Commercial genetic testing laboratories are increasingly employing genetic counselors. As a result, the role of these or many genetic counselors is shifting from primarily direct patient counseling in clinics and hospitals to more laboratory-centered activities that involve case coordination, customer liaison, variant classification, marketing, and sales. Given the importance of these commercial entities to the genetic counseling profession, this commentary examines the current financial situation of four publicly traded, genetic testing companies. It also explores how the various roles of genetic counselors are likely to be affected by the financial pressures these companies face. They arBRCA1 and BRCA2) but also prenatal genetic testing such as cell-free DNA screening , carrier screening, and microarray for prenatal diagnosis. It is important to note that many commercial genetic testing laboratories, including those we are reviewing, also have genetic-based products for somatic oncology . While such tests have potential diagnostic and therapeutic implications, they generally have less direct genetic counseling implications. As an example of tests for which genetic counseling is important are Myriad Genetics BRACAnalysis  or the myRisk®, 28-gene panel for evaluating a patient’s risk for developing various hereditary cancers. In contrast, Myriad’s Prolaris® test analyzes transcripts in prostate cancer for prognostic purposes and is not expected to have significant implications for genetic counseling.This article examines the financial health of such companies because this will affect their long-term influence on the practice of medical genetics and the employment of genetic counselors. It is primarily focused on publicly traded companies that offer genetic testing with significant genetic counseling implications. This includes not only genetic testing for hereditary cancer susceptibility . Some of the publicly traded companies that offer genetic testing are part of larger companies that perform many types of laboratory testing  with genetic testing being a smaller percentage of total sales. We choose not to look at these companies because their multifaceted activities limit our ability to delineate the financial aspects for their laboratories’ genetic tests. Eliminating companies that do not match our criteria, we are left with four publicly traded, commercial laboratories whose primary focus is genetic testing—Myriad Genetics, Natera, Invitae, and CombiMatrix.Before analyzing these four companies, it would be useful to review the typical trajectory of start-up companies in relation to their funding sources Fig. . This wiWhen a company becomes public, it typically tenders an initial public offering (IPO) which is the first time the company’s stock is offered to the public. Proceeds from the IPO are used to support the company’s growth. After a company becomes public, it may not be able to generate sufficient revenue to fuel its expansion, so it raises additional monies by having another public offering (2nd PO) or forming partnership with other companies.Typically, after about 5 years, the company’s early investors expect to make money off their investment in what is called a return-on-investment (ROI) event. This often happens after a company becomes public and has an IPO because it is easier for publicly traded shares to be sold. Another ROI event is when either a private or public company is bought by another company. Finally, if a company becomes profitable, it may provide a ROI by offering dividends to its shareholders. Other shareholders such as the founders and employees can also benefit financially from these ROI events. This makes a ROI event a key objective for start-up companies.Our four public companies under consideration—Myriad Genetics, CombiMatrix, Natera, and Invitae—went through these growth routes to have IPOs in 1995, 2007, 2015, and 2015, respectively. It is also of interest to consider the trajectories of some genetic testing companies that have been acquired. Sequenom, one of the pioneering companies in non-invasive prenatal screening, had their IPO in 2000 but was bought in 2015 by Laboratory Corporation of America (Lab Corp), a large and diversified testing company. Ariosa, another company specializing in non-invasive prenatal screening, never went public but was bought as a private company by Roche in 2015. GeneDx, a gene testing company founded in 2000, also never went public and was bought in 2006 by another large, general laboratory testing company, Bio-Reference Laboratories, which was in turn bought by Opko Health in 2015. Sequenom, GeneDx, and Ariosa continue to operate as semi-independent subsidiaries. In contrast, Signature Genomics, formed in 2003, was a leader in microarray-based testing, and was bought as a private company in 2010 by PerkinElmer, but subsequently shut its operations in 2014. During the review process of this paper, it was announced that CombiMatrix would essentially be acquired by Invitae via a merger process.A comprehensive assessment of the publicly traded, genetic testing companies can be obtained from their annual 10-K reports. Public companies are required to file these yearly with the U.S. Securities and Exchange Commission (SEC) and must include an extensive tally of financial information. Table It appears that Myriad is the only company, of our four selected, that was profitable in 2016. Of its total revenue of $723 million, the bulk of their revenue continues to result from their hereditary cancer testing franchise ($632 million). The other three companies, Invitae, Natera, and CombiMatrix, all had substantial net losses in 2016  by the total number of outstanding shares. Myriad’s market capitalization decreased from approximately $2.5 billion at the beginning of 2015 to approximately $1.2 billion at the beginning of 2017. Similarly, Natera’s market capitalization decreased from approximately $1.1 billion soon after it went public on 2 July 2015 to approximately $620 million at the beginning of 2017. Invitae’s market capitalization decreased from approximately $550 million soon after it went public on 12 February 2015 to approximately $300 million at the beginning of 2017. CombiMatrix market capitalization has been about $15 million over the last several years despite several swings up and down.In the reports filed with the SEC, each of these companies acknowledge their challenging financial situations and each company plans to respond to these challenges in various ways. Their 10-K annual reports detail their strategies for doing such.Mayo v. Prometheus and 2013 Molecular Pathology v. Myriad Genetics Supreme Court decisions . Five companies including Natera, recently formed the Coalition for Access to Prenatal Screening (CAPS) to “encourage appropriate legislative measures and reimbursement coverage policy changes” for prenatal screening using cell-free DNA (cfDNA).The role of health professionals in the testing process is also an issue being experimented with by genetic testing companies. There is a continuum of approaches from companies for how much genetic testing should be integrated into the broader healthcare system. While some tests are only available through health professionals, an approach pioneered by 23andMe is to offer direct-to-consumer tests that minimize involvement of health professionals. More companies, such as Natera, Invitae, and Myriad are offering a mixed approach, requiring a healthcare provider to order tests but also advertising directly to patients. Companies including Color Genomics take an approach that has been termed “physician-mediated” direct-to-consumer testing, in which the testing is requested and purchased directly by the patient, and the order is authorized by a physician that is contracted with the laboratory. How companies approach this issue will likely continue to evolve as the repercussions of recent FDA policies get integrated into the marketplace  or whole genome sequencing (WGS) instead of multi-gene panels. Illumina has announced a new sequencing machine that will be able to do a complete genome for $100 in the next several years Illumina . CompaniConceivably, the cost of genetic testing will be low enough that everyone, or at least the majority of people, will have their genome sequenced. Analysis and interpretation would then only be done on a patient-specific and population-based basis as is currently done for testing and screening purposes. Perhaps even current criteria, or some evolution of them, could be used for choosing which analysis and interpretation needs to be done on whom. If this future scenario is correct, then the hereditary genetic testing market will require two services; large-scale, high-quality genome sequencing and genomic information storage followed by analysis and interpretation of said data. Genome sequencing itself will likely become a “commodity” in that it will be a service that is widely available and similar in quality among different companies. Thus, it will be difficult to have it sufficiently profitable to fund genetic counseling services as currently provided.There are a few potential approaches by which companies could make money on the genetic analyses/interpretation component of this possible future. One is to maintain propriety  information on genotype-phenotype correlations. This has been Myriad’s approach for maintaining market share of BRCA testing. But the increased competition in BRCA testing has caused more publicly available BRCA genotype-phenotype correlation information to be available. There has been an increase in the sharing of information among commercial and non-commercial laboratories that has decreased the amount of proprietary information  Farr .The bulk data of genetic information without personal identifiers may also be monetized due to its inherent value to biopharmaceutical companies for the development of new therapies. Genotype-phenotype correlations could provide leads for new drug targets. Purveyors of “personalized medicine” could also use this information for the aim of tailoring biopharmaceuticals for specific populations that share certain genetic markers. Genetic testing companies have been monetizing this information for many years now and this trend should be expected to continue and expand. For example, 23andMe has been selling their customers’ genetic data to biopharmaceutical companies since it was founded and has even recently begun their own biopharmaceutical development program Herper .We conjecture several potential outcomes for these medical genetic testing companies as they attempt to provide a suitable ROI to their investors and other shareholders. Each of these potential trajectories will have a varying degree of impact on genetic counselors.Perhaps this is most clearly seen in the changing role of genetic counselors over the past decade. Because of limited support from clinics, Medicaid, Medicare, and insurance companies, many genetic testing companies directly provide pre- and post-test genetic counseling services to patients. Genetic counselors from these companies are also made available to advise physicians. With the exception of Myriad’s waning BRCA franchise legacy, most of these companies are not currently profitable and they must be funding their genetic counselor services from the monies they raise from investors. If they continue to be unprofitable or not sufficiently profitable to justify an acceptable ROI—then they will have difficulty raising additional funds. Thus, their penchant to support genetic counseling services will likely decrease.At the same time, companies are employing genetic counselors in less direct counseling roles such as sales and marketing, report writing, and variant interpretation. There is also the risk that employment of genetic counselors for these non-counseling activities may become less supported as investor funding becomes less available.While companies could become profitable, and stay independent from decreased testing costs and increased market size, independence does not imply a future reliance on genetic counselors. Companies could change their focus away from services that require genetic counselors. Myriad, for example, is investing more on non-hereditary testing, a pivot which will likely reduce their need for genetic counselors.Mergers between genetic testing companies could also have varied effects on genetic counselors. For example, counseling more directly related to individual patients is likely to scale with the number of test conducted by the merged entity, whereas jobs less directly involved in counseling individual patients  may be reduced by such mergers as the new entities attempt to become more efficient.Companies could maintain their interest in hereditary and prenatal genetic services and such services could evolve into more analytic and interpretative services . This would then likely cause an increased demand for genetic counselors to perform such activities. This would include not only work in the development and implementation of such genetic information technology but also patient counseling before and after specific analytic and interpretative services are rendered (akin to pre- and post-test counseling). In this scenario, it is likely that genetic counselors would be valued for their other non-counseling activities in customer liaison and marketing and sales.Genetic testing companies could also be bought by bigger reference laboratories. This would seem to be an attractive “exit strategy” for struggling companies given the current market and as evidence by the acquisition of Sequenom by Lab Corp and Ariosa Diagnostics by Roche Diagnostics for example. This could conceivably be the predominant plan for these independent companies as reflected in their keen interest in acquiring market share with the hope of seeming more valuable to potential buyers. It is not clear how such consolidating acts would affect genetic counseling. Some of the larger diversified companies provide pre- and post-test genetic counseling. Others refrain from any genetic counseling and adhere to a more restricted role of providing the testing with limited direct patient contact. LabCorp, for example, employs over 100 genetic counselors to provide direct patient care, while Quest Diagnostics provides neither pre- nor post-test counseling . In the future, these larger testing companies could also be less inclined to provide counseling services if their genetic testing volume were more dependent on bundling genetic tests with their panoply of other non-genetic tests and less dependent on offering genetic counseling services in order to facilitate the uptake of genetic tests. Given the anticipated decreased margins of genetic testing, greater interest will be had in off-setting genetic counseling costs to other payers.The continued transformation of healthcare systems could make genetic companies more valuable to other types of large companies. For example, Konica Minolta, a diversified, healthcare technology company, recently acquired Ambry Genetics (a privately held genetic testing company) so as to develop its precision medicine offerings. Highly profitable, electronic medical record (EMR) companies may also be inclined to acquire genetic information companies to better integrate genetic information into the wider sphere of electronic medical records Harris . It is lChanges in healthcare policy could also affect the role of genetic counselors who work for laboratories Pollock . Some stGenetic counselors are increasingly moving from their direct patient-care roles in clinics and hospitals to a more diversified role in commercial genetic testing companies. Several business and market forces are shaping the future role of genetic counselors in companies as these firms strive to achieve profitability and provide a suitable return-on-investment to their investors. Although not widely perceived, many commercial genetic companies are currently unprofitable. These companies have been relying on investments from outside groups to support their patient genetic counseling services and their more business-related activities of genetic counselors . It is unclear whether they will be able to create sufficient profits or continue to raise enough capital to continue to support such activities. In addition, some of the genetic testing companies are likely to be bought by bigger companies which will affect the role of genetic counselors. Finally, the entire paradigm of genetic testing is likely to shift to a one-test-many analyses/interpretation model in which the one test will be WES or WGS. How this all plays out and affects the genetic counseling work force is in flux and will require genetic counselors to be highly adaptive to these changing work environments.


Zostera marina (eelgrass) is of critical importance to ecosystem functions including fisheries. Here, we estimate connectivity of Z. marina in the Skagerrak–Kattegat region of the North Sea based on genetic and biophysical modelling. Genetic diversity, population structure and migration were analysed at 23 locations using 20 microsatellite loci and a suite of analytical approaches. Oceanographic connectivity was analysed using Lagrangian dispersal simulations based on contemporary and historical distribution data dating back to the late 19th century. Population clusters, barriers and networks of connectivity were found to be very similar based on either genetic or oceanographic analyses. A single‐generation model of dispersal was not realistic, whereas multigeneration models that integrate stepping‐stone dispersal and extant and historic distribution data were able to capture and model genetic connectivity patterns well. Passive rafting of flowering shoots along oceanographic currents is the main driver of gene flow at this spatial–temporal scale, and extant genetic connectivity strongly reflects the “ghost of dispersal past“ sensu Benzie, Maintaining and enabling evolutionary processes within meta‐populations are critical to resistance, resilience and adaptive potential. Knowledge about which populations act as sources or sinks, and the direction of gene flow, can help to focus conservation efforts more effectively and forecast how populations might respond to future anthropogenic and environmental pressures. As a foundation species and habitat provider,  Samples with ambiguous or rare alleles were re‐amplified and re‐genotyped for confirmation. We succeeded in amplifying all individuals at all loci.PCR products were diluted 1:100 , and fragment analysis was performed on an Applied Biosystems 3730 DNA Analyser with a 350 ROX internal size standard added to each well. Fragments were scored automatically using GeneMapperZ. marina  were identified and removed using RClone ) taking into consideration Hardy‐Weinberg equilibrium (HWE), and a threshold of 0.05.Because seagrasses can spread clonally via rhizome extension, a genetic individual (genet) may consist of hundreds or thousands of shoots (ramets) covering several metres. Even though a sampling distance of 1–1.5 m is generally adequate for Null alleles were tested for with MicroDrop  of 200 and a range of generations t (0–100) with 1,000 replicates and 100,000 batches.To analyse the statistical power of our set of microsatellites to discriminate clonal replicates, we calculated the probability of identity (PI) in GenAlEx 6.5  with the formula MLG‐1/N‐1, where N is the number of ramets  in R 2.15.3.We calculated heterozygosity‐based estimates in GenAlEx 6.5  with a burn‐in of 10,000 sweeps followed by 25,000 sweeps, with 100 independent runs conducted for each Kmax. The independent runs were averaged and compared to assess convergence. The average deviance information criterion (DIC) for each value of Kmax was used to evaluate the most likely number of genetic clusters by determining Kmax at which a higher number of parameters did not improve the model significantly. We used pophelper  and GENECLASS2  format from the Norwegian Environment Agency, the Swedish County Administrative Board of Västra Götaland and the Danish Nature Agency. Along the Swedish Skagerrak coast, distribution was based on satellite image analyses  circulation model and the offline Lagrangian particle tracking model TRACMASS .We employed a clustering method to identify partial dispersal barriers based on modelled dispersal probabilities in the seascape  and log10(multigeneration matrix/historic multigeneration matrix + 1e‐30).To test for isolation by oceanography (IBO), we correlated genetic distance with minimum oceanographic dispersal probabilities  defined here as oceanographic distance. We used minimum dispersal probability to generate a symmetric matrix of dispersal, because it may arguably be best correlated with (symmetric) geographic and genetic distance  and oceanographic distance applied to modelled dispersal probability matrices for single‐generation‐extant, multigeneration‐extant and multigeneration‐historic dispersal probabilities, and to highlight sites that are central to connectivity. Networks were drawn using the R packages igraph  was close to the intended value of 0.05 (Table The probability of identity (PI) by chance for a 20‐locus 05 Table .3.2MLG was shared among two populations  separated by 120 km. This is within our estimates for single‐generation northward oceanographic dispersal and reattachment at Gåsö. This genotype has three alleles at three loci not otherwise found at Gåsö. Population differentiation using pairwise shared allele distances  or FST (0.01–0.21) was significant  generated six oceanographic clusters with partial barriers among them Figure b. Connec3.6~10 to ~400 km. A significant pattern of isolation by “sea distance“ (IBD) with genetic differentiation (Dps) is evident . IBO was strongest for the dispersal probability based on the historic distribution of Z. marina, which reached a correlation coefficient as high as −0.59 (Table FST‐related genetic indices were similar (not shown). IBO was also observed for the correlation between genetic asymmetric migration rates and the directional dispersal probabilities  was similar to the TESS analysis and the network based on genetic distance . Interestingly, the highest values of allelic richness are found in the centre of the Skagerrak–Kattegat region around the Læsø Islands , where eelgrass was historically abundant  and multigeneration‐historic dispersal, explaining ~40% of genetic variability  distribution. In fact, it may even reflect the “ghost of original colonization“ after the last glacial maximum. Such mismatches have often been observed and explained by a time lag between current demographic processes and population genetic structure  and Gåso (G‐SG) have become oceanographically isolated over the last century Figure c,d, but 4.4Cross‐validations of the genetic and oceanographic modelling data show good agreement and provide different insights into the structure and connectivity of populations. The genetic survey integrates over many gene‐flow mechanisms and captures regional population history through deep time. In addition, diversity metrics and population differentiation, as well as inferences about demography, can only be determined with genetic data. In general, genetic methods are less well suited for inferring the spatial component and directionality of dispersal.In contrast, biophysical models provide insights about the generational time depth of dispersal and the shaping of populations with respect to barriers and circulation patterns. Although single‐generation dispersal may be a weak predictor, the ability to simulate a range of generational time depths through stepping‐stone simulations is a distinct advantage. When historical distribution records are also available, as is the case here, predictions of where populations should or could persist become very powerful. Biophysical models also offer better spatial coverage than is feasible with most genetic sampling efforts. The main shortcoming of biophysical models is that they say nothing about demographic history, adaptive potential or genetic health of the species in question. In terms of resource investment, initial front‐end development of suitable oceanographic models is both time‐consuming and cost‐intensive, and limited to the specific region of interest. In the absence of such oceanographic models, genetic surveys remain the best alternative. Microsatellite markers are available for many seagrass species, and an assessment such as the one reported here is standardized and easy to perform.The added value of the dual approach further strengthens conservation planning and eventual monitoring of a particular management plan, because it is possible to rerun a biophysical model with different data and under different scenarios to reflect adaptive management  will require further local management. Likewise, it is critical to protect the small meadows off the Island Læsø (7‐LS and 7‐480), remnants of the large historic offshore population that appear key for connectivity  in the Swedish Kattegat, where major losses of eelgrass have occurred and continue to occur. The oceanographic isolation could be related to two large rivers that enter the Kattegat just south of this area and may create a dispersal barrier. The genetic isolation indicates that natural replenishment from outside is unlikely, making protection of the remaining Z. marina, but more and new measures are needed to improve environmental conditions. In addition to increased efforts to reduce nutrient input to coastal waters, management should consider measures to enhance depleted populations of large predatory fish that would restore the trophic structure of coastal ecosystems . Despite protection by international conventions and directives, and large MPA networks (covering approximately 15% of the Skagerrak–Kattegat; Moksnes et al., 5Z. marina in the Skagerrak–Kattegat and is the main driver of observed population genetic structure and meta‐population dynamics. We show that the meta‐population is driven by stepping‐stone dispersal over many generations and that current genetic differentiation is best explained by connectivity considering the historic Z. marina distribution. This “;ghost of dispersal past“ is also evident in the distribution of allelic richness, where highest diversity is found in the Læsø Island area, where major historic losses occurred. Using two complementary methods to assess connectivity enabled us to investigate and compare dispersal and migration patterns at different temporal scales. In this study, we found strong concordance among the two methods in detecting sources, sinks and connectivity patterns. This information can be used to pinpoint areas where local protection is necessary or where populations could/should be managed in a network approach for MPAs. The temporally more dynamic oceanographic modelling was also able to highlight areas where connectivity has become limited over the last decades. Such information is additionally helpful for marine spatial management to pinpoint geographic areas where there is a need to improve environmental conditions. The large geographic scale study presented here forms a framework for future detailed assessments of connectivity and genetic diversity on smaller scales within the coastal archipelagos and fjords. Such multiscale information should aid managers at the local, national and international levels in marine spatial planning, for example, for the identification of hubs, and important extant or historic source meadows that should be targeted for protection, or key areas for restoration.Our analysis supports the notion that passive rafting of flowering shoots by oceanographic currents influence patterns of gene flow of https://doi.org/10.5061/dryad.2139f.The matrix of microsatellite genotypes and the oceanographic matrices of dispersal probability can be found on Dryad:  Click here for additional data file.


As this finding has substantial implications for social and health-care policies in inner cities with poor neighbourhoods, we would like to address two points:With great interest we read the paper of Sariaslan et al.1 observed that the heritability estimate for living in deprived neighbourhoods was considerably lower in the twin study (41%) compared to the sibling study (65%), with both twins and siblings being of younger adult age (twins 23−24 years and siblings 31−35 years of age). Whether living in poverty is more strongly influenced by genetic, including epigenetic, factors or by environmental factors and their interplay thus remains to be explored in further studies.First, not reported in the abstract but in the results section, Sariaslan et al.2 was 0.26, pointing to ‘substantial environmental contributions to socioeconomic status.' Genetic factors predisposing to living in a deprived neighbourhood can also be associated with unequal access to the housing market for phenotypically visible migrants and minorities due to discriminatory practices. We recently observed that, independent of personal income and education, the respective mental health burden was substantially higher in subjects living in socially deprived neighbourhoods;3 this effect was substantially stronger in migrants than in non-migrants and  was mainly attributed to perceived social exclusion and discrimination. In Europe, minorities tend to live in inner cities, and schizophrenia rates are substantially increased in first- and second-generation migrants.4 These findings call for a specific exploration of the interaction between genetic factors, social exclusion and schizophrenia manifestation.6Second, genetic factors contributing to neighbourhood deprivation are not necessarily limited to heritable cognitive capacities. For instance, the genetic correlation of general intelligence and socioeconomic status in the study of Marioni 7 and such effects may well confound the observed heritability estimates of living in deprived neighbourhoods. These considerations call for study designs that look at complex genotype−phenotype interactions and assess not only genetic but also epigenetic effects.8As a general remark, we would argue that both supra-additive genetic effects and the presence of complex genotype−phenotype interactions could lead to an overestimation of genetic effects, which could in turn override environmental effects in twin study designs that assume only additive interactions. Moreover, there is emerging evidence that the effects of genetic variation on behavioral phenotypes are amenable to targeted behavioral interventions such as cognitive training,


In the present study, we assessed the genetic diversities of the Chinese Kazak ethnic group on the basis of 30 well-chosen autosomal insertion and deletion loci and explored the genetic relationships between Kazak and 23 reference groups. We detected the level of the expected heterozygosity ranging from 0.3605 at HLD39 locus to 0.5000 at HLD136 locus and the observed heterozygosity ranging from 0.3548 at HLD39 locus to 0.5283 at HLD136 locus. The combined power of discrimination and the combined power of exclusion for all 30 loci in the studied Kazak group were 0.999999999999128 and 0.9945, respectively. The dataset generated in this study indicated the panel of 30 InDels was highly efficient in forensic individual identifcation but may not have enough power in paternity cases. The results of the interpopulation differentiations, PCA plots, phylogenetic trees and STRUCTURE analyses showed a close genetic affiliation between the Kazak and Uigur group. In the last few years, a novel polymorphic marker, insertion and deletion polymorphisms (InDels), gained increased concern and attention in the field of medical and forensic genetics. InDels as biallelic markers combine desirable characteristics of both single nucleotide polymorphisms (SNPs) and short tandem repeats (STRs), becoming promising genetic markers for forensic purposes. InDels could be readily analyzed by capillary electrophoresis which is relatively common in the forensic DNA laboratories . Moreovehttp://en.people.cn/102759/102835/7562907.html). To date, one commercial kit, Qiagen Investigator DIPplex reagent , has been applied in multiplex amplification of 30 autosomal InDels. Previous population studies have been done and published using the kit p values g Kazak1 , Tujia [g Kazak1 , South Kg Kazak1 , She [14g Kazak1 , three Hg Kazak1 , 14, 16 g Kazak1 , four Eug Kazak1 –21  were conducted between Kazak group and other 23 populations utilizing the softwares of EIGENSOFT 6.0.1 [K  and thus analyze population structure. Although the 30 InDel loci are not ideal ancestry informative markers and have limited differentiation power, they were still efficient to distinguish ancestries of the studied Kazak and other populations to some extent. As shown in Figure K value. Of course, it is still need more effective ancestry informative markers to identify and estimate ancestry components of admixtures better in the later studies. Moreover, we performed the population structure analysis again with the STRUCTURE program v2.2, which is given in Figure K=2, the Asians and Europeans were almost entirely filled with yellow and blue component, respectively. Meanwhile, the Kazak, Kazak1 and Uigur groups represented a mixture of blue and yellow components. Uigur and Kazak groups could be better separated from Europeans and East Asians at K=3, which was in accordance with the result of output posterior probabilities that K=3 was the most appropriate and suitable configuration  reveals the genetic divergence between different populations. Populations with similar allelic distributions have small genetic distances. In order to estimate the genetic distances between the Kazak and 23 reference populations, we calculated AD values on the basis of 30 InDels. As presented in Table AD=0.0007), followed by Uigur group with AD=0.0016. And the largest genetic distance was found with Mexican Amerindian group (AD=0.0526), which were consistent with the Fst and PCA results mentioned above.Genetic distance  method was in the Figure We further conducted phylogenetic reconstruction on the basis of two different methods. An unrooted tree was constructed by the PHYLIP software (version3.6) based on the allele frequencies of all InDel loci, revealing the genetic relationships between studied kazak group and other compared populations. As shown in the Figure Bloodstain samples were collected from 513 un- related healthy individuals  from Kazak group residing in Xinjiang Uygur Autonomous Region, China. Written informed consents were acquired from all participants involved in this study. The research was carried out according to the human and ethical research principles of Xi’an Jiaotong University Health Science Center, China and approved by the ethics committee of Xi’an Jiaotong University Health Science Center. In the process of collecting samples, it must be ensured that any two individuals have no any blood relationship within at least three generations. Human genomic DNA was isolated from bloodstain samples utilizing the method of Chelex-100 .A multiple PCR amplification with fluorescent of autosomal 30 InDels was conducted with Investigator DIPplex reagent  in a single multiplex reaction on GeneAmp PCR System 9700 thermal cycler  following manufacturer's instructions. InDel genotyping was performed with capillary electrophoresis on ABI 3500 Genetic Analyzer  and analyzed by GeneMapper v3.2 software .p values were estimated by Arlequin software (version3.0) using the AMOVA method. The heat map of pairwise Fst was carried out in R statistical software v3.0.2. The SNPAnalyzer  was used to test LD for all pair-wise InDels. Two PCA plots were carried out in EIGENSOFT v6.0.1 software and MATLAB 2007a , respectively. Population genetic structure analyses were performed with the ADMIXTURE v1.23 and STRUCTURE v2.2 programs. Two phylogenic trees were conducted by PHYLIP (version3.6) software via allele frequencies of 30 InDels, as well as MAGA software (version5.0) based on AD values, respectively.Allele frequency distributions and forensic statis-tical parameters including HWE, MP, Ho, PE, DP, PIC and TPI of 30 InDel loci were computed with the modified Powerstat (version1.2) spreadsheet . And the He values were calculated as described previously . The locIn this study, we obtained the allele frequencies and forensic parameters of the autosomal 30 InDels for the research of population genetics and forensic sciences. And we found the panel was highly efficient in forensic individual identification but could only be used as supplementary markers for STR loci in paternity cases. The results of the interpopulation differentiations, PCA plots, phylogenic trees and STRUCTURE analyses indicated a close genetic relationship between Kazak and Uigur groups. In order to better understand the origin and genetic background of Kazak group, further study should be conducted in later research.


Ovis gmelini musimon × Ovis sp.) population in southern France 60 years after its introduction. Based on multilocus genotypes at 16 loci of microsatellite DNA , we identified three genetic groups for females and two for males, and concurrently defined the same number of socio‐spatial units using both GPS‐collared individuals (n = 121) and visual resightings of marked individuals (n = 378). The socio‐spatial and genetic structures did not match, indicating that the former was not the main driver of the latter for both sexes. Beyond this structural mismatch, we found significant, yet low, genetic differentiation among female socio‐spatial groups, and no genetic differentiation in males, with this suggesting female philopatry and male‐biased gene flow, respectively. Despite spatial disconnection, females from the north of the study area were genetically closer to females from the south, as indicated by the spatial analysis of the genetic variability, and this pattern was in accordance with the common genetic origin of their founders. To conclude, more than 14 generations later, genetic signatures of first introduction are not only still detectable among females, but they also represent the main factor shaping their present‐time genetic structure.Fine‐scale spatial genetic structure of populations results from social and spatial behaviors of individuals such as sex‐biased dispersal and philopatry. However, the demographic history of a given population can override such socio‐spatial factors in shaping genetic variability when bottlenecks or founder events occurred in the population. Here, we investigated whether socio‐spatial organization determines the fine‐scale genetic structure for both sexes in a Mediterranean mouflon ( SpatialStudying the genetic structure is particularly suitable in (re)introduced populations as (re)introductions and translocations can have long‐term impacts on the genetic makeup of populations. They generally involve only a limited number of individuals thus retaining only a subset of the total genetic diversity of the source population, the occurrence of a strong genetic bottleneck being a likely event , studies are still scarce on species where group structure can be quite loose and characterized by fission–fusion dynamics, such as the large herbivores .The fine‐scale spatial genetic structure of gregarious species is also strongly influenced by their social structures  population in the Caroux‐Espinouse massif (southern France) 60 years after 19 founder individuals of diverse origins were introduced. Ungulates are especially well‐suited for such a task as most of their populations were (re)introduced worldwide during the last century  in the Caroux‐Espinouse massif . Females seem to be philopatric  and sexually mature from 1.5 years of age , that is short in comparison with the mouflon generation period . DNA extraction from hairs was performed on plates of 96 extraction columns  in the presence of negative and positive extraction controls. The samples were lysed overnight at 56°C (according to manufacturer) and then DNA was purified and isolated using purification columns and vacuum filtration. DNA was then eluted to obtain one tube of 140 μl concentrated at 20–100 ng/μl and stored in numbered 96‐tubes plates in a freezer at −20°C. For each DNA sample, 16 microsatellite markers  and consisted of an initial denaturing step  followed by 35 cycles of denaturation , annealing  and extension ; cycles were followed by a final extension . PCR products were resolved on a capillary sequencer ABI PRISM 3130 XL (Applied Biosystems) with formamide (denaturing conditions) and an internal size marker . Migration conditions and genotypes of control samples were systematically checked. The electropherograms were analyzed using GENEMAPPER software (Applied Biosytems/Life Technologies) and analyzed independently by two analysts to determine the allele sizes for each marker of each individual. Reading errors were resolved, and ambiguous results were considered as missing data. Using such reading process, genotyping error rates were estimated to be <2% in our dataset . Individuals were genotyped between one and four times to obtain at least 13 markers with no missing data.Genotyping was performed by the Antagene laboratory , as different genetic and socio‐spatial structures between the sexes were expected due to (i) their different utilization of space  in our population and assign each individual to a given cluster. K was determined by sequentially running a K‐means algorithm with increasing values of K. We used K ranging from 1 to 20 clusters and 1,000,000,000 iterations with 1,000 different starting values for each run of the algorithm. The clustering solutions were compared using the Bayesian Information Criterion (BIC). The optimal number of clusters was the one minimizing the BIC or, for competing solutions, the first one for which there was a marked break with the preceding BIC values. Once the optimal number of clusters had been identified, we used a cross‐validation procedure  to keep the optimal number of principal components (PCs) in the DAPC. The DAPC itself was then applied, performing first a Principal Component Analysis (PCA) on individual genotypes and then a Discriminant Analysis (DA) on principal components to assign each individual to a cluster with membership probabilities. We retained the minimal number of discriminant functions in order to maximize the total genetic variance explained. Genetic clustering reliability was assessed by looking at re‐assignment successes and proportions of individuals having membership probabilities lower than 0.80 for each genetic cluster.We first used a DAPC  proposed by Jombart et al. n = 5,291) of 378 marked animals  collected from 1990 to 2015 and GPS locations  of 121 additional animals  fitted with GPS collars from 2006 to 2016 and all having a fixed home range. Data from animals fitted with GPS were randomly subsampled to distribute evenly the number of GPS locations and visual resightings in the dataset. This step was performed to avoid an oversampling of a very local zone over the study area which may artificially bias the cluster analysis. We retrieved an average number of 20 locations for GPS females and males, both being in the order of magnitude of the average number of visual resightings per individual , males: 9.7 ± 8.9). Visual resightings had the advantage of providing rough spatial behavior from a large number of animals. Conversely, GPS data provided an intense and accurate monitoring  representative of habitats and time periods with very low (or null) visual detection probability  but concerned a more restricted number of individuals. We pooled both datasets to compute for each trap the arithmetic center of locations of all animals caught in that trap. A random spatial noise of a few meters using a uniform distribution was introduced to avoid getting duplicate coordinates for animals trapped at the same trap.In complement to the DAPC, we used a sPCA . We then performed a hierarchical cluster analysis (with unweighted pair group method of aggregation) based on the dissimilarities among arithmetic centers (Euclidean distances) to identify relevant trap clusters. The number of socio‐spatial units selected by hierarchical clustering was set to be equal to the number of genetic clusters so we could assess if the genetic structure matched the socio‐spatial structure. Once the socio‐spatial units were defined, each genotyped individual was assigned to one of them based on its last trap of capture. Finally, we computed the proportion of individuals from each socio‐spatial unit assigned to the same genetic cluster to assess the extent to which socio‐spatial units and genetic clusters match.We defined the socio‐spatial units  present in the population , allelic richness  and a heterozygosity excess is detectable  using the approach implemented in Bottleneck v.1.2.02 software .33.1Among the genotyped samples, 237 individuals with fixed home range and known socio‐spatial unit had at least 13 successfully genotyped markers. Two outliers and five pairs of twins were identified among these individuals. Data on the 230 remaining individuals  were considered in subsequent analyzes and were assigned to a socio‐spatial unit (see below). Comparison of the observed genotypes with the distribution of randomized genotypes generated with the program MICROCHECKER revealed that there were no null alleles in the dataset.3.2K = 3 because the break in BIC values occurred between two and three clusters and K = 3 was the only clustering solution with nonoverlapping clusters  for females, with only the first positive axis retained (Supplementary p = .84). The first sPCA axis accounted for most of the spatial genetic structure, covering a high proportion of the spatial autocorrelation and most of the variance in the genetic data . The first eigenvalue was 0.078 while the next eigenvalues were lower than 0.040 .The sPCA results corroborated those obtained with the DAPC. There was a significant global structure  and the other in the southern part (“Sm”) of the study area , we set the number of socio‐spatial units in the hierarchical clustering of spatial data to three for females and to two for males. For females, the three socio‐spatial units corresponded to groups of traps located in the northern, central and southern parts of the study area  and confirmed by Fis values which were not significantly different from zero  while it was very low and not significantly different from zero for males . All pairwise Fst values were significantly different from zero only in females , we found evidence supporting a recent bottleneck  for both sexes under both models of evolution  and Sf (64%) were grouped in genetic cluster 3 of the DAPC while females from socio‐spatial unit Cf were assigned to genetic cluster 1 (41%) or 2 . However, this socio‐spatial organization effect was not detected by DAPC and sPCA suggesting that other processes, such as a residual effect of introduction history . We also found a low but significant genetic differentiation between female socio‐spatial units   present in the founded population, they have more detrimental effects on the number of alleles than on heterozygosity  .We confirmed the previously highlighted low genetic differentiation between the different socio‐spatial units of the studied population , our results confirm the philopatric behavior of females  in the Cf socio‐spatial unit , for which Biebach and Keller (Odocoileus virginianus) populations regardless of their geographic separation. Finally, at intrapopulation and small geographic scale like in the present study, Simpson et al.  as they reveal important factors shaping the genetic structure of mouflon populations.Our findings highlighted important elements for wildlife management. First, despite the existence of male‐mediated gene flow, we showed that introduction history had long‐term consequences on intrapopulation spatial genetic structure, probably due to the high female philopatry which has favored the persistence of the historical genetic signature of introductions. These results confirm the importance for wildlife managers to account for sex‐specific spatial behaviors and gene flow when determining introduction/reinforcement strategies to ensure genetic mixing among introduced individuals from different sources. Indeed, as introductions often involve only a limited number of founders (Kekkonen & Brommer, All the authors disclose any potential sources of conflict of interest.Microsatellite and spatial data sets will be available from the Dryad Digital Repository.M.G., G.B., S.D., and D.M. conceptualized and designed the research. M.G., G.B., and P.M. conducted field sampling. J.A. conducted laboratory genetic analyzes. E.P., M.G., S.D., and P.M. conducted data analyzes. E.P., M.G., S.D., G.B., and P.M. interpreted the results. E.P., M.G., S.D., and G.B. wrote the manuscript with the help of all co‐authors. Click here for additional data file.


Heteromys desmarestianus goldmani). We sampled 136 individuals from one forest fragment and three coffee farms representing varying degrees of management intensity in southern Mexico. Using microsatellite markers, we evaluated the genetic structure of H. d. goldmani in the study area. Our results show higher genetic differentiation and lower connectivity for individuals within high and medium intensity coffee farms than for those near and within the forest fragments. Our results suggest that the population structure observed is driven by landscape characteristics other than distance.Most of the natural habitat in tropical regions exists as scattered fragments embedded in a matrix of different agricultural uses. As a result of this agricultural expansion, habitat loss and fragmentation have become the main drivers of biodiversity loss. Understanding the long-term effects of agricultural management on populations is of great importance for the development of successful conservation strategies. Our study uses genetic data to determine the effect of agricultural management practices on the population structure of a common tropical forest rodent (The online version of this article (10.1007/s10592-017-1016-9) contains supplementary material, which is available to authorized users. Agricultural production is expanding at rapid rates in the tropics. As a consequence, most tropical forests exist as fragments embedded within a mosaic of agricultural land . Additionally, we expect individuals within the forest fragment to show higher connectivity , than those found within the coffee farms.We studied the effect of different coffee management practices on the population genetic structure of This study was conducted at the Finca Irlanda Research Station located in the tropical montane region of Soconusco in Chiapas, Mexico. The area contains many coffee farms varying in management intensity, ranging from rustic to unshaded monocultures with forest patches scattered between them Fig. .2 . Sex and GPS coordinates for each individual sample were recorded , and DNA extracted from ear tissue samples. Microsatellite primers were designed specifically for H. d. goldmani by the Savannah River Ecology Laboratory. All samples were genotyped for 11 microsatellite loci . We estimated relatedness (r) within and between sites to: (1) identify siblings and (2) assess bias in dispersal of males and females. We calculated three different relatedness estimators , expected heterozygosity (HE) and fixation index (FIS) GenAlEx  for each locus at each cluster and conducted a bootstrapping analysis of the mean in R 3.2.4 (2016). To assess genetic differentiation between groups we calculated FST  and the locations of breaks in gene flow that delineate these clusters using Geneland 4.0.3  using Mantel tests Mantel  as imple Rousset  calculat Rousset . GeograpH. d. goldmani samples with an average of 7.8 alleles per locus (range 3–13). Primer and locus information is in Table S2 . Results for the relatedness estimators  indicate low relatedness of individuals within and between sampling sites. However, 0.2% of individual pairs show relatedness values of > 0.25. Although males appear to have lower levels of relatedness , we were not able to detect significantly different patterns between males and females from each cluster . This may be due to the low number of males present in the study, i.e. 11% of all samples .A total of 61 alleles were scored at 11 loci in all Geneland genetic clustering analysis was done with and without closely related individuals (r > 0.25). Results did not change; thus, we present results using all individuals sampled. Genetic structure emerged from Geneland, with a K of 4 as the optimal value in all the 20 runs (Table S4). Geneland assigned most individuals to one of four clusters: 1) Individuals from the forest fragment, low intensity coffee and individuals from medium and high intensity coffee close to the forest edge  among the three major clusters . The measure of genetic differentiation, FST indicated low but significant levels of differentiation between clusters , could limit gene flow. Resource availability in the coffee farms may explain some of the observed genetic structure. Coffee agroecosystems with low to medium management intensity  have a higher vegetation complexity  as a potential driver of population structure  will represent strong barriers to gene flow for forest dwelling small mammals. However, more research is needed in this area to understand the management practices that drive these genetic patterns. Replicating this study in other forest fragments and coffee farms in the area would help us gain a broader understanding of the effects of land use change and fragmentation on small mammal communities at a broader scale. Moving forward, it is important also to understand patterns of connectivity, and more importantly gene flow, among populations in natural continuous landscapes. Finally, our study demonstrates the impact of the agricultural matrix in dispersal of a species and the importance of understanding long-term responses of populations to these changes.Considering that a moderately intensified production system, such as the ones included in this study, appears to limit connectivity in Below is the link to the electronic supplementary material.Supplementary material 1 (DOCX 370 KB)


The genetic counseling profession is continuing to develop globally, with countries in various stages of development. In some, the profession has been in existence for decades and is increasingly recognized as an important provider of allied health, while in others it is just beginning. In this article, we describe the current global landscape of the genetic counseling specialty field's professional development. Using examples of the United States, United Kingdom, Canada, Australia, South Africa, and various countries in Asia, we highlight the following: (a) status of genetic counseling training programs, (b) availability of credentialing through government and professional bodies , and potential for international reciprocity, (c) scope of clinical practice, and (d) health‐care system disparities and cultural differences impacting on practice. The successful global implementation of precision medicine will require both an increased awareness of the importance of the profession of “genetic counselor” and flexibility in how genetic counselors are incorporated into each country's health‐care market. In turn, this will require more collaboration within and across nations, along with continuing engagement of existing genetic counseling professional societies. Since then, the profession has expanded globally, and in early 2018 we estimate there are nearly 7,000 genetic counselors in over 28 countries . We will also highlight efforts in South Africa and across Asia. We discuss similarities and differences in training, scope of practice, and types of clinical services where genetic counselors practice, as well as country‐ and region‐specific issues  that shape the manner in which genetic services, including genetic counseling, are offered. We refer readers interested in a more granular summary of development of the genetic counseling across the globe to a paper by Abacan et al. .2https://www.abgc.net/about-abgc/detailed-history.aspx/, accessed January 25, 2018). During this period, the curriculum and clinical training requirements were established and continue in similar form even in 2018 as described by the current ACGC standards and practice‐based competencies .In the late 1980s, nearly 20 years after the profession of genetic counseling was established, approximately 15 genetic counseling masters programs existed in the United States. A certification examination had been developed in 1981 in conjunction with credentialing of medical geneticists, and work was underway to establish more rigorous and consistent criteria for genetic counseling training , for example, crisis counseling or social support services. Some countries  have also focused on individuals with prior clinical training as physicians, physician assistants, or nurses, as these professions may already be recognized as health‐care providers, allowing different clinical practice options. The opportunity to enroll in a genetic counseling training program provides these health‐care professionals the chance to increase their knowledge of the genomic contribution to health, and addresses gaps when there are no medical genetics training programs or limited training slots for practitioners who are not specifically trained in pediatrics.3Table https://www.lkm.gov.my/).Only a handful of countries have a national statutory regulation of genetic counselors. The Health Professions Council of South Africa (HPCSA) governs the genetic counseling profession using Genetic Counselling South Africa (GCSA)‐developed standards of practice guidelines to guide the training and registration of genetic counselors in South Africa. Malaysia is currently the only Asian country wherein genetic counselor registration is in place with the Lembaga Kaunselor Malaysia , which allows registration as a clinical scientist (genomic counselor) with the Health and Care Professions Council (HCPC). Registration with the HCPC comes with a legal protection for the title and government‐recognized competency to practice. The genetic counseling profession in the United Kingdom has been campaigning for statutory regulation for more than 10 years and at the moment, the mechanisms to transition from voluntary to statutory regulation  are still being explored. The United Kingdom also has “registration” through a professional body, which is assessed via a portfolio of work submitted to the Genetic Counsellor Registration Board (GCRB) within the United Kingdom and Republic of Ireland (ROI).https://www.nsgc.org/p/cm/ld/fid=19, accessed February 12, 2018). Pending federal recognition and regulation in the United States, state‐based licensure functions as a recognition process to ensure an individual is competent to practice in a specific profession (in this case as a genetic counselor), and is meant to ensure public safety and title protection for those within the profession. Requirements for licensure vary by state, but most have accepted the ABGC certification examination as documentation of competency. India, Taiwan, and Japan have also established a certification process with oversight by their in‐country professional genetic counseling societies, but many other countries have no form of credentialing yet established.In areas where the profession of genetic counseling is well‐established but it is not yet a legal requirement and no registration or licensure systems are in place or available, the professional societies and/or registration bodies have taken on this self‐regulatory role while advocating for appropriate legislation and/or statutory regulation. The terms “registration” and “certification” are both used to describe voluntary credentialing by professional bodies. For example, in the European Union (EU), the European Board of Medical Genetics (EBMG) provides genetic counselor registration through an EBGC portfolio process similar to that established by the GCRB in the United Kingdom. In United States, Canada, and Australia, the term “certification” is used to describe this professional body credentialing, but it is achieved through different means; in this case, via examination through ABGC or CAGC, or through portfolio application to the Human Genetics Society of Australasia (HGSA). Additionally, the United States also has been lobbying for statutory regulation and since the early 2000s has achieved state‐based licensure, a governmental regulatory process, in 20+ of the 50 U.S. states (http://www.gcrb.org.uk/media/9339/applicant-guidelines-v3-july-2017.pdf).While a master's degree in genetic counseling is the minimum entry to the credentialing process for most countries where one exists, one of the most important conceptual differences is that some countries  consider their credential to measure entry level competency, while most other countries with registration or certification processes require a minimum of 2 years of supervised clinical work practice, postmaster's degree, and consider their credential to measure the competence to work autonomously. The various systems place emphasis on different competencies and measure them differently; some countries  measure work practice by an assessment of a portfolio of evidence, focusing on lived experience of genetic counseling in practice, demonstrating scientific knowledge, counseling skills, use of counseling supervision, and reflective practice. Notably, these portfolios require clinical practice, which impacts the ability of genetic counselors working in nonclinical settings to become credentialed. Other countries  require the prospective candidate to pass an examination that demonstrates applied clinical knowledge including both scientific and counseling skills. Additionally, countries vary in whether they allow alternative degrees or training approaches to be considered for genetic counseling registration, particularly in areas where professionals obtained on‐the‐job training to practice as genetic counselors in the early stages of the profession. For example, in Europe, the professional bodies that support genetic counselors were keen to keep the two professional groups—genetic counselors and genetic nurses, together. As such, the credentialing bodies that offer voluntary registration for genetic counselors  therefore recognizes two different pathways to register as a genetic counselor—the first via completing a Master's Genetic Counselling program, the second via a nursing route, which includes Master's level genetic nursing in Europe, or senior nursing practitioner plus training in genetics and counseling in United Kingdom and ROI  formed an International Committee on Genetic Counselor Credentialing in 2011 to examine and document some of these issues. The aim and scope of this group was merely to begin dialog in this space. Currently in most countries, the process of international recognition is rigorous, with the first formal criteria for true reciprocal registration established by the GCRB in 2006. This started with a recognition that genetic counselors registered with the relevant boards in Australia (HGSA), South Africa (HPCSA), and the United Kingdom (GCRB) would be able to receive individual registration (“certification”) in any of these countries without having to complete a whole new registration process. Several certification boards currently recognize Master's training programs overseas . These roles seem to occur at least in part across the globe by individuals trained as genetic counselors, though there is within and between country variation in how these roles are implemented, and specifically with regard to how psychotherapeutic the genetic counseling process is. Within the United Kingdom, Australia, and South Africa, genetic counselors aim to follow a patient‐centered psychotherapeutic process, integrating genetics/genomics knowledge into a consultation that is based on the clients' needs , process studies show that genetic counselors tend to practice in a more didactic, teaching model‐based manner  who face conditions with a genetic component in a clinical setting. The National Society of Genetic Counselors (NSGC) has framed the clinical scope of practice for genetic counselors to include medical roles , psychosocial support  and case management  .Finally, genetic counselors across the globe work both as part of a health‐care team and in some cases in a more autonomous role, and they practice across various specialities that may or may not include medical geneticists. One area of variation is the frequency with which genetic counselors work as “generalists” as part of a medical genetics team , versus within a specialty team  that is increasingly embedded within that medical speciality and working in conjunction with nongenetics trained physicians who become experts in genetic conditions within their specialty. In the United States, specialty practice has been increasing rapidly over the past 20 years, with substantial percentages of genetic counselors reporting that they work in cancer genetics (48%), cardiogenetics (10%), neurogenetics (∼8%), infertility genetics (∼5%), and other speciality areas  and workforce limitations require them to address all referral indications. For example, in South Africa, genetic counseling is primarily available in the major cities and mostly occurs at tertiary health‐care service centers. Patients in rural areas have limited access to genetic counseling and services by telephone and videoconference have recently been implemented to address this. Only 2 of the 11 provinces of South Africa have genetic counselors, 2 more provinces have genetic services provided by specialists in medical genetics, and for the rest of the country, genetic services are provided by nongenetics health‐care providers , variation in what tests are covered by public and private payer systems, as well as who is permitted to order (request) tests. Below, we provide several examples of practice variation based on health‐care system, contrasting systems that are primarily publically funded  with those who have mixed systems  and those that are primarily private payer systems (United States). We will also discuss how these variations include the clinical incorporation of noninvasive prenatal screening (NIPS) and whole exome/genome sequencing (WES/WGS).In the United States, health‐care services  are primarily funded through private payers, with around 25% of patients having publicly funded insurance. In recent years, genetic testing has moved from academic laboratories toward commercial laboratories, and this has accelerated the pace of clinical translation of new genomic technologies such as NIPS , WES (2011) and next‐generation sequencing‐based gene testing panels (2013). The commercial drive for genetic testing in the United States means that different companies offer different testing options; for example, cancer panels across different companies include a wide range of genes with a range of evidence around clinical validity and clinical utility, and a wide range of pricing. Their broad conceptual availability does not mean that all patients are able to access these technologies, however, and many are faced with justifying genetic test orders with letters of medical necessity, with high deductible co‐payments and with denial of coverage.https://sencanada.ca/content/sen/committee/372/soci/rep/repoct02vol6part7-e.htm, accessed February 12, 2018). In the United Kingdom, for example, NIPT is now available; whole genome sequencing (WGS) is only available within defined research projects, for example, in England via the 100,000 Genomes Project  and their scope of practice. Similarly, the specific types of genetic testing  that are covered by the health‐care system, and for whom, may vary on a provincial/territorial basis. In the United Kingdom, most genetic counselors practice clinically within Regional Clinical Genetics Services (or the newly formed Genomic Medicine Centres) within the publicly funded National Health Service . Genetic counselors work together with their clinical geneticist and clinical scientist colleagues as well as independently and autonomously with their own patient load. Although private practice for genetic counselors is on the increase, it is by no means the predominant role. As genomics becomes “mainstreamed,” that is, testing is offered throughout a whole health‐care setting, the roles for genetic counselors are evolving, with more involved in teaching, policy, research, and outreach clinical services  are largely on a fee‐for‐service basis. The private health insurance system does not cover the costs of the tests. Only a few tests are currently funded nationally with reimbursement to the patient through the national Medicare system, although that number is increasing. Similarly, in South Africa, some private medical insurance  schemes will cover local genetic testing (which is limited), others will cover a portion or none of the costs. In such cases patients are required to self‐pay for genetic testing. Because of the high costs of genetic tests in South Africa, tests are often performed by international laboratories that may cost up to 50% less than the local price. However, getting medical aids to cover the cost of international genetic testing is challenging. For example, access to array CGH only became locally available in 2017; gene panels and WES are available on a research basis in state laboratories or on a fee‐for‐service basis to individuals with private insurance, although the cost and funding remains problematic. Because there are a number of founder mutations for various genetic conditions in the local South African populations, founder testing is still being offered particularly through the state laboratory services. However, with the improvement in genetic testing technology it is becoming more cost‐effective to perform next‐generation sequencing on genes as opposed to testing for selective founder mutations.While genetic testing companies offering NIPT/NIPS are actively marketing in the Asia region, the limited number of trained providers offering quality genetic counseling and overall low medical genetic literacy may compromise the informed decision‐making process (Chandrasekharan, Minear, Hung, & Allyse, Finally, not surprisingly, cultural, linguistic, and religious issues also influence the provision of genetic counseling services. For example, South Africa has 11 official languages, while the Philippines has approximately 175 ethnolinguistic groups; this makes it challenging to provide counseling in a patient's native language. In addition, there are no words in many indigenous languages for “gene,” “chromosomes,” and “genetics,” making it difficult to follow the traditional western genetic counseling model. Research has also shown that across the world individuals have different worldviews and cultural practices, and these may differ significantly from a westernized and more biomedically focused approach to practice (Abad et al., 6Genetic counseling is a rapidly growing profession with the overarching goal to add value to the care of patients with genetic conditions and their families. There are many global similarities in the educational process, mechanisms of credentialing, and the scope of practice, but the profession has evolved in unique ways in different countries due to varying health‐care systems, legal restrictions, and cultural issues. The era of precision medicine is further challenging the way that genetic testing is offered, and the roles that genetic counselors play; thus far a “one size fits all” definition of the job title “genetic counselor” does not exist. Genetic counselors can learn from each other, sharing experiences, building on what works in other countries and adapting it to unique circumstances in one's own home country in order to improve care for our patients and their families. Together, we can be solution‐driven in strategically increasing professional recognition—both within and across nations.None.


ID) are identifiable by clinical genetic testing, offering the prospect of bespoke patient management. However, little is known about the practices of psychiatrists and their views on genetic testing.An increasing number of genetic causes of intellectual disabilities (We undertook an online survey of 215 psychiatrists, who were contacted via the Royal College of Psychiatrist's Child and Adolescent and Intellectual Disability Psychiatry mailing lists.In comparison with child and adolescent psychiatrists, intellectual disability psychiatrists ordered more genetic tests, referred more patients to genetic services, and were overall more confident in the genetic testing process. Respondents tended to agree that genetic diagnoses can help patient management; however, management changes were infrequently found in clinical practice.Differences are apparent in the existing views and practices of child and adolescent and intellectual disability psychiatrists. Developing training and collaboration with colleagues working in genetic services could help to reduce discrepancies and improve clinical practice. Intellectual disability is an aetiologically heterogeneous disorder, with both environmental and genetic causes. Rapid advances in genomics have resulted in many new genetic causes of intellectual disability being delineated  and gains (duplications) are referred to as copy number variations (CNVs). Loss or gain of genetic material can, in some instances, alter gene function and effect neurodevelopment. Several recurrent CNVs are associated with elevated risk for intellectual disability, as well as other co‐morbid phenotypes such as schizophrenia and epilepsy. For example the 22q11.2 deletion syndrome is associated with intellectual disability, but is also is one of the strongest risk factors for psychosis . Adult services are generally provided by specialist intellectual disability psychiatrists. Referrals for genetic testing can be made to National Health Service (NHS) Regional Genetics Centres (RGCs), which offer clinical genetics expertise in syndromes, cascade testing and counselling. Inequities in access to genetic testing have, however, encouraged the mainstreaming of genetic investigations, with an increased emphasis placed on medical specialists ordering tests directly Burton, .Intellectual disability is often associated with co‐morbid psychiatric disorders and/or behavioural problems. Recent estimates from United Kingdom (UK) primary care records show that approximately 21% of individuals with intellectual disability have a psychiatric disorder, 25% have some record of challenging behaviour, and 49% had been prescribed psychotropic drugs . The analysis compared CAMHS psychiatrists (referred to henceforth as child psychiatrists) and adult intellectual disability psychiatrists . Continuous outcome variables were analysed, using a 2.4™ (http://www.wordle.net/) for the creation of the word cloud.Open text responses were thematically coded using Nvivo qualitative data analysis software . Three open text questions were included in the survey, focusing on the benefits and concerns of genetic testing in clinical practice. For the word cloud, all open text responses were included, and a word frequency analysis was undertaken using Nvivo. Word stemming was undertaken to combine variations of words from the same root . All words mentioned greater than 5 times, excluding common words, were inputted into Wordle3n = 121). The majority of respondents worked in England , followed by Scotland , Wales  and Northern Ireland . The majority of respondents worked in community teams  followed by specialist assessment inpatient units  and specialist referral centres (outpatient) . A further 46 respondents (23%) reported that they worked in more than one of these settings. The median number of years working in the speciality was 10 .Responses were received from 215 psychiatrists, comprising 121 child psychiatrists (56%) and 94 intellectual disability psychiatrists (44%); 56% were females  were comparable to those of intellectual disability psychiatrists   p = .48). However, estimates by both child and intellectual disability psychiatrists of the percentage of patients on their caseloads with an established genetic diagnosis were much lower. Intellectual disability psychiatrists estimated a higher percentage of their own patients to have an established genetic diagnosis  compared to child psychiatrists , .Respondents were asked to estimate the percentage of people with intellectual disability for whom genetic factors make a significant contribution towards the cause of their intellectual disability. Estimates from child psychiatrists . Respondent's estimates of the percentage of intellectual disability caused by genetic factors did not influence the likelihood of them ordering a genetic test , p = .19). The percentage of patients on respondents’ caseloads with an established genetic diagnosis also did not affect the likelihood of ordering a genetic test , p = .33).More intellectual disability psychiatrists (77%), compared with child psychiatrists (56%), had ordered a genetic test in the last 10 years (3.3SD = 6.8) in comparison with intellectual disability psychiatrists . , p = <.001). In comparison with child psychiatrists, intellectual disability psychiatrists agreed that they were confident in: knowledge of genetic tests (69% versus 29%); assessing for dysmorphic features (63% versus 47%); ordering (47% versus 24%) and interpreting genetic tests (35% versus 12%); genetic counselling (22% versus 12%) and feeding back test results to patients (64% versus 32%) and their families (68% versus 34%).Respondents were asked how confident they felt in eight aspects of the genetic testing process, as presented in Table 3.4Respondents were asked what their main concerns were in relation to the genetic testing process, see Table 3.5As seen in Figure Respondents were also asked how they fed back results to their patients with intellectual disability. Of the 146 respondents eight (5%) had utilized videos, 20 (14%) had received input from speech and language therapists, 48 (33%) had used easy read materials, and 98 (67%) had used none of these aids. Responses were comparable for child and intellectual disability psychiatrists.Figure 3.6n = 214, χ² = 15.92, p = <.001). Intellectual disability psychiatrists also referred more patients per year to the genetics clinic compared with child psychiatrists .Respondents were asked if they had ever ordered a genetic test or made a referral to a clinical genetics service. Those who had made a referral were also asked to estimate the number of referrals in last year. A significantly higher percentage of intellectual disability psychiatrists, compared with child psychiatrists, had ordered a test or made a referral  followed by intellectual disabilities . The least likely reason for referral was pharmacological treatment .3.7n = 197). Respondents were also in agreement that they would prefer to refer to a regional genetics service rather than order a genetic test themselves . Finally, there was a consensus that further training in genetics would be beneficial .Both intellectual disability and child psychiatrists agreed that closer links with regional genetics services would be helpful  of the percentage of intellectual disability caused by genetic factors, these estimates were much higher than the actual percentage of patients on caseloads with a known genetic diagnosis . It is unclear why this disparity exists. It would be interesting to investigate whether the clinician responsible for ordering genetic testing is communicating the results to other professionals involved in the individual's care. This will be particularly important for individuals with intellectual disability and co‐morbid diagnoses who are under the care of multiple medical professionals.A high proportion of intellectual disability psychiatrists (77%) and just over half of child psychiatrists had directly ordered a genetic test. In comparison with child psychiatrists, intellectual disability psychiatrists were significantly more likely to order a genetic test and also referred more patients to the genetics clinic per year. This may have in part been a reflection of the intellectual disability psychiatrist's greater reported confidence in the genetic testing process. As evidenced in Table The survey highlighted a number of barriers to genetic testing in clinical intellectual disability services. Both child and intellectual disability psychiatrists reported that they were concerned about lack of available treatment and resources for genetic testing. Interestingly child psychiatrists had specific concerns about implications for insurance. The Department of Health has released a moratorium extending until 2019, whereby the only genetic test required to be disclosed is for Huntington's disease on life insurance sums worth more than £500,000  and intellectual disability (77%) psychiatrists agreed that they would prefer to refer to a Regional Genetics Centre (RGC) rather than directly order a genetic test themselves; however, links with NHS RGCs appeared to be variable. Some respondents reported good links with their local genetics services, whilst others felt that access to services was a barrier to referring for genetic testing. Both intellectual disability (83%) and child psychiatrists (72%) felt that better links with genetics services would be beneficial. Many of these clinicians felt that they do not have the knowledge or training to order genetic tests directly. This finding is supported by another survey, which found intellectual disability psychiatrists lacked adequate knowledge about genetics and testing processes . The curricula also fail to cover the genetic work‐up and basic genetic counselling skills that are required to take more of an active role in identifying and managing patients with genetic disorders. However, there are several recent initiatives to improve the psychiatry curriculum. For example, the Gatsby‐Wellcome initiative aimed to ensure that training focuses more on scientific advances in basic and clinical neurosciences . It is, therefore, hoped that future cohorts of psychiatrists will be more confident in utilizing technological advancements in the assessment and management of their patients.The majority of respondents expressed a wish for further training . Neither child and adolescent nor intellectual disability psychiatry curricula currently have learning objectives that specifically cover genetic disorders associated with intellectual disability (http://www.orpha.net/), and information guides on an extensive range of chromosomal disorders are available from the support group Unique (http://www.rarechromo.co.uk/html/DisorderGuides.asp). It would be of interest for further research to investigate whether psychiatrists are aware of these guidelines when they receive a genetic diagnosis for their patient.One of the reasons for undertaking genetic investigations is that a genetic diagnosis is likely to provide information about specific associated medical and psychiatric phenotypes and thus could improve treatment plans and clinical management for the patient. Whilst the majority of respondents felt that a genetic diagnosis would help with clinical management, fewer patients on their caseloads had a genetic diagnosis than they would expect, and clinical management changes following genetic diagnoses were not frequently seen in practice. There are published medical guidelines available for several genetic disorders, for example via the Orphanet portal for rare diseases (Another important consideration is that knowledge of behavioural phenotypes can place psychiatrists in a better position to deliver appropriate interventions and environmental adaptations. Whilst there is within syndrome variation it has been shown that certain behavioural features, such as repetitive and self‐injurious behaviours, are more common in particular syndromes. There are also implications for health screening, for example gastro‐intestinal problems are common in Cornelia de Lange syndrome and can exasperate self‐injurious behaviours (Waite et al., Both child and intellectual disability psychiatrists agreed that receiving a genetic diagnosis was more beneficial for family members than for the patient. Research has shown that there is a benefit to mothers in receiving a diagnosis for a child with intellectual disability; however, there is a lack of research as to the impact of a genetic diagnosis for adults with intellectual disability (Lingen et al., 4.1The survey was self‐reported which could have led to biases in estimations. There may have been a selection bias in the clinicians who chose to respond to the survey, perhaps those with more extreme views on genetics were more inclined to respond. This survey specifically focused on psychiatrists, who are one of the medical specialists frequently in contact with patients with intellectual disability in the UK. These findings may not be generalisable to other countries where services are organized differently.5Whilst a high number of child and intellectual disability psychiatrists appear to already be ordering genetic tests there remains a preference for referring directly to clinical genetics services. Respondents highlighted several areas of the genetic testing process in which they particularly lack confidence, such as indications for testing, interpretation and feedback of genetic results. Child psychiatrists in particular felt less confident, ordered fewer genetic tests, and referred fewer patients to genetic services.Genetic investigations are continuing to advance at a very rapid pace, with exome and whole‐genome sequencing beginning to enter clinical practice. In conjunction with other genetic investigations, it is likely that a genetic diagnosis will be identifiable in a much higher proportion of patients with intellectual disability in the future. This should facilitate early diagnosis and tailored interventions for patients and their families. However, as the landscape of genetic investigations becomes more complex it is going to be a challenge for psychiatrists to keep pace of developments. Improvements in training and closer links with genetics services would appear to be key areas to address to meet this challenge.There are no conflicts of interest.