Population Genetic Statistics Research Articles

The central limit theorem for the number of mutations in the genealogy of a sample from a large population.

The number K of mutations identifiable in a sample of n sequences from a large population is one of the most important summary statistics in population genetics and is ubiquitous in the analysis of DNA sequence data. K can be expressed as the sum of n-1 independent geometric random variables. Consequently, its probability generating function was established long ago, yielding its well-known expectation and variance. However, the statistical properties of K is much less understood than those of the number of distinct alleles in a sample. This paper demonstrates that the central limit theorem holds for K, implying that K follows approximately a normal distribution when a large sample is drawn from a population evolving according to the Wright-Fisher model with a constant effective size, or according to the constant-in-state model, which allows population sizes to vary independently but bounded uniformly across different states of the coalescent process. Additionally, the skewness and kurtosis of K are derived, confirming that K has asymptotically the same skewness and kurtosis as a normal distribution. Furthermore, skewness converges at speed 1/ln(n) and while kurtosis at speed 1/ln(n). Despite the overall convergence speed to normality is relatively slow, the distribution of K for a modest sample size is already not too far from normality, therefore the asymptotic normality may be sufficient for certain applications when the sample size is large enough.

Determination of HLA Tissue Type According to the Etiology of Patients with Chronic Renal Failure.

Chronic kidney disease (CKD) is a prominent public health concern, is defined as functional and structural damage to the kidneys. This study aims to investigate the association between human leukocyte antigen (HLA) alleles individuals with CKD and the different etiological subgroups of diesease. Genomic DNA was obtained from peripheral blood samples of 1,079 patients with retrospective CKD and 1,111 healthy control individuals. HLA genotyping was conducted using the Luminex based low-resolution method. Allele frequency distributions were calculated with the help of Arlequin v3.11 population genetics statistics program and SPSS v23.0 program, and p<0.05 values were accepted as significant by chi-square tests. HLA A*02 (21.83%), B*35 (18.30%), DRB1*11 (21.41%) alleles were observed most frequently in individuals with CKD, respectively. In our study, B*08, B*49, B*50 alleles in the HLA B locus (p=0.002, p=0.012 p=0.009) and DRB1*03, *04 alleles in the HLA DRB1 locus (p<0.001, p<0.001) were found positively associated with CKD. A*02, A*11, A*74 alleles at the HLA A locus (p=0.003, p<0.001, p=0.009) and B*27, B*39, B* alleles at the HLA B locus 40, B*59 (p<0.001, p<0.001, p<0.001, p=0.009), DRB1*07, *08, *09, *13, *16 (p<0.001, p=0.012, p=0.007, p<0.001, p<0.001) alleles were determined as negatively associated with the disease. Among the etiological groups of CKD, cystic kidney disease (36.8%), hypertension (16.8%) and urological anomalies (16.6%) were negatively associated with the HLA-DR*13 allele. Since CKD shows serious morbidity and mortality, this comprehensive study of HLA subgroups gave an explanatory idea about which alleles associated with the disease in terms of susceptibility and protection.

Grenedalf: population genetic statistics for the next generation of pool sequencing.

Pool sequencing is an efficient method for capturing genome-wide allele frequencies from multiple individuals, with broad applications such as studying adaptation in Evolve-and-Resequence experiments, monitoring of genetic diversity in wild populations, and genotype-to-phenotype mapping. Here, we present grenedalf, a command line tool written in C++ that implements common population genetic statistics such as θ, Tajima's D, and FST for Pool sequencing. It is orders of magnitude faster than current tools, and is focused on providing usability and scalability, while also offering a plethora of input file formats and convenience options. grenedalf is published under the GPL-3, and freely available at github.com/lczech/grenedalf.

Using Runs of Homozygosity and Machine Learning to Disentangle Sources of Inbreeding and Infer Self-Fertilization Rates.

Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g. from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data.

Next-generation data filtering in the genomics era.

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering -removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomicdata filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Insights into chloroplast genome evolution in Rutaceae through population genomics

Chloroplast genomes, pivotal for understanding plant evolution, remain unexplored in Rutaceae, a family with key perennial crops like citrus. Leveraging next-generation sequencing data from 509 Rutaceae accessions across 15 species, we conducted a de novo assembly of 343 chloroplast genomes, unveiling a chloroplast variation map highlighting the heterogeneous evolution rates across genome regions. Notably, differences in chloroplast genome size primarily originate from large single-copy and small single-copy regions. Structural variants predominantly occurred in the single-copy region, with two insertions located at the single-copy and inverted repeat region boundary. Phylogenetic analysis, principal component analysis, and population genetic statistics confirmed the cohesive clustering of different Citrus species, reflecting evolutionary dynamics in Citrus diversification. Furthermore, a close chloroplast genetic affinity was revealed among Atalantia (previously regarded as primitive citrus), Clausena, and Murraya. Zanthoxylum formed a distinct group with heightened genetic diversity. Through expanding our analysis to include 34 published chloroplast genomes, we explored chloroplast gene selection, revealing divergent evolutionary trends in photosynthetic pathways. While Photosystem I and Photosystem II exhibited robust negative selection, indicating stability, the Nicotinamide adenine dinucleotide (NADH) dehydrogenase pathway demonstrated rapid evolution, which was indicative of environmental adaptation. Finally, we discussed the effects of gene length and GC content on chloroplast gene evolution. In conclusion, our study reveals the genetic characterization of chloroplast genomes during Rutaceae diversification, providing insights into the evolutionary history of this family.

Potentials of single nucleotide polymorphisms and genetic diversity studies at HSP90AB1 gene in Nigerian White Fulani, Muturu, and N'Dama cattle breeds.

The study was aimed at genetic characterization of Nigerian breeds of Muturu, N'Dama, and White Fulani cattle breeds at heat shock protein 90AB1 locus. Also, the goal of the study was to detect the presence of single nucleotide polymorphisms (SNPs) at HSP90AB1 locus and consequently recommend them as bio-markers for thermo-tolerance potentials in Nigerian cattle breeds when exposed to assaults of thermal conditions/heat shock of tropical environment. Based on the previously published potentials of this candidate gene to lower assaults of thermal conditions/heat shock such as heat stress, the detected SNPs of HSP90AB1 within the population of the Nigerian cattle in this study will be recommended for population-based screening with a view to genetically improving those zebu cattle breeds that are more vulnerable to heat shock and assaults of thermal conditions. Total number of 200 blood samples were randomly collected from White Fulani (84 samples), Muturu (73 samples), and N'Dama (43 samples) breeds of cattle. Out of these, 20 DNA samples were randomly selected from each of the three cattle breeds and were used for DNA extraction and downstream analyses to further confirm findings of previous study, hence the goal of our study. DNA was extracted from the blood samples using the Zymo-bead DNA extraction kit and DNA sequencing of our samples was performed. A total number of 9 SNPs (within exons 5-6 coding regions) and 11 SNPs (within exons 12-13 coding regions) were detected at HSP90AB1 locus using the codon code aligner software. ARLEQUIN 2.0001 software was used to estimate the basic population genetic statistics while the DnaSP version 5.10.01 was used to estimate the genetic diversity indices. This study detected new SNPs (polymorphic sites) at HSP90AB1 locus within the DNAs of Nigerian White Fulani (WF), Muturu (MU), and N'Dama (ND) breeds of cattle. Within exons 5-6 coding regions, the N'Dama (ND) cattle breed had the highest for number of SNPs (5) and genetic diversity indices while White Fulani (WF) and Muturu (MU) had the least (2) number of SNPs each. Within exons 12-13 coding regions, WF had the highest numbers of SNPs (7) and genetic diversity indices while MU had the least number of SNPs (1) and genetic diversity indices. Some of the detected SNPs at HSP90AB1 locus were shared among the three breeds, suggesting that these three Nigerian cattle breeds showed shared ancestral alleles and lineage. Our study further revealed that HSP90AB1 is highly polymorphic/variable and diverse among the three Nigerian cattle breeds examined. Based on the previously documented thermo-tolerance potentials of members of HSP90 sub-family including the findings of our study, we hypothesize therefore that the presence of SNPs of HSP90AB1 within the DNAs of these three breeds of Nigerian cattle (WF, ND, and MU) may confer them thermo-tolerance potentials for thermal assault conditions and heat shock of the tropics at HSP90AB1 locus. Therefore, the detected SNPs can be recommended as bio-markers to improve the thermo-tolerance potentials of Nigerian breeds of zebu cattle raised under the challenges of heat shock for better adaptation and survival.

Genetic diversity of broomrape (Orobanche cumana Wallr.) populations from different geographical origins assessed by ISSR markers

Abstract The present study was focused on the assessment of genetic diversity in twenty-three populations of Orobanche cumana parasitizing on sunflower in Bulgaria, Turkey, Moldova and Romania using 13 ISSR markers. The obtained results on the genetic diversity parameters showed that the broomrape populations were characterised by a significant level of the intrapopulation diversity. In addition, descriptive population genetic statistics revealed that Turkish populations had a higher level of genetic diversity indices than populations from several areas of the northeast and east of the Balkan Peninsula included in Eastern Europe. The analysis of molecular variance showed that 38 % of the genetic variability was due to differences within populations, 34 % was due to differences among populations and the lowest molecular variation was among countries (28 %). According to clustering and PCA methods, Moldavian, Bulgarian and Romanian broomrapes shared more genetic traits with each other than with Turkish populations within a main gene pool. As a whole, all results of this study showed that there is a high intrapopulation diversity of the O. cumana gene pool in the Black Sea basin. From the clustering and PCA analyses, it can be concluded that the grouping of broomrape populations is partly determined by their geographical origin, as well as by the genetic differences and similarities accumulated over time, and is not related to virulence. The information obtained from this study may be highly relevant in contributing to the development of sustainable control strategies of the pathogen and breeding programmes for sunflower resistance to broomrape.

Genomic analysis of the rhesus macaque (Macaca mulatta) and the cynomolgus macaque (Macaca fascicularis) uncover polygenic signatures of reinforcement speciation.

Speciation can involve phases of divergent adaptation in allopatry and ecological/reproductive character displacement in sympatry or parapatry. Reproductive character displacement can result as a means of preventing hybridization, a process known as reinforcement speciation. In this study, we use whole-genome sequencing (WGS) of two closely related primate species that have experienced introgression in their history, the rhesus (Macaca mulatta) and cynomolgus (M. fascicularis) macaques, to identify genes exhibiting reproductive character displacement and other patterns consistent with reinforcement speciation. Using windowed scans of various population genetic statistics to identify signatures of reinforcement, we find 184 candidate genes associated with a variety of functions, including an overrepresentation of multiple neurological functions and several genes involved in sexual development and gametogenesis. These results are consistent with a variety of genes acting in a reinforcement process between these species. We also find signatures of introgression of the Y-chromosome that confirm previous studies suggesting male-driven introgression of M. mulatta into M. fascicularis populations. This study uses WGS to find evidence of the process of reinforcement in primates that have medical and conservation relevance.

Genetic variation in Loudetia simplex supports the presence of ancient grasslands in Madagascar

Societal Impact StatementRecognizing Loudetia‐dominated grasslands were widespread prior to human colonization highlights that open ecosystems were and continue to be an important component of Madagascar's biodiversity. A better understanding of the plant species that form grassland ecosystems is necessary for effective land management strategies that support livelihoods, but substantial financial and logistical barriers exist to implementing conservation genetic studies using contemporary genomic tools. Some challenges for population genetic analyses of non‐model polyploids lacking reference genomes can be ameliorated by developing computational resources that leverage a cost‐effective data generation strategy that requires no prior genetic knowledge of the target species. This may benefit conservation programs with small operating budgets while reducing uncertainty compared to status quo microsatellite assays.Summary The extent of Madagascar's grasslands prior to human colonization is unresolved. We used population genetic analyses of a broadly dominant C4 fire‐adapted grass, Loudetia simplex, as a proxy for estimating grassland change through time. We carefully examined the utility of target‐enrichment data for population genetics to make recommendations for conservation genetics. We explored the potential of estimating individual ploidy levels from target‐enrichment data and how assumptions about ploidy could affect analyses. We developed a novel bioinformatic pipeline to estimate ploidy and genotypes from target‐enrichment data. We estimated standard population genetic summary statistics in addition to species trees and population structure. Extended Bayesian skyline plots provided estimates of population size through time for empirical and simulated data. All Malagasy L. simplex individuals sampled in this study formed a clade and possibly indicated an ancestral Central Highland distribution of 800 m in altitude and above. Demographic models suggested grassland expansions occurred prior to the Last Interglacial Period and supported extensive grasslands prior to human colonization. Though there are limitations to target‐enrichment data for population genetic studies, we find that analyses of population structure are reliable. Genetic variation in L. simplex supports widespread grasslands in Madagascar prior to the more recent periods of notable paleoclimatic change. However, the methods explored here could not differentiate between paleoclimatic change near the Last Glacial Maximum and anthropogenic effects. Target‐enrichment data can be a valuable tool for analyses of population structure in the absence a reference genome.

Genetic inference of orchid colonization of a Costa Rican lava flow

AbstractThe dispersal and colonization of plant populations allow species to occupy novel habitats, migrate, and undergo range shifts in response to changing environmental factors and, as such, are fundamental ecological processes for ensuring the long‐term persistence of species. Natural landscape disturbance often generates habitats available for colonization. Patterns of colonization and population expansion can be inferred from the levels and partitioning of genetic variation of plant populations with known disturbance histories, such as recent volcanic eruptions. We sampled and mapped 496 individuals from two populations of the colonizing terrestrial orchid, Sobralia chrysostoma, on the 1992 lava flow of Volcán Arenal in central Costa Rica. We used neutral co‐dominant markers to genotype individuals and estimate population genetic statistics. Both populations had high mean levels of genetic diversity (P = 100%; AP = 3.31; He = 0.259) suggesting that the lava flow was colonized by numerous individuals that likely originated from multiple source populations. However, significant spatial genetic structure (SGS) was only present in one population at the smallest distance class (≤2 m) and was low (r = 0.032). That these large and genetically diverse populations had such low SGS and an absence of SGS, respectively is contrary to expectations and differs significantly from the pattern in Epidendrum radicans (Orchidaceae), with which S. chrysostoma is growing sympatrically. Our results suggest that these two populations either consist primarily of immigrant individuals or that seeds produced in situ dispersed over longer distances, thereby producing larger seed shadows and greater overlap of seed shadows.

Epidemiological inference for emerging viruses using segregating sites

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Genetic inference of orchid population dynamics on different‐aged lava flows in Costa Rica

AbstractColonization is a fundamental ecological process that is important for the persistence of species, particularly when a changing environment necessitates range shifts. Vacant habitats available for colonization often arise from landscape disturbance. Colonization and population expansion processes can be inferred by examining the levels and spatial distribution of genetic variation of plant populations with known disturbance histories. Samples (N = 690) of the terrestrial orchid, Epidendrum radicans, were collected from five lava flow sites on the slopes of Volcán Arenal in Costa Rica that last experienced major eruptions in 1968 and 1992. Individuals were also sampled (N = 188) from four regional populations. Samples were characterized using 15 nuclear genetic markers and analyzed using population genetics statistics. Genetic diversity within sites was moderate (He = 0.092–0.192). Contrary to expectation, diversity tended to be lower on the older lava flows (0.131 vs. 0.172) which may reflect their more sheltered topography that restricted pollen/seed immigration, and/or greater intra‐ and interspecific competition. Genetic diversity measures indicate that the lava flows were colonized by numerous individuals that likely originated from multiple sources while spatial genetic structure (SGS) statistics indicate that most recruitment in the study sites subsequent to colonization resulted from in situ reproduction and localized seed deposition. Younger sites had significantly greater SGS over larger distances which reflects fewer reproductive events, and less spatial and temporal overlap of seed shadows relative to the older sites. Clones were also generally smaller on the younger sites (≤3 m vs. ≤8 m).

MagicalRsq: Machine-learning-based genotype imputation quality calibration

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation.

Metagenomic datasets of host-associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two-step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low-depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low- and high-depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.

A distribution function from population genetics statistics using Stirling numbers of the first kind: Asymptotics, inversion and numerical evaluation

Stirling numbers of the first kind are common in number theory and combinatorics; through Ewen’s sampling formula, these numbers enter into the calculation of several population genetics statistics, such as Fu’s F s F_s . In previous papers we have considered an asymptotic estimator for a finite sum of Stirling numbers, which enables rapid and accurate calculation of Fu’s F s F_s . These sums can also be viewed as cumulative distribution functions, leading directly to the possibility of an inversion function, where, given a value for Fu’s F s F_s , the goal is to solve for one of the input parameters. We solve this inversion using Newton iteration for small parameters. For large parameters, we have to extend our earlier obtained asymptotic results to solve the inversion problem asymptotically. Numerical experiments are given to show the efficiency of both solving the inversion problem and the expanded asymptotic estimator for sums of Stirling numbers.

Utilizing Tajima's D to identify potential microbe-associated molecular patterns in Xanthomonas euvesicatoria and X. perforans

The coevolution between plants and pathogens constitutes a significant proportion of evolutionary history for both host and parasite. Here we used the population genetics test statistic Tajima's D to identify genes involved in plant-pathogen interactions between tomato and Xanthomonas euvesicatoria and X. perforans, the causal agents of bacterial spot disease of tomato. We assessed the usefulness of this method and experimentally confirmed alleles of known microbe-associated molecular patterns (MAMPs). Overall, we demonstrated the utility of population genetic statistics such as Tajima's D for the identification of genes that may be important for pathogen recognition by the host immune machinery.

Demographic history and patterns of molecular evolution from whole genome sequencing in the radiation of Galapagos giant tortoises.

Whole genome sequencing provides deep insights into the evolutionary history of a species, including patterns of diversity, signals of selection, and historical demography. When applied to closely related taxa with a wealth of background knowledge, population genomics provides a comparative context for interpreting population genetic summary statistics and comparing empirical results with the expectations of population genetic theory. The Galapagos giant tortoises (Chelonoidis spp.), an iconic rapid and recent radiation, offer such an opportunity. Here, we sequenced whole genomes from three individuals of the 12 extant lineages of Galapagos giant tortoise and estimate diversity measures and reconstruct changes in coalescent rate over time. We also compare the number of derived alleles in each lineage to infer how synonymous and nonsynonymous mutation accumulation rates correlate with population size and life history traits. Remarkably, we find that patterns of molecular evolution are similar within individuals of the same lineage, but can differ significantly among lineages, reinforcing the evolutionary distinctiveness of the Galapagos giant tortoise species. Notably, differences in mutation accumulation among lineages do not align with simple population genetic predictions, suggesting that the drivers of purifying selection are more complex than is currently appreciated. By integrating results from earlier population genetic and phylogeographic studies with new findings from the analysis of whole genomes, we provide the most in-depth insights to date on the evolution of Galapagos giant tortoises, and identify discrepancies between expectation from population genetic theory and empirical data that warrant further scrutiny.

MtDNA D-Loop Diversity in Alpine Cattle during the Bronze Age

The Bronze Age in Europe is characterized by major socio-economic changes, including certain aspects of animal husbandry. In the Alpine region archaeozoological data, though not very abundant, reveal that cattle were the most important domestic animals in this time period. They were probably used differently in the lowlands than at higher altitude, traction became more important and people increasingly exploited them for dairy products rather than for meat. Thus, a crucial question in this context is whether these major events are accompanied by changes in genetic diversity of cattle. Here we report partial mtDNA d-loop data (320 bp) obtained by PCR from 40 alpine cattle excavated at different sites in South Tyrol, Italy, and Grisons, Switzerland. Most cattle belong to the main European taurine T3 haplogroup, but a few members of T2 and Q haplogroups were identified. Moreover, genetic diversity measures and population genetic statistics indicate different cattle histories at different sites, including bottlenecks and potential admixture. However, Bronze Age Alpine cattle appear to be linked to modern rural cattle mainly from Italy.

Genomic differentiation with gene flow in a widespread Amazonian floodplain‐specialist bird species

AbstractAimEcological, climatic and palaeogeographical processes drive biological diversification. However, the evolutionary outcomes of those mechanisms are complex and difficult to discriminate. Here, we test how alternative drivers affected connectivity along the Amazonian floodplains generating current patterns of population structure and diversity within the Striped Woodcreeper, a widespread bird species tied to forests seasonally flooded by Amazonian rivers.LocationAmazonian floodplains.TaxonXiphorhynchus obsoletus (Aves, Furnariidae).MethodsWe sequenced 2213 loci of ultraconserved elements (UCEs) and 20 exons to investigate patterns of genetic structure and connectivity across the Striped Woodcreeper distribution, using a set of spatially explicit methods. Population genetics statistics were calculated for each sampled drainage and within each identified cluster. We tested alternative evolutionary scenarios and estimated past and current demographic parameters implementing a simulation‐based framework.ResultsThree genetic clusters with high admixture proportions were identified. These clusters are distributed in the western, central and eastern parts of the Amazon Basin and are not correlated with different river water types representing ecological gradients along the floodplains. Instead, migration rates indicate two putative historical barriers along the main channel of the Amazonas‐Solimões River. Demographic model tests suggest a process of sequential differentiation, partitioned across the Amazon Basin, with stable population sizes and continuous gene flow.Main conclusionOur results support the hypothesis that late Quaternary changes in connectivity between the eastern and western Amazonian drainages were responsible for driving genetic differentiation in the Striped Woodcreeper. Genomic differentiation occurred in the presence of long‐term gene flow throughout the X. obsoletus distribution, suggesting some degree of continuous historical connectivity across Amazonian floodplains. Compared to previous studies, our results suggest that although connectivity among populations of Amazonian bird species specialized in floodplain habitats varies in response to the same abiotic mechanisms, species‐specific differences in habitat use may be a strong predictor of population divergence.