR]

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h = 0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species. Author summary: The dominance coefficient (h) of a mutation determines its impact on organismal fitness when heterozygous. For instance, fully recessive mutations (h = 0) have no effects on fitness when heterozygous whereas additive mutations (h = 0.5) have an effect that is intermediate to the two homozygous genotypes. The extent to which deleterious mutations may be recessive, additive, or dominant is a key area of study in evolutionary genetics. However, dominance parameters remain poorly known in humans and most other organisms due to a variety of technical challenges. In this study, we aim to constrain the possible set of dominance and selection parameters for amino acid changing mutations in humans. We find that a wide range of models are possible, including models with a theoretically-predicted relationship between h and s. We then use a range of plausible selection and dominance models to explore how deleterious variation may have been shaped by the out-of-Africa bottleneck in humans. Our results highlight the subtle influence of dominance on patterns of genetic load in humans and demonstrate that models of partially recessive mutations at amino-acid-changing sites can explain the observed effects of inbreeding on mortality in humans. [ABSTRACT FROM AUTHOR]

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. Integrating neuroimaging and molecular brain data across people and brain regions and states can help to understand human brain individuality. This study presents a resource that uses computational genomics to estimate genetically regulated components of gene expression and infer associations of this expression with neuroimaging and clinical phenotypes. [ABSTRACT FROM AUTHOR]

Feralization is the process of domesticated animals returning to the wild and it is considered the counterpart of domestication. Molecular genetic changes are well documented in domesticated organisms but understudied in feral populations. In this study, the genetic differentiation between domestic and feral cats was inferred by analysing whole-genome sequencing data of two geographically distant feral cat island populations, Dirk Hartog Island (Australia) and Kaho'olawe (Hawaii) as well as domestic cats and European wildcats. The study investigated population structure, genetic differentiation, genetic diversity, highly differentiated genes, and recombination rates. Genetic structure analyses linked both feral cat populations to North American domestic and European cat populations. Recombination rates in feral cats were lower than in domestic cats but higher than in wildcats. For Australian and Hawaiian feral cats, 105 and 94 highly differentiated genes compared to domestic cats respectively, were identified. Annotated genes had similar functions, with almost 30% of the divergent genes related to nervous system development in both feral groups. Twenty mutually highly differentiated genes were found in both feral populations. Evolution of highly differentiated genes was likely driven by specific demographic histories, the relaxation of the selective pressures associated with domestication, and adaptation to novel environments to a minor extent. Random drift was the prevailing force driving highly divergent regions, with relaxed selection in feral populations also playing a significant role in differentiation from domestic cats. The study demonstrates that feralization is an independent process that brings feral cats on a unique evolutionary trajectory. [ABSTRACT FROM AUTHOR]

Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release. Author summary: Haplotype estimation, also known as phasing, is now applied to population scale projects, typically of hundreds of thousands of samples to millions of samples. Generally phasing relies on statistical methods as they provide very accurate results for common variations. However, for rare and very rare variants the lack of statistical power often results in poor phasing. The large amount of rare variations discovered with whole-genome sequencing as well as the number of samples makes it expensive to process. We have developed the SAPPHIRE method that leverages whole-genome sequencing data to verify and correct the phase at poorly phased variant loci. It does so by finding sequencing reads that contain both the poorly phased variant and an accurately phased common variant. SAPPHIRE scales with large data sets by specifically targeting variation where statistical phasing performed poorly, therefore it reduces the quantity of sequencing data to be processed and combines the advantages of both read-based and statistical approaches. We show the efficiency of SAPPHIRE by improving the estimated haplotypes for 200,031 samples in the UK Biobank. SAPPHIRE is free and available as open-source software. [ABSTRACT FROM AUTHOR]

Despite the economic importance of Piper nigrum (black pepper), a highly valued crop worldwide, development and utilization of genomic resources have remained limited, with diversity assessments often relying on only a few samples or DNA markers. Here we employed restriction-site associated DNA sequencing to analyze 175 P. nigrum accessions from eight main black pepper growing regions in Sri Lanka. The sequencing effort resulted in 1,976 million raw reads, averaging 11.3 million reads per accession, revealing 150,356 high-quality single nucleotide polymorphisms (SNPs) distributed across 26 chromosomes. Population structure analysis revealed two subpopulations (K = 2): a dominant group consisting of 152 accessions sourced from both home gardens and large-scale cultivations, and a smaller group comprising 23 accessions exclusively from native collections in home gardens. This clustering was further supported by principal component analysis, with the first two principal components explaining 35.2 and 12.1% of the total variation. Genetic diversity analysis indicated substantial gene flow (Nm = 342.21) and a low fixation index (FST = 0.00073) between the two subpopulations, with no clear genetic differentiation among accessions from different agro-climatic regions. These findings demonstrate that most current black pepper genotypes grown in Sri Lanka share a common genetic background, emphasizing the necessity to broaden the genetic base to enhance resilience to biotic and abiotic stresses. This study represents the first attempt at analyzing black pepper genetic diversity using high-resolution SNP markers, laying the foundation for future genome-wide association studies for SNP-based gene discovery and breeding. [ABSTRACT FROM AUTHOR]

The adaptative responses and divergent evolution shown in the environments habited by the Cichlidae family allow to understand different biological properties, including fish genetic diversity and structure studies. In a zone that has been historically submitted to different anthropogenic pressures, this study assessed the genetic diversity and population structure of cichlid Caquetaia kraussii, a sedentary species with parental care that has a significant ecological role for its contribution to redistribution and maintenance of sedimentologic processes in its distribution area. This study developed de novo 16 highly polymorphic species-specific microsatellite loci that allowed the estimation of the genetic diversity and differentiation in 319 individuals from natural populations in the area influenced by the Ituango hydroelectric project in the Colombian Cauca River. Caquetaia kraussii exhibits high genetic diversity levels (Ho: 0.562–0.885; He: 0.583–0.884) in relation to the average neotropical cichlids and a three group-spatial structure: two natural groups upstream and downstream the Nechí River mouth, and one group of individuals with high relatedness degree, possibly independently formed by founder effect in the dam zone. The three genetic groups show recent bottlenecks, but only the two natural groups have effective population size that suggest their long-term permanence. The information generated is relevant not only for management programs and species conservation purposes, but also for broadening the available knowledge on the factors influencing neotropical cichlids population genetics. [ABSTRACT FROM AUTHOR]

This article discusses the importance of genomic surveillance in studying pathogen invasions, using the example of the ash dieback invasion of Europe caused by Hymenoscyphus fraxineus. The ash dieback invasion has had significant economic and ecological impacts, and understanding the genetic variation within the pathogen is crucial for effective management strategies. The article highlights the significance of different types of genomic variation, such as nucleotide substitutions, haplotype diversity, and structural genomic variation, in invasion biology. The authors also discuss various genomic surveillance strategies, including targeted resequencing, whole genome resequencing, and pangenomics, and suggest the potential of air sequencing as a pathogen surveillance method. [Extracted from the article]

The striped catfish Pseudoplatystoma magdaleniatum is a large-sized migratory species from the north Andes region, endemic to Magdalena basin and one of the major fishery resources. Despite the estimated reduction of over 80% of the fisheries production of this species throughout the basin in recent decades, its population in the lower Magdalena-Cauca basin showed healthy genetics after molecular analyses. However, the current conservation status of this species and several habitat disturbances demand the re-evaluation of its population genetics to infer evolutionary risks and assess potential changes. This work analyzed a total of 164 samples from the Cauca River collected downstream the Ituango Dam between 2019–2021 using species-specific microsatellite markers to compare the genetic diversity and structure in samples collected between 2010–2014 from the lower Magdalena-Cauca basin, previously analyzed. Our results showed a relatively stable panmictic population over time (4 to 10 years), with high genetic diversity and evidence of recent bottleneck. Promoting habitat connectivity to conserve gene flow, characterizing diversity and genetic structure over the entire basin, and integrating the results with future monitoring are important aspects for the management planning for P. magdaleniatum in the Magdalena-Cauca basin. [ABSTRACT FROM AUTHOR]

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry. Author summary: Many diseases and complex traits, such as prostate cancer, exhibit differences in incidence across populations. Yet the potential contribution of genetic factors towards such disparities is unclear. Polygenic scores summarise genetic effects across the genome and can in principle provide a valuable tool for assessing and comparing disease risk across populations. In practise, current approaches based on polygenic scores assume that such scores perfectly measure genetic risk of disease without measurement error, and thus do not account for uncertainty that arises in the construction of the score from a finite genome-wide association study (GWAS) training sample, which can be substantial. We introduce a Bayesian approach based on the LDpred2 polygenic score model that accounts fully for training sample uncertainty, and we propose a Wald test for formally testing such genetic risk differences across populations. Simulations show that the method properly controls for type 1 errors assuming independent SNPs (achieved by pruning), and that statistical power is sensitive to both the genetic architecture (heritability and polygenicity) and training sample size. In application to prostate cancer, this framework enables us to identify a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry. [ABSTRACT FROM AUTHOR]

Cyphocharax magdalenae, a Colombian freshwater fish species, plays a vital role in nutrients distribution and serves as a significant food source for other fish species and local fishing communities. Considered a short-distance migratory species, C. magdalenae populations face substantial extinction risk due to human activities impacting their habitats. To address the lack of knowledge on genetic diversity and population structure, this study used next-generation sequencing technology to develop species-specific microsatellite loci and conducted a population genetics analysis of C. magdalenae in the middle and lower sections of the Cauca River, Colombia. Out of 30 pairs of microsatellite primers evaluated in 324 individuals, 14 loci were found to be polymorphic, at linkage equilibrium and, in at least one population, their genotypic frequencies were in Hardy-Weinberg equilibrium. Results showed high genetic diversity levels compared to other neotropical Characiformes, with inbreeding coefficients similar to those reported for phylogenetically related species. Moreover, C. magdalenae exhibits seasonal population structure (rainy-dry) consisting of two genetic stocks showing bottleneck signals and high effective population sizes. This information is essential for understanding the current species genetics and developing future management programs for this fishery resource. [ABSTRACT FROM AUTHOR]

The cocoa pod borer (CPB) Conopomorpha cramerella (Snellen) (Lepidoptera: Gracillaridae) is one of the major constraints for cocoa production in South East Asia. In addition to cultural and chemical control methods, autocidal control tactics such as the Sterile Insect Technique (SIT) could be an efficient addition to the currently control strategy, however SIT implementation will depend on the population genetics of the targeted pest. The aim of the present work was to search for suitable microsatellite loci in the genome of CPB that is partially sequenced. Twelve microsatellites were initially selected and used to analyze moths collected from Indonesia, Malaysia, and the Philippines. A quality control verification process was carried out and seven microsatellites found to be suitable and efficient to distinguish differences between CPB populations from different locations. The selected microsatellites were also tested against a closely related species, i.e. the lychee fruit borer Conopomorpha sinensis (LFB) from Vietnam and eight loci were found to be suitable. The availability of these novel microsatellite loci will provide useful tools for the analysis of the population genetics and gene flow of these pests, to select suitable CPB strains to implement the SIT. [ABSTRACT FROM AUTHOR]

Biomorphs, Richard Dawkins's iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems. Author summary: The question of how novel phenotypic variation arises has received much less attention than natural selection even though both are key processes in Darwinian evolution. Here we uncover striking anisotropic structure in phenotypic variation for biomorphs, Richard Dawkins's iconic schematic model of development, introduced in his book The Blind Watchmaker. We observe strong simplicity bias: upon uniform random sampling of genotypes, a simple biomorph can be exponentially more likely to appear as potential variation than any individual complex biomorph is. The mapping from genotypes to phenotypes (GP map) for biomorphs exhibits many similarities to molecular GP maps, including relatively high mutational phenotype robustness which is positively correlated with evolvability. Such structure in the arrival of variation can enhance the ability of evolution to find fitness maxima through neutral exploration. By using evolutionary simulations, we show that phenotypes that fix in a population may not be the fittest ones, but rather adaptive phenotypes that are most likely to appear as variation. We hypothesize that similar developmental bias as observed in the biomorphs system may occur more widely in developmental systems. [ABSTRACT FROM AUTHOR]

Corynorhinus mexicanus is an insectivorous bat endemic to Mexico that inhabits the high and humid regions of the Sierra Madre Oriental (SMO), the Trans-Mexican Volcanic Belt (TMVB), and the Sierra Madre Occidental (SMOC). A previous study suggested that C. mexicanus could be a cryptic species complex due to the genetic divergence observed between specimens from the TMVB and SMOC. The present study implemented phylogenetic, population genetics, and morphological analyses to evaluate the hypothesis that C. mexicanus is a species complex. The phylogenetic analysis indicated that C. mexicanus is a polyphyletic species composed of three indirectly related lineages. The estimated divergence times for the lineages suggest that they first originated during the Pliocene, while the second and third shared a common ancestor with C. townsendii 1.55 million years ago, and diverged 600,000 years ago during the Middle Pleistocene. The population genetics analysis reveals the SMO lineage of C. mexicanus is an isolated genetic group and highly diverged from the rest of lineages (SMOC and TMVB). The morphological analyses showed variation in the skull and mandible associated with the lineages and sex of the specimens, highlighting a difference in mandible shape between the specimens of the SMO and the rest of C. mexicanus. The results of this study suggest the presence of an undescribed species of the genus Corynorhinus. [ABSTRACT FROM AUTHOR]

Genome-wide genealogies of multiple species carry detailed information about demographic and selection processes on individual branches of the phylogeny. Here, we introduce TRAILS, a hidden Markov model that accurately infers time-resolved population genetics parameters, such as ancestral effective population sizes and speciation times, for ancestral branches using a multi-species alignment of three species and an outgroup. TRAILS leverages the information contained in incomplete lineage sorting fragments by modelling genealogies along the genome as rooted three-leaved trees, each with a topology and two coalescent events happening in discretized time intervals within the phylogeny. Posterior decoding of the hidden Markov model can be used to infer the ancestral recombination graph for the alignment and details on demographic changes within a branch. Since TRAILS performs posterior decoding at the base-pair level, genome-wide scans based on the posterior probabilities can be devised to detect deviations from neutrality. Using TRAILS on a human-chimp-gorilla-orangutan alignment, we recover speciation parameters and extract information about the topology and coalescent times at high resolution. Author summary: DNA sequences can be compared to reconstruct the evolutionary history of different species. While the ancestral history is usually represented by a single phylogenetic tree, speciation is a more complex process, and, due to the effect of recombination, different parts of the genome might follow different genealogies. For example, even though humans are more closely related to chimps than to gorillas, around 15% of our genome is more similar to the gorilla genome than to the chimp one. Even for those parts of the genome that do follow the same human-chimp topology, we might encounter a last common ancestor at different time points in the past for different genomic fragments. Here, we present TRAILS, a new framework that utilizes the information contained in all these genealogies to reconstruct the speciation process. TRAILS infers unbiased estimates of the speciation times and the ancestral effective population sizes, improving the accuracy when compared to previous methods. TRAILS also reconstructs the genealogy at the highest resolution, inferring, for example, when common ancestry was found for different parts of the genome. This information can also be used to detect deviations from neutrality, effectively inferring natural selection that happened millions of years ago. We validate the method using extensive simulations, and we apply TRAILS to a human-chimp-gorilla multiple genome alignment, from where we recover speciation parameters that are in good agreement with previous estimates. [ABSTRACT FROM AUTHOR]

This article discusses the importance of strain diversity within microbial species. While species classification is typically based on genetic similarity thresholds, recent research has shown significant heterogeneity in genotypes and phenotypes across isolates of the same species. This diversity can impact host health, antimicrobial resistance, and clinical outcomes. The use of reference strains in research can limit our understanding of a species and its diversity. Strain diversity can also affect susceptibility to antimicrobial agents. However, implementing strain diversity findings into clinical practice can be challenging due to the need for swift decisions and the feasibility of tailored treatments. [Extracted from the article]

The Coccinellidae is a highly diversified family of order Coleoptera. Coccinellid ladybirds are well known for their role as biological control agent against varied range of agricultural pests. The samples of coccinellid ladybird collected from Pakistan were identified and characterized as Micraspis allardi (Mulsant, 1866). This is one of the least-studied ladybird species with limited work on its ecological distribution as a biological control agent. The genus Micraspis has vast genetic diversity with a possible presence of unknown number of cryptic species. Sequence information of some species of the genus Micraspis are present in NCBI database. However, least molecular data or sequences describing M. allardi could be available from database. Therefore, morphological and molecular characterization was imperative for this species. Here, the samples collected from sugarcane field of Faisalabad District of Pakistan and were identified by using morphological and molecular protocols. For molecular identification, two different regions of mitochondrial cytochrome c oxidase I (COI) gene (COI-5′ and COI- 3′) were used as molecular markers for the identification of the species. Morphological appearance, DNA sequence similarity searches and phylogenetic analysis collectively indicated it as M. allardi. To the best of our knowledge, this is the first report providing molecular evidence of M. allardi using mitochondrial DNA barcode region (658bp) as well as mtCOI-3ʹ sequences (817bp). The study will help in understanding population genetics through diversity analysis, ecological role, and phenotypic structures associated with the geographic range of this species. [ABSTRACT FROM AUTHOR]

Background: Echinolaelaps fukienensis is the dominant mite species parasitic on the body surface of the genus Niviventer. The mitochondrial genome (mitogenome) has its own independent genetic material and genetic system, and is now widely used in population genetics, genealogical biogeography, phylogeny and molecular evolution studies. Species diversity of the superfamily Dermanyssoidea is very rich, but its mitogenomes AT content is high, and it is difficult to amplify the complete mitogenome by routine PCR. To date, we have only obtained the mitogenomes of 6 species, scarcity on sequence data has greatly impeded the studies in the superfamily Dermanyssoidea. Methods: Echinolaelaps fukienensis were collected in 2019 from the body surface of Niviventer confucianus (Rodentia, Muridae) in Yunnan Province. The E. fukienensis mitogenome was determined and analyzed for the first time using the Illumina Novoseq 6000 platform. Phylogenetic analyses of the superfamily Dermanyssoidea were conducted based on the entire mitogenome sequences. Results: The E. fukienensis mitogenome was 14,402 bp, which is known the smallest genome of the superfamily Dermanyssoidea, encoding a total of 37 genes, including 13 PCGs, 22 tRNAs, 2 rRNAs and 1 control region. Most protein-coding genes use ATN as the start codon and TAN as the stop codon. AT and GC skew of atp8 genes in E. fukienensis were both 0. The average length of 22 tRNA genes of E. fukienensis was 64 bp, and secondary structures of tRNAs showed base mismatches and missing D-arms in many places. Compared with gene arrangement pattern of the hypothetical ancestor of arthropods, the E. fukienensis mitogenome shows a novel arrangement pattern. Phylogenetic tree supported the monophyly of the superfamily Dermanyssoidea. Echinolaelaps fukienensis being the least genetic distant (0.2762) and most closely related to Varroa destructor. Conclusions: This study analyzed comprehensive the structure and evolution of the E. fukienensis mitogenome for the first time, enriches molecular data of the genus Echinolaelaps, which will contribute to further understand phylogeny and rearrangement patterns of the superfamily Dermanyssoidea. [ABSTRACT FROM AUTHOR]

Being able to properly quantify genetic differentiation is key to understanding the evolutionary potential of a species. One central parameter in this context is FST, the mean coancestry within populations relative to the mean coancestry between populations. Researchers have been estimating FST globally or between pairs of populations for a long time. More recently, it has been proposed to estimate population-specific FST values, and population-pair mean relative coancestry. Here, we review the several definitions and estimation methods of FST, and stress that they provide values relative to a reference population. We show the good statistical properties of an allele-sharing, method of moments based estimator of FST (global, population-specific and population-pair) under a very general model of population structure. We point to the limitation of existing likelihood and Bayesian estimators when the populations are not independent. Last, we show that recent attempts to estimate absolute, rather than relative, mean coancestry fail to do so. Author summary: We present a general model for the evolution of coancestries within and among populations possibly of different sizes and connected by migration, allowing for any type of mating pattern. We describe a moment-based, allele-sharing estimator of global, population-specific and population-pair FST, the coancestries within or between pairs of populations relative to the average mean coancestry between populations and show its good statistical properties using both simulated and real, published data from the 1,000 genomes project. As few as 10 individuals per population with 104 independent SNPs are sufficient to obtain accurate estimates. We evaluate statistically how our estimate compares to others, and discuss how population-pair (as opposed to pairwise) FST could become a useful metric in molecular ecology and conservation genetics. [ABSTRACT FROM AUTHOR]

A database for the Iraqi Sorani Kurds, specifically focused on the 12 X-short tandem repeat (STR) loci, has been developed to fascilitate forensic and population genetics investigations. The present study involved genotyping 117 unrelated individuals from the Sorani Kurds ethnic group using the Investigator Argus X-12 QS kit. The analysis revealed that the DXS10135 locus exhibited the highest degree of polymorphism, as indicated by a polymorphism information content (PIC) value of 0.94565 and a gene diversity (GD) value of 0.95623. Conversely, the DXS8378 locus displayed the lowest level of polymorphism, with a PIC value of 0.61026 and a GD value of 0.68170. Notably, two individuals were found to possess a rare allele (allele = 6) at the DXS8378 locus, which was not included in the allelic ladder of the kit. Furthermore, a significant linkage disequilibrium (LD) (p < 0.05/117) was observed between the DXS10103 and DXS10101 loci on linkage group 3 (LG3). The ancestral composition of the five primary geographic regions, namely Africa, Middle East, East Asia, Europe, and South America, was determined through the utilization of the FST/FSTmax ratio. The findings of this analysis revealed that the Middle Eastern populations exhibited the lowest FST/FSTmax ratio, measuring at 0.23243, indicating a relatively lower ancestral diversity. Conversely, the European populations showcased the highest FST/FSTmax ratio, measuring at 0.27122, indicative of a greater ancestral diversity within this region. Additionally, the allelic richness indicators, namely distinctive and private alleles, indicated that Africa and the Middle East displayed the highest levels, while Far East Asia exhibited the lowest. This analysis supports the hypothesis of repeated founder effects during outward migrations, as evidenced by both the ancestry variability and the allelic richness. Consequently, the findings of this study have important implications for forensic genetics and population genetics research, particularly in relation to the consideration of genetic predispositions within specific ethnic groups. [ABSTRACT FROM AUTHOR]

A central challenge in population genetics is the detection of genomic footprints of selection. As machine learning tools including convolutional neural networks (CNNs) have become more sophisticated and applied more broadly, these provide a logical next step for increasing our power to learn and detect such patterns; indeed, CNNs trained on simulated genome sequences have recently been shown to be highly effective at this task. Unlike previous approaches, which rely upon human-crafted summary statistics, these methods are able to be applied directly to raw genomic data, allowing them to potentially learn new signatures that, if well-understood, could improve the current theory surrounding selective sweeps. Towards this end, we examine a representative CNN from the literature, paring it down to the minimal complexity needed to maintain comparable performance; this low-complexity CNN allows us to directly interpret the learned evolutionary signatures. We then validate these patterns in more complex models using metrics that evaluate feature importance. Our findings reveal that preprocessing steps, which determine how the population genetic data is presented to the model, play a central role in the learned prediction method. This results in models that mimic previously-defined summary statistics; in one case, the summary statistic itself achieves similarly high accuracy. For evolutionary processes that are less well understood than selective sweeps, we hope this provides an initial framework for using CNNs in ways that go beyond simply achieving high classification performance. Instead, we propose that CNNs might be useful as tools for learning novel patterns that can translate to easy-to-implement summary statistics available to a wider community of researchers. Author summary: The ever-increasing power and complexity of machine learning tools presents the scientific community with both unique opportunities and unique challenges. On the one hand, these data-driven approaches have led to state-of-the-art advances on a variety of research problems spanning many fields. On the other, these apparent performance improvements come at the cost of interpretability: it is difficult to know how a model makes its predictions. This is compounded by the computational sophistication of machine learning models which can lend an air of objectivity, often masking ways in which bias may be baked into the modeling decisions or the data itself. We present here a case study, examining these issues in the context of a central problem in population genetics: detecting patterns of selection from genome data. Through this application, we show how human decision-making can encourage the model to see what we want it to see in various ways. By understanding how these models work, and how they respond to the particular way in which data is presented, we have a chance of creating new frameworks that are capable of discovering novel patterns. [ABSTRACT FROM AUTHOR]

Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods—SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics. Author summary: Population genetic simulation is a powerful tool in the study of evolution. A number of supervised machine learning methods have been developed that take advantage of inexpensive simulations as training data. Despite their outstanding performance in benchmarks, these models can fail when the simulated training data deviate from the real data. In this work, we employed domain adaptation techniques to address this "simulation mis-specification" problem by training the machine learning model jointly with simulated and real data. We performed extensive benchmark experiments to demonstrate the improvement of the domain-adaptive models over standard machine learning models in the presence of different types of mis-specification. In addition, we applied dadaSIA, a domain-adaptive selection inference model, to improve the estimates of selection coefficients at selected loci in a European population. The domain adaptation framework proposed in our work is widely applicable to models relying on synthetic training data and therefore opens the door to many more applications in population genetics. [ABSTRACT FROM AUTHOR]

The mitochondrial genome (mt-genome) is one of the promising molecular markers for phylogenetics and population genetics. Recently, various mt-genomes have been determined rapidly by using massively parallel sequencers. However, the control region (CR, also called D-loop) in mt-genomes remain difficult to precisely determine due to the presence of repeat regions. Here, using Nanopore sequencing, we succeeded in rapid and collective determination of complete mt-genome of the hot-spring frog, Buergeria japonica, and found that its mt-genome size was 22,274 bp including CR (6,929 bp) with two types of tandem repeat motifs forming repeat regions. Comparison of assembly strategies revealed that the long- and short-read data combined together enabled efficient determination of the CR, but the short-read data alone did not. The B. japonica CR was longer than that of a congenic species inhabiting cooler climate areas, Buergeria buergeri, because of the long repeat regions in the former. During the thermal adaptation of B. japonica, the longer repeat regions in its CR may have accumulated within a period after divergence from B. buergeri. [ABSTRACT FROM AUTHOR]

Teak (Tectona grandis Linn. f.) is considered one of the most expensive hardwoods in the world. The dispersion of the species over the years has taken the teak beyond its first sources of diversity and little is known about the genetic origin and genetic variability. Thus, this study aimed to investigate the genetic diversity and genetic population structure existing in a representative teak germplasm bank collection. DNA was extracted from young leaves and each sample were genotyped by whole genome sequencing at 3 giga bases per sample, the sequences are aligned using the genome, and SNPcalls and quality control were made. To study the population structure of the genotypes, Bayesian variational inference was used via fastStructure, the phylogenetic tree was based on the modified Euclidean distance and the clustering by the UPGMA hierarchical method. Genetic diversity was analyzed based on the pairwise genetic divergence (Fst) of Weir and Cockerham. Genotyping by sequencing resulted in a database of approximately 1.4 million of variations SNPs were used for analysis. It was possible to identify four populations with considerable genetic variability between and within them. While the genetic variability in teak is generally known to be narrow, this study confirmed the presence of genetic variability scale in teak, which is contrary to what was initially expected. [ABSTRACT FROM AUTHOR]

The horizontal transfer of genes is fundamental for the eco-evolutionary dynamics of microbial communities, such as oceanic plankton, soil, and the human microbiome. In the case of an acquired beneficial gene, classic population genetics would predict a genome-wide selective sweep, whereby the genome spreads clonally within the community and together with the beneficial gene, removing genome diversity. Instead, several sources of metagenomic data show the existence of "gene-specific sweeps", whereby a beneficial gene spreads across a bacterial community, maintaining genome diversity. Several hypotheses have been proposed to explain this process, including the decreasing gene flow between ecologically distant populations, frequency-dependent selection from linked deleterious allelles, and very high rates of horizontal gene transfer. Here, we propose an additional possible scenario grounded in eco-evolutionary principles. Specifically, we show by a mathematical model and simulations that a metacommunity where species can occupy multiple patches, acting together with a realistic (moderate) HGT rate, helps maintain genome diversity. Assuming a scenario of patches dominated by single species, our model predicts that diversity only decreases moderately upon the arrival of a new beneficial gene, and that losses in diversity can be quickly restored. We explore the generic behaviour of diversity as a function of three key parameters, frequency of insertion of new beneficial genes, migration rates and horizontal transfer rates.Our results provides a testable explanation for how diversity can be maintained by gene-specific sweeps even in the absence of high horizontal gene transfer rates. Author summary: Our study investigates the interactions between gene-specific selective sweeps, horizontal gene transfer (HGT), and genome diversity within microbial communities. Using mathematical models and simulations, we examine the time scales involved in these processes to gain insights into their complex dynamics. Our findings highlight the importance of considering both HGT and metacommunity structure. Contrary to previous assumptions that gene-specific selective sweeps lead to a decrease in biodiversity, we find that genome diversity can be regenerated under moderate rates of HGT. Our results offer a verifiable explanation for the preservation of diversity in the presence of gene-specific sweeps, even when high rates of horizontal gene transfer are absent. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce "AlignStatPlot," a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software. [ABSTRACT FROM AUTHOR]

Plant pathogens secrete proteins called effectors that target host cellular processes to promote disease. Recently, structural genomics has identified several families of fungal effectors that share a similar three-dimensional structure despite remarkably variable amino-acid sequences and surface properties. To explore the selective forces that underlie the sequence variability of structurally-analogous effectors, we focused on MAX effectors, a structural family of effectors that are major determinants of virulence in the rice blast fungus Pyricularia oryzae. Using structure-informed gene annotation, we identified 58 to 78 MAX effector genes per genome in a set of 120 isolates representing seven host-associated lineages. The expression of MAX effector genes was primarily restricted to the early biotrophic phase of infection and strongly influenced by the host plant. Pangenome analyses of MAX effectors demonstrated extensive presence/absence polymorphism and identified gene loss events possibly involved in host range adaptation. However, gene knock-in experiments did not reveal a strong effect on virulence phenotypes suggesting that other evolutionary mechanisms are the main drivers of MAX effector losses. MAX effectors displayed high levels of standing variation and high rates of non-synonymous substitutions, pointing to widespread positive selection shaping the molecular diversity of MAX effectors. The combination of these analyses with structural data revealed that positive selection acts mostly on residues located in particular structural elements and at specific positions. By providing a comprehensive catalog of amino acid polymorphism, and by identifying the structural determinants of the sequence diversity, our work will inform future studies aimed at elucidating the function and mode of action of MAX effectors. Author summary: Fungal plant pathogens use small secreted proteins, called effectors, to manipulate to their own advantage their host's physiology and immunity. The evolution of these effectors, whether spontaneously or in response to human actions, can lead to epidemics or the emergence of new diseases. It is therefore crucial to understand the mechanisms underlying this evolution. In this article, we report on the evolution of effectors in one of the prime experimental model systems of plant pathology, Pyricularia oryzae, the fungus causing blast diseases in rice, wheat, and other cereals or grasses. We further characterize in this fungus a particular class of effectors, called MAX effectors, using structural models based on experimental protein structures of effectors. We show that this class of effector is produced by the pathogen during the early stages of infection, when plant cells are still alive. By comparing the gene content of isolates infecting different plant species, we show that the MAX effector arsenal is highly variable from one isolate to another. Finally, using the inferential framework of population genetics, we demonstrate that MAX effectors exhibit very high genetic variability and that this results from the action of natural selection. [ABSTRACT FROM AUTHOR]

Determining the extent and distribution of genetic diversity is an essential component of plant breeding. In the present study, we explored the genetic diversity and population structure of Vernonia amygdalina, a fodder, vegetable and medicinal species of Africa and some parts of Yemen. Most empirical studies demonstrate that populations that are separated by geographic or ecological factors may experience genetic differentiation resulting from restricted gene flow between populations. A total of 238 individuals were sampled from two populations: i) Lake Victoria crescent (LVC) and ii) Southern and Eastern Lake Kyoga basin (SEK) agroecological zones of Uganda and genotyped using DArT platform. Of the two populations, the overall mean observed heterozygosity (Ho) was low to medium (Ho = 0.07[silicoDArTs] and 0.2[SNPs]). Inbreeding levels were also very low (-0.04 to -0.08) suggesting the presence of random mating. Partitioning of genetic structure in the two populations indicated that SEK exhibited a higher genetic diversity than LVC. The principal coordinates analysis (PCA) showed no geographical structuring, consistent with the low genetic differentiation (Fst = 0.00) and the low Euclidean genetic distance (1.38–1.39) between the LVC and SEK populations. However, STRUCTURE analysis with admixture models revealed weak possible genetic clusters with very small genetic distance among them. Overall, the results suggest low genetic diversity and weak genetic differentiation between the two populations. One possible explanation of the results could be the presence of human assisted gene flow over long distances. [ABSTRACT FROM AUTHOR]

Castanopsis carlesii (Hemsl.) Hay. is a widely distributed and dominant tree species native to subtropical China with significant ecological and economic value. Due to serious human-related disturbance, its wild resources have been increasingly reduced, and whether may result in the loss of genetic diversity. However, no population genetics studies of natural C. carlesii have been reported to date. Microsatellite markers have been a useful tool in population genetics. Therefore, we developed EST-SSR markers based on the transcriptome sequencing of C. carlesii leaves. A total of 149,380,224 clean reads were obtained, and 63,012 nonredundant unigenes with a mean length of 1,034 bp were assembled and annotated based on sequence similarity searches in the Nr, Nt, KO, SwissProt, PFAM, KOG, and GO databases. The results showed that only 5,559 (8.82%) unigenes were annotated in all seven databases, but 46,338 (73.53%) could be annotated in at least one database. A total of 31,459 potential EST-SSRs were identified in 18,690 unigenes, with an average frequency of one SSR approximately 2 kb. Among the 100 EST-SSR primer pairs designed, 49 primer pairs successfully produced the expected product by amplification, with a success rate of 49%, but only 20 primer pairs showed abundant polymorphisms. Polymorphisms were verified using 25 samples from C. carlesii in Qimen, Anhui. A total of 119 alleles were detected, with a mean number of alleles (Na) of 5.95 per locus and a mean polymorphism information content (PIC) of 0.6125. All the 20 newly developed EST-SSR markers were verified in other Castanopsis species (C. sclerophylla, C. lamontii, C. fargesii, C. eyrei and C. jucunda). Sixteen primer pairs showed successful amplification in all five Castanopsis species (80%), and the transferability ratios ranged from 90% to 100%. These developed EST-SSR markers can be applied to population genetic and germplasm evaluations of C. carlesii and related species. [ABSTRACT FROM AUTHOR]

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics inference methods. Previous models have demonstrated that nucleotides flanking polymorphic sites–the local sequence context–explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways–first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data. Author summary: Many biological questions rely on accurate estimates of where and how frequently mutations arise in populations. One factor that has been shown to predict the probability that a mutation occurs is the local DNA sequence surrounding a potential site for mutation. It has been shown that increasing the size of local DNA sequence immediately surrounding a site improves prediction of where, what type, and how frequently the site is mutated. However, current methods struggle to take full advantage of this trend as well as capturing how certain our estimates are, in practice. We have designed a model, implemented in software (named Baymer), that is able to use large windows of sequence context to accurately model mutation probabilities in a computationally efficient manner. We use Baymer to identify specific DNA sequences that have the biggest impacts on mutability and apply the model to find motifs that have potentially evolved mutability between different human populations. We also apply it to show that germline mutations observed as polymorphic sites in humans—those that have occurred in our recent evolutionary history—can model very young mutations (de novo mutations) as well as polymorphism observed in populations of closely related great ape species. [ABSTRACT FROM AUTHOR]

Lumpfish (Cyclopterus lumpus) is a transatlantic marine fish displaying large population sizes and a high potential for dispersal and gene-flow. These features are expected to result in weak population structure. Here, we investigated population genetic structure of lumpfish throughout its natural distribution in the North Atlantic using two approaches: I) 4,393 genome wide SNPs and 95 individuals from 10 locations, and II) 139 discriminatory SNPs and 1,669 individuals from 40 locations. Both approaches identified extensive population genetic structuring with a major split between the East and West Atlantic and a distinct Baltic Sea population, as well as further differentiation of lumpfish from the English Channel, Iceland, and Greenland. The discriminatory loci displayed ~2–5 times higher divergence than the genome wide approach, revealing further evidence of local population substructures. Lumpfish from Isfjorden in Svalbard were highly distinct but resembled most fish from Greenland. The Kattegat area in the Baltic transition zone, formed a previously undescribed distinct genetic group. Also, further subdivision was detected within North America, Iceland, West Greenland, Barents Sea, and Norway. Although lumpfish have considerable potential for dispersal and gene-flow, the observed high levels of population structuring throughout the Atlantic suggests that this species may have a natal homing behavior and local populations with adaptive differences. This fine-scale population structure calls for consideration when defining management units for exploitation of lumpfish stocks and in decisions related to sourcing and moving lumpfish for cleaner fish use in salmonid aquaculture. [ABSTRACT FROM AUTHOR]

In Brazil, Leishmania braziliensis is the main causative agent of the neglected tropical disease, cutaneous leishmaniasis (CL). CL presents on a spectrum of disease severity with a high rate of treatment failure. Yet the parasite factors that contribute to disease presentation and treatment outcome are not well understood, in part because successfully isolating and culturing parasites from patient lesions remains a major technical challenge. Here we describe the development of selective whole genome amplification (SWGA) for Leishmania and show that this method enables culture-independent analysis of parasite genomes obtained directly from primary patient skin samples, allowing us to circumvent artifacts associated with adaptation to culture. We show that SWGA can be applied to multiple Leishmania species residing in different host species, suggesting that this method is broadly useful in both experimental infection models and clinical studies. SWGA carried out directly on skin biopsies collected from patients in Corte de Pedra, Bahia, Brazil, showed extensive genomic diversity. Finally, as a proof-of-concept, we demonstrated that SWGA data can be integrated with published whole genome data from cultured parasite isolates to identify variants unique to specific geographic regions in Brazil where treatment failure rates are known to be high. SWGA provides a relatively simple method to generate Leishmania genomes directly from patient samples, unlocking the potential to link parasite genetics with host clinical phenotypes. Author summary: Leishmania braziliensis is the main cause of cutaneous leishmaniasis in Brazil. Due to limitations in culturing, it is important to study the parasite in a culture-independent manner. We use selective whole genome amplification (SWGA) to explore parasite genomic diversity directly from patient biopsies. This method is inexpensive and can be broadly used to generate parasite genome sequence data sampled from different Leishmania species infecting different mammalian hosts. We found high diversity among the L. braziliensis genomes from Bahia, Brazil, which correlated with geographic location. By integrating these data with publicly available genome sequences from other studies spanning four countries in South America, we identified variants unique to Northeast Brazil that may be linked to high regional rates of treatment failure. [ABSTRACT FROM AUTHOR]

The genetic composition of mallards in eastern North America has been changed by release of domestically-raised, game-farm mallards to supplement wild populations for hunting. We sampled 296 hatch-year mallards harvested in northwestern Ohio, October–December 2019. The aim was to determine their genetic ancestry and geographic origin to understand the geographic extent of game-farm mallard introgression into wild populations in more westward regions of North America. We used molecular analysis to detect that 35% of samples were pure wild mallard, 12% were early generation hybrids between wild and game-farm mallards (i.e., F1–F3), and the remaining 53% of samples were assigned as part of a hybrid swarm. Percentage of individuals in our study with some form of hybridization with game-farm mallard (65%) was greater than previously detected farther south in the mid-continent (~4%), but less than the Atlantic coast of North America (~ 92%). Stable isotope analysis using δ2Hf suggested that pure wild mallards originated from areas farther north and west than hybrid mallards. More specifically, 17% of all Ohio samples had δ2Hf consistent with more western origins in the prairies, parkland, or boreal regions of the mid-continent of North America, with 55%, 35%, and 10% of these being genetically wild, hybrid swarm, and F3, respectively. We conclude that continued game-farm introgression into wild mallards is not isolated to the eastern population of mallards in North America, and may be increasing and more widespread than previously detected. Mallards in our study had greater incidence of game-farm hybridization than other locales in the mid-continent but less than eastern North American regions suggesting further need to understand game-farm mallard genetic variation and movement across the continent. [ABSTRACT FROM AUTHOR]

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies. [ABSTRACT FROM AUTHOR]

Microorganisms produce costly cooperative goods whose benefit is partially shared with nonproducers, called 'mixed' goods. The Black Queen Hypothesis predicts that partial privatization has two major evolutionary implications. First, to favor strains producing several types of mixed goods over nonproducing strains. Second, to favor the maintenance of cooperative traits through different strains instead of having all cooperative traits present in a single strain (metabolic specialization). Despite the importance of quorum sensing regulation of mixed goods, it is unclear how partial privatization affects quorum sensing evolution. Here, we studied the influence of partial privatization on the evolution of quorum sensing. We developed a mathematical population genetics model of an unstructured microbial population considering four strains that differ in their ability to produce an autoinducer (quorum sensing signaling molecule) and a mixed good. Our model assumes that the production of the autoinducers and the mixed goods is constitutive and/or depends on quorum sensing. Our results suggest that, unless autoinducers are costless, partial privatization cannot favor quorum sensing. This result occurs because with costly autoinducers: (1) a strain that produces both autoinducer and goods (fully producing strain) cannot persist in the population; (2) the strain only producing the autoinducer and the strain producing mixed goods in response to the autoinducers cannot coexist, i.e., metabolic specialization cannot be favored. Together, partial privatization might have been crucial to favor a primordial form of quorum sensing—where autoinducers were thought to be a metabolic byproduct (costless)—but not the transition to nowadays costly autoinducers. [ABSTRACT FROM AUTHOR]

Livestock European diffusion followed different human migration waves from the Fertile Crescent. In sheep, at least two diffusion waves have shaped the current breeds' biodiversity generating a complex genetic pattern composed by either primitive or fine-wool selected breeds. Among primitive breeds, aside from sharing common ancestral genomic components, they also show several traits such as the policeraty, large horns in the ram, short tail, and a moulting fleece, considered as ancestral. Although most of the primitive breeds characterized by these traits are confined on the very edge of Northern Europe, several residual populations are also scattered in the Mediterranean region. In fact, although in Italy a large number of local breeds are already extinct, others are listed as critically endangered, and among these there is the Quadricorna breed which is a four-horned sheep characterized by several ancestral traits. In this context we genotyped 47 individuals belonging to the Quadricorna sheep breed, a relict and endangered breed, from Central and Southern Italy. In doing so we used the Illumina OvineSNP50K array in order to explore its genetic diversity and to compare it with other 33 primitive traits-related, Mediterranean and Middle-East breeds, with the specific aim to reconstruct its origin. After retaining 35,680 SNPs following data filtering, the overall genomic architecture has been explored by using genetic diversity indices, Principal Component Analysis (PCA) and admixture analysis, while the genetic relationships and migration events have been inferred using a neighbor-joining tree based on Reynolds' distances and by the maximum likelihood tree as implemented in treemix. Multiple convergent evidence from all our population genetics analyses, indicated that the two Quadricorna populations differ from all the other Italian breeds, while they resulted to be very close to the Middle Eastern and primitive European breeds. In addition, the genetic diversity indices highlighted values comparable with those of most of the other analyzed breeds, despite the two populations exhibit slightly different genetic indices suggesting different levels of genomic inbreeding and drift (FIS and FROH). The admixture analysis does not suggest any signal of recent gene exchange with other Italian local breeds, highlighting a rather ancestral purity of the two populations, while on the other hand the treemix analysis seems to suggest an ancient admixture with other primitive European breeds. Finally, all these evidences seem to trace back the residual Quadricorna sheep to an early Neolithic spread, probably following a Mediterranean route and that urgent conservation actions are needed in order to keep the breed and all related cultural products alive. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Slavic speakers in the second half of the first millennium CE remains a controversial topic in archaeology, and academic passions on the issue have long run high. Currently, there are three main hypotheses for this expansion. The aim of this paper was to test the so-called "hybrid hypothesis," which states that the movement of people, cultural diffusion and language diffusion all occurred simultaneously. For this purpose, we examined an archaeological Deep Data set with a machine learning method termed time series clustering and with emerging hot spot analysis. The latter required two archaeology-specific modifications: The archaeological trend map and the multiscale emerging hot spot analysis. As a result, we were able to detect two migrations in the Eastern Alps between c. 500 and c. 700 CE. Based on the convergence of evidence from archaeology, linguistics, and population genetics, we have identified the migrants as Alpine Slavs, i.e., people who spoke Slavic and shared specific common ancestry. [ABSTRACT FROM AUTHOR]

Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest, but is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes from genomic sequence data are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve along the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly. Here, we present our method CHIMP (CHMM History-Inference Maximum-Likelihood Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes. We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data. Author summary: The demograpic history of natural populations shapes their genetic variation. The genomes of contemporary individuals can thus be used to unravel past migration events and population size changes, which is of anthropological interest. Moreover, it is also important to uncover these past events for studies investigating disease related genetic variation, since past demographic events can confound such analyses. Here we present a novel method for inferring the size history of a given population from full-genome sequencing data of contemporary individuals. Our method is based on a Coalescent Hidden Markov model framework, a model frequently applied to this type of inference. A key component of the model is the representation of unobserved local genealogical relationships among the sampled individuals as latent states. This is achieved by numerically solving certain differential equations that describe the distributions of these quantities and ultimately enables inference of past population size changes. Other methods performing similar inference rely on availability of high quality genomic data, whereas we demonstrate that our method can be applied in situations with limited data quality. [ABSTRACT FROM AUTHOR]

Extracting information on the selective and demographic past of populations that is contained in samples of genome sequences requires a description of the distribution of the underlying genealogies. Using the Laplace transform, this distribution can be generated with a simple recursive procedure, regardless of model complexity. Assuming an infinite-sites mutation model, the probability of observing specific configurations of linked variants within small haplotype blocks can be recovered from the Laplace transform of the joint distribution of branch lengths. However, the repeated differentiation required to compute these probabilities has proven to be a serious computational bottleneck in earlier implementations. Here, I show that the state space diagram can be turned into a computational graph, allowing efficient evaluation of the Laplace transform by means of a graph traversal algorithm. This general algorithm can, for example, be applied to tabulate the likelihoods of mutational configurations in non-recombining blocks. This work provides a crucial speed up for existing composite likelihood approaches that rely on the joint distribution of branch lengths to fit isolation with migration models and estimate the parameters of selective sweeps. The associated software is available as an open-source Python library, agemo. Author summary: For simple models of idealised populations, the process that generates the observed sequences can be mathematically described. For a given number of samples, we can enumerate all possible genealogies. We can even incorporate the impact of past events like population size reductions on the observed sequence variation. However, the number of possible genealogies will become very large, very fast. So, to extract information from the observed mutations, we need mathematical tools and efficient algorithms to use the information contained within the large collection of possible genealogies. The Laplace transform is one such mathematical tool that allows us to recursively generate the branch length distribution of all genealogies. Here I show how the transform can be represented as a graph. Using this nonlinear data structure, I define a general procedure to efficiently evaluate the associated mathematical expressions. And I further show how this can be used to speed up existing composite likelihood approaches to fit demographic models and estimate sweep parameters. The associated software, agemo, has a well-documented Python API and has been designed with extensibility in mind, making it an ideal back-end for many other inference approaches in population genetics. [ABSTRACT FROM AUTHOR]

Genetic diversity of plant populations on islands is likely to be influenced by characteristics such as island origin (oceanic or continental) and their age, size, and distance to continental landmasses. In Japan, Planchonella obovata sensu lato which is found on both continental and oceanic islands of varying age, size, and distance to East Asian continental areas—is an ideal system in which to investigate the factors influencing genetic diversity of island plant species. In this study, we examined the genetic diversity of P. obovata s.l. populations, in the context of the species population genetic structure, demography, and between island migration, from 668 individuals, 28 populations and 14 islands including both continental (the Yaeyama Islands) and oceanic islands (the Daito, Bonin, and Volcano Islands) using 11 microsatellite markers. The Yaeyama and Volcano Islands respectively had the highest and lowest genetic diversity, and island origin and age significantly affected genetic diversity. Clustering analysis revealed that populations were grouped into Bonin, Volcano, and Yaeyama + Daito groups. However, Bonin and Volcano groups were distinct despite the relatively short geographical distance between them. Approximate Bayesian Computation analysis suggested that the population size was stable in Bonin and Yaeyama + Daito groups, whereas population reduction occurred in Volcano group, and migration between groups were very limited. Younger oceanic islands showed lower genetic diversity, probably due to limited gene flow and a lack of time to accumulate unique alleles. Genetic structure was generally consistent with the geographic pattern of the islands, but in Volcano, a limited number of founders and limited gene flow among islands are likely to have caused the large genetic divergence observed. [ABSTRACT FROM AUTHOR]

The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in such a fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids. Author summary: This work introduces a method that combines the singular value decomposition (SVD) with Archetypal Analysis to perform fast and accurate genetic clustering by first reducing the dimensionality of the space of genomic sequences. Each sequence is described as a convex combination (admixture) of archetypes (cluster representatives) in the reduced dimensional space. We compare this interpretable approach to the widely used genetic clustering algorithm, ADMIXTURE, and show that, without significant degradation in performance, Archetypal Analysis outperforms, offering shorter run times and representational advantages. We include theoretical, qualitative, and quantitative comparisons between both methods. [ABSTRACT FROM AUTHOR]

Central and eastern chimpanzees are infected with Simian Immunodeficiency Virus (SIV) in the wild, typically without developing acute immunodeficiency. Yet the recent zoonotic transmission of chimpanzee SIV to humans, which were naïve to the virus, gave rise to the Human Immunodeficiency Virus (HIV), which causes AIDS and is responsible for one of the deadliest pandemics in human history. Chimpanzees have been infected with SIV for tens of thousands of years and have likely evolved to reduce its pathogenicity, becoming semi-natural hosts that largely tolerate the virus. In support of this view, central and eastern chimpanzees show evidence of positive selection in genes involved in SIV/HIV cell entry and immune response to SIV, respectively. We hypothesise that the population first infected by SIV would have experienced the strongest selective pressure to control the lethal potential of zoonotic SIV, and that population genetics will reveal those first critical adaptations. With that aim we used population genomics to investigate signatures of positive selection in the common ancestor of central-eastern chimpanzees. The genes with signatures of positive selection in the ancestral population are significantly enriched in SIV-related genes, especially those involved in the immune response to SIV and those encoding for host genes that physically interact with SIV/HIV (VIPs). This supports a scenario where SIV first infected the central-eastern ancestor and where this population was under strong pressure to adapt to zoonotic SIV. Interestingly, integrating these genes with candidates of positive selection in the two infected subspecies reveals novel patterns of adaptation to SIV. Specifically, we observe evidence of positive selection in numerous steps of the biological pathway responsible for T-helper cell differentiation, including CD4 and multiple genes that SIV/HIV use to infect and control host cells. This pathway is active only in CD4+ cells which SIV/HIV infects, and it plays a crucial role in shaping the immune response so it can efficiently control the virus. Our results confirm the importance of SIV as a selective factor, identify specific genetic changes that may have allowed our closest living relatives to reduce SIV's pathogenicity, and demonstrate the potential of population genomics to reveal the evolutionary mechanisms used by naïve hosts to reduce the pathogenicity of zoonotic pathogens. Author summary: Chimpanzees are at the origin of HIV-1, a virus that generates an incurable disease and that generated a pandemic that has claimed 35 million lives. Chimpanzees have evolved to control the pathogenicity of the virus, which does not typically develop into AIDS in the same way as in humans. Identifying the genetic adaptations responsible for this process provides critical knowledge about SIV and HIV. Our analysis of chimpanzee genetic adaptations identified specific genes and molecular pathways involved in adaptation to SIV, providing important insights into the mechanisms that likely allowed our closest living relatives to control SIV/HIV. Further, we establish SIV as a strong and recurrent selective pressure in central and eastern chimpanzees, two important subspecies of large mammals that are currently endangered. [ABSTRACT FROM AUTHOR]

Intraspecies nucleotide sequence variation is a key to understanding the evolutionary history of a species, such as the geographic distribution and population structure. To date, numerous phylogenetic and population genetics studies have been conducted based on the sequences of a gene or an intergenic region on the mitochondrial genome (mtDNA), such as cytochrome c oxidase subunits or the D-loop. To evaluate the credibility of the usage of such 'classic' markers, we compared the phylogenetic inferences based on the analyses of the partial and entire mtDNA sequences. Importantly, the phylogenetic reconstruction based on the short marker sequences did not necessarily reproduce the tree topologies based on the analyses of the entire mtDNA. In addition, analyses on the datasets of various organisms revealed that the analyses based on the classic markers yielded phylogenetic trees with poor confidence in all tested cases compared to the results based on full-length mtDNA. These results demonstrated that phylogenetic analyses based on complete mtDNA sequences yield more insightful results compared to those based on mitochondrial genes and segments. To ameliorate the shortcomings of the classic markers, we identified a segment of mtDNA that may be used as an 'approximate marker' to closely reproduce the phylogenetic inference obtained from the entire mtDNA in the case of mammalian species, which can be utilized to design amplicon-seq-based studies. Our study demonstrates the importance of the choice of mitochondrial markers for phylogenetic analyses and proposes a novel approach to choosing appropriate markers for mammalian mtDNA that reproduces the phylogenetic inferences obtained from full-length mtDNA. [ABSTRACT FROM AUTHOR]

Populations within schistosomiasis control areas, especially those in Africa, are recommended to receive regular mass drug administration (MDA) with praziquantel (PZQ) as the main strategy for controlling the disease. The impact of PZQ treatment on schistosome genetics remains poorly understood, and is limited by a lack of high-resolution genetic data on the population structure of parasites within these control areas. We generated whole-genome sequence data from 174 individual miracidia collected from both children and adults from fishing communities on islands in Lake Victoria in Uganda that had received either annual or quarterly MDA with PZQ over four years, including samples collected immediately before and four weeks after treatment. Genome variation within and between samples was characterised and we investigated genomic signatures of natural selection acting on these populations that could be due to PZQ treatment. The parasite population on these islands was more diverse than found in nearby villages on the lake shore. We saw little or no genetic differentiation between villages, or between the groups of villages with different treatment intensity, but slightly higher genetic diversity within the pre-treatment compared to post-treatment parasite populations. We identified classes of genes significantly enriched within regions of the genome with evidence of recent positive selection among post-treatment and intensively treated parasite populations. The differential selection observed in post-treatment and pre-treatment parasite populations could be linked to any reduced susceptibility of parasites to praziquantel treatment. Author summary: Schistosomiasis is caused by parasitic helminths of the genus Schistosoma. Schistosoma mansoni is the primary cause of intestinal schistosomiasis, a devastating and widespread parasitic infection that causes morbidity, death and socio-economic impact on endemic communities across the world and especially sub-Saharan Africa. Using whole-genome sequencing, we were able to elucidate the parasite population within Lake Victoria island fishing communities in Uganda which are among the major hotspots for schistosomiasis. We further assessed genetic markers that might be linked to recent observations concerning reduced susceptibility to praziquantel, the major drug used in the treatment of this disease. Whole-genome data on the population genetics of S. mansoni in an African setting will provide a strong basis for future functional genomics or transcriptomic studies that will be key to identifying drug targets, improving existing drugs or developing new therapeutic interventions. [ABSTRACT FROM AUTHOR]

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean square error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown. Author summary: single-layer feedforward neural networks learn the established model-based linear estimators for high and low recombination rates. neural nets learn good estimators for intermediate recombination rates where computation of model-based optimal estimators is hardly possible. a single neural network estimator can automatically adapt to a variable recombination rate and performs close to optimal this is advantageous when recombination rates vary along the chromosome according to a recombination map using the known estimators as a benchmark to adapt the training error function improves the estimates of the neural networks [ABSTRACT FROM AUTHOR]

In an empirical analysis of transposable element (TE) abundance within natural populations of Mimulus guttatus and Drosophila melanogaster, we found a surprisingly high variance of TE count (e.g., variance-to-mean ratio on the order of 10 to 300). To obtain insight regarding the evolutionary genetic mechanisms that underlie the overdispersed population distributions of TE abundance, we developed a mathematical model of TE population genetics that includes the dynamics of element proliferation and purifying selection on TE load. The modeling approach begins with a master equation for a birth-death process and extends the predictions of the classical theory of TE dynamics in several ways. In particular, moment-based analyses of population distributions of TE load reveal that overdispersion is likely to arise via copy-and-paste proliferation dynamics, especially when the elementary processes of proliferation and excision are approximately balanced. Parameter studies and analytic work confirm this result and further suggest that overdispersed population distributions of TE abundance are probably not a consequence of purifying selection on total element load. [ABSTRACT FROM AUTHOR]

Snake fungal disease (SFD; ophidiomycosis), caused by the pathogen Ophidiomyces ophiodiicola (Oo), has been documented in wild snakes in North America and Eurasia, and is considered an emerging disease in the eastern United States of America. However, a lack of historical disease data has made it challenging to determine whether Oo is a recent arrival to the USA or whether SFD emergence is due to other factors. Here, we examined the genomes of 82 Oo strains to determine the pathogen's history in the eastern USA. Oo strains from the USA formed a clade (Clade II) distinct from European strains (Clade I), and molecular dating indicated that these clades diverged too recently (approximately 2,000 years ago) for transcontinental dispersal of Oo to have occurred via natural snake movements across Beringia. A lack of nonrecombinant intermediates between clonal lineages in Clade II indicates that Oo has actually been introduced multiple times to North America from an unsampled source population, and molecular dating indicates that several of these introductions occurred within the last few hundred years. Molecular dating also indicated that the most common Clade II clonal lineages have expanded recently in the USA, with time of most recent common ancestor mean estimates ranging from 1985 to 2007 CE. The presence of Clade II in captive snakes worldwide demonstrates a potential mechanism of introduction and highlights that additional incursions are likely unless action is taken to reduce the risk of pathogen translocation and spillover into wild snake populations. Snake fungal disease is an emerging disease in eastern North America, but the origins of the disease have been unclear. This study uses population genetic data to show that the fungus that causes the disease was introduced multiple times to North America over the last few hundred years. [ABSTRACT FROM AUTHOR]

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies. Author summary: Codon usage bias (CUB), or the unequal usage of codons of the same amino acid (i.e. synonymous codons), has been observed in species across all domains of life. CUB is known to be shaped by both non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolution. A key challenge for researchers is disentangling the role of various processes shaping codon usage, often for the purpose of identifying codons favored by natural selection, sometimes referred to as "optimal" or "preferred" codons. Despite large variation in non-adaptive nucleotide biases within a genome, most methods to quantify natural selection typically ignore this variation for the sake of simplicity. Here, we combine a population genetics model with unsupervised machine learning to identify genes evolving under different non-adaptive nucleotide biases across 49 budding yeasts species. We find that ignoring for variation in non-adaptive nucleotide biases can obfuscate signals of selection on codon usage. Our results indicate the need for more nuanced models of coding sequence evolution. [ABSTRACT FROM AUTHOR]

Background: Leishmaniases are neglected tropical diseases that inflict great burden to poor areas of the globe. Intense research has aimed to identify parasite genetic signatures predictive of infection outcomes. Consistency of diagnostic tools based on these markers would greatly benefit from accurate understanding of Leishmania spp. population genetics. We explored two chromosomal loci to characterize a population of L. braziliensis causing human disease in Northeast Brazil. Methodology/Principal findings: Two temporally distinct samples of L. braziliensis were obtained from patients attending the leishmaniasis clinic at the village of Corte de Pedra: (2008–2011) primary sample, N = 120; (1999–2001) validation sample, N = 35. Parasites were genotyped by Sanger's sequencing of two 600 base pairs loci starting at nucleotide positions 3,074 and 425,451 of chromosomes 24 and 28, respectively. Genotypes based on haplotypes of biallelic positions in each locus were tested for several population genetic parameters as well as for geographic clustering within the region. Ample geographic overlap of genotypes at the two loci was observed as indicated by non-significant Cusick and Edward's comparisons. No linkage disequilibrium was detected among combinations of haplotypes for both parasite samples. Homozygous and heterozygous genotypes displayed Hardy-Weinberg equilibrium (HWE) at both loci in the two samples when straight observed and expected counts were compared by Chi-square (p>0.5). However, Bayesian statistics using one million Monte-Carlo randomizations disclosed a less robust HWE for chromosome 24 genotypes, particularly in the primary sample (p = 0.04). Fixation indices (Fst) were consistently lower than 0.05 among individuals of the two samples at both tested loci, and no intra-populational structuralization could be detected using STRUCTURE software. Conclusions/Significance: These findings suggest that L. braziliensis can maintain stable populations in foci of human leishmaniasis and are capable of robust genetic recombination possibly due to events of sexual reproduction during the parasite's lifecycle. Author summary: Leishmania braziliensis affects poor human populations in the tropics, may cause face disfiguring lesions and may also resist treatment. There has been intense research for markers in these parasites genetic contents for helping predict if an infected human being would be of greater chance of severe disease or treatment failure. The consistent identification of such markers requires a deep understanding of how genes circulate within these parasites' natural populations. We explored two small segments of DNA (i.e. loci), one on chromosome 24, the other on chromosome 28 of L. braziliensis to characterize a population that causes human disease in Northeast Brazil. We employed two samples of parasites obtained from lesions of patients diagnosed from 1999 to 2001, and from 2008 to 2011. We sequenced the DNA of those loci in each parasite of the two samples. Then, we evaluated the status of several population genetics parameters among them. Based on our findings to that region, we concluded that L. braziliensis can maintain populations that are genetically stable for several years in foci of human leishmaniasis and are capable of robust recombination of their genetic contents, probably due to events of sexual reproduction during its lifecycle. [ABSTRACT FROM AUTHOR]

Backgrounds: Plasmodium vivax is the predominant Plasmodium species distributed extensively in the Americas and Asia-Pacific areas. Encoded protein by Plasmodium vivax Reticulocyte Binding Proteins (PvRBPs) family member are of critical prominence to parasite invasion and have been considered the significant targets in development of malaria vaccine for the blood stage. As high genetic polymorphism of parasites may impede the effectiveness of vaccine development, more research to unraveling genetic polymorphism of pvrbp2b from various geographical regions seems indispensable to map the exact pattern of field isolates. Methodology/Principal findings: The aim of this study was to determine the sequences of Iranian pvrbp2b (nt: 502–1896) gene and then, to ascertain polymorphism of pvrbp2b gene, recombination, the level of genetic distances, evaluation of natural selection, and the prediction of B-cell epitopes of Iranian and global P. vivax isolates. Pvrbp2b partial gene was amplified and sequenced from 60 Iranian P. vivax isolates. Iranian pvrbp2b sequences as well as 95 published sequences from five countries were used to evaluate the genetic diversity and neutral evolution signature in worldwide scale. A total of 38 SNPs were identified among 60 Iranian pvrbp2b sequences (32 non-synonymous and 6 synonymous mutations), and 32 amino acid substitutions were observed in 29 positions as compared to Sal-1 sequence. Worldwide sequence analysis showed that 44 amino acid changes had occurred in 37 positions of which seven polymorphic sites had trimorphic mutations while the rest was dimorphic. The overall nucleotide diversity for Iranian isolates was 0.00431 ± 0.00091 while the level of nucleotide diversity was ranged from 0.00337 ± 0.00076 (Peru) to 0.00452 ± 0.00092 (Thailand) in global scale. Conclusions/Significance: Of amino acid substitutions, 12 replacements were located in the B-cell epitopes in which nine polymorphic sites were positioned in N-terminal and three polymorphic sites in predicted B-cell epitopes of C-terminal, signifying both variable and conserved epitopes for vaccine designing. Using the achieved outcome of the current investigation interrogate questions to the selection of conserved regions of pvrbp2b and understanding polymorphism and immune system pressure to pave a way for developing a vaccine based on PvRBP2b candidate antigen. [ABSTRACT FROM AUTHOR]