Sillago sinica are fragmented or even absent.Genetic diversity and heterogeneous genomic signatures in marine fish populations may result from selection pressures driven by the strong effects of environmental change. Nearshore fishes are often exposed to complex environments and human activities, especially those with small ranges. However, studies on genetic diversity and population selection signals in these species have mostly been based on a relatively small number of genetic markers. As a newly recorded species of Sillaginidae, the population genetics and genomic selection signals of S. sinica individuals from Dongying (DY), Qingdao (QD) and Wenzhou (WZ) populations and obtained 4,878,771 high-quality SNPs. Population genetic analysis showed that the genetic diversity of S. sinica populations was low, but the genetic diversity of the WZ population was higher than that of the other two populations. Interestingly, the three populations were not strictly clustered within the group defined by their sampling location but showed an obvious geographic structure signal from the warm temperate to the subtropics. With further analysis, warm-temperate populations exhibited strong selection signals in genomic regions related to nervous system development, sensory function and immune function. However, subtropical populations showed more selective signalling for environmental tolerance and stress signal transduction.To address this theoretical gap, we performed whole-genome resequencing of 43 S. sinica populations. The reduction in genetic diversity may be related to the bottleneck effect. Considering that low genetic diversity leads to reduced environmental adaptability, conservation efforts and genetic diversity monitoring of this species should be increased in the future. Differences in genomic selection signals between warm temperate and subtropical populations may be related to human activities and changes in environmental complexity. This study deepened the understanding of population genetics and genomic selection signatures in nearshore fishes and provided a theoretical basis for exploring the potential mechanisms of genomic variation in marine fishes driven by environmental selection pressures.Genome-wide SNPs provide high-quality data to support genetic studies and localization of selection signals in  A central challenge in evolutionary ecology is to identify the underlying genomic variation in natural populations responsible for environmental adaptation , 2. QuitCoilia nasus populations revealed differences in immune, visual, migratory and osmoregulation between these two ecotypes [Oncorhynchus mykiss gairdneri) populations [Marine fish often inhabit a wide variety of complex and changing environments . Howeverecotypes . Whole-gulations . The larS. sinica, is a newly identified species belonging to Sillaginidae (Perciformes), that was found in the inshore waters from the Bohai Sea to northern part of the South China Sea. [S. sinica, Xue found that there was no random mating between its different populations, which might be related to the living habits of S. sinica [S. sinica might be an ideal model for exploring local adaptation. Key loci for local environmental adaptations in Sillago japonica, a close relative of S. sinica, have been explored by whole-genome resequencing and species distribution models, and the results revealed that candidate genes related to membrane fluidity may underlie their adaptation to cold environments [S. sinica populations remain largely unexplored. Our laboratory has assembled the complete genome map of S. sinica in the previous stage [ina Sea. . This spina Sea. . By expl. sinica . Similar. sinica . These rronments . As a neus stage , which hS. sinica individuals collected from three sites across the coastal waters of China, covering warm temperate  and subtropical  populations. The aims of this study were, to summarize, (1) to reveal differences between populations’ genetic diversity and genetic structure based on the obtained genome-wide variation data. (2) to clarify effective population size and evolutionary history, and (3) to infer local adaptive mechanisms based on selection signals and gene function. To our knowledge, this may be the first study to sequence the whole genome of S. sinica and may provide a new perspective on its population genetics and local environmental adaptation.In the present study, we sequenced the whole genomes of S. sinica generated a total of 576.43 G raw data, with an average of 13405.42 M raw data per sample, and a total of 574.60 G of filtered clean data, with an average of 13362.97 M per sample. The Q20 and Q30 of the clean reads were both approximately 90%  decreased to half of its maximum value at 31.2 kb in the WZ population but at 51.2 kb and 57.5 kb in the QD and DY populations, respectively  of the WZ population reached a minimum of 5 × 104 years ago, while the minimum Ne values of QD and DY populations were 4 × 104 years ago  of the WZ, QD and DY populations was calculated to be 0.002444, 0.002417, and 0.002418, respectively Fig. A. The reion Fig. D. In totago Fig. .Fig. 2GeFst&π to mine functional regions that are closely related to the survival environment. Considering that there were no significant genetic differences between the QD and DY populations, we selected the WZ population as a control group to reveal candidate warm temperate environmental selection genes in the QD and DY populations. Using the top 5% of maximum Fst (Fst ≥ 0.0834) and π ratio (πQD/WZ ≥ 0.2813) values, a total of 474 candidate genes (corresponding to 7.78 Mb in size) were identified in the QD population  were identified in the DY population , calcium signaling pathway , long-term potentiation , cAMP signaling pathway , circadian entrainment , neuroactive ligand-receptor interaction  and glutamatergic synapse . However, 59 subtropical adaption candidate genes were significantly enriched in the phagosome , neuroactive ligand-receptor interaction , Hippo signaling pathway , glycosylphosphatidylinositol (GPI)-anchor biosynthesis , and collecting duct acid secretion   and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the candidate genes. GO analysis showed that the main GO terms for the potential genes associated with adaptation to warm temperate and subtropical environments included cellular process GO: 0009987) and single-organism process (GO: 0044699) in biological processes, cell (GO: 0005623) and cell part (GO: 0043226) in cellular component, binding (GO: 0005488) and catalytic activity (GO: 0003824) in molecular function  Fig. .Fig. 9GOp = 0.00), circadian entrainment , dopaminergic synapse , glutamatergic synapse , FC gamma R-mediated phagocytosis , Rap1 signaling pathway  and calcium signaling pathway  , cAMP signaling pathway , long-term potentiation , Fanconi anemia pathway , circadian entrainment , and Ras signaling pathway   caused by selection can be observed on the genome. We used the XP-EHH (cross population extended haplotype homozogysity) method to detect selection signals between populations. No strong selection signals were detected between QD and DY populations. We set "XP-EHH < -1" between the WZ and QD populations to screen for selected loci and obtained 83 genes Fig. A. Simila02) Fig. A. The se04) Fig. B. IntereS. sinica, such as mitochondrial DNA and microsatellite markers [S. sinica populations has been limited largely by the lack of abundant applicable molecular markers compared to other fish. The labor-wasting, limited numbers and genetic information of traditional markers make population genetic research difficult [Oreochromis niloticus, Micropterus salmoides and Larimichthys polyactis have been carried out using SNP markers [S. sinica to our knowledge. It has been shown that a sufficient number of SNPs can be obtained from second-generation high-throughput sequencing data at 15 × sequencing depth [In this study, we compared three populations  based on genetic diversity, population structure, and selective signals, with a focus on genomic variations. Previous studies have identified a few traditional molecular markers for  markers . Howeverifficult . SNP marifficult . Many po markers –27. Thisng depth . In thisS. sinica populations based on mitochondrial DNA, but the results are not always consistent. For example, Zhang et al. found that the nucleotide diversities of the WZ, QD and DY S. sinica populations were 0.012, 0.005 and 0.001, respectively [S. sinica populations is at a low level, but the genetic diversity of the WZ population is higher than that of the other two populations. Our results validated previous main findings and extended these earlier results to genome-wide variant loci. Linkage disequilibrium analysis seems to support the genetic diversity results. Studies have shown that if the population undergoes positive selection, the frequency of the surrounding sites linked to the favorable site will increase rapidly due to the piggyback effect, so the haplotype containing the favorable site has a higher frequency on the one hand, and on the other hand, because the duration is short, it also has a long range of LD influence [S. sinica populations experienced the first bottleneck effect during the last interglacial , with a sharp decline in effective population size. Although the population size of the S. sinica rapidly increased during the pre-Last Glacial Period, the genetic diversity of the S. sinica populations did not increase sufficiently, as there are highly similar “redundant copies” in the increased number of individuals from a genetic point of view [According to the theory of genetics and evolutionary biology, genetic diversity is the basis and core of biodiversity and the fundamental guarantee of the evolutionary potential of species . Severalectively . Howeverectively . Althougnfluence . This fenfluence . This ph of view . This exConsidering the existence of geographical isolation, the grouping of species is usually based on the geographical location of sampling first, but the flaw of this method usually breaks our logical chain. The results of population genetic structure research based on genome-wide variation loci are usually incompatible with geographical grouping. In this study, the NJ tree showed that all the individuals were clustered into two large clades: the QD and DY populations were clustered into one clade and the WZ populations were clustered into the other clade. The PCA and admixture results also showed confounding effects of the QD and DY populations. The population genetic differentiation index can serve as further evidence. Our results showed no significant genetic differentiation between the QD and DY populations, but both populations developed a moderate degree of genetic differentiation from the WZ population. This result is inconsistent with the results of mitochondrial DNA . The mosFst&π to identify functional regions that are closely related to the survival environment. Considering the significant genetic structure of the three S. sinica populations, we defined the QD and DY populations as warm temperate populations to screen for selection signals. We identified 95 overlapping genes between the QD and DY populations as potential genes associated with adaptation to warm temperate environments. KEGG enrichment results showed that these genes were related to environmental adaptation, the nervous system, signal transduction and signaling molecules and interactions. Dopaminergic synapse, long-term potentiation and glutamatergic synapse pathways are considered to be related to the development of the nervous system, vision and hearing of fish [S. sinica warm temperate populations. Selection signals in the nervous system may be related to reduced environmental complexity due to human activities, but the deeper mechanisms need to be further explored [We detected strong selection signals in the genome based on  of fish –38. We i of fish . The D1B of fish . Althougexplored .We defined the WZ population as the subtropical population to screen for selection signals. A total of 59 overlapping genes were identified as potential genes associated with adaptation to subtropical environments. KEGG enrichment results showed that these genes were related to transport and catabolism, signaling molecules and interactions, signal transduction, glycan biosynthesis and metabolism and excretory system. We identified a key gene in the phagosome pathway, ATP6V1B2 (V-type proton ATPase subunit B), which was shown to help fish acidify external media to improve environmental ammonia tolerance . This seS. sinica warm temperate and subtropical populations. In conclusion, these results further support the results of the Fst&π analysis. We used the XP-EHH method to further detect strong selection signals between populations. No strong selection signals were detected between the QD and DY populations. We detected 83 genes between the WZ and QD populations, which were mainly associated with the nervous system, environmental adaptation, immune system, and signal transduction. Based on the KEGG enrichment results, we again localized to the GRIN2A gene. In addition, guanine nucleotide-binding protein G (q) (GNAQ) and dedicator of cytokinesis protein 2 (DOCK2) were also identified as candidate genes. GNAQ is involved as a modulator or transducer in various transmembrane signaling systems . DOCK2 hS. sinica populations. In the present study, we detected low levels of genetic diversity in the QD, DY and WZ populations. The reduction in genetic diversity may be related to the bottleneck effect. Considering that low genetic diversity leads to reduced environmental adaptability, conservation efforts and genetic diversity monitoring of this species should be increased in the future. Interestingly, the three populations did not strictly cluster within the group defined by their sampling location, but showed an obvious geographic structure signal from the warm temperate to subtropics. Warm-temperate populations exhibit strong selection signals in genomic regions related to nervous system development, immune regulation and sensory function. However, subtropical populations show more selective signaling for environmental tolerance and stress signaling. We speculated that this result may be related to human activities and changes in environmental complexity. In conclusion, these results deepen our understanding of genetic diversity and selection signals in fishes, especially nearshore demersal fishes, and provide a theoretical and data basis for exploring potential mechanisms of local environmental adaptations in marine fishes.Genome-wide SNPs provide high-quality data to support genetic studies and the localization of selection signals in S. sinica from three populations distributed in the Yellow Sea, Bohai Sea and East China Sea  using BWA software with the parameter “mem -t 4 -k 32 -M”, and a bam file was generated [The raw image data obtained by sequencing were converted into raw sequence data (raw reads) by base calling, and raw reads contained linker sequences, low-quality bases and undetected bases (represented by N). We filtered this interference information and finally obtained clean reads. Xu et al.  have pubenerated . The BAMenerated .P value using Fisher’s exact test to detect strand bias) > 60.0, SOR (symmetric odds ratio of 2 × 2 contingency table to detect strand bias) > 4.0, MQRankSum  < 12.5, or ReadPosRankSum (Z score from Wilcoxon rank sum test of Alt vs. Ref read position bias) < 8.0. ANNOVAR was used to annotate the genomic distribution of variants [We called and filtered the raw SNPs according to GATK Best Practices from the alignment files . SNPs wevariants . SNP var2) dropped to half of the maximum.After filtering, we generated a set of high-quality SNPs for the subsequent population genetic analysis. First, Treebest software (v.1.9.2) was used to calculate the distance matrix and construct the phylogenetic tree by the neighbor-joining (NJ) method. The number of bootstrap replicates was set to 1000 to test the reliability of the NJ clustering tree. PCA of whole-genome SNPs for all 43 individuals was performed with the PLINK software . Populat−8 and 1 year respectively. TreeMix (v.1.13), which inferred the maximum-likelihood (ML) tree for the QD, DY and WZ populations. A window size of 1000 was used to account for linkage disequilibrium (–k) and “–global” to generate the ML tree. Migration events (–m 0 1 2) were sequentially added to the tree [The effective population size of the three populations was analysed using the PSMC model in the PSMC package . The “fqthe tree .S. sinica populations, we calculated the genome-wide distribution of population nucleotide diversity (π) and genetic differentiation indices (Fst) using VCFtools with a window size of 40 kb and a step size of 20 kb [Fst and π ratios were simultaneously used as a candidate region under strong selective sweeps. Based on these candidate regions, we localized to the corresponding SNPs and genes. We also performed the XP-EHH test for every SNP using the default settings of selscan v1.3.0 [http://www.omicshare.com/tools) [To uncover the genetic variants of warm temperate and subtropical of 20 kb . The subn v1.3.0 , which wn v1.3.0 . Gene Onm/tools) –61.


Artificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes  of birth date, in four pig populations  to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives.Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates  of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations.The GPSM identified 49 to 854 loci as under selection (This work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.The online version contains supplementary material available at 10.1186/s12711-023-00836-9. Broadly, population genetic methods are used to identify three types of directional selection in genomic data. First, hard selective sweeps are the signatures of rapid selection in which one haplotype is selected to fixation within a population. Under this rapid selection, variation surrounding the selected mutation is dragged or hitchhikes with the selected mutation resulting in large tracks of reduced nucleotide diversity and increased haplotype homozygosity. Soft selective sweeps are similar to hard sweeps in that the diversity is reduced around the selected locus, but the selected DNA variants are on more than one haplotype, via selection on standing variation, recurrent mutation or migration , 2. FinaOver the past 300 years, artificial selection in pigs has led to the formation of pig breeds with well-defined breed characteristics and considerable across-breed variation in phenotypes that are related to economically relevant traits . Pig breDeciphering the genetic basis of phenotypic diversity in species that are raised for meat production is a much studied research area in livestock genomics , 15. UndGeneration proxy selection mapping (GPSM) has been used as an analytical method for the detection of polygenic selection loci in populations , 15, 20.longissimus muscle area. In addition, selection indices for two of the four breeding populations (Landrace and Yorkshire) also emphasized maternal reproductive traits and included EPD for increased number and weight of piglets born and weaned.In this study, we used four populations of pigs, using data owned by The Maschhoff’s, Limited Liability Company (LLC), Carlyle, IL, USA. Within each population, a selection index was used to identify boars and gilts with superior genetic merit to return to the breeding population at the nucleus level. Breeding population-specific selection indices for all populations included expected progeny differences (EPD) for growth and carcass traits such as increased feed efficiency and average daily gain, decreased backfat depth, and increased A pedigree consisting of individual, sire, and dam identification, birth date, and genetic line for 1,247,982 pigs was provided by The Maschhoff’s, LLC. Information regarding the number of sires and dams, founder pigs, and generations within each population is summarized in Table , i.e., chromosomes 1–18. Genotype quality control was performed using the PLINK v1.9 software [The dependent variable for all analyses was birth date (AGE) calculated as the difference, in months, between each pig’s birth month and January 2006. Pigs from the entire dataset of genotyped pigs were separated into 15 subsets based on population or combination of populations. Analyses were conducted using SNPs located on the pig autosomes onlysoftware  for eachK parameter set to 3. Purebred pigs that were predicted to have a breed proportion less than 95% of their assigned genetic line  were removed from all subsequent analyses, as these may be due to sample swaps. While predicted breed proportions were estimated for the crossbred pigs, none were removed from the genotyped sample, as deviations from expected breed proportions cannot be distinguished from deviations due to Mendelian sampling or noise of ancestry prediction. Genomic relationship matrices (GRM) were estimated for each subset using the GCTA v1.93.2 software [The percentage of Duroc, Landrace, and Yorkshire ancestry was predicted for each pig using the fastSTRUCTURE algorithm , with thsoftware  and the software , and thesoftware  was alsosoftware  in the ssoftware . Figuressoftware  package Depending on data subset, certain combinations of the following three statistical analyses were performed on AGE: (1) univariate variance component estimation, (2) bivariate variance component estimation, and (3) univariate genome-wide association using a mixed linear model to estimate SNP associations.g, and To estimate the proportion of variance in AGE explained by genome-wide SNPs (PVE) for each data subset Table , the folGenetic correlations  corrected Q-values using the ‘qvalue’ package [Q < 0.10 was used for all analyses.Generation proxy selection mapping analyses were conducted to detect SNPs with changes in allele frequency over time within each subset Table . To acco package  of R, anVariance component and GPSM analyses of purebred pigs  using the likelihood ratio test. Previous GPSM simulations have shown that GPSM PVE is indicative of population demographic history [P < 0.001) based on the likelihood ratio test. Between the univariate variance component estimation analyses using real and simulated genotype data, the pedigree structure, AGE values, and number of SNPs were the same for each subset; however, the genomic relationship between each pairwise combination of pigs was different. For the Landrace and Yorkshire populations, the real PVE was higher than the simulated PVE by only 0.02 and 0.01, respectively. However, for the Duroc population, the real PVE was 0.11 higher than the simulated PVE. In addition, PVE from the univariate variance component analyses were similar across replicates of simulated genotype data. For example, PVE ranged from 0.82 to 0.84 (mean of 0.83 ± 0.004), 0.85 to 0.87 (mean of 0.86 ± 0.003), and 0.84 to 0.85 (mean of 0.842 ± 0.0020) for the Duroc, Landrace, and Yorkshire populations, respectively, across five replications of simulated genotype data per population  based on the likelihood ratio test. Within the purebred subsets  based on the likelihood ratio test (Table Table st Table . This reQ < 0.10) associated with AGE for each subset is in Table P-values for independent SNP genotype association tests with AGE were well calibrated . However, in commercial pig populations, divergence likely occurred in a scenario that resembles a blending of Methods 1 and 2; thus, the higher error rate in Method 2 could be inflated compared to reality in the swine industry. Nevertheless, these results suggest that GPSM is robust to allele frequency changes due to genetic drift over time. Furthermore, simulation results show that the genomic relationship matrix appropriately accounts for population stratification , preventing spurious associations.Analyses using Method 2 to simulate founder genotypes, which simulated completely different founder genotypes for each population, had a significantly larger number of spurious associations than those using Method 1, which simulated a single founder population for all three purebred populations . Selection objectives within The Maschhoffs are highly similar between the Landrace and Yorkshire populations and are the most dissimilar between the Duroc and each of the Landrace and Yorkshire populations. Estimated genetic correlations in the present study followed this pattern, as the estimated genetic correlations between the two maternal breeds were higher than those estimated between the Duroc population and either the Landrace or Yorkshire populations  between populations in their demographic and selection histories. A genetic correlation of nearly 1 for AGE between two populations suggests a high proportion of autosomal loci that are statistically associated with AGE undergoing similar changes in allelic frequency over time, while a genetic correlation between 0 and -1 suggesting the contrary . The results of the simulation analysis, where randomly generated founder pig SNP genotypes are randomly dropped through the real pedigree of each population (mimicking genetic drift), validate this assumption, since the genetic correlations between populations were not significantly different from 0 (regardless of the most recent common ancestor in simulations) based on the likelihood ratio test (ns Table . Howeverns Table  and therns Table .Q < 0.10) in most populations  of the GPSM analyses, with more even sampling providing more power and uneven sampling decreasing power . The ageQ-values for each population allowed us to identify several regions along the autosomal genome that expressed similar patterns of GPSM significance across populations  identified in the region of chromosome 9 that is associated with selection in all three purebred populations, which are all differentially expressed in ovarian tissues [MIOS gene, which is commonly referred to as the “missing oocyte gene”, is well known for its role in regulating meiosis during oocyte development. In a study using Drosophila, a mutation in the MIOS gene caused erroneous oocyte development, i.e. instead of stimulating progression through each stage of meiosis, the described mutation caused oocyte progression towards polyploid nurse cells as opposed to fully functional, mature haploid gametes [MIOS gene, two genes of particular interest, HOMER2 and WHAMM, were identified near significant GPSM SNPs on chromosome 7 in all four populations. In humans, both these genes are associated with lung function. However, the HOMER2 gene is also associated with traits related to human body mass index. In addition, these two genes are located in regions of the pig genome that are associated with carcass traits such as backfat thickness, loin muscle depth and area, carcass length, dressing percentage, and estimated carcass lean content. The HOMER2 and WHAMM genes were likely identified in each population due to the strong emphasis placed on carcass feed efficiency and lean meat production in selection indices for the Duroc, Landrace, and Yorkshire populations of the current swine breeding company. However, whether the HOMER2 and WHAMM genes influence carcass traits through their effect on lung function  or whether they have carcass-specific effects in swine is not known. Thus, further quantitative trait association studies and bioinformatics analyses are required to test these alternatives. The region containing the UTRN and STX11 genes had AnimalQTL annotations related to white blood cell counts and virus titers (immunity) as well as adiposity measures (production). Genome-wide association studies in humans have shown that the UTRN and STX11 genes are associated with lung function [A number of SNPs were detected by GPSM across at least two populations Fig. . Visual ons Fig. . Of part tissues . Most no gametes . While tfunction  and pre-function , respectThe detection of significant associations across the autosomal genome in each of the Duroc, Landrace, Yorkshire, and crossbred populations indicates that artificial selection has influenced numerous genes in each of these populations of pigs. Furthermore, for the power to increase when pooling data across populations and shared signal across populations, common causal variants  must be segregating in the populations, and the variants must be responding to similar selection objectives. Thus, concordant traits across selection indices for maternal and terminal pig breeds are likely influenced by the same quantitative trait loci in the genome of each breed.We confirmed that GPSM is robust in separating the changes in allele frequency due to genetic drift and artificial selection, through simulations. In each of the 11 gene-drop simulations, GPSM found very few spurious associations between SNP genotype and AGE Table . Rowan eExcept for two outliers [SNP 30819 in the crossbred population (14.39 months) and SNP 3420 in the Duroc population (13.32 months); Table We evaluated generation proxy selection mapping as an analytical method for detecting large and small signatures of artificial selection in a large commercial population of pigs from three purebred populations and one crossbred population. Numerous significant SNPs were detected across the genome in each genetic line, indicating that GPSM is effective to detect changes in pig genomes due to polygenic selection over relatively short time scales (~ 4 to 10 years). In addition, simulations proved that GPSM is well-calibrated to distinguish between changes in allele frequency over time resulting from genetic drift or artificial selection. Several SNPs were identified as significantly associated with AGE across multiple populations, which indicates that the selection objectives, genetic architectures, and causal variants underlying the quantitative traits that influence allele frequencies at loci are similar in each population over time. The results from this analysis and future analyses using GPSM will provide valuable insight into the biological mechanisms underlying selection on quantitative phenotypes in the commercial swine industry. Lastly, SNPs identified as being significantly associated with AGE have the potential to serve as indicators of genomic regions to prioritize in the development of genetic prediction models and selection schemes in swine breeding programs.Additional file 1: Table S1: Proportion of variation in AGE explained by SNPs for each purebred subset using five replications of randomly simulated genotype data.Additional file 2: Table S2: Number of SNPs significantly associated with AGE for each subset using five replicates of randomly simulated genotype data.Additional file 3: Table S3: Population, chromosome, SNP effect, Q-value, gene identifier, associated human traits, and associated pig traits for SNPs associated with AGE from GPSM analyses.


The insertion/deletion polymorphism (InDel), an ideal forensic genetic marker with a low spontaneous mutation rate and small amplification product fragments, is widely distributed in the genome, combining the advantages of STR and SNP genetic markers. The X-chromosome has high application value in complex paternity testing, and it is an excellent system for evaluating population admixture and studying evolutionary anthropology. However, further research is needed on the population genetics of X-chromosome InDels (X-InDels).In this article, a system composed of 38 X-InDel loci was utilized to analyse and evaluate the forensic parameters of the Guizhou Han population in order to explore its forensic application efficiency.Fst genetic distance, and phylogenetic reconstruction were employed for dissecting the genetic structure of the Guizhou Han population by comparing it with previously reported populations. As expected, the studied Han population displayed relatively close genetic affinities with the East Asian populations. At the same time, there were obvious genetic differentiations between the Guizhou Han population and other continental populations that were discerned, especially for the African populations.The results showed that expected heterozygosities spanned from 0.0189 to 0.5715, and the cumulative power of discrimination of the 32 X-InDels and three linkage blocks was 0.9999999954 and 0.999999999999741 for males and females, respectively. The combined mean exclusion chance of these loci for trios and duos is 0.999999 and 0.999747, respectively. Multiple methods like principal component analysis, This study further verified the applicability of 38 X-InDels for human personal identification and kinship analyses of Han Chinese, and also showed the application potential of X-InDels in population genetics. Kinship testing and individual identification are the mainstays of forensic genetic research . ForensiThe X-chromosome has a special inheritance pattern whereby, during female meiosis, X-chromosome markers are recombined along the entire chromosome and passed on to both female and male offspring. In males, however, the X-chromosome markers are passed exclusively to the female offspring . Due to The Chinese Han nationality is the largest ethnic group in the world, and its origin, development and expansion are highly complex. According to the relevant historical documents, the most widely accepted view today is that the origin of the Han nationality can be traced back to the Huaxia ethnic group in the Central Plains of China during the Shang and Zhou dynasties (21st-8th century BC) . The HanFingertip blood samples were collected from 264 unrelated Guizhou Han people  in accordance with the principle of informed consent. After explaining the objectives and procedures of our study, all participants provided their written informed consent. Ethical permission was warranted by the Ethics Committee of Guizhou Medical University , and followed the recommendations provided by the revised Helsinki Declaration of 2013.https://www.r-project.org/), as shown in At the same time, we also collected the raw data and allele frequencies of 38 X-InDels from 26 populations in the 1,000 Genomes Project  and the via the ABI 3500xL Genetic Analyzer . The GeneMapper v 4.0 was used to perform electropherogram analysis and allele assignments.Without DNA extraction, all blood samples were directly amplified. The Thermo 96-Well PCR System  was used to genotype a total of 264 unrelated samples. A total of 38 X-InDel markers were included in the analysis panel. The specific procedures for PCR and the general information of different markers were described in a previous publication . Isolatip values. HWE was assessed in females, and LD was tested by combining the Chi-square test and p-values from male haplotype counts (The 38 X-InDel loci were examined for the Hardy-Weinberg Equilibrium (HWE) using the Genepop 4.7 software package . In addie counts . We estie counts . In addie counts  performeF-statistics, Fst) were calculated by the Genepop 4.7 software package (via the R 3.3.0 software (https://www.r-project.org/). MVSP 3.22 software was used to perform allele frequency-based principal component analysis (PCA) to obtain a better understanding of the demographic relationships of the different populations. With the data from the Fst genetic distance matrix, MEGA software v7.0 (The genetic distances ( package  based onare v7.0  was utilp = 0.00732), there were no discernible differences between males and females for these X-InDels (p > 0.05). The allele frequencies were calculated separately for males and females of the rs57608175 locus based on the results of the Fisher’s exact test, and the allele frequencies of the remaining 37 X-InDel loci were calculated by combining males and females. We found that the minor allele frequencies of most loci were larger than 0.2. In addition, a total of 136 unique haplotypes are observed in all males for these 38 X-InDels and no shared haplotypes are discerned among these individuals. Therefore, the haplotype diversity of 38 X-InDels in males is 1.The raw genotype of the 38 X-InDel loci for 264 individuals of the Guizhou Han Chinese are shown in p < 0.05). Even so, after the Bonferroni correction (p > 0.05/38), there was only one p-value for the rs56820033 locus that was not within the allowable range (2 ≥ 0.8 and p > 0.05/703 (after Bonferroni correction), LD test results showed that rs3859989 and rs61260787, rs36094418 and rs79829945, rs3216913 and rs10699224 formed three linkage blocks, respectively. In accordance with the recommendations of the DNA commission of the ISFG on X genetic markers, pairs of loci must be analysed together , South Chinese  and Henan Han Chinese . On the contrary, the genetic affinities between the Guizhou Han population and the African populations were the furthest (with the largest genetic distance), especially with the Luhya in Webuye, Kenya .Combined with Fst genetic distances, as shown in In order to explore phylogenetic relationships among the Guizhou Han population and other reference populations, we constructed a neighbor-joining (NJ) phylogenetic tree based on their pairwise The analysis of allele frequencies of the 38 X-InDel loci showed they are evenly distributed in the Guizhou Han population. However, the allele frequency distribution at locus rs57608175 showed a significant difference between males and females. The same situation has not been found in other studies , which mAccording to the statistical calculation results of forensic parameters, it is revealed that the combined PDs and MECs of the Guizhou Han population calculated by the 38 X-InDels panel are higher than those in alternative X-InDels studies , althougThe distribution of allele frequencies in 28 populations showed that the loci rs16368, rs25581, rs11277082, rs1160845, rs2307741, rs16367, and rs34763847 have great potential as ideal AIMs. Particularly in African, American, and East Asian populations, it appears that the majority of the 38 X-InDel loci can manifest relatively high genetic divergences, which is of great utility for forensic ancestry origin analyses.Fst genetic distance showed that the genetic distances between the Guizhou Han and other populations in the same East Asian region are relatively small, and studies on the autosomal or Y-chromosomes STRs of Guizhou Han Chinese also showed similar clustering patterns (The results of PCA analysis and patterns . It has patterns , which iIn our study, based on the newly built 38 X-InDels system, the X-InDels typing data of 264 individuals from the Guizhou Han population was analyzed, and the forensic application effectiveness of the system was explored and verified. The statistical results of the forensic parameters indicate that the system can be used as an excellent auxiliary tool for the forensic paternity testing and individual identification of Han Chinese, which also support its potential use in population genetics. Population genetic analyses of Guizhou Han and other reference populations showed Guizhou Han population had relatively close genetic affinities with Han populations from East Asia. In addition, some loci out of these 38 X-InDels could be viewed as candidate AIMs for distinguishing different continental populations. In summary, the data in this study have laid the groundwork of demographic data for the use of the 38 X-InDels in individual identification, kinship testing and biogeographic ancestry analysis.10.7717/peerj.14964/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14964/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14964/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.14964/supp-4Supplemental Information 4Click here for additional data file.


Halictus tripartitus. We examined wing morphology of specimens sampled from a reproductively isolated population of H. tripartitus on Santa Cruz Island . Our analysis revealed significant differentiation in wing venation in this island population relative to conspecific mainland populations. We additionally found that this population‐level variation was less pronounced than the species‐level variation in wing venation among three sympatric congeners native to the region, Halictus tripartitus, Halictus ligatus, and Halictus farinosus. Together, these results provide evidence for subtle phenotypic divergence in an island bee population. More broadly, these results emphasize the utility and potential of wing morphometrics for large‐scale assessment of insect population structure.Phenotypic divergence is an important consequence of restricted gene flow in insular populations. This divergence can be challenging to detect when it occurs through subtle shifts in morphological traits, particularly in traits with complex geometries, like insect wing venation. Here, we employed geometric morphometrics to assess the extent of variation in wing venation patterns across reproductively isolated populations of the social sweat bee,  Halictus tripartitus. The two populations show subtle morphological differentiation in the venation patterns on their forewings. We used geometric morphometrics to demonstrate phenotypic divergence in island and mainland populations of the sweat bee,  Bees typically forage within a few kilometers of their nesting sites, and dispersal distances are generally well under the 30 km water barrier separating Santa Cruz Island from the mainland  were eradicated from the island by 2004 and have not been observed there since , H. ligatus (nmainland = 43), and H. farinosus . The basal tip of some forewings were removed if they were heavily sclerotized and prevented the wings from lying flat. We plotted 9 homologous wing venation landmarks  tests using R package “RRPP” version 1.3.1 ;  and accuracy  than for the three congeneric Halictus species.Population‐level discrimination was also successful. The two populations of n Figure . This an4Halictus bees, as well as significant (though less pronounced) differentiation across island and mainland populations of H. tripartitus. Species‐level variation in wing venation is well established for many bee taxa, serving as useful characters for identification to family, genus, or species levels , when lowered sea levels reduced the distance from the mainland to about 6 km  may contribute to this population divergence, in addition to founder effect and genetic drift. Future sampling across the entire Channel Island archipelago could shed light on historical patterns of dispersal and population divergence, inferred from patterns of differentiation in wing landmark geometries. Finally, comparisons to other populations across the considerable geographic range of H. tripartitus would provide interesting context for assessing the relative magnitude of phenotypic divergence in the Santa Cruz Island population.As for many island bee populations, it is unknown how and when this population of  Miller, . Gene flHalictus species likewise supports a role for geometric morphometrics in taxonomic identification to the species level. Further, geometric wing morphometrics may be useful for distinguishing among populations  and between species within complexes ; supervision ; validation (supporting); visualization ; writing – original draft ; writing – review and editing (lead). Charles N. Thrift: Conceptualization (supporting); data curation (lead); formal analysis (lead); funding acquisition (supporting); investigation (lead); methodology ; resources (supporting); software (lead); validation ; visualization ; writing – original draft ; writing – review and editing (supporting). Katja C. Seltmann: Conceptualization (lead); data curation (supporting); funding acquisition (lead); investigation (supporting); methodology (supporting); project administration (lead); resources (lead); supervision (lead); validation ; writing – original draft (supporting); writing – review and editing (supporting).Table S1.Click here for additional data file.Table S2.Click here for additional data file.Figure S1.Click here for additional data file.Data S1.Click here for additional data file.


Avicennia marina is one of the most distributed mangrove species throughout the world. The populations of A. marina occur in a limited region in southern parts of Iran. Very few genetic and spatial analyses are available on these plants from our country. Therefore, the present study was planned to provide detailed information on Avicennia marina populations with regard to genetic diversity, gene flow versus genetic isolation, effects of spatial variables on connectivity and structuring the genetic content of trees populations and also identifying adaptive genetic regions in respond too spatial variables. We used SCoT molecular markers for genetic analyses and utilized different computational approaches for population genetics and landscapes analyses. The results of present study showed a low to moderate genetic diversity in the studied populations and presence of significant Fst values among them. Genetic fragmentation was also observed within each province studied. A limited gene flow was noticed among neighboring populations within a particular province. One population was almost completely isolated from the gene flow with other populations and had peculiar genetic content.Spatial PCA analysis revealed both significant global and local genetic structuring in the studied populations. Spatial variables like humidity, longitude and altitude were the most important spatial features affecting genetic structure in these populations.Mangrove forests grow in coastal areas, lagoons, estuaries, and deltas and form the main vegetation in tidal and saline wetlands. Due to the mankind activities and also changes in climate, these forests face degradations and probably extinction in some areas.  Mangroves are very specialized plants and have adapted to unfavorable environmental conditions, strong tides, high salinity, high temperature, strong winds and anaerobic conditions in the habitat. In such conditions, most of the plant species of dry habitats are not able to adapt. These trees have unique physio-morphological adaptations and tolerance to hyper-saline environments, tidal cycles and soil chemistry , 2.The most-extensive mangrove areas are situated in Asia 42%), Africa (20%), North and Central America (15%), Oceania (12%), followed by South America (11%) . The man2%, AfricApproximately only 6.9% of world’s mangrove areas are protected under the International Union for Conservation of Nature (IUCN) program . These mAvicennia marina (Forsk.) Vierh., is an important mangrove species which can grow and reproduce across a wide range of climatic, saline and tidal conditions. However, its growth is only confined to the southern part of Iran where in comprises a limited number of geographical populations  and therefore, they may grow either close to the mother tree or in other suitable locations. Therefore, the barriers against the water current or circulation patterns are important for mangrove species’ abundance and distribution . MoreovePopulation and landscape genetic studies provide data on patterns of gene flow and connectivity among populations and have become an important basis for planning conservation management .Mangrove populations can face environmental changes if they contain an adequate genetic variability. Gene flow and population connectivity may act as a source for providing genetic material for persistence of these populations. With increase in pressures on natural populations due to anthropogenic and climate disturbances, the knowledge of population connectivity becomes vital for identifying vulnerable Mangrove populations and informing management actions at appropriate spatial scales.The mountainous rims which surround the high interior basins of Iran, and also presence of latitudinal range result in a range of climate change in from arid to sub-tropical across the country. For example, the eastern part of the country is covered by deserts but the northern regions that are covered by the Alborz mountain chain, contains dense forests in the vicinity of the Caspian Sea. The Zagros Mountain is located in the country’s western part and extends from north to south. The Persian Gulf and the Oman Sea coastlines form the country’s southern border.Population and landscape genetics studies provide important data on genetic structure of species by identifying the gene flow corridor versus the populations with low genetic variability. These data may be used in planning conservation programs for a specific species .These two disciplines combine genetic structure data, landscape ecology, and spatial statistics to investigate the relative influence of landscape and environmental features on gene flow, genetic discontinuities and genetic population structure .Population and landscape genetics studies utilize different molecular markers and computational approaches to produce related data. Molecular markers used are SNPs (Single nucleotide polymorphism), SSRs (Simple sequence repasts), AFLP (Amplified fragments length polymorphism), and SCoT markers (Start codon targeted), etc.The spatial methods include univariate to multivariate analysis and use multiple regression methods to Bayesian approaches, and data mining analyses . to inveAvicennia marina plants in Iran, the present study was performed based on both population genetic analysis and landscape investigation by spatial principal components (sPCA) analysis, with the following aims: 1- Provide data on genetic structure, and genetic diversity of Avicennia marina populations of Iran. 2- Produce data on the genetic discontinuity of these populations versus gene flow. 3- To identify gene flow corridor in southern part of the country and also to illustrate the path of gene migration among different populations. 4—To analyses spatial variables and their effects on spatial genetic structuring of A. marina populations, and 5- Identify potentially adaptive genetic regions related to local environmental conditions.Identification of potentially adaptive genetic regions or alleles in different local populations is also an important step in these studies . TherefoWe used SCoT (Start codon targeted) molecular markers which are suitable molecular marker for both population genetics and landscape genetic investigations . These mWe used different approaches for populations and landscape genetics analyses, due to computation complexity of analytical approaches and a likelihood of difference in some parts of the results obtained.The analytical methods used in population genetic study ranged from DCA , DAPC , RDA , and CCA . We used RASP , for phylogeography analysis.Similarly, in landscape genetic studies, we used spatial principal components (sPCA), as it can analyze data in a reduced space and can be used for co-dominant markers as well as presence / absence data as is the case in SCoT molecular markers . This meWe also used random forest machine learning approach to identify the importance of spatial and geographical variables on genetic diversity.In total 119 plants growing in nine geographical populations were studied for both populations genetics as well as landscape genetic studies  was applied to extract the genomic DNA. The extraction was done by activating charcoal for binding of polyphenolics during extraction. DNA isolation was boosted without the interference of impurities .The extracted DNA was examined in terms of quality by running on 0.8% agarose . In thisFour primers  were selected based on Collard and Mackill . The priThe amplification reactions were performed in Techne thermocycler (Germany) with the following program: 4 min at 94 ºC, 35 cycles of 1 min at 94 ºC, 1 min at 50–60 ºC and 2 min at 72 ºC and a final cycle of 5min at 72 ºC. The amplification products were visualized by running on 2% agarose gel, stained with cyber green . The fragment size was estimated by using a 100 bp molecular size ladder .The SCoT bands obtained were treated as binary characters and coded accordingly . For genetic diversity analyses,we estimated population genetic diversity parameters as performed in GenAlex ver.6 . SimilarGrouping of the populations based on genetic data was performed by both clustering and ordination methods. We used both Jaccard and Dice genetic similarity index for these analyses, as they are appropriate for binary data as in SCoT markers. We used PAST program ver.4 for these analyses .DAPC method was used to show genetic admixture and assignment of individuals to different genetic groups. This was done in adegenet package of R. 4.1 .Association between genetic data and geographical variables was determined by RDA and CCA methods by 999 permutations as performed in PAST program ver.4. . SimilarCCA , is based on the regression of the SNPs and ecological features. It uses an approach similar to principal components analysis (PCA). However, in PCA, we have a maximized variance of data, while CCA tries to maximize the association of data (SNPs), to geographical variables .RDA is a form of constrained ordination that suits for genomic data sets, where we are interested in understanding how the multivariate environmental factors shape the patterns of genomic composition across geographical areas. RDA is a direct gradient analysis technique, which summarizes linear relationships between components of response variables that are "redundant" with (i.e. "explained" by) a set of explanatory variables. It is based on multivariate regression .LFMM is a Bayesian method for testing associations between loci and geographical gradients using latent factor mixed models. It performs a regression analysis in which the confounding variables are modeled with unobserved (latent) factors. The program estimates correlations between geographical and ecological variables and allelic frequencies, and simultaneously infers the background levels of population structure .We used spatial PCA method (sPCA) to investigate the spatial pattern of genetic variability based on allelic frequency data of individuals or populations . This apThe Spatial autocorrelation is measured using Moran’s I , that isRF (Random Forest) is a machine learning method which is used in spatial analysis with the aim to illustrate the importance of spatial factors affecting genetic differentiation  in the studied populations. This is a supervised machine learning algorithm that is used in classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression . This apRandom Forests illustrates the feature importance, based on calculated Gini Index. A lower value of this index shows the importance of that variable and reveal association between genetic data and landscape features . RF analIn phylogenetic approach we used a maximum likelihood phylogenetic tree obtained from SCoT molecular markers as the base tree which was later on used for reconstruction of ancestral area analysis and connectivity as performed in RASP ver. 4 . We usedWe obtained 48 distinct and stable SCoT loci for population genetic analyses, which were used in sPCA analysis. DCA plot Fig. , showed Avicennia marina geographical populations are provided in , and percentage of genetic polymorphism (%P = 66.67%) compared to the other studied populations.Genetic diversity parameters in the studied populations are provided in Table The Nie’s Gst analysis of SCoT loci with regard to gene flow (Nm) and genetic differentiation (Gst) is provided in Table  , among the studied populations, which shows in spite of potential across populations gene flow, they still have a mean genetic distinctness that differentiate them from each other.The results show that there is a genetic diversity moderate(25–66 percent of polymorphism)in the studied populations. AMOVA revealed a significant difference among the studied populations. About 48% of total genetic difference was among populations, while about 52% was within population genetic variability. These results indicate that The Nei’s genetic distance of the studied populations is provided in Table .Table 5NIn general, a high degree of genetic similarity was observed among the studied populations, which ranged from 0.70 to 0.96.The highest degree of genetic distance 0.34), occurred between populations 1 and 8, followed by populations 1 and 9 (0.279). Grouping of these populations based on genetic data by LDA method is provided in  between SCoT loci obtained and geographical and environmental variables studied. Therefore, only RDA plot is presented in Fig. Both RDA and CCA plots after 999 permutations produced a similar and significant association . Significant Mantel test and IBD indicate that with increase in geographical distance of A. marina populations, the genetic difference increases between them. Similarly, as presented in , followed by humidity . The lowest importance value was obtained for temperature , followed by rain .RDA plot constructed based on both Fst values and spatial variables Fig. , supportp < 0.01), with two components. This indicates a strong correlation between spatial features studied and Fst values. It also shows that the higher value of Fst classes (F and E classes), are structured by longitude, altitude and humidity.In contrast, temperature and rainfall mainly structure the populations with a lower Fst class (D classes). These results indicate the complex effects of spatial features on structuring genetic content of Avicennia marina populations.RDA analysis produced significant results . However, population 1 is genetically differentiated from the other studied populations and shows no genetic admixture with them. Therefore, we may suggest a loss of common shared ancestral alleles in this particular population due to local adaptation/ or selection.we should consider the other areas which are not studied by us as the source of parental alleles for this locality.The gene flow corridor has been marked and shown in Fig.  Circled. The popA. marina populations ranging from 0.25 to 66%. A similar study performed on genetic diversity of A. marina by SSR markers in three populations of Bushehr province of Iran by Valipour Kahrood et al. [We obtained almost a low genetic polymorphism in d et al. , also reA. marina from other parts of the world are also in agreement with results of present study. For example, Maguire et al. [Avicennia marina by using micro-satellite markers. They reported that the levels of heterozygosity detected for each population, overall loci, ranged from 0.0 to 0.8, with an average of 0.407, indicating that some populations had little or no genetic variation, whereas others had a large amount. They noticed that populations at the extremes of the distribution range showed reduced levels of heterozygosity, and significant levels of inbreeding, which is in agreement with our results. We observed a unique genetic structure in population 1 , much different from the other studied populations. This locality is placed far from the other studied localities and showed no signs of genetic admixture. Local genetic structuring by spatial variables was also significant in this region. Therefore, plants in this population may have faced inbreeding to lack of connectivity with the other populations.The reports on genetic diversity of e et al.  studied Rhizophora apiculata and Avicennia marina, in a heavily disturbed area in Tielu Harbor, Sanya City, Hainan Island, China, by using SSR markers and reported a significant positive Fst values and high levels of inbreeding A. marina populations. The reason for the high level of inbreeding in A. marina thought to be the habitat degradation and fragmentation, which was also reported for Avicennia germinans L. [In a similar study, Lu et al. , investiinans L. .We considered Hormozgan province with its populations as the corridor of gene flow among the studied populations. We noticed migration and gene flow from this central region to the marginal population of Bushehr (population 2).In a similar study Rolland et al.  used nicA. marina populations studied. This species is the most widely distributed mangrove species worldwide, which is considered due to long distance dispersal (LDD) of its buoyant propagules, coupled with tolerance to a wide range of environmental conditions [A. marina is rare and the majority of propagules disperse less than 1 km from their release point and rarely over 10 km [The present study showed significant paired Fst and populations’ genetic differentiation. Moreover, a significant global and local spatial structuring prevails on genetic structure of nditions . Howeverer 10 km .A. marina populations are genetically distinct and exhibit a new connectivity level.These results are in accord to the present study findings. Different studies have reported both long-range connection of mangrove trees in relation to ocean currents and direction, especially along same coastlines, and also the occurrence of a stepping-stone model of migration between estuaries , 37. TheA. marina, these genetically distinct populations and subpopulations should be treated as separate management units. Similarly, it was suggested [Binks et al. , suggestuggested , that fouggested , suggestuggested , 39.The present study identified genetic regions by SCoT loci, which are significantly correlated with geographical and landscape variables. These loci were identified by different analytical approaches employed. It seems therefore, using different approaches may improve understanding of associated SNPs or genetic regions with geographical and ecological variables and such a combined data evaluation, give insights into contemporary evolutionary processes, and may explain how environmental factors influence selective and neutral genomic diversity within and among related species or different geographical populations within a single species .Presence of heterogenous environmental conditions bring about changes in the genetic diversity of plant species, which in turn results in local adaptations , 40, 41.A. marina. In conclusion, we provided novel findings on genetic structure, gene flow and spatial structuring of genetic content in A. marina populations. We also identified the genetic regions associated to the geographical, and land scape variables in A. marina.Identification of important genes with specific functions is a critical task in genetic studies of


Experimental introductions of species have provided some of the most tractable examples of rapid phenotypic changes, which may reflect plasticity, the impact of stochastic processes, or the action of natural selection. Yet to date, very few studies have investigated the neutral and potentially adaptive genetic impacts of experimental introductions. We dissect the role of these processes in shaping the population differentiation of wall lizards in three Croatian islands , including the islet of Pod Mrčaru, where experimentally introduced lizards underwent rapid (~30 generations) phenotypic changes associated with a shift from an insectivorous to a plant‐based diet. Using a genomic approach , we confirmed a founder effect during introduction and very low neutral genetic differentiation between the introduced population and its source. However, genetic depletion did not prevent rapid population growth, as the introduced lizards exhibited population genetic signals of expansion and are known to have reached a high density. Our genome‐scan analysis identified just a handful of loci showing large allelic shifts between ecologically divergent populations. This low overall signal of selection suggests that the extreme phenotypic differences observed among populations are determined by a small number of large‐effect loci and/or that phenotypic plasticity plays a major role in phenotypic changes. Nonetheless, functional annotation of the outlier loci revealed some candidate genes relevant to diet‐induced adaptation, in agreement with the hypothesis of directional selection. Our study provides important insights on the evolutionary potential of bottlenecked populations in response to new selective pressures on short ecological timescales. Our study provides unprecedented information on how demographic expansion can help genetically impoverished populations successfully adapt to new environments. Such interplay between demography and selection can have a major role in the evolution of species responding to global changes, such as introduced species and species impacted by climate change. DNA fragments were purified with 1.5 volumes of AMPureXP beads (Agencourt) and subsequently ligated with 180 U of T4 ligase (New England Biolabs) in a 50 μL reaction at 23°C for 60 min and at 20°C for 60 min, followed by a heat‐inactivation step at 65°C (10 min). After purification (1.5 volumes of AMPureXP beads), size selection was performed with BluePippin (Sage Science Inc.), setting the internal fragment size to 250–500 bp. The gel‐eluted fraction was amplified using Phusion High‐Fidelity PCR Master Mix (New England Biolabs) in a 50 μL reaction. PCR conditions followed an initial denaturation at 95°C (3 min), 10 cycles of 95°C (30 s), 60°C (30 s), 72°C (45 s), and a final extension at 72°C (2 min). Amplified products were purified with 1 volume of AMPureXP beads and sequenced on NovaSeq 6000 .DNA extraction was performed using the DNeasy Blood & Tissue kit (Qiagen). Individuals were genotyped using double‐digest restriction‐site‐associated DNA sequencing (ddRADseq). Genomic DNA (300 ng) was digested with 2.4 U of both 2.3P. siculus, ddRAD loci were assembled de novo using Stacks v.2.61 , expected heterozygosity (He), allelic richness (AR), inbreeding coefficient (FIS), population‐specific FST, and the number of private alleles (PA). We determined the 95% confidence intervals (95% CI) of Ho, He, AR, FIS, and FST by performing 1000 bootstraps over loci. Individual Ho was also used to test the difference in genetic diversity among populations using ANOVAs and Tukey posthoc statistics (significance: p = .05) in R. Diversity indices were estimated using the SNP dataset, excluding the outlier loci identified in the analysis of selection (see below).We calculated genetic diversity indices using the packages hierfstat v.0.04‐22 Goudet,  and poppFST (WC FST) and 95% CI (1000 bootstraps) using hierfstat v.0.04‐22  in R.To assess the potential effect of our sample sizes, we performed a rarefaction analysis, which showed that good estimates of s Figure , supports Figure . Althougs Figure . NeverthD at each locus. We compared Tajima's D for three sets of variants: (1) common polymorphism in all three populations; (2) shared polymorphism in Pod Kopište and Pod Mrčaru (monomorphic in Sušac); and (3) private polymorphism in each population. Tajima's D was calculated on the set of loci obtained after removal of loci identified as outliers in the analysis of selection (see below). Under these circumstances, D most likely represents the effect of demographic history. We predicted that common polymorphism would reflect the ancestral history in the region, while shared polymorphism in Pod Kopište and Pod Mrčaru and population‐specific polymorphism in Pod Mrčaru would reflect the demographic history of introduced populations. Positive Tajima's D values can indicate population contraction, while negative D values can indicate recent population expansion after a bottleneck  using Q‐values < 0.01, generating a list of highly differentiated SNPs for each method and analysis. For pcadapt, Q‐values were computed using the R package qvalue v.2.6.0  . Genetic differentiation at outlier loci was quantified using the allele frequency difference (AFD) between populations by assessing the change in allele frequency among populations. We assessed whether the Pod Mrčaru population shows limited differentiation from its source, except at very few loci showing an extreme level of differentiation. To do this, we compared the distributions of per locus 2.8P. siculus is not available, contigs of these catalog loci were aligned to the closely related Podarcis muralis  million high‐quality reads per individual on average Table . We asse3.2K = 1  and Sušac and Pod Kopište  than between Pod Kopište and Pod Mrčaru . The PCA differentiated the three islands on the two first PCs , allele richness (AR), and the number of private alleles (PA) showing a gradual decrease from Sušac to Pod Kopište to Pod Mrčaru (Table FIS) were all significantly different from zero but only slightly positive. Population‐specific FST was not different from zero in Sušac, suggesting that the genetic variability of this population does not differ from the ancestral population, while population‐specific FST was significantly positive in the two islets, indicative of genetic drift, especially in Pod Mrčaru , being highest in Sušac (0.330 ± 0.003 (SE)), while Ho was not significantly different between Pod Kopište (0.293 ± 0.005) and Pod Mrčaru (0.279 ± 0.003) , which was highest in Sušac (1547 ± 70 (SE)), but was not significantly different between Pod Kopište (764 ± 91) and Pod Mrčaru (658 ± 100)  among the three populations, 53% were carried by the Sušac population, whereas only 27% and 19% were carried by the Pod Kopište and Pod Mrčaru populations, respectively (Table  among th) Figure .D was positive in Sušac , Pod Kopište , and Pod Mrčaru , consistent with a population contraction experienced by the lizards in the region. For the shared polymorphisms, the mean Tajimia's D was positive but lower in Pod Kopište  compared to Pod Mrčaru , suggesting an impact of genetic drift in the latter population. Tajima's D for the private polymorphisms in Pod Kopište  and in Pod Mrčaru  were all negative, consistent with a population expansion after a recent bottleneck. As a comparison, Tajima's D for private polymorphisms in Sušac was closer to equilibrium .Population‐level summary statistics differed between common polymorphisms in all three populations, shared polymorphisms between Pod Kopište and Pod Mrčaru (monomorphic in Sušac), and private polymorphisms Figure . For the3.4Q‐values ≤ 0.01) for BayeScan and pcadapt, respectively, of which 22 were detected by both methods and were therefore considered significant outliers  and locus‐specific BayeScan FST  were high for the 29 identified outlier loci, as compared to the average differentiation at 52,470 neutral loci . The distributions of locus‐specific FST revealed a higher proportion of SNPs with low differentiation and a lower proportion of SNPs with intermediate differentiation in the Pod Kopište vs. Pod Mrčaru analysis compared to the among‐populations analysis  at significant outlier loci revealed high differentiation among the three populations, ranging between 0.75 and 1.00 Figure . Only fos Figure .42) than Pod Kopište (0.03 km2), and Pod Mrčaru, which is even smaller (0.02 km2). Furthermore, the two islet populations appeared to show a loss of rare alleles and a reduced gene pool compared to Sušac lizards. In principle, such small populations should have reduced standing genetic variation upon which selective forces can act . Despite generally high conserved synteny in Podarcis lizards  showed particularly striking shifts in allele frequency: 0.43 in Sušac, 0.08 in Pod Mrčaru, and 1  in Pod Kopište. This locus is positioned approximately 800 bp upstream and is therefore likely in linkage with the gene periostin (POSTN). This protein has various roles in a range of tissues, with a major role in bone development , a homolog of the OCD1 protein involved in intracellular organic acid catabolism and metabolism . Nonetheless, the role of gene flow is an alternative hypothesis to be further considered given the genetic admixture and diet similarities between the lizards in Pod Mrčaru and Sušac. The overall low signal of selection could reveal that a small number of loci have large effects on the observed phenotypic differences. Nevertheless, the statistical methods used here perform better at detecting highly differentiated loci compared to small‐effect loci. Subtle allele frequency change at small‐effect loci may be further confounded by the effects of founding events and genetic drift ; investigation (lead); writing – original draft ; writing – review and editing . Josephine R. Paris: Formal analysis ; writing – original draft ; writing – review and editing . Iolanda Silva‐Rocha: Conceptualization (supporting); investigation ; writing – review and editing . Viola Di Canio: Investigation (supporting); writing – review and editing (supporting). Miguel Angel Carretero: Conceptualization ; writing – review and editing . Gentile Francesco Ficetola: Conceptualization ; writing – review and editing . Daniele Salvi: Conceptualization ; writing – review and editing .The authors declare that they have no conflict of interest to disclose.Appendix S1Click here for additional data file.


Mercenaria mercenaria , is an economically valuable aquaculture species along the North American Atlantic and Gulf coasts. Hard clams have a pelagic larval phase that allows for dispersal, but the level of genetic connectivity among geographic areas is not well understood. To better inform the establishment of site‐appropriate aquaculture brood stocks, this study used DArTseq™ genotyping by sequencing to characterize the genetic stock structure of wild clams sampled along the east coast of North America and document genetic diversity within populations. Samples were collected from 15 locations from Prince Edward Island, Canada, to South Carolina, USA. Stringent data filtering resulted in 4960 single nucleotide polymorphisms from 448 individuals. Five genetic breaks separating six genetically distinct populations were identified: Canada, Maine, Massachusetts, Mid‐Atlantic, Chesapeake Bay, and the Carolinas . This is the first study to assess population genetic structure of this economically important hard clam along a large portion of its native range with high‐resolution genomic markers, enabling identification of previously unrecognized population structure. Results of this study not only broaden insight into the factors shaping the current distribution of M. mercenaria but also reveal the genetic population dynamics of a species with a long pelagic larval dispersal period along the North American Atlantic and Gulf coasts.Aquaculture is growing rapidly worldwide, and sustainability is dependent on an understanding of current genetic variation and levels of connectivity among populations. Genetic data are essential to mitigate the genetic and ecological impacts of aquaculture on wild populations and guard against unintended human‐induced loss of intraspecific diversity in aquacultured lines. Impacts of disregarding genetics can include loss of diversity within and between populations and disruption of local adaptation patterns, which can lead to a decrease in fitness. The northern hard clam,  Genetic data are essential to mitigate the genetic and ecological impacts of aquaculture on wild populations and guard against unintended human‐induced loss of intraspecific diversity in aquacultured lines. When wild and captive animals interbreed, the impacts on the wild populations can include loss of genetic diversity within and between populations and disruption of patterns of local adaptation via outbreeding depression leading to a decrease in fitness , hereafter hard clam, is a marine bivalve inhabiting soft substrates of mud, sand, and shell fragments in coastal lagoons and estuaries from the Gulf of St. Lawrence, Canada, through the northern Gulf of Mexico . The fine‐scale population structure delineated by this study allows a better understanding of the mechanisms involved in maintaining population structure for marine bivalves along the North American east coast. Additionally, it provides baseline measures of standing genetic variation in wild populations that may be valuable for monitoring and quantifying changes in genetic diversity and effective population size in the face of warming coastal waters and proliferation of genetically focused aquaculture stocks.22.1n = 452 total) were collected from wild populations from 15 sites along the east coast of North America from Prince Edward Island, Canada, to North Inlet, SC, USA, with more intensive sampling in the Mid‐Atlantic region, including Chesapeake Bay, Virginia. Sampling locations included: Prince Edward Island, Canada (PI), Middle Bay, ME (ME), Harbor Cove, MA (HC), Cape Cod, MA (CC), Orient Harbor, NY (OH), Raritan Bay, NY (RB), Great Bay, NJ (GB), Atlantic City, NJ (AC), Assateague, MD (AT), Wachapreague, VA (WP), James River, VA (JR), Mobjack Bay, VA (MB), Pocomoke Sound, VA (PS), Bogue Sound, NC (BS), and North Inlet, SC (NI)  at −20°C. Genomic DNA was isolated from foot and mantle tissue using the DNeasy Blood & Tissue Kit (Qiagen) with the optional RNase A treatment as per the manufacturer's protocol. DNA quantity and purity were assessed using the NanoDrop Spectrophotometer (NanoDrop Technologies Inc.) and quality was assessed by horizontal agarose gel electrophoresis of samples alongside a 1 Kb Plus DNA ladder (Invitrogen).2.2To identify SNP loci, isolated DNA was sent to Diversity Arrays Technology (DArT Pty Ltd) at concentrations between 50 and 100 ng/μL for high‐throughput GBS using DArTseq™  in multiple sampling locations, likely because of genotyping errors and other technical artifacts, and to compare individual levels of average heterozygosity across loci to look for evidence of cross‐contamination in the R package radiator v1.1.4  was used, and the false discovery rate (FDR) method was implemented, with loci having q‐values less than the FDR of α set to 10% considered outliers. The outliers identified by both OutFLANK and pcadapt were compared and the subset of loci identified by both methods was retained. Therefore, along with the “full dataset,” two additional datasets were created; the “outlier dataset” included only the loci identified using both methods, and the “neutral dataset,” which excluded those loci included in the outlier dataset. All three datasets were analyzed as described below. BLAST searches  and expected heterozygosity  were calculated in the R package adegenet v2.1.2  iterations followed by 500,000 MCMC iterations. Results were compiled in Structure Harvester  to identify de novo the optimal number of clusters based on the Bayesian information criterion (BIC) value, and individuals were assigned to these clusters with the DAPC.Several alternative methods with differing underlying assumptions were used to delineate population structure. A principal component analysis (PCA) was used to visually explore the population structure and the level of genetic differentiation among clams within and between each sampling location using Francis, . A discrFrancis, , which dgenodive  of each genetic population was determined to assess the impacts of genetic drift and inbreeding on the populations using NeEstimator v2.1 , a Mantel test Mantel,  of the c33.1n = 452, Table DArTseq™ of the complete sample set , neutral loci (4865), and outlier loci (95), with the samples from Prince Edward Island removed (no PI dataset) for some analyses. PI samples were removed from the datasets when they obscured lower levels of population structure. Comparison of results of the analyses that included all loci and the analyses that included only neutral loci were congruent, therefore only the results based on all loci and outlier loci datasets, with and without PI, are presented below.There were 184 loci identified as outliers in 3.2He) generally increased with latitude except for the PI sample, which had the lowest He (0.186). For all sampling locations, observed heterozygosity was significantly lower than expected heterozygosity   where all comparisons were significantly different (p < 0.0001). Middle Bay, ME, was also significantly different from all other sampling locations . Samples collected from the two locations in Massachusetts (HC and CC) were not significantly different from each other  but were significantly different from samples from all other sampling locations . Samples from locations between Cape Cod and Cape Hatteras had varying levels of differentiation. Clams from Orient Harbor, NY, were not significantly different from Great Bay, NJ  but were significantly different from all other locations . Clams ranging between Raritan Bay, NY, and Wachapreague, VA  were not significantly different from each other . Samples from the three locations within the Chesapeake Bay, VA, were not significantly different from each other  but all were significantly different from all other samples including clams collected from Wachapreague on the oceanside of Virginia . Samples from the two southernmost locations collected south of Cape Hatteras in the Carolinas (BS and NI) were not significantly different from each other  but were significantly different from samples from all other sampling locations .Pairwise 47 Table . The higThe PCA of all loci resulted in 1.25% variance explained by principal covariate 1 (PC 1), 0.65% by PC 2, and 0.56% by PC 3 Figure . The larstructure and DAPC produced similar results  PI, (2) ME, (3) MA (HC and CC), (4) Mid‐Atlantic (OH to WP), (5) the Chesapeake Bay , and (6) the Carolinas (BS & NI). When the PI sample was excluded from analyses, the most supported numbers of clusters, K = 3 and K = 5, showed a similar pattern  until six groups were tested   (Table GIS ranged from 0.087 (Maine) to 0.151 (Massachusetts) , and the smallest difference was between the Chesapeake Bay and the Mid‐Atlantic .Diversity generally increased with latitude except for the finding of lower diversity in the Canadian population. Excluding the Canadian population, which had a 1) Table . For alls) Table . All genNe) for all populations at all assessed allele frequency categories ranged from 461 in Maine to 27,815 in the Carolinas .Estimated effective population size  indicated a significant signal of IBD in the Mid‐Atlantic region. There was a positive correlation between Nei's genetic and geographic distance matrices; 9 Figure .3.4K = 8 was the most optimal number of clusters based on comparison of BIC values . Pairwise comparisons between some locations that were not significantly different based on analysis with the full dataset were significantly different from each other using the outlier dataset including HC and CC , OH and GB , and WP and RB, GB, and AC .Pairwise 25 Table . Locatio44.1Mercenaria mercenaria, across a large portion of its native range. Samples from 15 locations from PI to SC resulted in delineation of 6 genetically distinct populations based on analyses with 4960 SNP loci. The five genetic breaks identified by the whole dataset were also recovered by the outlier dataset, increasing the resolution of previous studies based on fewer genetic markers and sampling locations , Atlantic cod (Gadus morhua), sea scallop (Placopecten magellanicus), northern shrimp , European green crab (Carcinus maenas), and the Atlantic silverside (Menidia menidia)  Maine and Massachusetts, (2) Cape Cod, (3) between the bayside and oceanside of the Delmarva Peninsula, and (4) Cape Hatteras. The observed genetic discontinuities were most pronounced when comparing samples across Cape Hatteras and Cape Cod—two barriers known to disrupt gene flow in other marine species  are separated by the Delmarva Peninsula. Optimum salinity for the hard clam is between 20 and 38 parts per thousand (ppt), but they can survive in salinities as low as 12.5  between samples from the two Massachusetts locations, as well as between samples from locations in the Mid‐Atlantic that were not observed with the dataset that included all loci  and then identifying fine‐scale structuring of 11 putative genetic populations of lobster from 17 sampling locations . A study of the giant California sea cucumber  also revealed the presence of two main genetic populations along the northeastern Pacific Ocean , with “significant, albeit weak, substructure within regions”   were identified along the Atlantic coast of Canada with significant neutral genetic differentiation  between sampling locations. When assessing diversity across the whole range for the black‐lip pearl oyster (Pinctada margaritifera), high levels of differentiation were found between the Indian Ocean and Pacific Ocean basins (FST > 0.25) and within each basin   were similar to those found in the Eastern oyster, higher than those found in the American lobster or giant Californian sea cucumber, and much lower than those found with the black‐lip pearl oyster . The pattern of increased homozygosity and positive inbreeding values that appear as observed heterozygote deficiencies are common in marine bivalves , and while it could be an artifact of marker type used for the analyses, it may also reflect a true difference from a region not sampled in this study.Genetic diversity estimates among the six hard clam populations along the coast of North America were similar. Expected heterozygosity was consistently higher than observed heterozygosity in all groups and both values were relatively uniform across the populations, with positive inbreeding values in all locations (Ne), inferences about the effect of genetic drift among the populations can be made by comparing the Ne values of the clam populations in this study. The populations with the lowest estimated Ne were Maine and Canada, while the populations in the Carolinas had the highest Ne, suggesting that the effective population size of hard clams decreases with increasing latitude. This pattern of Ne supports the hypothesis of a northward range expansion of the hard clam from common Pleistocene refugia around the Carolinas (Baker et al., Although there is no available hard clam census size data to compare to the estimated effective population sizes (5This study was the first to use GBS to assess the population structure and genetic diversity of hard clams along a large portion of their native range. Six genetically distinct populations of hard clams were identified: Canada, Maine, Massachusetts, Mid‐Atlantic, Chesapeake Bay, and the Carolinas. Results support the findings of previous genetic studies, which not only identified breaks at Cape Cod and Cape Hatteras (Baker et al., These markers also provide a baseline for future research on this economically important species to support fisheries and aquaculture. Highly informative marker subsets can be identified for assay panels that can be used for many applications including stock assignment of individuals from unknown populations. These panels also could be used with aquacultured clams to assess heterogeneity and genetic ancestry, track parentage, monitor genetic diversity, and identify desirable brood‐stock source populations. In addition, how genotypes and genetic populations correlate with environmental data can be examined. Physiological experiments paired with transcriptomic analysis could provide a better understanding of differences in salinity, hypoxia, and temperature tolerances among hard clam genetic populations. Identification of genes associated with traits of interest for selective breeding will benefit the aquaculture industry.The authors have no conflict of interest to declare.Appendix S1.Click here for additional data file.