Population Structure Information Research Articles

Population structure of three invasive congeneric teasel (Dipsacus) species

AbstractThree species of the Old World genus Dipsacus L. are considered invasive in the Americas, yet they may differ in how they spread and reproduce and in their genetic diversity. Differences in invasion method may suggest that different management techniques are needed for each species. We performed genetic analyses on 572 plants in 69 populations from the United States, Argentina, and Eurasia with the goals of analyzing taxonomy, diversity, mode of reproduction, population structure, and founder effect of each of these species’ invasions, as well as looking for evidence of recent or ongoing hybridization. We found Indian teasel [Dipsacus sativus (L.) Honck.] to be lowest in diversity and possibly reliant on self-pollination more than the other species, Fuller’s teasel (Dipsacus fullonum L.) and cutleaf teasel (Dipsacus laciniatus L.). We found no evidence of hybridization within the invasions and no support for D. sativus as a subspecies of D. fullonum. The closest genetic matches of D. fullonum from the United States to the native range were with Hungary and Spain, while the closest match for D. fullonum between Argentina and the native range was with Spain. Dipsacus laciniatus from the United States most closely matched with samples from Russia. Population structure information regarding these three weedy Dipsacus species can help us understand their invasive processes as well as give insight into their management and the development of a biological control program.

Genetic diversity, population structure and kinship relationships highlight the environmental influence on Uganda's indigenous goat populations.

Knowledge about genetic diversity and population structure among goat populations is essential for understanding environmental adaptation and fostering efficient utilization, development, and conservation of goat breeds. Uganda's indigenous goats exist in three phenotypic groups: Mubende, Kigezi, and Small East African. However, a limited understanding of their genetic attributes and population structure hinders the development and sustainable utilization of the goats. Using the Goat Illumina 60k chip International Goat Genome Consortium V2, the whole-genome data for 1,021 indigenous goats sourced from 10 agroecological zones in Uganda were analyzed for genetic diversity and population structure. A total of 49,337 (82.6%) single-nucleotide polymorphism markers were aligned to the ARS-1 goat genome and used to assess the genetic diversity, population structure, and kinship relationships of Uganda's indigenous goats. Moderate genetic diversity was observed. The observed and expected heterozygosities were 0.378 and 0.383, the average genetic distance was 0.390, and the average minor allele frequency was 0.30. The average inbreeding coefficient (Fis) was 0.014, and the average fixation index (Fst) was 0.016. Principal component analysis, admixture analysis, and discriminant analysis of principal components grouped the 1,021 goat genotypes into three genetically distinct populations that did not conform to the known phenotypic populations but varied across environmental conditions. Population 1, comprising Mubende (90%) and Kigezi (8.1%) goats, is located in southwest and central Uganda, a warm and humid environment. Population 2, which is 59% Mubende and 49% Small East African goats, is located along the Nile Delta in northwestern Uganda and around the Albertine region, a hot and humid savannah grassland. Population 3, comprising 78.4% Small East African and 21.1% Mubende goats, is found in northeastern to eastern Uganda, a hot and dry Commiphora woodlands. Genetic diversity and population structure information from this study will be a basis for future development, conservation, and sustainable utilization of Uganda's goat genetic resources.

A Principal Component Informed Approach to Address Polygenic Risk Score Transferability Across European Cohorts.

One important confounder in genome-wide association studies (GWASs) is population genetic structure, which may generate spurious associations if not properly accounted for. This may ultimately result in a biased polygenic risk score (PRS) prediction, especially when applied to another population. To explore this matter, we focused on principal component analysis (PCA) and asked whether a population genetics informed strategy focused on PCs derived from an external reference population helps in mitigating this PRS transferability issue. Throughout the study, we used two complex model traits, height and body mass index, and samples from UK and Estonian Biobanks. We aimed to investigate 1) whether using a reference population (1000G) for computation of the PCs adjusted for in the discovery cohort improves the resulting PRS performance in a target set from another population and 2) whether adjusting the validation model for PCs is required at all. Our results showed that any other set of PCs performed worse than the one computed on samples from the same population as the discovery dataset. Furthermore, we show that PC correction in GWAS cannot prevent residual population structure information in the PRS, also for non-structured traits. Therefore, we confirm the utility of PC correction in the validation model when the investigated trait shows an actual correlation with population genetic structure, to account for the residual confounding effect when evaluating the predictive value of PRS.

Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data

<p indent=0mm>Crop diseases are biological disasters that affect grain production and quality. The infestation of diseases consumes the nutrients and water, disrupts its normal life process, and causes changes in the internal physiological and biochemical state and external appearance of the crop. Canopy reflectance spectrum can detect crop population structure information well, and chlorophyll fluorescence data can sensitively reflect changes in crop photosynthetic physiology, both methods are capable of detecting crop diseases via remote sensing technology. This article outlined the current research status of crop diseases detection based on reflectance spectrum through remote sensing technology from the aspects of monitoring methods and monitoring scales, summarized the research progress of using active fluorescence, passive fluorescence and coordinated solar-induced chlorophyll fluorescence and reflectance spectroscopy to monitor crop diseases, analyzed the advantages and disadvantages of reflectance spectrum and chlorophyll fluorescence data in crop disease early warning detection, and discussed the possible problems in the remote sensing detection of crop diseases. On the basis, we made a prospect for the development of remote sensing monitoring crop diseases. This paper provides an important reference for the subsequent applications of crop diseases detection based on reflectance spectrum and chlorophyll fluorescence data.

Elucidating SNP-based genetic diversity and population structure of advanced breeding lines of bread wheat (Triticum aestivum L.).

Genetic diversity and population structure information are crucial for enhancing traits of interest and the development of superlative varieties for commercialization. The present study elucidated the population structure and genetic diversity of 141 advanced wheat breeding lines using single nucleotide polymorphism markers. A total of 14,563 high-quality identified genotyping-by-sequencing (GBS) markers were distributed covering 13.9 GB wheat genome, with a minimum of 1,026 SNPs on the homoeologous group four and a maximum of 2,838 SNPs on group seven. The average minor allele frequency was found 0.233, although the average polymorphism information content (PIC) and heterozygosity were 0.201 and 0.015, respectively. Principal component analyses (PCA) and population structure identified two major groups (sub-populations) based on SNPs information. The results indicated a substantial gene flow/exchange with many migrants (Nm = 86.428) and a considerable genetic diversity (number of different alleles, Na = 1.977; the number of effective alleles, Ne = 1.519; and Shannon’s information index, I = 0.477) within the population, illustrating a good source for wheat improvement. The average PIC of 0.201 demonstrates moderate genetic diversity of the present evaluated advanced breeding panel. Analysis of molecular variance (AMOVA) detected 1% and 99% variance between and within subgroups. It is indicative of excessive gene traffic (less genetic differentiation) among the populations. These conclusions deliver important information with the potential to contribute new beneficial alleles using genome-wide association studies (GWAS) and marker-assisted selection to enhance genetic gain in South Asian wheat breeding programs.

Integrating Genetic Data and Demographic Modeling to Facilitate Conservation of Small, Isolated Mountain Goat Populations

ABSTRACTAcquisition of field data and analytical methods needed for conservation and management of wildlife populations represent significant challenges, particularly for species that inhabit landscapes that are difficult to access or species that persist in small, isolated populations. In such instances, integrating diverse and complementary data streams, such as genetic and non‐genetic data, can advance our understanding of population dynamics and associated management implications. We examined how genetic and morphologic data can be used to articulate population structure of a low‐density, peninsular population of mountain goats (Oreamnos americanus) on the Cleveland Peninsula, Alaska, USA, and surrounding areas, 2005–2018. We then use a population demographic modeling approach to examine how the use of population structure information influences sustainable harvest quotas, as compared to a panmictic, null model. Specifically, we conducted extensive field sampling of genetic (n = 446) and morphologic (i.e., horn length, n = 371) data to characterize population structure. We conducted demographic analyses and examined harvest modeling scenarios using a sex‐ and age‐specific matrix population modeling approach. Genetic and morphologic data analyses suggested peninsular subpopulations were demographically isolated, relative to surrounding mainland populations. Specifically, genetic structuring was evident and followed an isolation‐by‐distance, stepping‐stone pattern indicating limited interchange, low effective population sizes, and reduced genetic diversity along a peninsular extremity to mainland gradient. Harvest modeling indicated that overharvest would likely occur if the panmictic, null model was used to guide harvest because the smallest genetically defined population at the peninsular extremity was too small to permit any level of sustainable harvest. Our analyses illustrate the importance of using genetic and morphologic data, in combination with demographic modeling, to quantitatively delineate population boundaries and dynamics for ensuring viability of small, isolated populations. © 2020 The Wildlife Society.

Application of next generation sequencing (NGS) for descriptive analysis of 30 genomes of Leishmania infantum isolates in Middle-North Brazil

Visceral leishmaniasis (VL) is a life-threatening disease caused by the protozoa Leishmania donovani and L. infantum. Likely, L. infantum was introduced in the New World by the Iberic colonizers. Due to recent introduction, the genetic diversity is low. Access to genomic information through the sequencing of Leishmania isolates allows the characterization of populations through the identification and analysis of variations. Population structure information may reveal important data on disease dynamics. Aiming to describe the genetic diversity of L. infantum from the Middle-North, Brazil, next generation sequencing of 30 Leishmania isolates obtained in the city of Teresina, from where the disease dispersed, was performed. The variations were categorized accordingly to the genome region and impact and provided the basis for chromosomal ploidy and population structure analysis. The results showed low diversity between the isolates and the Iberic reference genome JPCM5. Most variations were seen in non-coding regions, with modifying impact. The ploidy number analysis showed aneuploid profile. The population structure analysis revealed the presence of two L. infantum populations identified in Teresina. Further population genetics studies with a larger number of isolates should be performed in order to identify the genetic background associated with virulence and parasite ecology.

Integrated Population Modeling for White‐Tailed Deer in Saskatchewan, Canada

ABSTRACTMonitoring annual change and long‐term trends in population structure and abundance of white‐tailed deer (Odocoileus virginianus) is an important but challenging component of their management. Many monitoring programs consist of count‐based indices of relative abundance along with a variety of population structure information. Analyzed separately these data can be difficult to interpret because of observation error in the data collection process, missing data, and the lack of an explicit biological model to connect the data streams while accounting for their relative imprecision. We used a Bayesian age‐structured integrated population model to integrate data from a fall spotlight survey that produced a count‐based index of relative abundance and a volunteer staff and citizen classification survey that generated a fall recruitment index. Both surveys took place from 2003–2018 in the parkland ecoregion of southeast Saskatchewan, Canada. Our approach modeled demographic processes for age‐specific (0.5‐, 1.5‐, ≥2.5‐year‐old classes) populations and was fit to count and recruitment data via models that allowed for error in the respective observation processes. The Bayesian framework accommodated missing data and allowed aggregation of transects to act as samples from the larger management unit population. The approach provides managers with continuous time series of estimated relative abundance, recruitment rates, and apparent survival rates with full propagation of uncertainty and sharing of information among transects. We used this model to demonstrate winter severity effects on recruitment rates via an interaction between winter snow depth and minimum temperatures. In years with colder than average temperatures and above average snow depth, recruitment was depressed, whereas the negative effect of snow depth reversed in years with above average temperatures. This and other covariate information can be incorporated into the model to test relationships and provide predictions of future population change prior to setting of hunting seasons. Likewise, post hoc analysis of model output allows other hypothesis tests, such as determining the statistical support for whether population status has crossed a management trigger threshold. © 2020 The Wildlife Society.

Updating hosts and distribution range of the pea crab Pinnotheres bicristatus (Brachyura: Pinnotheridae)

Small crabs belonging to the family Pinnotheridae are characterized by living inside mainly bivalves. In European waters the recently-described pea crab Pinnotheres bicristatus increased to five the known number of pinnotherid species. The know distribution of the recently-described pea crab Pinnotheres bicristatus encompassed the area between the Gulf of Cádiz (NE Atlantic) and the western Alboran Sea (SW Mediterranean). The species was so far reported to occur in the pallial cavity of the common saddle oyster, Anomia ephippium, between 11 and 52 m depth. During two years, a total of 43 specimens of Pinnotheres bicristatus were collected in two different projects/surveys along the Mediterranean coasts of the Iberian Peninsula, thus enlarging its known occurrence to the eastern Alboran Sea and the NW Mediterranean, at depths between 37 and 71 m. We also report a new bivalve host for the species, the flat oyster Ostrea edulis, and population structure information of these new records. As a conclusion, this study considerably extends both in latitude and longitude, as well as in depth, the known geographical distribution of P. bicristatus.

Development of a Large Gene-Associated SSR Marker Set and in-Depth Genetic Characterization in Scarlet Sage.

Salvia splendens, scarlet or tropical sage, is a tender perennial herbaceous flowering plant popularly grown in public and private gardens all over the world. In this study, we developed a set of simple sequence repeats (SSRs) from genome-wide sequences to assess the genetic diversity and population structure among 112 cultivars. We obtained 364,379 SSRs by mining scarlet sage’s recently published whole genome sequence; 14,545 gene-associated SSR loci were identified in 2 kb gene flanking regions. Among the 768 gene-associated SSR primer sets we screened, 576 loci successfully amplified in DNA pools of 3–4 different cultivars, of which 271 remained polymorphic when tested across eight individual plants. We searched for the related gene functions attributable to these gene-associated SSRs using diverse databases, resulting in 259 Non-redundant matching sequences, 205 individual Gene Ontology (GO) terms, 236 assigned to eukaryotic orthologous groups, and 67 KEGG-annotated (Kyoto Encyclopedia of Genes and Genomes) sequences. We finally selected 41 polymorphic SSR loci to infer genetic diversity and population structure among 112 S. splendens accessions. Based on the developed gene-associated SSRs, clustering analyses consistently revealed two distinct genetic groups within the core collection of S. splendens cultivars. This work developed and characterized an exhaustive set of genome-wide gene-associated SSR markers for scarlet sage. These SSRs can provide species identification, genetic diversity and population structure information for S. splendens, and will therefore be important tools for the management and protection of S. splendens germplasm.

Population Structure Analyses Provide Insight into the Source Populations Underlying Rural Isolated Communities in Illinois

We have previously hypothesized that relatively small and isolated rural communities may experience founder efffects, defijined as the genetic ramifijications of small population sizes at the time of a community’s establishment. To explore this, we used an Illumina Infijinium Omni2.5Exome-8 chip to collect data from 157 individuals from four Illinois communities, three rural and one urban. Genetic diversity estimates of 999,259 autosomal markers suggested that the reduction in heterozygosity due to shared ancestry was approximately 0, indicating a randomly mating population. An eigenanalysis, which is similar to a principal component analysis but run on a genetic coancestry matrix, conducted in the SNPRelate R package revealed that most of these individuals formed one cluster, with a few putative outliers obscuring population variation. An additional eigenanalysis on the same markers in a combined data set including the 2,504 individuals in the 1000 Genomes database found that most of the 157 Illinois individuals clustered into one group in close proximity to individuals of European descent. A fijinal eigenanalysis of the Illinois individuals with the 503 individuals of European descent (within the 1000 Genomes Project) revealed two clusters of individuals and likely two source populations; one British and one consisting of multiple European subpopulations. We therefore demonstrate the feasibility of examining genetic relatedness across Illinois populations and assessing the number of source populations using publicly available databases. When assessed, population structure information can contribute to the understanding of genetic history in rural populations

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.

Application of Next-Generation Sequencing for Characterization of Surveillance and Clinical Trial Isolates: Analysis of the Distribution of β-lactamase Resistance Genes and Lineage Background in the United States.

BackgroundSequencing technologies and techniques have seen remarkable transformation and innovation that have significantly affected sequencing capability. Data analyses have replaced sequencing as the main challenge. This paper provides an overview on applying next-generation sequencing (NGS) and analysis and discusses the benefits and challenges. In addition, this document shows results from using NGS and bioinformatics tools to screen for β-lactamase genes and assess the epidemiological structure of Escherichia coli– and Klebsiella pneumoniae–causing bloodstream (BSIs) and urinary tract (UTIs) infections in patients hospitalized in the United States during the SENTRY Antimicrobial Surveillance Program for 2016.MethodsA total of 3525 isolates (2751 E. coli and 774 K. pneumoniae) causing BSIs (n = 892) and UTIs (n = 2633) in hospitalized patients in the United States were included. Isolates were tested for susceptibility by broth microdilution, and those that met a minimum inhibitory concentration (MIC)–based screening criteria had their genomes sequenced and analyzed.ResultsA total of 11.6% and 16.1% of E. coli–causing UTIs and BSIs, respectively, met the MIC-based criteria, whereas 11.0% and 13.7% of K. pneumoniae isolates causing UTIs and BSIs, respectively, met the criteria. Among E. coli, blaCTX-M variants (87.6% overall) prevailed (60.5% of CTX-M group 1 and 26.9% of group 9). A total of 60.3% of K. pneumoniae isolates carried blaCTX-M variants (52.7% and 7.6% of groups 1 and 9, respectively). Two E. coli (0.6%) and 13 K. pneumoniae (12.9%) isolates harbored blaKPC. Among KPC-producing K. pneumoniae (2 from BSIs and 11 from UTIs), 84.6% (11/13) were ST258 (CC258). Seventeen and 38 unique clonal complexes (CCs) were noted in E. coli that caused BSIs and UTIs, respectively, and CC131 (or ST131) was the most common CC among BSI (53.6%) and UTI (58.2%) isolates. Twenty-three and 26 CCs were noted among K. pneumoniae–causing BSIs and UTIs, respectively. CC258 (28.3%) prevailed in UTI pathogens, whereas CC307 (15.0%) was the most common CC among BSI isolates.ConclusionsThis study provides a benchmark for the distribution of β-lactamase genes and the population structure information for the most common Enterobacteriaceae species responsible for BSIs and UTIs in US medical centers during the 2016 SENTRY Program.

Population Structure Analyses Provide Insight into the Source Populations Underlying Rural Isolated Communities in Illinois.

We have previously hypothesized that relatively small and isolated rural communities may experience founder effects, defined as the genetic ramifications of small population sizes at the time of a community's establishment. To explore this, we used an Illumina Infinium Omni2.5Exome-8 chip to collect data from 157 individuals from four Illinois communities, three rural and one urban. Genetic diversity estimates of 999,259 autosomal markers suggested that the reduction in heterozygosity due to shared ancestry was approximately 0, indicating a randomly mating population. An eigenanalysis, which is similar to a principal component analysis but run on a genetic coancestry matrix, conducted in the SNPRelate R package revealed that most of these individuals formed one cluster, with a few putative outliers obscuring population variation. An additional eigenanalysis on the same markers in a combined data set including the 2,504 individuals in the 1000 Genomes database found that most of the 157 Illinois individuals clustered into one group in close proximity to individuals of European descent. A final eigenanalysis of the Illinois individuals with the 503 individuals of European descent (within the 1000 Genomes Project) revealed two clusters of individuals and likely two source populations; one British and one consisting of multiple European subpopulations. We therefore demonstrate the feasibility of examining genetic relatedness across Illinois populations and assessing the number of source populations using publicly available databases. When assessed, population structure information can contribute to the understanding of genetic history in rural populations.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81–0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

Population genetics of a Chinese endemic, Gymnocypris potanini Herzenstein, threatened by population isolation: conflicting patterns between microsatellites and mitochondrial DNA

Freshwater fish population isolation is concerning considering few major rivers are undammed. Key prerequisites for scientific genetic management of threatened species are genetic diversity and population structure information. Population genetic analyses based on microsatellites and mtDNA control region were conducted in Gymnocypris potanini, an endangered Chinese endemic schizothoracine fish in the Tibetan Plateau. The results showed that three populations from the dammed Zagunao River possessed higher levels of microsatellite diversity than three other populations in different, but nearby, dammed river systems. However, mitochondrial data showed the lowest variability in Zagunao populations and neutrality tests were significant. The relatively low mtDNA variability and the relatively high nuclear variability might be explained by selective sweep in Zagunao populations. Isolation might have already influenced the genetic divergence of Zagunao populations considering the highly significant F ST values (P < 0.001). Microsatellite data of Zagunao populations could be used as a baseline for investigating changes in genetic status of the separated populations given the ongoing operation of the hydroelectric projects. It appears that geologic isolation influenced the distribution and evolution of G. potanini in the Tibetan Plateau, which may benefit predictions of genetic changes to anthropogenic-caused separation in the future.

Genetic relatedness of previously Plant-Variety-Protected commercial maize inbreds.

The emergence of high-throughput, high-density genotyping methods combined with increasingly powerful computing systems has created opportunities to further discover and exploit the genes controlling agronomic performance in elite maize breeding populations. Understanding the genetic basis of population structure in an elite set of materials is an essential step in this genetic discovery process. This paper presents a genotype-based population analysis of all maize inbreds whose Plant Variety Protection certificates had expired as of the end of 2013 (283 inbreds) as well as 66 public founder inbreds. The results provide accurate population structure information and allow for important inferences in context of the historical development of North American elite commercial maize germplasm. Genotypic data was obtained via genotyping-by-sequencing on 349 inbreds. After filtering for missing data, 77,314 high-quality markers remained. The remaining missing data (average per individual was 6.22 percent) was fully imputed at an accuracy of 83 percent. Calculation of linkage disequilibrium revealed that the average r2 of 0.20 occurs at approximately 1.1 Kb. Results of population genetics analyses agree with previously published studies that divide North American maize germplasm into three heterotic groups: Stiff Stalk, Non-Stiff Stalk, and Iodent. Principal component analysis shows that population differentiation is indeed very complex and present at many levels, yet confirms that division into three main sub-groups is optimal for population description. Clustering based on Nei’s genetic distance provides an additional empirical representation of the three main heterotic groups. Overall fixation index (FST), indicating the degree of genetic divergence between the three main heterotic groups, was 0.1361. Understanding the genetic relationships and population differentiation of elite germplasm may help breeders to maintain and potentially increase the rate of genetic gain, resulting in higher overall agronomic performance.

Microsatellite variation in Donax trunculus from the Iberian Peninsula, with particular attention to Galician estuaries (NW Spain)

Genetic variation and population structure information is essential for conservation and stock management policies. The wedge clam Donax trunculus is an important fishing resource in the Iberian Peninsula and in some areas, such as the northwestern Spain, wild stocks have decreased greatly. Despite this, information is mainly from the southwestern Atlantic to the northwestern Mediterranean of the Iberian Peninsula. In this study, fifteen microsatellite loci were examined at 17 localities along the Iberian Peninsula to characterize its genetic diversity and population structure. Particular attention was paid to the northwestern Atlantic area, and to test if the pattern previously described for this species is confirmed when localities distributed across the Atlantic coast are included and different microsatellite markers are used. All localities displayed similar allelic richness values and heterozygosity levels but when genetic diversity levels were compared among groups of localities, tests were significant and samples from the northwestern area (Galicia) showed the lowest values. The analysis of population structure indicated that localities from the Atlantic coast are genetically homogeneous although some samples showed significant pairwise Fst values. These values were low and Bayesian analysis of genetic differentiation did not show a consistent structure along the Atlantic coast of the Iberian Peninsula. However, Atlantic samples were genetically different from those located in Mediterranean coast, which may be explained by the existence of the Almeria-Oran front. Moreover, Fuengirola, locality situated in the Alboran Sea between the Strait of Gibraltar and Mediterranean Sea, showed significant differences from all remaining localities included in the study. Overall, the data showed the existence of genetic homogeneity along the Atlantic coast of the Iberian Peninsula and support the three management units (Atlantic Ocean, the Alboran Sea and the northwestern Mediterranean) previously described in this species. Reduced diversity in the northwestern Spain samples could be related to the exploitation of this resource.

Comparative Cost and Effort of Fish Distribution Detection via Environmental DNA Analysis and Electrofishing

Fisheries conservation requires accurate knowledge of species identities and distributions. Fisheries are typically assessed via capture‐based sampling, but managers frequently are unable to conduct extensive surveys due to budgetary constraints. Sampling of environmental DNA (eDNA) released by fish is a potentially cost‐effective approach that could improve species detection per unit effort. However, eDNA methods have not been widely adopted, in part because the cost and effort of eDNA versus traditional sampling are often unclear to managers. We compared the monetary costs and sampling effort required to assess the distribution of Brook Trout Salvelinus fontinalis in a Wisconsin watershed using both electrofishing and eDNA. We detected Brook Trout via both electrofishing and eDNA. The eDNA analysis required lower sampling effort and 67% less cost than triple‐pass electrofishing. However, eDNA was more expensive than presence–absence electrofishing, and no population structure information was obtained. Our study illustrates the potential of eDNA to complement traditional sampling methods during fish surveys.

SNP Marker Based Genetic Diversity and Population Structure Study of Rice Germplasm of Arunachal Pradesh

Rice genetic resource is the primary source for rice breeding. It also makes a concrete contribution to global wealth creation and food security. India is one of the centers of origin for rice and the nutritional security of the North-Eastern (NE) states of India predominantly depends on rice. The NE region especially Arunachal Pradesh is considered to be one of the hot pockets of rice genetic resources in the world and also has diverse rice growing conditions as compared to other parts of the country. Genetic diversity of 662 rice accessions originating from the state of Arunachal Pradesh was assessed using 36 single nucleotide polymorphism (SNP) markers distributed across all 12 chromosome of rice genome. These SNP markers generated a total of 36 allelic combinations. Polymorphic Information content (PIC) ranged from 0.006 to 0.375 and major allele frequency ranged from 0.51 to 0.99. Similarly, heterozygosity and gene diversity ranged from 0.003 to 0.162 and 0.006 to 0.49, respectively. A picture of genetic relatedness was inferred using Neighbor joining tree, which grouped all the genotypes in to three major clusters. AMOVA analysis showed that, among population 31% whereas, among individual 51% diversity. Population structure from K=1 to K=20 were tested and Δk was found maximum at three population (K=3). Principal Coordinate Analysis (PCoA) showed that rice germplasm were intermixed as per Neighbour joining tree but showed three major groups based three population structure information. Hence, PCoA supports the population obtained from the model based approach. This study shows that large diversity exists in the rice germplasm of Arunachal Pradesh and can be utilized for trait-specific breeding.