Dioscorea alata) accessions using 24 phenotypic traits and 6,918 single nucleotide polymorphism (SNP) markers. Multivariate analysis for phenotypic variability indicated that all phenotypic traits assessed were useful in discriminating the yam clones and cultivars. Cluster analysis based on phenotypic data distinguished two significant groups, while a corresponding analysis with SNP markers indicated three genetic groups. However, joint analysis for the phenotypic and genotypic data provided three clusters that could be useful for the identification of heterotic groups in the D. alata breeding program. Our analysis for phenotypic and molecular level diversity provided valuable information about overall diversity and variation in economically important traits useful for establishing crossing panels with contrasting traits of interest. The selection and hybridization of parental lines from the different heterotic groups identified would facilitate maximizing diversity and exploiting population heterosis in the D. alata breeding program.A better understanding of the structure and extent of genetic variability in a breeding population of a crop is essential for translating genetic diversity to genetic gain. We assessed the nature and pattern of genetic variability and differentiation in a panel of 100 winged-yam ( Dioscorea spp.) is an important tuber crop supporting the livelihood of millions of people in the tropics and subtropics2. Globally, the average annual yam production is about 60 million tons with a gross value of ~14 billion US dollars. West Africa is the major yam growing area accounting for 94% of global yam production3. There are over 600 Dioscorea species of which ten are cultivated and are of economic importance4. The winged yam , which is also called water yam or greater yam, is widely distributed in Africa and other tropical areas of the world5. Dioscorea alata is known for its vigorous plants with high tuber yield and plays a vital role in food security and income generation6. Different ecotypes are currently grown and used7.Yam  as well as a high-density genetic map of winged yam2. The availability of genomic resources in D. alata enables genome-wide study on genetic diversity with large datasets of high-density SNP markers from the next-generation sequencing platforms. Diversity Array Technology (DArT) characterized by high call rates and scoring reproducibility is among the sequencing techniques that are successfully applied in exploring genetic diversity and population genetic parameters of diploid root crops including cassava17 or polyploids such as potato18 but has not yet been applied to yam.Recent advances in sequencing techniques and their cost reduction has provided new insight into population structure and genetic variability in crops. Reference genomes are now available for both 22. The usefulness of this approach for genetic variability analysis in yam was demonstrated by Sartie et al.4. Employing a combination of data from 32 microsatellite markers and 24 phenotypic traits, D. alata and D. dumetorum which hitherto were categorized as sub-groups were clearly separated into distinct genetic groups4.Previous diversity studies in yam have generally been conducted using either morphological or low throughput molecular markers. However, joint analysis of morphological and molecular information has the potential to provide in depth insight into population structure and genetic variability and it has been successfully applied in other cropsD. alata clones and cultivars using phenotypic traits and SNP markers. These results will serve as a basis for a protocol to evaluate the genetic diversity and make an informed choice of parental lines to design new varieties in D. alata.The objective of this study was to assess the genetic structure and diversity in a panel of We performed principal component analysis to identify the most discriminative variables among the accessions. The first seven principal components cumulatively explained 76% of the total phenotypic variation Table . The firCorrelation analysis revealed high and positive correlation between tuber yield components such as total tuber number , the weight of the tuber  and the length  while negative correlations were observed between the tuber yield components and the two disease variables  with the highest FST (0.13) obtained between accessions in cluster one and cluster three, while the lowest value was found between accessions of DAPC clusters one and two (FST = 0.06). Assessment of the genetic diversity among accessions within each cluster revealed very low FST variability of −0.012, −0.011 and −0.014 for clusters 1, 2 and 3, respectively  and the discriminant analysis of principal components (DAPC), a maximum of k = 3 was obtained  Fig. . Out of Hierarchical cluster generated from the phenotypic information was compared to that originating from the genotypic data, and very few clones (2%) were identified to be clustered into the same position across the two hierarchical clusters Fig. . We notiGenetic diversity assessment using the combined phenotypic and molecular information revealed the presence of three well defined genetic groups in the current set of materials  was observed between the two matrices  value among accessions between clusters and low FST value among accessions within clusters. This indicated high connectivity among accessions within clusters and low connectivity among accessions between clusters as also reported in Cowpea27 and Cassava28 using DAPC analysis with SNP markers from genotyping by sequencing. The high cophenetic coefficient correlation (>0.70) observed here for the hierarchical cluster constructed based on the molecular data indicates the reliability of this approach to summarize the information of dissimilarity matrices21.Various types of molecular markers ranging from dominant to codominant are available to estimate and assess the level of genetic diversity. This study also used 6918 SNP markers from Diversity Array Technology (DArT) platform to elucidate the genetic diversity and population structure of the D. alata. Many of the accessions in our study are breeding lines emanating from the hybridization of genotypes of diverse genetic backgrounds. Such hybridization has enhanced the genetic diversity as rightly asserted by Arnau et al.7. The few accessions identified with no admixture can be utilized to develop bi-parental crosses for QTL identification while focusing on the traits of interest in the breeding program.The high proportion of admixture observed in our study is an indication of gene flow in the population of 21, Cupuassu22, Jatropha29 and yellow passion fruit30, and in agreement with the inconsistencies observed between the clusters generated from the phenotypic and the molecular diversity analyses. The negligible association observed between the phenotypic and genotypic data could be because the variation in phenotypic traits may be as a result of one or few mutations and therefore often not concordant with the overall genetic distance between two accessions. Consequently, to assess the amount  of total anonymous variability, DNA markers are often more efficient since they rely on numerous mutations along the entire genome. This low correlation between phenotypic and molecular diversity matrices could also be because the variation often detected by molecular markers is commonly of the non-adaptive type and hence not liable to natural and or artificial selection, unlike phenotypic characters which are usually subject to natural and artificial selection31. Additionally, molecular markers have been assumed to be selectively neutral, whereas the portions of the genome associated with phenotypic expression are subject to selection under environmental influence33. Several authors, therefore, suggest that the best way to identify divergence among accessions is the combined use of molecular and phenotypic data as these tools are complementary32. The low correlation between morphological and molecular data in the D. alata accessions generally suggests that the two data types are appropriate for a combined use, which can deepen understanding and discriminate the genotypes better due to the non-overlapping information.The low correlation observed between the genotypic and phenotypic distance matrices observed in this study was also reported in sweet potatoeset al.4, Andrade et al.21, Alves et al.22, Reis et al.30 and Alves et al.32. By analyzing morphological and molecular data together, Alves et al.32 found that the average dissimilarity among physic nut accessions improved by 156% and 64% with respect to the original mean phenotypic and molecular dissimilarities, respectively. Similarly, Martins et al.34 and Bosettiet al.35 observed a marked increase in the sampling intensity of the genetic diversity by combining phenotypic and molecular data. This could be as a result of the combination of non-overlapping information into one data set32.The genetic diversity and differentiation in the population were also assessed using a joint dissimilarity matrix of the phenotypic and molecular marker data resulting in three distinct clusters. The high and moderate correlation observed between the combined dissimilarity matrix and the genotypic and phenotypic dissimilarity matrices, respectively, is an indication that genetic diversity analysis based on the joint matrix is a very valuable tool to cumulatively and reliably allocate genotypic and phenotypic information. The usefulness of the combined dissimilarity matrix of morphological and molecular data in deciphering the genetic variability and differentiation among accessions in this study corroborate the findings of Sartie D. alata clones and cultivars using genotypic, phenotypic, and combined genotypic/phenotypic distances revealed ample genetic variation in the population. This diversity can be explored for D. alata improvement and genetic resource conservation. As a result of the inconsistencies and reshuffling of cluster membership observed across the phenotypic and genotypic characterizations, the combined matrix could be employed for genetic diversity analysis if one desires to use both the phenotypic and genotypic information as this approach maintains high correlation with both matrices and gives a cumulative overview of the total diversity in the population. Future analysis to estimate the combining ability of the accessions in the three clusters identified by the combined distance will enhance the partitioning of the accessions into distinct heterotic groups. The selection and hybridization of parental lines from the three genetic groups identified by the combined morphological and molecular marker analysis is expected to maximize genetic diversity and heterosis in yam breeding program.In conclusion, our analysis of the diversity in the A panel of 100 winged yam accessions obtained from the IITA yam breeding program was used for this study. These accessions comprised of 97 breeding lines emanating from full-sib and half-sib breeding and three landrace cultivars  and Ikenne  in the south-west, and Ubiaja  in the south-central part of the country. The experiment was laid out in a simple lattice design with three plants per genotype. Twenty-four phenotypic variables were recorded according to the yam crop ontology37. DNA quality was estimated for each sample based on electrophoresis in an agarose gel.One gram of young, healthy and fully expanded leaves from fully grown plants in the field were collected per genotype. The leaf samples were immediately put in Ziploc bags and kept on ice before transferring to −80 °C for freeze drying. Deoxyribonucleic acid (DNA) was extracted from the leaf samples using the CTAB procedure with slight modificationet al.38 with slight modification. For the sequencing-based DArT genotyping, complexity reduction methods optimised for yam at DArT was used: PstI_ad/TaqI/HpaII_ad with TaqI restriction enzyme used to eliminate a subset of PstI –HpaII. PstI-site specific adapter was tagged with 102 different barcodes enabling encoding a plate of DNA samples to run within a single lane on an Illumina GAIIx. After the sequencing, FASTQ files generated by DArT were aligned against the yam genome reference available on (https://drive.google.com/drive/folders/0B54dLJoyUZ5SUDY5TzV2eGFWaWs) and hapmap file was generated by DArT.DArT genotyping was performed using the methodology described by Sansaloni 39 according to the model:Analysis of variance was performed to determine differences among the accessions for the various traits across the three locations using the SAS version 9.440. The optimal number of factors to be retained was determined using the dimdesc function in R, and the contribution of each trait was determined using the principle of Peres-Nero et al.41. A dissimilarity matrix was generated using the Gower method implemented in cluster and graphics R packages42. Count variables  were transformed using log ratio function in R. The final hierarchical cluster was then performed using the ward.D2 method in cluster R package42. Correlation among the different phenotypic variables was performed using the R software and result was displayed as heatmap.The LSmeans from the genotype by environment analysis was used for principal component analysis in the FactorMiner and missMDA R packages43 for MAF (0.05), no missing data, depth (>5), Genotype Quality (GQ = 20), maximum and minimum allele = 2 and no indels, a total of 6,918 SNP markers were retained for further analysis.To evaluate the sequencing error and reliability of the Diversity Array Technology for yam, two intentionally duplicated DNA samples were included. The Duplicated DNA was used to define the threshold for duplicate identification in the sample. The HapMap file received from the DArT platform was converted into a variant call format (VCF). A total of 22,140 SNP markers were identified from the raw data and after filtering with VCFtools44 and the plink command–freq and –hardy45. A population structure analysis was performed based on Admixture46 through the Bayesian Information Criterion (BIC). The optimal number of clusters was inferred using k-means analysis after varying the possible number of clusters from 2 to 40. A discriminant analysis of principal components (DAPC) was carried out on the identified clusters using the first 40 principal components in the adegenet R package47. Membership probabilities of the individuals for the different groups were estimated using a cut-off value of 40% suggested through the DAPC. Pairwise population differentiation statistics (FST) was calculated using VCFtools43 to estimate the genetic distance and the relationships among the different DAPC groups formed. A pairwise genetic distance matrix was calculated using the Jaccard method implemented in the phylentropy R package48. A Ward’s minimum variance hierarchical cluster dendrogram was then generated from the genetic distance matrix using the analyses of phylogenetics and evolution (ape) R package50.Summary statistics including major and minor allele frequencies, heterozygosity both for SNP markers and genotypes were assessed using TASSEL software version 5.2.5151 and the similarity of the two dendrograms assessed using the tanglegram function implemented in the dendextend R package52.Genetic groups were defined using a combination of the phenotypic and genotypic dissimilarity matrices. This joint matrix was generated by the summation of the genotypic and phenotypic dissimilarity matrices. A Mantel test was performed for analysis of correspondence between the dissimilarity matrices obtained from the phenotypic, genotypic and joint distance matrices using the Monte-Carlo method with 9999 permutations for estimation of significance. The hierarchical clusters generated from the phenotypic and genotypic data sets were compared using the viridis R packageSupplementary InformationPairwise genetic distance between the 100 D. alata accessions studiedJaccard dissimilarity matrix of the three genetic groups formed through the hierarchical clustering methodList of accessions, pedigree information and categorisation into different clusters by DAPC and Hierarchical clustering


Euschistus heros, in Brazil. To better access the risk of increased pests like E. heros and to determine probabilities for insecticide resistance spreading, it is necessary first to access the levels of the genetic diversity, how the genetic diversity is distributed, and how natural selection is acting upon the natural variation. Using the genotyping by sequencing (GBS) technique, we generated ~60,000 single-nucleotide polymorphisms (SNPs) distributed across the E. heros genome to answer some of those questions. The SNP data was used to investigate the pattern of genetic structure, hybridization and natural selection of this emerging pest. We found that E. heros populations presented similar levels of genetic diversity with slightly higher values at several central locations in Brazil. Our results also showed strong genetic structure separating northern and southern Brazilian regions  with a very distinct hybrid zone at the central region. The analyses also suggest the possibility that GABA channels and odorant receptors might play a role in the process of natural selection. At least one marker was associated with soybean and beans crops, but no association between allele frequency and cotton was found. We discuss the implications of these findings in the management of emerging pests in agriculture, particularly in the context of large areas of monoculture such as soybean and cotton.Recent changes in soybean management like the adoption of transgenic crops and no-till farming, in addition to the expansion of cultivated areas into new virgin frontiers, are some of the hypotheses that can explain the rise of secondary pests, such as the Neotropical brown stink bug,  Habitats and landscapes featuring extensively cultivated areas foist new challenges upon ecologists and populations geneticists every time a new technology emerges and is incorporated into the set of pest management tactics. The implementation of such technologies—including no-till farming, novel pesticides, and transgenic crops—modify the agricultural conditions for resident insect populations setting the stage for the potential rise of new pests problems . Albeit Bt crops represent an important landmark in insect control, but the use of Bt has also been linked to an increase in the densities of sap-sucking insects, in particular, hemipterans , located at the central portion of the country and where soybean has been produced intensively and in large-scale in the last 40 years , Nezara viridula (L.), and Euschistus heros (F.) for their major importance in agricultural ecosystems  that can be considered valuable adaptation for feeding on annual crops such as soybean  is composed of approximately 4,123 species distributed around the world from which we can highlight osystems . Euschis America . Not onl soybean . Additiodynamics . UnderlyE. heros lineages were uncovered in a recent phylogeographic study using mitochondrial markers that showed that the pattern of the spatial distribution of the two groups is still greatly preserved to this day  in Brazil  protocol  with thePstI and MseI restriction enzymes (The reduced representation libraries were prepared according to the protocol of GBS using  enzymes . Sequencprocess-rad-tags in STACKS v.2 (M = 4) and a minimum of three reads were allowed for the stack depth (m = 3) to form the RADloci (Samples were demultiplexed using ACKS v.2 . Illumin RADloci . Our fil RADloci . Minor a RADloci . More st RADloci . Outputs RADloci .O), expected heterozygosity (HE), nucleotide diversity (π), and the inbreeding coefficients (FIS). The genetic differentiation was estimated through the overall and pairwise Fixation Index FST  and the geographic distance using the Mantel test with 10,000 permutations using matrix in three different scales  using the R library ecodist . The more stringent filtering parameters forced us to remove two populations  from the dataset due to excess of missing data. The program was set to run 1.5 × 105 burn-in interactions followed by 5 × 106 Markov chain Monte Carlo steps. We simulated the number of clusters ranging from 1 to 15 (K = 1 to K = 15) using 10 replications. Most likely value of K was selected by Evanno method implemented in Structure Harvest  and identifies loci that have excessively high or low FST compared to expectations under neutrality conditions. LOSITAN was run with 100,000 simulations, with infinite alleles mutation model, 0.995 confident interval and false discovery rate (FDR) of 0.05. Additionally, we used a Bayesian approach to identify candidates using differences in allele frequencies between populations using the software Bayescan . We also adopted an FDR of 0.05 to select outlier SNP markers in Bayescan. The RADtags in which outlier SNPs were identified were used in nucleotide searches with BLASTx against the genomic database of the National Center for Biotechnology Information (NCBI) using Blast2go. For the sequences with significant BLAST hits, functional annotations were taken using the ontology system “Gene ontology”.The strategy for identifying loci putatively under selection was based on the detection of loci at extreme F LOSITAN . This meBayescan . BayescaGlycine max), common bean (Phaseolus vulgaris), and cotton (Gossypium hirsutum) crops in varies ratios] using a latent factor mixed-effect model (LFMM) implemented in the R package LEA , which is a repository containing statistics from the Brazilian Institute of Geography and Statistics (IBGE). The agriculture census information referred to the three crops was extracted from 5,564 municipalities providing a grid with crop prevalence in the whole territory. The relative yield and presence of each crop per unit of the area were the two standardized metrics used as environmental factors. The crop variables were first scaled  and then compressed onto a lower-dimensional feature subspace using PCA analysis. Considering only the subset of sampled areas, cotton occurrence in the agriculture landscape varied from 0 up to 8.42% (MGPO) in contrast to soybean that varied from 1% (SPAN) to 76.2% (TOPA). Common bean crops were also present in lower proportions in sampled areas varying from ~0.25% (SPPI) to 9% (PITE). In summary, PC1 (39.3%) captured the major differences regarding areas and yield in soybean, PC2 (25.2%) captured differences associated with cotton and PC3 (13.8%) with common bean , we only considered putative outliers found by both LFMM and pcadapt methods   and MAF adjusted 10%. Data imputation was performed to deal with remaining missing data . Gibbs s methods . The pcais (PCA) .The GBS library generated a total of 226,947,346 reads that were retained after multiplexing and quality checking, the sequencing was successful, considering the high number of reads obtained. After demultiplexing and quality control, a total of 673,844 loci were genotyped giving an effective mean coverage per-sample of 12.4× (SD = 8.7). Minimum and maximum coverage per loci were 3.6× and 61.6×, respectively, and the average number of sites per locus of 90.7. After filtering, 28,188 loci were kept composed of 2,597,430 sites from which 61,876 variant sites remained.E. heros nucleotide diversity across the different regions in Brazil. The lowest nucleotide diversity value was found from the northern location PITE (π = 0.0017), and the highest diversity values (π = 0.0062) were found from two central locations MTCA (π = 0.0062) and GOLB (π = 0.0062) (IS) ranged from 0.0009 to 0.0052, showing the populations are largely in equilibrium or outbred rather than inbred.We have found a great variation in ST = 0.22 (p = 0.000) when comparing northern versus southern locations  and southern locations    compared to the scenario where just the southern and central location  were included, or when just the central locations where taken into account . These results indicate that isolation by distance here is likely an artefact due to genetic structure and the variation present in gene pools can be better described as discrete rather than continue across the range of geographic distribution.We have also found a significant relationship between genetic and geographical distance  was also detected by all other methods tested  (Using 2 as the latent factor (K = 2) and controlling the false discovery rate (FDR) at q = 0.05, we have not found any SNP associated with the cotton crop gradient (PC2) in sampled areas. However, a total of 18 candidate SNPs were detected associated with soybean (PC1) and 7 associated with the common bean gradients (PC3) (ST relations between populations using this single markers (27994_38), we were able to see that the separation of the northern area TOPA (land cover: ~76% soybean and ~1% common bean) from the other areas indicates that this marker is, at least in part, associated with local adaptation. Moreover, a genetic substructure involving MTCA (~76% soybean and ~0.4% beans) and the two SP locations where soybean is not intensively cultivated (< 2% soybean and <0.5% common bean) show that this marker is responding more strongly to soybean than the common bean gradient  and recurrent events  have been a point of debate in population genetics over the years and more recently in the field of molecular ecology . The comE. heros populations are undergoing in Brazil. Yet, to avoid pitfalls, we must discuss the two major modes of selection, balancing and positive selection, taking in account temporal, spatial, and the biological context  is a possible mechanism that can lead to insecticide resistance in insects  were largely used to control stinkbugs, but the molecular mechanism of action is still not well understood to this day. Moreover, this insecticide group has restricted usage in Brazil , detected by all tested methods, seems to be associated with both soybean and common bean crops. Locations where soybean crops are the most abundant (POTA and MTCA) reaching up to 76% of the covered area, and in places where it is almost completely absent (SPAN and SPPI), helped form a pattern of substructure based on this marker. However, we still cannot completely discard possible loci association with cotton crops as our sampled were not broad enough to cover areas with high cotton prevalence . Moreover, in our samples, cotton crops were intermingled with soybean crops, which can create unaccounted interactions between E. heros genotypes and the environmental gradients.E. heros is becoming more prevalent in cotton crops. Host plant shift can be defined as the process by which the organism abandoned one or more host species in favor or a new host . Unpredictable results might be expected in the central region where the two populations are exchanging adaptions. Resistance evolution, host adaptation, and changes in the dormancy behavior are expected to evolve first in the Cerrado. At the same time, the spreading of resistance or other adaptations to other areas is expected to happen slowly given the limited gene flow between the two areas.Bt plants in Brazil was the sensitive reduction in the frequency of insecticide applications targeting lepidopterans. However, an inconvenient side effect of such adoption is the now frequent hemipterans outbreak that has become an undesirable trend in soybean field throughout the Cerrado. Here, we provided shreds of evidence that the admixture of the two E. heros lineages and possible the evolution of adaptations associated with host recognition and increase in survival and reproduction might why E. heros is so dominant in the soybean ecosystems. The results presented here provide a valuable foundation for future studies to elucidate the mechanisms by which host range and resistance evolutions are affecting natural populations aiding the development of effective tools for pest control.In conclusion, one of the direct benefits attributed to the adoption of MZ conceived and designed the study, generated the molecular data, conducted the data analysis, and drafted the manuscript. EC conducted the data analysis, and drafted the manuscript. XW conducted the data analysis. LML conducted the data analysis on lab. PB conducted the data analysis and the manuscript review. SM conducted the data analysis on lab. JV conducted the data analysis. CO conducted the field sampling and the manuscript review. JP conducted the field sampling and the manuscript review. SC conceived and designed the study, and manuscript review.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RAD‐seq) require large amounts of high‐quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods  can provide genetic information on individuals or populations. Such samples typically yield low‐quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next‐generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g.,  These were the first studies to document successful amplification of DNA from hair and faecal samples of wild species. Soon researchers were amplifying nuclear DNA to determine sex  or to maximise recaptures using high intensity sampling over a limited geographic range , habitat‐use and availability of the material produced . This is, in part, because it is important to maximise sampling opportunities for elusive species, given the labour‐intensive nature of field work, but also because certain parameters  require the application of assumptions about sampling that may or may not be satisfied if sampling is conducted incorrectly.Consideration also needs to be given to the most suitable collection and storage method for the study species and sample type. For example, the time since deposition, environmental conditions, part of faeces sampled and storage medium can influence the quality of genotypes obtained, showing the importance of sampling protocol . The Amplifluor SNP loci had a comparable error rate  to the microsatellite loci  in this study  is then used to infer genotypes based on the position of the peaks in the spectra, corresponding to different alleles at different loci  to assess abundance, migration timing and stock composition . The enriched libraries were sequenced with Illumina HiSeq and provided sufficient genomic markers to undertake pedigree reconstruction . Such automated calling is not always accurate, and it is recommended that researchers visually check the data for error. This is particularly true for noninvasively collected samples, which can have higher error rates  as well as erroneous genotypes , employing good laboratory practices and other technical improvements that are beyond the scope of this review  can be used to estimate individual‐ and population‐level parameters, such as vital rates and population genetic parameters.3.13.1.1Okapia johnstoni). Of the 24 faecal samples detected, only 12 yielded DNA but of these six were identified as okapi and these yielded four mitochondrial haplotypes . Advances in environmental DNA (eDNA) analysis are enhancing our ability to examine past and present distribution and diversity of various species and communities , those panels have been used to monitor the existing relationships between individuals of a given population, either to investigate genetic and social structure, gene flow, reconstruct pedigrees or minimise inbreeding .O) was 0.32 ± 0.01 in juveniles whereas adult HO was 0.35 ± 0.01, a significant statistical difference consistent with expectations of viability selection. Unfortunately, the types of SNP arrays often used in MIS studies preclude the evaluation of other genetic diversity metrics that will likely be important in the future . The CBD's 2020 Targets include a commitment to “sensu Aichi Target 13).In the absence of samples from a population over time, analysis of genetic data using single point samples can provide insights into recent demographic change  outperformed all other potential indicators across all scenarios. However, the power with which to detect a decline in diversity in K varied with more samples or markers with, for example, 2500 SNPs being effective at detecting minor demographic declines after 8–10 generations . Hoban et al.  also fouFigure: Modified from Hoban et al. : Compari3.3.2Populations of conservation concern are usually small and thus experience inbreeding and genetic drift that could lead to a depletion of genetic variation. The parameter effective population size measures the strength of the stochastic processes of inbreeding and genetic drift Wright,  in a popPongo abelii; Nater et al., Ursos actos; Miller & Waits, Acrocephalus sechellensis; Spurgin et al., While this approach has its limitations and caveats  where, even if individuals can be observed and identified, invasive sampling is regarded as unethical and is often prohibited. Such studies may allow identification of social group‐mediated genetic structure and inferences on sex‐biased dispersal and how these may be modified by habitat fragmentation , and to assign individuals to those populations using the correct allele frequency data. Over recent years, numerous studies have successfully investigated genetic structure in wild populations using MIS .The sheer abundance of microsatellite data sets associated with MIS conservation studies is impressive. Thus, it would be desirable if future monitoring efforts could tie an individual's established microsatellite DNA profile to a new SNP profile. Many individuals of long‐lived species like trees, whales, bears or eagles have already been genetically tagged using microsatellites. In an ideal world, a new DNA profile generated with SNPs would be matched to those generated previously with microsatellites. Unfortunately, this is time‐consuming and expensive because it would require SNP genotyping a reference sample for each individual or having a way to link the SNP genotype to the microsatellite genotype. In principle, it might be possible using a high‐density SNP array to genotype individuals at each microsatellite locus. However, in practice, this depends on the availability of the SNPs, the extent of linkage disequilibrium, recombination rates, nucleotide substitution rates, effective population size, as well as the practicalities of designing assays for the repetitive genomic regions that microsatellites represent. In practice, it is an easy decision to forego microsatellites and establish a new SNP array when monitoring a “new” species. There are online tools, such as the ConGress website that contains a Decision Making Tool, that can help managers to use power analyses to identify optimal methods for a MIS analysis  to leverage microsatellite‐based fingerprinting against SNP‐based fingerprinting.4.2Evolving technology means that genetic monitoring of populations is expanding beyond genotypes. We broadly categorise these methods into those that will help enhance understanding of population demography, health and “functional” or adaptive genetic monitoring. The latter moves beyond using neutral alleles for individual identification and estimation of population genetic parameters to assay loci linked to processes such as inbreeding and adaptation Table . WildlifBox 41Ceratotherium simum). Credit: J. A. DeWoody.Image: White rhinoceros  and abiotic  pressures. Conventionally, longitudinal studies that track individuals in well‐studied populations have been the only way to estimate age for many species (Clutton‐Brock & Sheldon, Epigenetic markers might have utility in monitoring other facets of population demography, as epigenetic changes have been linked to early life conditions (Gapp, von Ziegler, Tweedie‐Cullen, & Mansuy, 4.2.2The microbial communities living on or in multicellular organisms or “hosts,” termed microbiomes, are a rich area of study in humans and, increasingly, wild animals. Host health and fitness can be affected by the microbiome through different mechanisms: the microbiome could act directly to protect health, through competitive exclusion or by stimulating immunity, or act indirectly, by modifying metabolism or development (Bahrndorff, Alemu, Alemneh, & Lund Nielsen, As the gut microbiome is closely related to diet (Amato et al., 4.2.3Traditional genetic monitoring has focused on presumably neutral markers to identify individuals and to assess genetic diversity. When whole‐genome data are available, investigators have the choice of using intergenic SNPs from gene deserts or of using “non‐neutral” markers derived from protein‐coding genes thought to be targets of natural selection (DeWoody et al., Only a few studies have yet investigated the possibilities of using MIS approaches for such a purpose. Russello et al.  used hai5Genetic monitoring with MIS has proven to be a valuable tool to monitor and manage species and populations. With increasing access to new technological advances, researchers will be able to go beyond identifying individuals to investigate their role in the ecosystem and assess population‐level dynamics. Such tools will be necessary to meet the challenges of conservation biology in a rapidly changing environment.The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO.The author declares no conflict of interest.


Zostera marina) genotypes from five sites within Bodega Harbor, CA. We measured traits related to nutrient uptake, morphology, biomass and growth, photosynthesis, and chemical deterrents for all genotypes. We used these trait measurements to calculate a multivariate pairwise trait distance for all possible genotype combinations. We then estimated pairwise relatedness from 11 microsatellite markers. We found significant trait variation among genotypes for nearly every measured trait; however, there was no evidence of a significant correlation between pairwise genetic relatedness and multivariate trait distance among individuals. However, at the subpopulation level (sites within a harbor), genetic (FST) and trait differentiation were positively correlated. Our work suggests that pairwise relatedness estimated from neutral marker loci is a poor proxy for trait differentiation between individual genotypes. It remains to be seen whether genomewide measures of genetic differentiation or easily measured “master” traits (like body size) might provide good predictions of overall trait differentiation.Ecological studies often assume that genetically similar individuals will be more similar in phenotypic traits, such that genetic diversity can serve as a proxy for trait diversity. Here, we explicitly test the relationship between genetic relatedness and trait distance using 40 eelgrass ( EelgrasOur previous analyses of the covariance between genetic relatedness and trait differentiation in eelgrass suggested that the two were uncorrelated  patches of eelgrass near the entrance to the harbor, which are all subtidal  . Eelgrass grows in Bodega Harbor from about 0.25 m above MLLW in the intertidal to 3 m below MLLW in the deepest parts of the harbor adjacent to a dredged channel. The five eelgrass collection sites are distributed throughout the harbor, between 0.45 and 3.2 km apart. We transported the 260 eelgrass ramets collected from these sites to the Bodega Marine Laboratory (~2 to 4 km), where we trimmed the ramets to a single shoot with 3 cm of rhizome and 30 cm of leaf length and planted them in 11.4 cm diameter by 9.5‐cm high‐plastic flowerpots. We placed all pots in a single common garden flow‐through seawater tank; we randomly assigned the pots to an initial position and rotated the pot position weekly. We collected leaf clips from each ramet for genetic analysis.In May 2012, we collected 20 eelgrass ramets harvested at 2‐m intervals along a 40‐m transect at each of three tidal heights  at four sites within Bodega Harbor, CA. We collected an additional 20 ramets along a 40‐m transect from small  independently .We delineated genotypes and estimated relatedness using 11 microsatellite loci selected from a pool of >30 loci developed specifically for 2.3FST) using ARLEQUIN 3.5.1.3 and tested for significance by 10,100 permutations of the data  a wide range of pairwise relatedness values and (b) genotypes from all tidal heights and sites .We collected eelgrass shoots from the primary common garden of 40 genotypes, cleaned them of all sediment, epiphytes, and invertebrates, and cut their rhizomes to 3 cm the day prior to allow wound healing. The roots and rhizomes of each shoot were compartmentalized from the leaf shoots by inserting the shoot through a slit in a watertight rubber stopper that was then inserted into a 40‐ml opaque plastic chamber filled with 35 ml of nitrogen‐free artificial seawater spiked with ammonium to 100 μM using a 5 M NHWe took water samples from the root/rhizome chambers and shoot chambers prior to the start of the experiment, then sampled the shoot chambers every hour for 4 hr, at which time we detached the root/rhizome chambers from the shoot chamber, removed the shoots, and took a final sample. We analyzed the shoot chamber samples (nitrate) using a Lachat 8000 series flow injection autoanalyzer and root/rhizome chamber samples (ammonium) using a Beckman Coulter DU640 spectrophotometer .2.7Z. marina  fluorometer  to measure maximum quantum yield  and rapid light curves (RLC), which determine the effective quantum yield as a function of irradiance and can be used to assess light adaptation , and the resulting yield measurements were converted into a relative electron transport rate (rETR) using the following equation:ΔF/Fm′ is the effective quantum yield, ΔF is the difference between background fluorescence F and F at each PAR increment, Fm′ is the maximum fluorescence, 0.5 assumes that photons absorbed are equally distributed between photosystems I and II ; β, slope of the curve where yield declines (strength of photoinhibition); and Ps, which is a scaling factor used to determine the maximum relative electron transport rate (rETRmax). We fit each curve to a double exponential decay function  to account for correlations among traits, and used the principal component scores for PC1 and PC2 to calculate the Euclidean distance between all pairs of genotypes , and among genotypes from different sites or tidal heights (mean PC scores) in two‐dimensional trait space (hereafter “PCA trait distance”). We performed the PCA using scaled and centered (mean = 0 and variance = 1) trait values and used a correlation matrix to calculate principal components. The analysis was performed in R 3.0.3 using the prcomp function, which conducts the PCA using singular value decomposition of the data matrix. Separately, we assessed correlations among traits using pairwise regression  and trait differentiation (PCA trait distance) among genotypes from different sites and tidal heights within Bodega Harbor, CA. We also tested for a relationship between genetic differentiation (FST) and geographic distance among sites. All analyses were performed using R 3.3.3  using a Mantel test (Vegan package) to account for nonindependence of the data caused by the presence of the same genotype in multiple pairwise combinations. We also used Mantel tests to evaluate the relationship between genetic differentiation  have been criticized for having low power and an inflated type I error rate , and the null expectation under drift is that QST‐FST = 0 . Significant deviation from the null expectation indicates that traits are under selection and local adaptation is occurring. We tested whether QST was significantly different from the mean FST among subpopulations using a method developed by Whitlock and Guillaume (QST and FST (QST‐FST) using a mixture of parametric simulations and bootstrapping. The uncertainty of mean FST is modeled with a bootstrap across loci and a distribution of neutral QST values is simulated using the estimated additive genetic variation and assuming the null hypothesis (that QST = FST) is true. The observed QST values are then compared to the tails of the simulated null distribution. An extension of this procedure allows for the relatedness of offspring  to be specified  was greater or less than expected due to drift alone, we used uillaume , which uuillaume . In brie33.1Ps, rETRmax, and β) showed no significant variation among genotypes.Seventeen of the 21 traits differed among the 40 genotypes we measured for this study (ANOVA results in Supporting information Appendix α) and phenolic content, and negatively associated with traits related to clonal expansion. Higher order PCs contributed minimally to explaining variation in traits, and we did not consider them further. These patterns show that nutrient uptake rate and the size and growth rate of the terminal shoot are negatively correlated, whereas measurements of clonal expansion show a more orthogonal (independent) relationship with nutrient uptake rate and terminal shoot traits. Phenolic content and α also tend to show an orthogonal relationship with other traits  and maximum root length  differed significantly by tidal height, when both site and tidal height were included in a model. We did not have enough replication to rigorously evaluate an interaction between site and tidal height.We also analyzed trait differences as a function of the sites and tidal heights from which genotypes were collected to assess whether genetically based trait variation measured in a common garden was nonrandomly distributed in the field . Out of the 21 traits, we found that shoot length differed significantly by site . Our two trait distance measures (multivariate and PCA) were highly correlated .The multivariate trait distance we calculated for all possible pairs of the 40 genotypes was uncorrelated with their pairwise genetic relatedness (Mantel statistic 3.3FST) and trait differentiation (PCA trait distance) across the sites from which the genotypes were sampled . We found no evidence of a correlation between genetic and trait differentiation across tidal heights , as trait and genetic variation among tidal heights was modest and with only three pairwise comparisons our power to detect patterns was low . Thus, the correlation between genetic relatedness and trait differentiation among eelgrass genotypes appears to be scale dependent, just as has been found for correlations between phylogenetic distance and trait distance among species , so this relationship is not entirely due to a single differentiated site.The relationship between phenotypic and genetic differentiation that we observed at the site (or subpopulation) level could be explained by a combination of selection and genetic drift. Environmental differences across sites could create varying selection pressures that drive trait differences, while dispersal limitation among sites could reduce gene flow and allow divergence of neutral genetic markers. Alternatively, both traits and allele frequencies may diverge as a result of genetic drift due to low connectivity. Although we could not reject the null model of drift in the The scale‐dependence of the within‐species relatedness versus trait differentiation relationship  is analogous to the idea that the relationship between phylogenetic and trait differentiation among species depends on the phylogenetic scale under consideration  or at loci that actually control the measured traits could provide a stronger genetic proxy for trait differentiation at the among‐individual scale, but such data are currently unavailable for eelgrass. Independent of its relationship to traits, genetic relatedness in eelgrass influences the outcome of interactions, possibly as an indicator of the intensity of kin interactions (Abbott et al., None declared.JMA, JJS, SLW, and RKG conceived and designed the study. JMA and KD collected data for the study with assistance from SLW. JMA, KD, and JJS analyzed the data. JMA lead the manuscript preparation. All authors contributed critically to the drafts and gave final approval for publication.Susan Williams, renowned marine biologist and devoted mentor, lost her life in a car accident in Petaluma, CA on April 24th 2018. Susan, a Distinguished Professor at the University of California, Davis, was a leader in ocean conservation, who focused on community engagement, science communication, and international collaboration to understand and protect marine ecosystems. Susan served as the director of the Bodega Bay Marine Laboratory from 2000 to 2010 and was an essential advisor to legislators, playing a key role in enacting legislation that expanded the boundaries of two national marine sanctuaries. Among many honors, in 2010, Susan received the UC Davis Academic Senate Distinguished Scholarly Public Service Award for her efforts in marine conservation. Susan was a pioneer in marine biology, paving the way for women to follow in her footsteps. She was a caring and dedicated mentor who used her success to support and empower her students. In 2009, she was honored by the UC Davis Consortium for Women and Research as an outstanding mentor for championing inclusion and diversity in the sciences. Susan's passion was inspiring, and the tremendous impacts of her mentorship, research, and leadership will continue her legacy. Click here for additional data file.


Anopheles balabacensis, the primary vector of Plasmodium knowlesi in Sabah, Malaysia, is both zoophilic and anthropophilic, feeding on macaques as well as humans. It is the dominant Anopheles species found in Kudat Division where it is responsible for all the cases of P. knowlesi. However there is a paucity of basic biological and ecological information on this vector. We investigated the genetic variation of this species using the sequences of cox1  and cox2 (685 bp) to gain an insight into the population genetics and inter-population gene flow in Sabah. A total of 71 An. balabacensis were collected from seven districts constituting 14 subpopulations. A total of 17, 10 and 25 haplotypes were detected in the subpopulations respectively using the cox1, cox2 and the combined sequence. Some of the haplotypes were common among the subpopulations due to gene flow occurring between them. AMOVA showed that the genetic variation was high within subpopulations as compared to between subpopulations. Mantel test results showed that the variation between subpopulations was not due to the geographical distance between them. Furthermore, Tajima’s D and Fu’s Fs tests showed that An. balabacensis in Sabah is experiencing population expansion and growth. High gene flow between the subpopulations was indicated by the low genetic distance and high gene diversity in the cox1, cox2 and the combined sequence. However the population at Lipasu Lama appeared to be isolated possibly due to its higher altitude at 873 m above sea level. Anopheles spp. are the only vectors of human and zoonotic malaria caused by five malaria parasite species namely Plasmodium falciparum, P. vivax, P. malariae, P. ovale and P. knowlesi. Approximately 70 Anopheles species have been known to transmit these malaria parasites in nature and 41 of them are considered as dominant vector species/species complex [Anopheles dirus, An. balabacensis, An. latens and An. introlatus belonging to the Leucosphyrus group [Anopheles dirus, a member of the Dirus complex found mainly in China, Cambodia, Vietnam, Laos and Thailand is the primary vector for human and simian malaria in Vietnam [An. balabacensis, An. latens and An. introlatus, all members of the Leucosphyrus complex have been incriminated as primary vectors for P. knowlesi [ complex . Ninetee complex  with fouus group –4. Anoph Vietnam . In Malaknowlesi –8.Anopheles balabacensis is found in the forested areas of Philippines , Indonesia , East Malaysia (Sabah and Sarawak) and Brunei [An. balabacensis prefers to bite humans outdoors rather than indoors [d Brunei –4, 9. Re indoors  and duri indoors , 11–12.P. knowlesi malaria in the world with most of the cases reported in 2013 occurring in the interior areas [P. knowlesi among the indigenous malaria cases for 2014–2016 was respectively 66%, 80% and 92%. Asymptomatic infection has also been detected in the community. A survey conducted in Kudat and Kota Marudu districts found that 9.8% (112/1147) of the collected blood samples were positive for P. knowlesi with the majority of the infected individuals not having a history of fever [P. knowlesi has caused most of the malaria deaths in adults [An. balabacensis has been confirmed as the main vector for both P. falciparum [P. knowlesi [An. balabacensis in Sabah not been investigated.Sabah has the highest incidence of n adults . Althouglciparum  and P. kAn. balabacensis in Sabah based on the cox1, cox2 and the combined cox1 and cox2 sequences (“combined sequence”) of mitochondrial DNA. The mitochondrial DNA was used in the study as it is a suitable marker in a wide range of taxonomic, population and evolutionary studies in animals including malaria vectors [An. balabacensis populations.We conducted a study on the genetic variation between subpopulations of  vectors –18. SuchP. knowlesi cases. The inter-site distance varied from 2.4 km to 237.2 km with the GPS coordinates varying from 5.33192N - 7.21578N to 116.04140E - 117.10292E   . Each moion keys ,19–20 anAn. balabacensis using the DTAB-CTAB method [cox1 and cox2 genes. Details of the PCR primers used are shown in 2 (25 mM), 2.0 μl of the forward and reverse primers (10 μM), 1.0 μl of Taq DNA polymerase (5.0 U/μl), 3.0 μl of DNA template and 26.0 μl sterile dH2O. After the first PCR reaction was completed, 3.0 μl of the PCR product was used as a DNA template in the second PCR. The PCR reaction was performed using a thermal cycler  with an initial denaturation at 95°C for 5 min followed by 30 cycles of denaturation at 94°C for 1 min, annealing at 55°C for 1 min, extension at 72°C for 1 min and one final extension step at 72°C for 10 min.Genomic DNA was extracted from each B method  and storcox1—COIF+UAE10; cox2—X2F+COIIR). In order to determine the consistency of the Taq DNA polymerase, eleven PCR products of An. balabacensis cox1 and cox2 genes from Paradason were cloned into pGEM-TEasy vectors  and the plasmids were extracted from the transformed E. coli (JM109) using DNA-spin Plasmid DNA Purification Kit  all according to the manufacturer’s procedure. The extracted plasmid was restricted using EcoRI restriction enzyme  and the two plasmids from each gene containing the correct size of PCR amplicon were sent to AITBIOTECH, Singapore for sequencing at both directions using forward and reverse M13 primers.After the PCR was completed, the PCR products were purified using MEGA quick-spin PCR & Agarose Gel DNA Extraction System  according to the manufacturer’s procedure. The purified PCR products were analyzed on 1.5% agarose gel electrophoresis stained with RedSafe nucleic acid staining solution (iNtRON Biotechnology) and visualized using UV transilluminator. The purified PCR products were sent to AITBIOTECH (Singapore) for sequencing using forward and reverse primers  database with accession number starting from MH032606 to MH032747  for cox2 with reference to the nucleotide sequence of An. cracens (JX219733) [Cox1, cox2 and the combined sequence were translated into proteins based on the genetic code for mitochondrial DNA of Drosophila using DnaSP software (ver. 5.10.01) [An. balabacensis subpopulations was explored with molecular variance analysis (AMOVA) using Arlequin 3.11 [ST values for genetic distance between the subpopulations were tested for significance as well as used for estimating gene flow, using 1,000 permutations [The sequences were multi-aligned using ClustalW incorporated in the MEGA4.1 software . The nucX219733) . Cox1, c5.10.01) . The popuin 3.11 . The poputations . The numutations  and nuclutations  were alsD [Fs [−8 [Neutrality test using Tajima’s D  and Fu’sD [Fs  was carr[t=τ/2uk , where τ5 x 10−8  was usedhttp://ibdws.sdsu.edu/) with 10,000 permutations to assess the significance of correlation between genetic distance and linear geographical distance [http://popart.otago.ac.nz).Mantel test for isolation by distance (IBD) was performed online . Consent to carry out mosquito collection was obtained from the village council or the village headman and the land owners. All volunteers who carried out mosquito collections signed informed consent forms and were provided with antimalarial prophylaxis during the study period.cox1 sequence had 31.1% A, 38.4% T, 15.8% C and 14.7% G with an A + T bias of 69.5%, and can be translated into 461 amino acids. There were more mutations by transition (93.65%) than transversion (6.35%). In the transition mutations-, inter-changes between the two-ring purines: A → G (22.17%); G → A (47.03%) were more frequent than one-ring pyrimidines: C → T (17.31%); T → C (7.14%).The cox2 sequence had 35.9% A, 38.4% T, 13.4% C and 12.3% G with an A + T bias of 74.3% and can be translated into 228 amino acids. Mutation by transition (86.89%) was more common than by transversion (13.11%). However in the transition, only inter-changes between two-ring purines: A → G (22.11%); G → A (64.78%) were detected.The cox1, the subpopulations have the following genetic statistics: number of haplotypes 1–5, haplotype diversity 0–1, nucleotide diversity 0–0.00231, average number of nucleotide differences 0–3.2 and number of segregating sites 0–8 , while Timbang Dayang the lowest (-0.082)  to 0.600 (between Lipasu Lama and Sorinsim) with gene flow among the subpopulations varying from 0.333 to ∞  . The pai333 to ∞ .cox2, the variance component was also higher within subpopulations compared to among subpopulations  while Lipasu Lama the lowest (-0.014)  to 0.634 (between Lipasu Lama and Tinukadan Laut) with gene flow among the subpopulations varying from 0.289 to ∞ (As for (-0.014) . The pai289 to ∞ .ST value (0.304), while Mambatu Laut the lowest (0.082)  to 0.667 (between Lipasu Lama and Sorinsim and Minikodong and Sorinsim) with gene flow among the subpopulations varying from 0.25 to ∞  . The pai.25 to ∞ .D (p<0.01) or Fu’s Fs (p<0.001) values for either cox gene or for the combined sequence. Sorinsim subpopulation had zero values for Tajima’s D and Fu’s Fs for cox1, cox2 and the combined sequence  had either significant Tajima’s sequence .cox genes , but the SDD value was significant for the combined sequence   . At the cox1, cox2 and the combined sequence showed a unimodal peak indicating the population expansion model is applicable ) on geographical distance was not significant  were detected .An. balabacensis populations in Sabah was explored by analyzing the partial sequence of cox1 , cox2 (685 bp) and the combined sequence  of the mitochondrial DNA from 71 specimens collected from 14 different sites each representing a different subpopulation.The genetic variation of ST value: cox1: 0.102; cox2: 0.153; combined sequence: 0.129), likely to be a result of inter-breeding and gene flow between the subpopulations.Overall, the genetic distance between the subpopulations was low , Kudat , Kota Marudu (Sorinsim), Ranau (Paus) and Keningau (Keritan Ulu) which are geographically far apart from each another, while based on the cox2 the ancestral was detected in all the subpopulations. However, the combined sequence showed that the ancestral was found only at two districts viz. Kudat  and Keningau (Keritan Ulu). It is unlikely that the total number of haplotypes in a subpopulation had been sampled in our study, for this would depend on the sample size [cox1 was observed in the Mambatu Laut subpopulation while the highest nucleotide diversity was in Tomohon subpopulation, both situated in the low lying areas of Kudat. Based on cox2 sequence, the Sinangip subpopulation has the highest haplotype diversity and Lipasu Lama has the highest nucleotide diversity, both located in higher altitude areas. The Sorinsim subpopulation had zero diversity for both sequences which could probably be due to sampling error.Based on ple size . Howeverple size . The higAn. balabacensis especially those collected from the higher altitude areas. Cox1 has higher haplotype diversity and nucleotide diversity compared to cox2, which would suggest that cox1 is a more suitable molecular marker for investigating genetic variation and structure of An. balabacensis. It has been shown the number of samples and the area covered in such population study would influence the data obtained and thus the interpretation [cox1 or cox2 alone, the complete mitochondrial DNA sequence would be required [Our results provide some basic information on the genetic structure of retation –37. To orequired –39.cox1, cox2 and the combined sequence among the An. balabacensis subpopulations was moderate to high. This indicates high gene flow between the subpopulations which may imply that An. balabacensis has high dispersal rate contributing to its success as a vector for P. knowlesi.In general, our results show that the genetic diversity in the cox1 and cox2 of An. balabacensis contain a high ratio of A + T similar to other Anopheles species [Anopheles spp.: An. oswaldoi in Brazil and for An. minimus in China and South East Asia [The sequences of both  species –42, with species . Similarast Asia –43. Howeast Asia .An. balabacensis subpopulations in Sabah lie within subpopulations rather than among subpopulations. Similar finding has also been recorded for other Anopheles species, e.g. An. baimaii  populations in India [An. lesteri [An. sinensis [An. sinensis population was unique compared to other subpopulations in China [An. balabacensis in this study, the evidence is not strong enough to substantiate the conclusion that any subpopulation is isolated geographically. Nevertheless the high pairwise genetic distance and the low gene flow for the Lipasu Lama subpopulation located in the mountainous area (873 meter above the sea level), may indicate possible early stage of isolation. Another subpopulation that may also show early stage of isolation is Sorinsim, as indicated by the combined sequence analysis. It is possible that the presence of cryptic species may also contribute to the observed high pairwise of genetic distance in these two subpopulations, but this needs to be confirmed by further study.AMOVA of the sequences showed that the genetic variation in in India , An. les lesteri  and An. sinensis ,47 populin China . It had An. balabacensis adults being transported unintentionally in boats or ferry along with the daily movement of people between the main island and Banggi Island.The Limbuak Laut and Timbang Dayang subpopulations located in Banggi Island which is separated from the main island by 44 km showed small to moderate pairwise genetic distance and high gene flow, indicating that there is also sharing of genetic material between Banggi and the main island subpopulations through breeding and migration. This could possibly be achieved by D values obtained in the overall subpopulations suggest that the DNA sequences are evolving in a non-random manner and many rare alleles are present in the subpopulations which are expanding demographically [Fs which is a more sensitive statistic for detecting deviations from neutrality and indicators for population expansion and growth [An. dirus and An. baimaii in South East Asia [An. baimaii in north-east India [An. sinensis [An. lesteri [The negative Tajima’s phically . This isd growth . A singld growth . Furtherast Asia , An. baist India , An. sinsinensis  and An.  lesteri  both in cox1, cox2 and the combined sequence showed that An. balabacensis population of Sabah belongs to one cluster, suggesting that the subpopulations are expanding from a single ancestral haplotype. Based on cox1, this ancestral haplotype (Hap_2) was found in nine subpopulations, whereas based on cox2, the ancestral haplotype (Hap_1) was found in all the subpopulations. However the combined sequence showed that the ancestral haplotype (Hap_1) was found in Kudat and Keningau districts. It appears that the mutation rate of cox1 and cox2 differ [The genealogy networks of 2 differ –50, resuAn. balabacensis population of Sabah is undergoing population growth and expansion. The low genetic distance in the overall population of An. balabacensis based on the mitochondrial DNA indicates that there is high genetic diversity in the subpopulations, a likely consequence of inter-population migration and breeding, resulting in gene flow between the subpopulations.This study has shown that S1 Fig(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableThe haplotypes marked with asterisk (*) were detected only in one subpopulation.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 TableNm values are shown above the diagonal while FST values below the diagonal. Values marked with asterisk indicate the genetic distances between two subpopulations are significant: *p<0.05, **p<0.01.(PDF)Click here for additional data file.S7 TableNm values are shown above the diagonal while FST values below the diagonal. Values marked with asterisk indicate the genetic distances between two subpopulations are significant: *p<0.05.(PDF)Click here for additional data file.S8 TableNm values are shown above the diagonal while FST values below the diagonal. Values marked with asterisk indicate the genetic distances between two subpopulations are significant: *p<0.05, **p<0.01, ***p<0.001.(PDF)Click here for additional data file.


Leptobrachium liui (Megophryidae), we made one of the earliest attempts to examine the interglacial high‐elevation refugia scenario in these Asian mountains. Based on our current understanding of the study system, we formulated a hypothesis that these frogs of western origin were distributed more widely and continuously during glacial phases, allowing eastward dispersal, and that they are currently isolated in interglacial refugia at higher elevations. Microsatellite data and mitochondrial and nuclear sequence data were obtained with extensive sampling followed by the synthesis of phylogeographic and population genetic analyses and modeling of the species distribution. The analyses revealed a sequential eastward divergence of microsatellite clusters and gene lineages accompanied by a decline in genetic diversity. Molecular dating estimates revealed divergence events in the Pleistocene, and a reduction in local populations was inferred to have occurred at a time comparable to the end of the last glacial. Strong genetic isolation by distance reflecting a more continuous historical distribution was detected. Furthermore, environmental niche models inferred a wide planar distribution during the last glacial maximum, providing further support for the hypothesis.The effects of Quaternary climatic oscillations on the distributions of organisms in different parts of the world are not equally well understood, limiting the ability to understand the determinants of biodiversity. Compared with the mountain regions in southern Europe and southwestern North America, such effects on high‐elevation species in the East Asian subtropical mountain systems located in southern and southeastern China have seldom been addressed. In this study, using  Such efPinus kwangtungensis and noted another three plant species with similar distributions and genetic patterns ] of the Quaternary Guo, . With thLeptobrachium liui   and is found in several major southern and southeastern China mountain ranges that are separated by low elevations or connected only by middle‐elevation passes and that form the northeastern range limit of the genus. This distribution may have evolved via west‐to‐east dispersal followed by long‐term occupation, as suggested by the deep eastward divergences among regional mitochondrial lineages  Figure , is ideaL. liui that is not found in L. leishanense; however, both species call underwater  pattern of genetic structure across the range. The dispersal abilities of amphibians are generally limited  sequential eastward divergence of microsatellite clusters and gene lineages; (b) molecular date estimates of the Pleistocene for at least the more recent divergences; (c) west‐to‐east decline in genetic variation consistent with the loss of diversity during dispersal; (d) an estimated reduction in local populations at a time comparable to the end of the last glacial (~0.01 Ma); (e) two‐dimensional IBD fitting the genetic structure better than one‐dimensional IBD if an IBD pattern was found; and (f) a wider and more continuous distribution during the LGM inferred by correlative species distribution modeling. We examined these predictions based on the combined data of L. liui and L. leishanense because we detected lower genetic differentiation between L. leishanense and nearby L. liui than between the latter and the other populations of L. liui, suggesting that L. leishanense is a synonym of L. liui.In this study, using extensive sampling of both species with 907 individuals from 25 sites, 14 microsatellite loci, one mitochondrial locus, and one nuclear locus, we tested a hypothesis integrating eastward dispersal with the interglacial high‐elevation refugia scenario. We hypothesised that 22.1L. liui and 108 individuals from three sites of L. leishanense were included as the ingroup, and the sampling spanned the distributions of both species  and NADH dehydrogenase subunit 1 genes; the nuclear rag1 locus contained an 800‐bp partial sequence of the recombinase activating protein 1 gene. The primers used in the PCR and Sanger sequencing (ABI 3730) experiments included Leu‐1L, Leu‐5L, Rag1‐3L, Rag1‐2H, Rag1‐6H  clones were sequenced to distinguish among the haplotypes.The microsatellite loci included VIB‐B4, VIB‐C10, VIB‐D5, CHA1, CHA6, CHA9, LSMT6, LSMT8, CHA4, CHA7, CHA8, CHA11, LSMT4, and LSMT9  most closely related to L. liui and L. leishanense  evaluated ranged from one to the number of sampling sites included plus one. For each K, 20 independent runs were conducted, and the 10 runs with the highest likelihoods were included in the follow‐up analyses. In STRUCTURE HARVESTER 0.6.94 , and a reasonable biological interpretation  was used to present spatial patterns in diversities. For the microsatellite data, the expected heterozygosity was estimated using Arlequin 3.5.1.2  was conducted using Arlequin. To assess the major pattern of differentiation between regional population groups, principal coordinate analysis (PCoA) was conducted on individual interpopulation difference matrices using GenAlEx 6.502  and geographic distances, and 10,000 randomizations were applied, with the negative genetic distances set to zero. The geographic distances were calculated using coordinates and analyzed with and without logarithmic transformation.Various measures of population differentiation were calculated and used for further analysis. For the microsatellite data, the  to Weir  using th to Weir , applyinue Jost,  was estiue Jost, . For the2.4Addressing the predicted recent decline ~0.01 Ma in each local population, the approximate Bayesian computations were applied to the microsatellite data using DIYABC 2.1.0 , namely, larger contribution to model development and putative greater biological importance. As a result, 10 variables were retained as follows: annual mean temperature, mean monthly temperature range, annual temperature range, mean temperature of the wettest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the driest month, precipitation seasonality, and precipitation of the warmest quarter.The LGM and current species distributions were modeled for comparison. The analyses were conducted at a spatial extent of 21.25–31°N and 106.5–122.25°E. This area did not extend substantially from the ranges of the study species and the mountains Figure . The MaxIn the Maxent analysis, we mainly used the default settings and ran models with 10 bootstrap replicates by randomly assigning localities to the training (75%) and test (25%) data sets. The default settings have been validated over a wide range of species using different sets of environmental variables in various regions of the world and have been shown to achieve good performance , with one to three cases per locus. After sequential Bonferroni corrections, four cases (1.5%) of significant (0.05 level) deviation from Hardy–Weinberg equilibrium were identified in three populations, and 31 departures (2.7%) from linkage equilibrium (one or two cases per locus pair) were significant at the 0.05 level.Eight microsatellite loci, VIB‐B4, VIB‐C10, VIB‐D5, CHA1, CHA6, CHA9, LSMT6, and LSMT8, were successfully genotyped for all sampling sites included, forming the 8‐loci data used for interpopulation analyses and comparisons. From the 14‐loci data with missing genotypes, Micro‐Checker did not return evidence of stuttering or allele dropout and detected 13 cases (4.8%) of potential null alleles  with nearby L. liui (WM) . The samples from areas WW and WM and those from WE and E were separately assigned to two groups, which were then analyzed individually. Admixture was observed in the intermediate locality 12. Within the WW + WM group, interpretations of the clustering results were challenging. For example, five clustering modes were found among the 20 runs when K = 2. The mode with the lowest likelihood divided WW from WM, whereas the mode with the highest likelihood clustered one northern and two southern insular sites (20–22) as a single population. Relevant to these results, a population structure driven by IBD complicated the STRUCTURE clustering analysis )), with a 1.5–0.6 Ma divergence date between WE and E. In E, the EE lineage was nested within the EW lineages and was estimated to have diverged from the latter 0.6–0.2 Ma. The distribution could be divided into eight areas/subareas  that correspond exclusively to various genetic lineages that diverged approximately 4.2–0.4 Ma  and 12, forming two separate lineages. These and another two WE lineages were nested within several WM and WW lineages.The 3.3A general pattern of a decline in genetic diversity was detected moving eastward along the distribution, with low levels of genetic diversity found in both the most southern (22) and northern (20) sites  for the group pair WM and WE + E, 0.047 (p = 0.198) for WM and WW, and 0.422 (p < 0.05) for WW and WE + E. These lines of comparison had not been applied to the nad1 data, in which the gene tree showed a sister‐group relationship between WW and the others.The close relationship between Ws Figure . The ragFST and Dest estimates were 0.31 and 0.63, respectively. For rag1 and nad1, as revealed by AMOVA, the among‐population variation accounted for 54.7% and 95.2% (both p < 0.05) of the total variation, respectively. These genetic variations could be primarily explained by logarithmic geographic distances, which consistently performed better than the linear geographic distances  were used. Fairly consistent results of scenario choice were produced by the direct and logistic regression approaches. The one‐decline scenario resulted in the highest probabilities in 21 (95.5%) and 19 (86.4%) of the 22 populations in the direct and logistic regression approaches, respectively. In the other cases, the growth‐after‐decline scenario resulted in the highest probabilities for population 3 + 4 for both approaches and populations 5 and 22 for the logistic regression approach. In population 3 + 4, if the growth‐after‐decline scenario was not considered, the most likely scenario would have been distinct growth ~3.6 thousand generations ago. It was not surprising that the one‐change scenario was not the most likely in any comparisons because all the special cases of it were covered in other scenarios. However, demographic parameter estimates under the one‐change scenario and the most likely scenario were highly consistent, suggesting that the data had sufficient information to distinguish between these simple scenarios  and an AUCtest of 0.971 ± 0.014. Two extreme variables, Twar  and Precdry , provided the greatest contributions to model development , 1.0 (1.5–0.6), and 0.4 (0.6–0.2) Ma. The two Pleistocene divergence events, WE‐E and then EW‐EE, could not be assigned to a single glacial phase lasting ~0.1 million years. Therefore, dispersals might have occurred in different glacial periods. Across the distribution, the mean estimates between regional populations ranged from 4.2 to 0.4 Ma, indicating that they have persisted through at least several late Quaternary glacial cycles  Figure . The meaFor the predicted population size decline caused by the contraction after the end of the last glacial period approximately 0.01 Ma, a slight reduction occurring on average three thousand generations ago was inferred for all local populations (Supporting Information Table R2) up to 0.630, the two‐dimensional IBD pattern detected was fairly strong. Various measures of population differentiation also showed generally high levels of genetic turnover  might have been relevant to the failure to detect a strong IBD pattern across the range of L. liui. More generally, these differences again demonstrated the substantial effects of the geographic sampling scheme on spatial genetic structure analyses.Since this work was submitted for publication, Li et al.  reportedi et al.  and the i et al. . A compai et al. . Howeveri et al. . Thus, ai et al.  and the None declared.Y.Z. and X.Z. conceived the study. Y.Z. conducted the field and molecular lab work. Y.Z. and J.H. performed the analyses. Y.Z. led the writing with assistance from J.H. and X.Z.nad1, and rag1 datasets are available on Dryad Digital Repository: https://doi.org/10.5061/dryad.29kb682.DNA sequences: GenBank accessions KJ484817–KJ485696 and KJ538788–KJ538949. The 14‐loci microsatellite,  Click here for additional data file. Click here for additional data file.


By studying human pairs of twins, researchers can learn the relative contributions of genetic and environmental factors to the development of alcoholism. Identical  twins share 100 percent of their genes, whereas fraternal  twins generally share only 50 percent of their genes. Using certain techniques and theoretical models, researchers can compare the two types of twin pairs for how often alcoholism occurs in both members of a twin pair. If alcoholism occurs more often in both members of MZ twins, genetic factors are implicated in the origin of the disorder. Twin research also has been applied to studies of differences between men and women in their genetic contribution to alcoholism. Humans are biologically similar, sharing almost all of their genetic material. Many questions relevant to alcoholism,Twin studies have been used to address important questions about the causes of alcoholism, including the following:How important to the development and course of alcoholism are genetic and environmental influences?Do genetic influences differ in importance for different groups of people ?Based on partial answers from these investigations, new twin studies are seeking answers to even more specific questions, such as the following:Do the same sets of genes (or the same environments) produce the same effects in males and females?What environmental characteristics are most hazardous for people at high genetic risk for developing alcoholism?protect against alcoholism among people who have a strong family history of the disorder?What environmental characteristics To what degree is the frequently seen overlap between alcoholism and other disorders (such as depression and anxiety) a result of the same sets of genes influencing both disorders?These questions have important implications for understanding the causes of alcoholism and ultimately providing knowledge to help design treatment and prevention strategies for people at high risk of developing the disorder. This article describes how twin studies are designed, offers examples of research questions for which twin studies are useful, and reviews their limitations.Because of their unique genetic status, twins play a valuable role in teasing apart genetic and environmental influences on people’s development of alcoholism. Genes are segments of DNA that form the blueprints for the development of the human body. Genetic expression that influences behavior is only partly preprogrammed; the biological processes regulated by genes are modified in complex ways by experiences with the environment. Thus, neither genes nor experiences operate independently of one another. In this sense, the often-cited dichotomy of “genes versus environment” is false; both are required for the development and expression of human characteristics. Knowledge of how genetic and environmental factors act both separately and together to influence the development of alcoholism can inform strategies  for intervention and prevention.When researchers study genetic influences on behavioral variation, they focus on the small percentage of genes that differ among people. Fraternal, or dizygotic (DZ), twin pairs, like ordinary siblings, have, on average, 50 percent of these genes in common, whereas identical, or monozygotic (MZ), twin pairs have 100 percent of these genes in common. By comparing MZ with DZ twin pairs, researchers can use this difference in the twins’ degrees of genetic resemblance to estimate the relative importance of genetic and environmental influences on the development and course of disorders such as alcoholism.liability for alcoholism Environmental effects that twins do not share . ; common environmental (C); and individual, specific environmental (E). Each type is described below.Additive Genetic Sources. Humans have 46 chromosomes,alleles.dominance  and epistasis . However, the most common mechanism of allelic action for complex behavioral traits is believed to be through additive genetic action. In the case of alcoholism, this would mean that all alleles relevant for the development of alcoholism combine and are not suppressed or magnified by alleles on the paired chromosome or any other location. The A component estimated in twin studies represents the total additive genetic action relevant to a person’s risk for developing alcoholism. Most twin models assume that dominance and epistasis are negligible. For example, genes that predispose a person to have a higher risk for alcoholism  might combine with other genes that reduce risk  to produce a moderate level of risk. Identical, or monozygotic (MZ), twins have 100 percent of their genes—including those that influence risk for alcoholism—in common, whereas fraternal, or dizygotic (DZ), twins share (on average) only 50 percent of the genes that vary in the population , these components also include the prenatal environment. In studies, researchers assume these factors correlate perfectly for twins or siblings reared together, shown by a correlation of 1.0 in the figure for the C components.Individual, Specific Environmental Sources. These include any experiences or environmental influences not shared by siblings, such as unshared peers and stressful life events . The estimate of this term usually includes random measurement error (such as that from misdiagnosis), which cannot be separated from E in studies in which twins are measured only once. Because the members of a twin pair do not share specific environmental sources, no correlation exists between the E components in the figure shown.Liability ModelsL) for an individual (i) can be expressed as: Li = a(Ai) + c(Ci) + e(Ei). The uppercase letters A, C, and E represent theoretical “scores” on each of the three components; these scores vary across people. The lowercase letters a, c, and e designate the amount each component contributes to the outcome, and these are constant for everyone in a group.Under the usual liability model, the three components described above are assumed to combine. The liability  + c2. With just these two correlations, the values of a, c, and e can be determined using algebraic rules.The similarity, or correlation, in liability for two people is estimated as the sum of the components that they have in common. Because members of MZ twin pairs are identical for both their A and C components, their correlation is estimated as a2 + c2); 0.4 = (0.5(a2) + c2)] results in estimates of a2 = 0.6, c2 = 0.1, and e2 = 0.3. This means that 60 percent of the differences among people in the tendency to develop alcoholism would be attributable to differences in their genotypes, 10 percent would be attributable to environmental factors shared with siblings, and 30 percent would be attributable to unshared environmental effects.For a simplified illustration of these formulas at work, imagine that a study finds that MZ twin pairs correlate at +0.7 for alcoholism or not, whereas DZ pairs correlate at +0.4 for this characteristic. This pattern would occur if MZ pair members were much more likely than DZ pair members to both be alcoholic or both be nonalcoholic. Inserting these numbers into the equations above [0.7 = .Carol A. Prescott and Kenneth S. Kendler—1For a definition of this and other technical terms used in this sidebar, see central glossary, pp. 182–183.Researchers assume that the three components listed above combine to produce total liability. Only people who exceed a specific level, or threshold, of liability will manifest the clinical aspects of the disorder. Thus, people with low genetic liability require higher levels of environmental risk before they are likely to develop alcoholism, whereas those with high genetic liability may develop alcoholism in response to lower levels of stressful events.At conception, MZ twins are genetically identical. Although mutations in some genes are likely to occur, and adult MZ twins may differ in which genes currently are active , these differences are believed to be small. MZ twins are assumed in studies to be perfectly correlated for genes relevant to the development of alcoholism. Because first-degree relatives, including DZ twins, have, on average, one-half their genes in common, DZ twins have only one-half the degree of genetic correlation seen between MZ twins. Thus, if members of MZ twin pairs are much more alike for liability for alcoholism than are DZ twins, genetic influences can be implicated as a major contributor to liability. Conversely, if MZ and DZ twin pairs are equally similar for alcoholism liability, then any similarity is attributed to shared environmental factors. Researchers use equations based on the MZ–DZ comparison to estimate the proportions of liability resulting from the genetic and environmental components . For example, the estimates of genetic influence from studies of male twin pairs could be compared with the estimates resulting from female twin pair studies to identify differences between the groups. Other variations on traditional twin study designs could address important questions about differences in the mechanisms of genetic influence (discussed below).Although the risk for alcoholism may vary somewhat in different demographic groups , the difference between sexes in prevalence of alcoholism is particularly dramatic. Because the genetic differences between males and females are larger than between any other groups, comparing the sexes can provide more specific information about genetic mechanisms of risk for alcoholism.Studying male and female same-sex pairs allows the calculation of separate estimates of genetic and environmental effects for each sex. Although the data can show if the sexes differ in the relative importance of genetic and environmental sources of liability for alcoholism, the data cannot determine whether these genetic and environmental influences are the same in both sexes. Researchers attempt to solve this question by studying opposite-sex twin pairs (which are never MZ pairs).By using opposite-sex twin pairs, sex differences in genetic and common environmental sources of liability sometimes can be estimated. For example, if DZ opposite-sex twin pairs are less similar in their alcoholism liability than DZ same-sex pairs, this finding would be evidence that the two sexes differ either in the genes or environments (or both) relevant for the development of alcoholism. For statistical reasons, the twin design cannot always reveal the degree to which these differences result from genetic or environmental distinctions between the sexes. Evidence that such differences exist, however, provides an important starting point for researchers studying sex differences in alcoholism. Thus far, the available opposite-sex twin data are limited  in the sexes: A woman may become depressed, but a man may become alcohol dependent. In another example, genetic liability may be expressed as alcoholism in one generation and drug dependence in another. Studying opposite-sex twin pairs or twins from different age cohorts can address these important issues regarding the origins of alcoholism and its overlap with other disorders.Another major application of the twin model is in the study of the different ways the same genetic liability can be expressed, often called etiologic heterogeneity—when the same clinical disorder  occurs through multiple pathways. For example, women with alcoholism are at higher risk for anxiety and depression than are alcoholic men, whereas alcoholic men are more likely than women to have antisocial characteristics , researchers also can better understand the clusters of symptoms and subtypes that constitute alcoholism. Furthermore, these studies can show whether the basis for the overlap is genetic, environmental, or both. For example, Genetic liability for alcoholism also may interact and combine with genetically influenced characteristics other than sex  or with environmental influences , resulting in varying outcomes for different people. More sophisticated behavioral genetic models are being developed that can address how these factors may mediate the development of alcoholism.As with any research design, results from twin studies are generalizable only to the extent that twins are representative of the entire population. Evidence indicates that the prevalence of psychiatric symptoms among twins does not differ from the prevalence in the general population . People Some studies obtain alcoholic twin subjects from treatment settings, whereas other studies assess twins from the general population. Because treatment-based studies typically identify only a minority of the affected cases in the general population, researchers must determine whether these cases differ in severity or symptomatology from those not included in the research. A particularly important consideration for twin research is whether twins in studied pairs are more likely to be similar than pairs in the general population. For example, twin pairs in which both twins are alcoholic may be more likely to participate in treatment or attract the attention of researchers (and thus be included in studies) than pairs in which only one member is alcoholic, thereby leading to biases in estimates of twin similarity . Alcohol consumption, however, is an integral aspect of social activity for many people, and spouses tend to resemble each other regarding alcohol use and misuse . Gene-environment interactions occur when persons with high genetic risk are particularly sensitive to the effects of alcoholism-promoting environments. Twin data can be applied to these issues if a range of environmental conditions is available for study. Data from studies of adopted children also are useful for testing for gene-environment interactions, but these studies have other methodological limitations. A further limitation of the twin study design is that the separation of liability into distinct genetic and environmental components may be invalid if sizable gene-environment Twin studies can be a powerful tool for addressing important questions about the development and course of alcoholism, including the role of genetic and environmental factors, as well as sex differences in the mechanisms and the magnitude of genetic and environmental influences. The results from opposite-sex twin studies, along with those from adoption and twin-family studies, promise to improve our understanding of the roles of genetic factors and of experiences and their interactions in the causes of alcoholism.