Given increased prevalence of direct-to-consumer (DTC) genetic health tests in recent years, this paper delves into discourses among researchers at professional genomics conferences and lay DTC genetic test users on popular discussion website Reddit to understand the contested value of genetic knowledge and its direct implications for health management. Harnessing ethnographic observations at five conferences and a text -analysis of 52 Reddit threads, we find both experts and lay patient-consumers navigate their own versions of “productive uncertainty.” Experts develop genetic technologies to legitimize unsettled genomics as medical knowledge and mobilize resources and products, while lay patient-consumers turn to Internet forums to gain clarity on knowledge gaps that help better manage their genetic risk states. By showing how the uncertain nature of genomics serves as a productive force placing both parties within a mutually cooperative cycle, we argue that experts and patient-consumers co-produce a form of relational medicalization that concretizes “risk” itself as a disease state. Since the inception of the Human Genome Project 1990–2003), an endeavor to map the genetic composition of human DNA, medicine has undergone a dramatic reorientation. Medical advancements have steered increasingly toward personalized understandings of the human condition 990–2003,. These fWithin the genomics revolution, direct-to-consumer (DTC) genetic technologies are transforming what it means to be ‘healthy.’ In “recreational genetics” , individWe analyze discourses among researchers at professional genomics conferences and lay DTC genetic test users in Reddit patient communities to understand the contested value of genetic knowledge and its direct implications for health management. We argue that both groups cooperate in continually medicalizing risk as a diagnostic state, which has implications on lifestyle and health interventions. Genomics’ uncertain nature and the current limits of this science serve as a productive force in positioning lay and expert actors within a mutual feedback cycle . Lay actWe underscore how medical uncertainty fundamentally shapes one’s diagnostic odyssey–the journey from diagnosis to intervention to resolution (or lack thereof) . BorrowiGenetic health unknowns  produce innovative approaches when conceptualizing one’s health status and future in a technology- reliant world. Though they do so differently, both researchers and patient-consumers strive to mitigate uncertainty surrounding genetic health tests while also leveraging this ambiguity to achieve disparate goals in their own right. Ultimately, both groups invest in uncertain genomic information–developing technologies or providing biodata and market interest–for the potential promise of clarity down the line. Given today’s technologically empowered patient-consumer and their abundant access to genetic data, it is increasingly important to uncover how medical innovations disrupt and re-envision the management of health, risk, and existence.What constitutes ‘healthy’ or ‘normal’ is far from stagnant or essentialized, bearing on how certain conditions become medicalized as diagnoses while others remain contested , 19. DefGenetic testing is a crucial site where health is constructed. Genetic sequencing, the technique grounding genetic tests, constitutes the body as “interchangeable parts, […] dismembering ‘physical health’ itself into separate territories to be divided and conquered” . Making Despite diagnostic uncertainty, sequencing advances have made consumer genetic testing increasingly affordable and accessible via private biomedical companies. The birth of this privatized marketplace fulfills “a drive to develop valid disease risk predictions and consequently offer tailor-made disease management and treatment,” which was left largely unaddressed in primary healthcare . ConsumeScholars describe our contemporary health era as one that embraces the “increasingly technological and scientific nature of biomedicine;” is characterized by ongoing “elaboration of risk and surveillance;” and allows a radical diversification in how medical knowledge is “produced, distributed, and consumed” . These tThere are several concerns with individualizing health management, especially via genetic testing. First, as discussed, genetic determinism is flawed given the complexity of interpreting genetic health, gene expression nuances, and epigenetic interactions , 22, 23.Medical knowledge production is multi-dimensional and often created outside laboratories, hospitals, and medical schools. Key non-medical actors include health advocates, political stakeholders, private industries, and patients themselves, all of who contribute to how diseases and conditions are framed and socially prioritized (or neglected) , 11, 12.Turning to genomics, we see collaboration and contestation among lay and expert actors. Alongside physicians, ‘expert’ actors include genetic counselors and genomics scientists. Timmermans and Stivers show the divergent ways patients and genetic counselors make sense of genetic information, noting discrepancies in interpreting results and making subsequent life decisions . On the The private industry around DTC genetic testing also impacts genetic knowledge. Patient-consumers seek DTC genetic services to glean health risks, inform lifestyle modifications, and satisfy curiosity about genetic futures . HoweverAugmented access to medical information, however, does not mean patients accurately understand their health states , 39. StuAside from a 2007 American Society of Human Genetics statement discrediting the medical utility of DTC genetic testing and urging more regulation over companies, consumers have little official guidance on navigating this marketplace . With loThis study utilized a multi-method approach over a two-year period (2018–2020). Relying on established offline and online ethnographic methods commonly deployed in the sociology of health and medicine , 47. We Ethnographic observation is particularly useful for understanding the medicalization of genetic information for two reasons. First, because genetic information is fraught with medical uncertainty , sociologists have shown that what makes genetic information useful or “actionable” to both physicians and patients is by situating such information in the social contexts of patients’ lives and the clinical contexts of researchers lives , 17. It Our ethnography included five major genetics conferences across the U.S. during Spring 2020 . We seleWe attended three conferences in person but participated in the rest virtually due to the COVID-19 pandemic. At least two researchers attended each conference and observed attendee presentations and interactions . Further, at least two researchers gathered data on each presentation panel to ensure consistency and reliability. In addition, we could revisit virtual conference recordings for clarification and comprehensiveness. We analyzed the data as a team. The codebook, a mix of theory and emergent themes from data, identified how scientific experts purported links between genotype and phenotype, ratio between questions and answers, scientific puzzles and hopes for solutions, genes posited as health markers, and degree of collaboration across fields and agendas.To gather data on lay experiences of DTC genetic testing and the diagnostic odyssey, we selected and coded relevant Reddit threads. Reddit is a popular online platform where users can create and engage with communities and discuss a wide range of topics. Founded in 2005, Reddit is the 7th most visited website in the U.S. and the 17th most visited in the world. As a digital forum where people often engage frankly, the platform holds rich text-based data. Reddit’s popularity, combined with its community emphasis on information sharing, makes it an excellent site for interactional content-analysis . Other To build our Reddit dataset, we first assessed a sample genetics report provided to users of 23andme, the most popular and influential DTC genetic testing company in the U.S., to determine the health risks and conditions being tested for 23andme is the only DTC genetic testing company to receive marketing authorization by the FDA in four health categories: genetic health risk, carrier screening, pharmacogenetics and cancer predisposition . PatientOne researcher coded each Reddit thread, which was then analyzed and reviewed collectively to resolve any discrepancies. This codebook represents existing scholarship and emergent ideas from the data. Codes overall reflect interactions about health states and futures based on genetic information, including the diagnostic odyssey, emotionality, perceptions of test reliability and purposes, uncertainty management, and life interventions.While conferences and Reddit threads represent differing avenues and approaches to comprehending genetic information, both yield critical insight into persisting knowledge gaps. For Reddit users, individualized risk responsibility, as well as possibly resolving genetic health uncertainty, ensures buy-in to medical knowledge co-production. Genomic experts, eager to capitalize on the next wave of biomedical advancement and respond to demand for personalized treatments, simultaneously create idealized prospects for genetic testing even as they contend with limitations of current science.For experts across conferences, it was uncertainty about the scientific basis of precision medicine that was leveraged strategically to elevate genetic testing’s efficacy and importance to health in the future. Specifically, accounts of potential breakthroughs in genetic health were centered around current diagnostic uncertainty and limits of clinical data on disease phenotypes. By leveraging uncertainty, experts were able to underscore the need for better data and highlight the institutional barriers preventing genomics from reaching its maximum potential. It was the limitations of science–not its correctness–that was invoked to solicit resources and bolster investments in the field.A stark example of the way that uncertainty is positively deployed are discussion of market opportunities at scientific conferences. Although many scientists acknowledge the unsettled nature of genomics, little stops products from being released into the market and medicalizing risk. Furthermore, rapid entries into the market engender supplementary services and cursory products designed to ameliorate uncertainty surrounding the first batch of products. For example, after acknowledging that physicians struggle to interpret genetic data, a software company presented their latest product aimed at tackling this issue. Moreover, presentations emphasized that because precision medicine remains a nascent field, mistakes and errors should be expected (and forgiven).To illustrate uncertainty being leveraged, a conference tour is instructive. After briefly reviewing the genetics revolution, a keynote speaker at a Molecular Medicine Tri-Conference told an anecdotal story that caught the attention of many during a heavily attended plenary session. The story–which was awarded a Pulitzer Prize in journalism–was about an effort by doctors using genetic screening to diagnose a young boy’s mystery illness. Before the geneticist got to him, the boy was near death, and his doctors were resorting to drastic measures . The speaker regaled the audience of scientists:There was no code. In two months, we worked weekends, middle of the night, to write the software to analyze his [genetic] data. One evening we identified the [genetic] variant… we put it on a list of ten possible culprits and had to meet with the immunologists to eliminate them one by one. Much wasn’t known about this variant and a paper happened to come out that connected it to a pathway. With that, we finally could convince the immunologist to confirm with tests that he was suffering from the rare disease. Nic is now 15 and he’s doing reasonably well with the missing intestine and does suffer from PTSD. He was in pain for a long time and was away from his family. We could have done a lot better if we had just sequenced [the boy] when it first showed up.The anecdote (and the audience’s positive reception) encapsulates the idealized future of precision medicine. It evinces the field’s public-facing frame, as experts offer a hopeful narrative of genomics’ heroism in the face of medical uncertainty. Somewhat emblematic of a social movement within healthcare, precision medicine researchers state their objective as ending patients’ diagnostic odysseys using genetic testing—a future wherein patients would present doctors their entire sequenced genome. As another researcher said during their presentation at this conference, “The idea is to get the right patient, the right drug, at the right dose, at the right time.”Similarly, at The Allied Genetics Conference (TAGC) put on by the Genetics Society of America, one speaker looked excitedly towards a future where “most humans will have their genomes sequenced” as scientific discoveries become both more human-focused and more industry-linked. In his vision, with the best of academia and industry, we should be able to scale genetic research rapidly and ultimately understand and cater to specific genetic needs of “every single person on the planet.” In stressing a more genomic-centric future, experts emphasize the uncertain state we currently live in (and need to move away from). These framings of uncertainty strategically spotlight turning unsettled science into a settled one.Throughout the conferences, medicine researchers underscore one specific challenge to the idealized future: the field could solve more health problems if only it had more access to patient data. Many presentations referred to the linking of electronic health records–with their detailed medical coding schemes and physician descriptions of patients–to laboratory data. Participants suggested linking these sources with data that captures individuals’ social determinants of health, such as geo-tracking and wearable devices. Additionally, researchers emphasized the need to get genetic data from people of color, though they recognized the challenges with doing so given systemic experiences of medical trauma. With such data, they posited that machine learning could definitively link genetic variation to phenotypic disease expressions .Uncertainty within this public frame is about the availability of data, not underlying methods of measuring and analyzing it (though many participants posed questions critiquing presenters’ methods). That is, if only researchers could get access to enough data, they could establish links between genetic variation and phenotypic diseases that inform treatments for patients. There is little mention of the downsides to medicalizing risk. It is common for conference presenters to fawn over the willingness of health systems to share large EHR datasets, especially those reaching far back in time. This corroborated the underlying principle of precision medicine seeking data to justify its idealized ends (and present uncertainty); as one presenter said, “For people in our genetics clinic … what the patient population can benefit from are these… rare disease patients are all unique and can have the same variations but different clinical paths for outcomes. If I can get the tickets of the patients by identifying the modifier, I can get them a few more years of health. And maybe the tech will help them … hopefully the rare becomes the chronic.”denovo variation occurs during the life course, social determinants are hard to measure, medical coding practices vary across health systems, researchers disagree over the best statistical models to link disease to genetic variation, and most importantly, practicing physicians are unclear on how this knowledge informs clinical decision-making.This idealized future of precision medicine is contrasted with experts’ strong understanding of current scientific limitations. Such complications include, but are not limited to: genetic variation only explains a small fraction of phenotypic disease, A poignant example comes from one presenter who launched his talk with claims that within the next ten years geneticists will have identified nearly all genetic variants causing human disease to, later admitting during his talk the great challenges in understanding how variations contribute to disease and how genetic risk translates into treatments. He noted, “We really don’t know a lot about many of the genes in humans” and pointed to the fact that half of the papers published are on 5% of known genes. Thus, his positivity was less a promise and more a call to action to transform this genetic dataset into ‘real’ medical advancements. The unsettled science proliferates into each presentation and subsequent question and answer periods (Q&A) and is collectively brainstormed by researchers as something they must resolve.The tenuous link between genetic variation and phenotypic disease expression is a central discussion at precision medicine conferences. Scientists widely acknowledged difficulties around identifying a genetic variation of possible interest. In one example, a researcher described how, previously, they would assume a particular variation was too messy and it would thus go ignored until they discovered the same variation could be layered amidst normal genetic groupings. Acknowledging the evolving science, the researcher said, “It will be much easier in the future.” In one Q&A session, an audience member asked what a presenter’s sense of the human genome’s dynamism was and how much it varied over a person’s lifetime. “It’s an obscure phenomenon,” responded the presenter, “it happens much more in complicated variations that we know of. We are seeing some (variations) override others at certain points, and then that one will override it back.” Another speaker noted, in humans, the ‘noise’ caused by myriad gene-environment interactions–whether that be diet, multiple disease states, or something as simple as aspirin—results in the admission that “the power to detect these interactions is very low.” One scientist dramatically said that “God’s punishment for geneticists’ hubris” was reflected in how a single genotype may influence multiple seemingly unrelated phenotypic traits. These technical negotiations show that scientists are grappling with genetic variation and its linkage with disease.Another major issue pertains to how genetic data is analyzed. Human genetic code is too complex for current computers to adequately analyze its entirety. Instead, computers use algorithms to sample and predict portions of the code. Choosing how best to do this engenders great consternation among researchers. Discussing a $100 genetic sequencing in China, a presenter said, “Every method has its artifacts and problems. At some point, you start to say to yourself: why would I convert? Since they all have their problems … There is a base and then they come in later with a model that is more accurate.” Another researcher spent five minutes of a twelve-minute presentation discussing a “consensus approach” to choose from four algorithms in their analysis.Electronic health records have their problems for researchers too. Local coding practices is one major hurdle. Medical codes that represent diseases and treatments are a key for how precision medicine tries to link phenotypic expressions with genetic variation. But what if hospitals and physicians’ offices practice coding differently? Pointing to ‘too much variation’ within hospital systems in one city that made EHR data available, a researcher described manually translating all data into a common language. Because EHR records were not designed for research, interpretability of phenotypes in medical databases is limited by the subjectivity of medical terminology, lack of standardization, and error, resulting in extreme difficulty extracting relevant concepts from records.Unsettled science within precision medicine results in two practices: focus on single-gene variations to demonstrate scientific utility, and efforts to obtain buy-in from physicians. Despite the public-facing idealized future that more data on variation will explain diseases, most conference papers focus on unraveling a single, highly visible disease. Usually, after highlighting the robustness of their dataset, presenters focused on one disease. One presenter said, “This is nation-wide data. We were funded to investigate macular degeneration. It is the leading cause of adult-onset blindness. Diagnosis is made through imaging. It has a sizable genetic component … Two variants account for about 20% of the risk.” The researchers said this “unusual” situation in which so much of the disease (20%) could be predicted by genetic variation was a good reason to study it. This conveys the widespread nature of unsettled genomic science: there are enough controversies in measuring genetic variation and disease linkages that research is valued even if it works to solve only highly visible examples of its variants.Physicians are seen on a continuum of potential allies to a major obstacle in the precision medicine movement. There is frustration around physicians having little knowledge about translating genetic risk to clinical action. In response to an audience member asking what the biggest challenge facing precision medicine is, a presenter answered:The medical workforce is entirely unprepared for the genomic revolution. How do we upscale regular working physicians? So they can know enough to save children’s lives! … Most physicians have never seen these rare diseases before; they have no frame of reference. That their patients would benefit from a treatment regimen … We want NICU teams to practice this. In the next five years, it will be routine to do it. It gets better all the time. The biggest problem is physicians knowing about this. We need to convince people that this is solid, share the information, and build this.Another participant spoke about developing a software platform to educate physicians to use genetic data. The platform was marketed to physicians as an easy way to provide “evidence-based” knowledge utilizing genetics for clinical decision-making. Importantly, these efforts and frustration with lack of physician buy-in co-exist alongside acknowledging that scientists themselves struggle to find clinical relevance for genetic information. Here, uncertainty is clearly leveraged–by bringing a product to clinicians, genetics companies instill a sense of legitimacy that the product may have productive potential in the future. The unsettledness of the backstage motivates the forward-facing science that appears to be certain and ready for application. The call for further integration of genome sequencing into routine medicine is based on the hope that alongside integration will be a demonstration of clinical utility and an implicit legitimization of genomic medical science. However, what makes a genetic risk result medically actionable and what percentage of patients will benefit from the medicalization of risk is left vague.Genetic counselors play an ambiguous role in this discussion. At some junctures, conference participants lamented physicians as a lost cause and focused on increasing patients’ access to genetic counselors. As one presenter discussed, “There is an inequality to this … we lack the analysis part. It is direct-to-consumer now. We need to make the analysis of it better.” There may be an imminent professional battle over the control and authority of genetic data. Who should professionally analyze it—physicians, genetic counselors, or patients? Per precision medicine researchers, it matters little so long as products are brought to market fast and at scale.To synthesize, it is through ethnographic observation that we are able to uncover the link between the public facing rhetoric and private consternation of precision medicine scientists. While conversations amongst scientists may signal important aspirational rhetoric as well as the need for scientific caution, highlighting key scenes at conferences allows us to understand the co-construction of meaning during interaction. In this section we showed that precision medicine scientists highlight the uncertainty in the field as a justification for continued investment (access to new and better data) while spending significant time trying to overcome serious scientific controversies . These observations of presentations, audience feedback, applause, and shared themes across the conferences illustrate the degree to which leveraging uncertainty characterizes the scientific field of precision medicine.While experts utilize uncertainty to project optimistic futures, accrue resources, and gain market traction, DTC testers represent the consumer side of the same equation. Consumers inherit these experts’ unsettled science and attempt to turn ambiguous diagnostics into useful health directives. Embarking on diagnostic odysseys, patient-consumers of DTC genetic tests assume a status identity—a ‘responsible’ individual assuming control over their health decisions and destinies. However, like experts, resources for patient-consumers fall short of allowing them to realize their goal. With few interpretive tools and general skepticism, consumers seek insights and affirmation from online communities to fill knowledge gaps within genetic test reports.The information patient-consumers use to fill these gaps come from a variety of sources ranging from anecdotal to professional. On Reddit, users (or posters) post questions or topics of discussion, which other users engage with through written replies and other indications of opinion . Unsettled genomic science leaves room for interpretation—online forums become important sites of contestation and collaboration as users share and compare framings for patient status/disease states .Original posters (OPs) on threads begin with a level of uncertainty. Although they provide varying degrees of knowledge of their diagnosis, nearly all posters engage in the virtual community to triangulate and “cobble together” a sufficient body of knowledge to feel more secure in their understanding of their condition. These are typically conditions that have come to light during a genetic test or within one’s family medical history. In cases where a person has taken a genetic test, the typical post begins with some information about their results, a statement about their personal or family background, questions they have for the community, and sometimes solutions or pathways they are contemplating. In other instances, posters may ask whether others in the Reddit community recommend taking a genetic test for a particular condition, and a similar discussion ensues based on others sharing test results and interpretations.In one case, OP, who admitted he does not know “a lot about health sequencing,” took a genetic test and was “shocked” that his results showed an increased risk of age-related macular degeneration. He wanted to know if he should take the results to a specialist and if the specialist might laugh at him or help him decrease his disease chances. His post highlights a trend that pervades Reddit threads–testers often do not know if medical experts take consumer-driven genetic testing seriously. This imbalance in acceptance and use of genetic testing across the medical profession might explain one of many motivations behind why many turn to Reddit forums. In contrast to the previous section in which an expert described understanding genetically-induced macular degeneration as developing and unsettled, patient-consumers interpreted an increased risk for macular degeneration as a certain diagnosis, eliciting strong emotional responses despite the uncertain nature of their case.In most cases, however, subsequent posters recommend seeking medical attention to verify results. Ultimately, the medical institution, be it via physicians or genetic counselors, is invoked as the arbiter of legitimacy on genetic health (not DTC testing results). Here we see a central tension in individualized health management—at the end of the day, our ability to control our bodies and certain disease states is still dependent on medical experts and institutions. Many genetic hobbyists recognize the ‘recreational’ or ‘incomplete’ aspects of consumer-driven genetic health testing. For example, in response to an OP’s post on finding out that they don’t have a BRCA gene despite their “dad getting breast cancer,” a poster replied with:23andMe is NOT the same as getting BRCA testing through a medical lab. They only check 3 out of thousands of possible mutations in the BRCA genes (and these 3 are only relevant if you have Jewish ancestry).Another poster added:Please consider using proper medical care to evaluate this situation, and not rely on23andMe—It is NOT a medical test despite their ambiguous marketing.Above, we see a clear hierarchy that pits DTC genetic tests against more “legitimate” tests. Many times, recipients of worrying results from companies such as 23andme are directed to more formal lab examinations to ascertain the odds of them having a particular disease or health risk.In another example about perceived uncertainty or unreliability of 23&Me tests, an OP was encouraged to pursue additional testing even though “it was unlikely that [they] had both genes for MCADD (a rare genetic condition limiting a person’s ability to break down fat in their body) due to the low accuracy of 23andme.” The OP further notes, “the genetic counselor basically told me that 23andme is pretty notorious for false positive results.” Subsequently, some posters also challenge the test’s legitimacy by noting the FDA’s actions to control and regulate this industry. One poster mentions “The FDA came down on 23andMe really hard for providing genetic analysis of data they provide without a doctor’s prescription.” The poster refers to an ongoing FDA attempt to regulate DTC genetic testing and better inform the public about what these technologies can  say about one’s health . GeneralDespite significant evidence pointed at the shaky science behind DTC health testing, posts most commonly focus on sharing knowledge on lifestyle interventions in response to genetic test results . This contradiction is crucial to highlighting how much genetic insight has altered our conception of health. Even though Reddit users frequently raise doubt regarding DTC tests’ reliability and urge people to get ‘proper’ verification from medical institutions, many simultaneously advocate for lifestyle changes that suggest, to a certain degree, that one should take DTC test results seriously.Because those who frequent these forums tend to have little or no medical background, lifestyle interventions are generic or low-stakes solutions that most feel equipped to offer. In other words, lifestyle interventions appear to be a safe and accessible approach to providing a poster with some reprieve while also allowing users to participate in discussions without bearing too much responsibility for another person’s health outcomes. Furthermore, lifestyle changes may also be a low-cost way to mitigate anxiety associated with medical uncertainty. Several users echoed this sentiment: “While I am waiting for verification, why not try to improve my health?” Although this medical management strategy is not as promising for those with dire or untreatable diseases, for many, it is a coping mechanism that gives a sense of control during times of uncertainty.Recall the OP who posted about their health risk of age-related macular degeneration. In the thread, there were discussions that the odds of him getting the disease could be better ascertained by performing additional testing through more verified genomic medicine services like Promethease and ClinVar. The OP, however, reflects an awareness of the multifaceted causality of such a health condition and adds nuance to the perception of risk as a medical state. He notes, “It is usually just different probabilities. Most things are at least somewhat environmental. The advice is similar to what we’ve always heard. Eat your vegetables, watch your weight, exercise, and don’t smoke.” He understands that genetic insight can only say so much and underscores external factors, such as environmental and structural conditions and lifestyle choices, as modes of medical uncertainty management.In another thread discussing results around Alzheimer’s and Parkinson’s disease, one poster mentioned:I didn’t do the health report but I ran my raw data through Promethease. I have one copy of the Alzheimer’s gene, APOE4, which doubles my risk. My grandpa had Alzheimer’s so I wasn’t surprised. I’m glad I know this because there is research showing that E4 carriers can significantly lower their risk with exercise and with DHA supplementation, that saturated fat seems to be a unique risk factor, and that a more Mediterranean style diet may be protective. If I hadn’t have known (sic), I wouldn’t have been as likely to make some dietary and lifestyle changes.Again, we see how genetic insight can spark low-burden lifestyle changes, which act both as a direct response to test results and enable patients-in-waiting to actively cope with their ongoing health states. In rare cases, DTC results led to an immediate change in individuals’ existing medical treatments: “The one good thing that came out of taking the test was that I was able to make some better-informed choices about my medications. Knowing that I have an increased risk of alzhiemers (sic), I talked to my doctor about getting off of a medication that has been associated with alzhiemers (sic). It hasn’t been confirmed yet, but given my genetic risk I no longer want to take any chances.” It appears that DTC genetic tests, as uncertain as they may be, reify health risks and motivate behavioral changes. Reflecting the medicalization of risk, consumers assume the role of patients even in the absence of concrete medical confirmation and commit to health and lifestyle interventions as a result of risk-based information. Such findings indicate that DTC tests not only have direct implications on people’s health management strategies, but they provide an important data point for clinicians, researchers, and biotech companies to understand how such strategies are mediated through how individuals make sense of their health data outside institutionalized medical purview.Less frequently, expert opinions from medical or genetic professionals enter the forum, either directly from Redditors who are professionals in these fields or through second-hand diagnostic relays from lay Redditors. Experts’ posts carry extra weight on these forums in terms of their credibility. Their advice is most effective at minimizing user uncertainty because it represents a highly informed viewpoint, per institutional standards. In one popular thread, a Ph.D. researcher who developed a system to help diagnose celiac disease hosted an “Ask Me Anything” (AMA) event for other Redditors. This thread reveals the uncertainty around diagnosing celiac disease in the medical field. In particular, OP emphasizes that celiac disease might not be entirely genetic:We need to be clear [that] having the [celiac] gene[s] does not mean one has celiac. One dataset reported over 30% of the non-celiac disease population has the genes but not the disease.While agreeing that a genetic test is a reliable means of confirming a professional diagnosis of celiac disease, this expert OP also noted the dangers of relying on DTC testing, or what they call “internet tests,” and using symptoms to self-diagnose. Here, we see a re-emphasis on ‘legitimate’ biomedical knowledge and distinguishing DTC kits as sub-par to that standard:If done the proper way the genetic test is highly reliable. There are some odd internet test kits that I have discovered were inaccurate once I ran the sample through a licensed laboratory. Please also note, and I can’t stress this enough, non-celiac gluten intolerance is real and presents with very similar symptomatology.In another thread, an OP writes asking if a popular DTC genetic testing kit would address his concerns about a predisposition to heart attacks:Hi guys recently found out some family members have had heart attacks on my biological dads side.  He says they have a faulty gene called FH or familial hypercholesterolemia. I was wondering if that came up on the 23 and me kit?A self-professed medical professional responds to the post, cautioning against relying too much on genetic markers for a condition that can be easily diagnosed and treated regardless of genetic makeup:The easiest way to find out if you have the severe genetic type of high cholesterol is to have your doctor check your cholesterol and lipid levels!And you(sic) insurance will pay for it!And if it is high, your doctor can treat it to reduce your risk of heart attack! Your doctor can’t treat with the DNA result. (Doctor and DNA tester here).In this case, the professional diverts the OP’s interpretation from an overly geneticized health conceptualization to one that relies on other physiological markers.The experts posting on Reddit embody the uncertainty that medical knowledge production across various spaces creates. Outside of a clinical setting, and without any way to assert (or prove) their identities as medical experts, they join the melee of posters whose advice runs from dietary changes to ethical reasons for forgoing reproduction. While these experts are joined by many lay posters encouraging a more systematic approach to diagnosis and treatment, they are also left somewhat adrift by individuals’ new and evolving predisposition to geneticize healthiness. In one thread begun by a poster titled “Got the new type 2 diabetes results and they’re not good. Basically I have a 50/50 shot of getting diabetes?”, a self-described former nurse encourages a cautious read of these results:If your lifestyle (diet & exercise) is the same as the research participants, yes. That may have been a small number of participants  and they might have all had a wretched lifestyle. You can beat those odds by having a better-than-average diet & exercise program. You might consider addressing your concerns with your primary healthcare provider and/or a Registered Dietitian.When the OP responds that he plans on consulting a doctor within the next couple of weeks and already eats well and exercises, the nurse replies by affirming his strategy and expressing support for health responsibility:I understand. Sounds like you already have a head start on beating the odds. I’m retired from nursing now but I still like seeing people succeed in their own healthcare management. I wish you all the best.This expert moves away from complete reliance on genetic testing insights to determine health futures, but recognizes that the OP occupies a patient-in-waiting status. As seen in lay community interactions, where posters underscored structural and environmental interactions that contour gene expression, lifestyle interventions were posed as an imperfect but best-possible strategy to manage health uncertainty as patients await more results. Like almost all posters, the exchange between the nurse and the OP brings to light the emphasis on individuals being the mediator and manager of their own health uncertainty in the age of genetic health. The compulsion for individuals to manage their health state through genetic tests discussed on these Reddit posts signals almost an obligation to doing so . As the Due to the voluntary nature of Reddit narration, not all diagnostic odysseys come to a legible conclusion. While some posters returned to their threads to share final results of their genetic tests or updates about their condition, most threads are left unresolved. These half-complete health journeys hold valuable clues about the moral and social valuation placed on health and related decision-making, regardless of their outcomes. For lay posters, Reddit provides a support group for those seeking validation or guidance through a deeply personal and scientifically fraught new territory. Ultimately, as patients’ uncertainty about their health states and genetic health results drive the need for more testing, discussions around interpretations, and potential interventions, risk becomes medicalized as a health state. Our data shows the tangible impact DTC genetic health tests have on patient-consumers’ medicalized worldview, their understanding of their risk states, and eventual health management decisions.Examining the landscape of genetic health testing as it continues to grow unabated, this article connects two distinct spheres where medical knowledge is produced and contested in the age of genomics: professional conference spaces for experts, and virtual communities for patient-consumers. We show that despite a distinct knowledge gap separating these two groups, both conceptualize genetic risk through an increasingly medicalized lens and exert pressure on the traditional medical establishment to increase focus on the topic of genetic risk.In sharing advances and discoveries with their colleagues, experts at conferences are stating a case for an idealized future where all disease risk states can be genetically treated. Uncertainty in the present is leveraged by strategically underscoring stories of success and triumph, all while acknowledging the fact that more work needs to be done if medical genomics potential is to be realized. This not only propels the enterprise by calling attention to the need for more institutional resources to be poured into research, but it also galvanizes participants by reminding them of the work ahead. While presenting this future ideal–where all possible disease is predicted and addressed—our conferences ethnographies allowed us to see how experts co-create a vision where present risk-states are necessarily under-treated. Building off of each other’s works and contributions, experts at these conferences remind us that our bodies, having yet to be fully genetically sequenced and analyzed, are inherently and continuously at risk. Such a perspective is then leveraged by experts as a form of motivation to bring in like-minded clinical practitioners who can transform theoretical genetics claims into practical medicalized interventions.Patient-consumers, like experts, traverse their own version of relating risk to healthiness. One could argue that those who purchased DTC test kits from companies such as 23andme inherited the promises shared by experts (after careful appropriation and packaging by marketeers), believing that genetic health testing can provide answers for health management. That said, for many, the post-test reality is one rife with interpretive troubles and aggravated uncertainty about their health. Without prior training and knowledge, they turn to platforms such as Reddit, where they crowdsource knowledge to help them navigate what to ‘do’ with this newly gained information about themselves and potential risk states. Although the threads examined suggest such crowdsourcing efforts do not provide clear, conclusive answers to diagnostic odysseys (at times confusing them even more), the interactions on these platforms offer both support and a forum to solidify genetic risk as a medically relevant diagnostic state for individuals seeking a framework through which to understand new bodily information. The analysis is summarized below .Examining the relationship between genomic information-making and health perceptions, we show how genomic researchers and patient-consumers harness unsettled science to co-produce the medicalization of genetic risk. Scientific experts embrace genetics-based risk assessments and view uncertainty as further reason to improve their craft’s efficacy and accuracy, while simultaneously courting doctors and medical establishments with the promise of medical breakthroughs. On the other side, patient engagement with DTC genetic testing feeds data back into the medical machine, generating more expectations around understanding genomic insights in ways that address health risks. As risk becomes medicalized as a health state necessitating care or treatment in itself, it transforms how society approaches health management and intensifies existing tensions in our biomedicalized landscape. For example, medicalizing risk may magnify pressure on individuals to expend resources in the private health market, as responsible individuals who must manage or ‘treat’ risk consistently  to stay healthy. Further, given how risk propels the genomic revolution in healthcare, providers may eventually be inclined or expected to treat risk rather than expressed disease. Taken across all fields of medicine, this could substantially alter how healthcare is delivered and exacerbate disparities in access and quality of care. In the larger picture, using risk as a medicalized marker of health may also impact sociocultural values around who is ‘healthy,’ and can precipitate discriminatory insurance coverage, employment, and social stigma toward those with certain risk profiles. If genetic risk becomes akin to sickness, hurdles commonly faced by those struggling with disease may become familiar to those faced with only the potential for disease. Understanding how these online spaces shape peoples’ health, especially as they increasingly seek digital ways to try to make sense of their genetic data, provides valuable insight into how we can better provide clinical care, implement protections and regulations for how DTC tests are delivered, and develop supportive resources like ease of access to genetic counselors via telehealth.There are a few limitations in this study. For one, while the conferences we attended allowed us an exclusive look into the world of genomics science and knowledge production, our view was limited to the conference attendees who showed up at these events. Other components and actors of the enterprise, including but not limited to research labs or corporations, influence on policy, and marketing are areas of importance that we did not have robust access to. Future research should fill this gap by examining other players and processes tied to genomic knowledge-making. Furthermore, regarding the lay population, we acknowledge that Reddit is a self-selective community. Thus, their attitudes or approaches may not pertain to everyone, especially those who are not as health- and tech-savvy. There remains a larger population of individuals who seek health via other forums , and future research should capture these experiences.S1 DatasetText-based data.(TXT)Click here for additional data file.S1 FilePython code.(PY)Click here for additional data file.S1 Data(TXT)Click here for additional data file.


Stress is thought to increase mutation rate and thus to accelerate evolution. In the context of antibiotic resistance, sub-inhibitory treatments could then lead to enhanced evolvability, thereby fuelling the adaptation of pathogens. Combining wet-lab experiments, stochastic simulations and a meta-analysis of the literature, we found that the increase in mutation rates triggered by antibiotic treatments is often cancelled out by reduced population size, resulting in no overall increase in genetic diversity. A careful analysis of the effect of ecological factors on genetic diversity showed that the potential for regrowth during recovery phase after treatment plays a crucial role in evolvability, being the main factor associated with increased genetic diversity in experimental data. A key aspect of the fight against antibiotic resistance concerns the origin of the genetic innovations generating resistance phenotypes. The generation of genetic diversity on which natural selection can act being at the heart of evolutionary biology, the genetic and environmental factors influencing mutagenesis have been under close scrutiny. Beyond the well-known role of mutator alleles which constitutively increase the mutation rate of a genome , it has The actual –realized– genetic diversity, however, is not only determined by mutation rate. Ecological factors, and population size in particular, further plays a crucial role , 10: thematerial and methods section 5.3 for details). While the observed number of mutants is not exactly the mutation supply, because the same mutation event can give several clones in the final population, both variables are closely related and the former can be directly measured in practice .Properly isolating and quantifying mutation rates, doubling times and death rates is not always possible. The estimation of mutation rate in particular brings intrinsic difficulties, because it relies on sophisticated mathematical models making strong biological assumptions (including the absence of cell death) that are not always pertinent, especially under stress . InsteadTo assess the role of antibiotic treatments on the generation of genetic diversity in bacteria, we collected raw data from primary literature on the effect of antibiotic treatments on mutation rates using fluctuation test and its variants, and conducted a meta-analysis with emphasis on the effect of treatments on both mutation rate and population size. Additionally, we performed our own set of experiments for the most commonly studied antibiotics, to mitigate the difficulty of comparing data from different labs in meta-analyses. We show that the decrease in population size due to the treatments often cancels the potential increase in mutation rate, resulting in no overall increase of genetic diversity. We further combine the experimental data with simulations to explore the ecological conditions in which antibiotic treatments may still foster the generation of genetic diversity.n = 1 observation [decreases the generated genetic diversity , Fig. Our work stems from the simple intuition that an antibiotic treatment which increases mutation rate by tenfold but decreases population size by 100-fold is not likely to increase genetic diversity . This iervation , 16), anervation . We confmaterial and methods section 5.3), this test is the de facto standard for mutation rate estimation. It is very closely related to Ames test, frequently used to assess mutagenecity of chemicals in a more medical context [This observation questions whether the antibiotic treatments reported in the literature to increase mutation rate also have an effect on population size, and whether this effect may outbalance the reported increase in mutation rate, resulting in unchanged or even decreased genetic diversity. To address this question, we re-analyzed the raw data from ten published studies on the effect of sub-inhibitory antibiotic treatments on mutation rate (Table  context .Table 1Ep = 2.32 × 10−9), Fig. p = 1.34 × 10−5, Fig. We first evaluated the effect of the antibiotic treatments on both computed mutation rates and population sizes. Estimating mutation rates with a widely used modern method . Conversely, data points from standard fluctuation assay protocol show a moderate decrease in genetic diversity . The difference between the genetic diversity for the data points from the two categories is highly significant .Integrating these factors into our analysis of the effect of antibiotic treatment on genetic variability, we found that data points associated with regrowth after treatment or with increased population size have a significantly increased genetic diversity relative to untreated  is a consequence of effective population size: natural selection can only act on fitness differences of the order of 1/Ne, below which drift is dominant. As a consequence, mutation rate is predicted to be inversely correlated with population size among microbial species. Similarly, our observation could be thought in the framework of the drift-barrier hypothesis. While error-prone polymerases [Studies of microbial mutation rates demonstrated that both genetic and environmental factors can affect mutation rate , 33, 34.ymerases , 40 are ymerases , 42, in ymerases –45, an ai.e. the raw number of observed mutants for an arbitrary selectable phenotype in the treated population relative to the untreated controls. We conclude that, at the chosen concentrations, antibiotic treatments do not increase genetic diversity when the bacteria are not regrown after treatments. While mutation rate is a crucial variable for the understanding of the molecular effects of various stresses, we show here that it is a poor predictor of genetic diversity and thus of evolvability when stress also exerts ecological effects.With mutation rate computation posing many theoretical challenges, we here proposed to estimate the generation of genetic diversity using a simpler and directly measured variable, We focused on studies posterior to 2008 (no more than ten years old at the beginning of this project) which quantified the effect of sub-inhibitory antibiotic treatments on bacterial mutagenesis. We were able to gather raw data for ten studies  for the analyzed dataset are available in We performed a protocol closely inspired by the historical fluctuation test . The aimEscherichia coli MG1655, we applied a strong bottleneck to initiate 12 parallel replicate populations. Specifically, we diluted the culture 105 times and inoculated 10 μL into 1 mL of fresh LB  supplemented or not with antibiotics, in 13 mL tubes . We used 20 antibiotic conditions with 12 replicates each for a total of 240 cultures . We further plated serial dilutions of six randomly chosen populations for each treatment onto LB agar to estimate total bacterial densities.After 24 h of growth in the presence of the different antibiotic treatments, we estimated the number of de novo mutants by plating 200 μL of each culture onto LB agar supplemented with 100 μg/mL rifampicin . The underlying assumption for this generalisation is not that mutation rate is uniform over the genome, but rather that mutation rate is increased  in a similar fashion at all positions on the genome.It is however known that mutation spectrum may be affected by stress , and thuDepending on the chosen marker, this protocol may therefore not allow to study the effect of stress on the mutation spectrum.Due to the large effect of the timing of appearance of mutations on the final number of mutants in the fluctuation test, this method requires a relatively high level of replication . This is the case for all methods derived from the fluctuation test.E. coli [To estimate mutation rate from fluctuation tests, the phenotype used to score mutants should be neutral. We used rifampicin resistance (conferred by substitutions in rpoB), which is frequently used in the literature. However, some rifampicin resistance mutations have been shown to confer a fitness defect in E. coli , potentiThe reproduction of mutants that emerged early in the culture implies that each observed mutant in the final population of a fluctuation test does not necessarily correspond to a unique mutational event and thus to a unique genotype. However, as we verified from simulations shown on Fig. As explained two paragraphs above, the inability to consider a varying mutational spectrum is a general caveat of the fluctuation test, which scores specific mutations in a specific locus. But this may be even more important in the specific context of stress-induced mutagenesis, where the rate of different types of mutations may be differently altered by the antibiotic treatment.In particular, the SOS-induced polIV has been reported to leave a very specific and restricted mutation spectrum, different from the one observed in normal, stress-free growth conditions . ConsequWe should note that this effect would actually amplify the main effect we report in this work: not only we showed that sub-inhibitory antibiotic treatments overall decrease the number of mutational events, but additionally these events would be less likely to correspond to unique alleles.Evolvability is defined as the capacity of a population to generate adaptive genetic diversity, and therefore to evolve by natural selection. This can be decomposed in two parts: the quantity of genetic diversity generated, and the fraction of this diversity which is adaptive. This work only concerns the first part, and for example does not consider questions such as the fitness landscape under antibiotic stress.Stochastic simulations are useful to give empirical support to seemingly intuitive arguments (such as a treatment which increases mutation rate by tenfolds and decreases population size by 100-folds ought to decrease genetic diversity), but also to properly formalize such arguments. They further permit to explore more complex scenarios for which intuition shows its limitations, such as the effect of death and the potential difference between bacteriostatic and bactericidal treatments.Our simulations feature two populations: a wild-type and a mutant. Individuals grow, die, and mutate, at given rates. They are initialised with a small number of wild-type bacteria, and continue until total population size reaches a predefined threshold (carrying capacity). The number of mutants is recorded at the end of the simulation. This mimics the experimental conditions of the fluctuation test.Our simulation method relies on Gillespie algorithm , and thuThe simulation scheme is represented graphically in The source code of our simulation program is included in the data deposit. More details on the parameters are given in Supplementary material


Panax ginseng and P. quinquefolius). Ginseng roots are threatened by the infection of the most notorious Cylindrocarpon-like fungi (CLF), which are a complex containing important soilborne pathogens. Although the disease is economically important, little is known about the genetic diversity and population structure of the pathogenic CLF complex. This knowledge will help in developing effective disease management strategies. To conduct this study, diseased ginseng roots were collected from 12 regions representing the main ginseng-growing areas in Northeast China, and CLF were isolated. A total of 169 isolates with CLF anamorph were identified in two Dactylonectria species and six Ilyonectria species using morphological and molecular methods. Cross pathogenicity tests showed that all species were pathogenic to both P. ginseng and P. quinquefolius, and most of them had slightly stronger aggressiveness in P. ginseng. The analysis of partial sequences of the Histone H3 gene generated a high level of genetic diversity and geographic differentiation. A total of 132 variable sites were detected in 169 sequences, which formed 20 haplotypes with a haplotype diversity of 0.824. Genetic differentiation was positively correlated with geographic distance. The geographic populations in the range of Changbai Mountain were distinctly discriminated from those in other non-Changbai Mountain populations. No significant genetic differentiation was found between ginseng hosts. Cylindrocarpon-like fungi causing ginseng root diseases are geographically correlated in the genetic distance in Northeast China and should be managed correspondingly.Northeast China is well known for cultivating ginseng ( Panax ginseng M.) and American ginseng (P. quinquefolius L.) are two important medicinal herbs in the genus Panax, and they are mainly cultivated in the Northeastern region of China. Particularly, the region of Changbai Mountain has a long cultivation history of Asian ginseng. American ginseng has been planted in Northeast China since the 1980s. Notably, commercial growers commonly plant Asian ginseng and American ginseng on the same farm. More interestingly, they rotate the two species sequentially to avoid replant obstacles and fully utilize the farmland. Ginsengs are perennial plants, and their roots are usually harvested after four to six years of growth in the field. Such a long time of soil exposure makes them vulnerable to infection by soilborne pathogens.Asian ginseng  and mitochondrial small subunit (mt SSU) rDNA; however, in the study, only less than 20 isolates were investigated [his3) gene had the highest resolution among several gene loci [It is a challenge to effectively manage such a pathogen complex in ginseng root disease control. Clarification of the population structure and host specificity of pathogens will be significant for making targeted disease management strategies. Geographic factors may affect the pathogen complex through temperature, humidity and other conditions. In addition, some CLF species not only infect mum spp. . Geneticstigated ,13. Althene loci .In the present study, our aims were to (i) identify the CLF isolates causing American ginseng root rot in Northeast China; (ii) determine the cross-pathogenicity between Asian ginseng and American ginseng; (iii) characterize the genetic structure of the CLF isolates from ginseng across the main growing regions in Northeast China; and (iv) investigate the geographic and host-origin effects on the population structure. Knowledge of the population genetics of CLF isolates would be important for developing sustainable and effective strategies for ginseng root disease control.his3 gene were amplified using the primer pair CYLH3F and CYLH3R [One hundred and sixty-nine isolates of CLF were obtained from diseased roots of Asian ginseng representing 13 locations and American ginseng representing 9 locations in Northeast China . Isolated CYLH3R . PCR proRandomly selected 1–3 isolates from each species and each host were evaluated for their pathogenicity on both Asian ginseng and American ginseng as previously described [his3 gene of 99 isolates from P. ginseng were obtained previously [P. quinquefolium were obtained in this study. The information of some CLF isolates obtained from Panax spp. worldwide was collected for population structure analyses  values [STF examines the extent of genetic differentiation between every two groups and takes values between zero and one. If STF is less than 0.05, it means slight genetic differentiation; if STF is more than 0.25, it means very high genetic differentiation. PopART v. 1.7 was used to calculate the relationships among haplotypes and to generate a visible TCS network with default settings [DnaSP v. 6.12 was also used to generate the Arlequin haplotype data files . Arlequi) values . FST exasettings ,19.K distinct populations [K = 1 to 13 were performed in independent runs with 100,000 generations and a burn-in of 100,000. The number of genetically homogeneous clusters (K) was identified by following the method developed previously [Three methods were used to determine the population structure. Firstly, the STRUCTURE v.2.3.4 software was used to access the number of genetic clusters and estimated admixture, which implements a clustering algorithm based on Bayesian Monte Carlo Markov Chain approach to assign individuals into ulations . Searcheeviously . Secondleviously , and a peviously . Thirdlyeviously , which rTo determine whether genetic variation was correlated with geographic distance among CLF populations, the relationship between the genetic distance and the geographic distance was investigated by using a Mantel test implemented in GenALEx6 . The paiIlyonectria and Dactylonectria  in I. mors-panacis were collected from American ginseng, while isolates in I. changbaiensis and I. qitaiheensis were obtained from Asian ginseng only.Seventy CLF isolates were obtained from diseased American ginseng roots and classified into two genera onectria . Among t species . All 169ollected . Ilyonec species . Eighty-Cylindrocarpon-like isolates tested on ginseng seedlings. No lesions were observed or minor lesions developed in the healthy control roots treated with sterile distilled water. All isolates were re-isolated from disease lesions and identified to the species inoculated initially, but no pathogenic fungi were isolated from mock roots. These isolates exhibited pathogenicity on both Asian ginseng and American ginseng, regardless of their original hosts  was observed in the JBCM population, while the lowest nucleotide diversity (0.004) was observed in the JYW population  of haplotypes with 1–3 mutations. Species I. communis had only three haplotypes, although it was widely distributed in 10 locations  with significant differentiation was observed between the JTJ and JBL populations, and the lowest STF value (−0.097) without significant differentiation was between JYH and HHB.The ntiation . The higK = 2 had both the highest likelihood values and Evanno’s ΔK index, which were significantly higher than other clusters. At K = 2, the five populations in the Changbai group were separated from non-Changbai populations .AMOVA also revealed significant variation among geographic regions (Changbai and non-Changbai), accounting for 36% of the total genetic variation . Among iSTF values were consistently higher between pairs of two different geographic groups (Changbai and non-Changbai) than between pairs of the same geographic groups , but exhibited slight differentiation between two host groups . No significant differentiation was found between populations from two different hosts, P. ginseng and P. quinquefolius, by using STRUCTURE analysis (P. ginseng were genetically far away from other haplotypes, such as haplotypes belonging to I. changbaiensis and I. qitaiheensis (The Mantel test showed that there was no correlation between geographic distance and genetic differentiation among all 12 geographic populations . Howeveranalysis . Haplotyiheensis .Panax spp. showed a significant population differentiation (I. mors-panacis were nearby each other. Among them, Shandong and Canadian populations with the same host P. quinquefolius were closely neighbored (PCoA of worldwide CLF isolates from ntiation . Axes 1 ntiation . PCoA alighbored .I. robusta from 11 locations exhibited the most diversity and belonged to eight haplotypes (I. robusta sequences downloaded from the GenBank, 15 haplotypes were found from worldwide 15 host genera or habitats (I. robusta, I. mors-panacis exhibited a low diversity and narrow host range (P. notoginseng and P. quinquefolius populations but not from P. ginseng. Correspondingly, these unique haplotypes were exclusively from Yunnan, Shandong and Canada, which are production areas of P. notoginseng and P. quinquefolius, respectively.Among these CLF species, plotypes . In furthabitats . Haplotyst range . Unique I. robusta, I. communis and I. mors-panacis are the top three most frequently isolated species causing ginseng root diseases in Northeast China, which was also supported by previous studies [I. robusta and I. communis were more frequently isolated than I. mors-panacis, they were less virulent than I. mors-panacis. Ilyonectria mors-panacis usually causes root rot on ginseng and was a highly virulent CLF species shown in this and other studies [Ilyonectria communis is widely distributed in Northeast China, and its host range should be addressed in future research. In addition to Ilyonectria species causing ginseng root diseases described in the present study, I. crassa and I. panacis have been reported to cause root diseases in American ginseng in Canada [We provided a whole picture of the pathogen complex of CLF populations associated with ginseng root diseases in Northeast China by analyzing 169 isolates. Among the CLF species,  studies . Althoug studies ,25,26. In Canada .STF value between two geographical groups and the genetic variation contributed by geographical regions also supported the two distinct genetic clusters. This could be due to a similar climate and longer ginseng growing history along the range of Changbai Mountain than in other areas in Northeast China. The Changbai group was even distinctly different from all other groups, including worldwide isolates. The retrieved Northeast group was close to the non-Changbai group of this study. This provides another evidence for the Changbai group with unique genetic populations.We have provided the first in-depth assessment of the genetic structure of CLF populations associated with ginseng in Northeast China, which were clustered into two distinct genetic groups. The two groups were discriminated against by Changbai Mountain. The Changbai populations generated more haplotypes and higher nucleotide diversity than the other populations. The high and significant pairwise I. liriodendri causing black foot disease in New Zealand vineyards in a previous study [Ilyonectria species are soilborne and limited for long-distance spread, and their reproduction is restricted locally. Although seedling transportation can cause a long-distance spread of soilborne pathogens, the current data implied this was not a main contributing factor.We found a significantly positive correlation between genetic distance and geographic distance among CLF populations. This indicated that geographic and climatic factors contributed to the overall genetic differentiation in CLF populations associated with ginseng plantations. A similar result has been found in us study . BesidesP. ginseng and P. quinquefolius in Northeast China, based on STRUCTURE analysis. The conclusion could have been strengthened if the genetic relationships among these host populations were examined by using discriminant analysis of principal components and Bruvo genetic distance [P. ginseng than on P. quinquefolius. This partially explains why it is practicable to plant P. quinquefolius following harvesting of P. ginseng to avoid replant diseases in Northeast China. The species I. robusta has a broad host range, including woody plants and herbs [I. mors-panacis, which has a narrow host range, suggesting that the genetics of the pathogen complex could be affected by alternative hosts. Given that various plant hosts are around ginseng fields in Northeast China, host shifts may be frequent. This could increase the possibility that I. robusta on nearby hosts could mate, which could increase the genetic differentiation of the I. robusta population.There was no tight association of CLF populations with their hosts distance . Howevernd herbs  and contI. robusta [I. communis and I. mors-panacis [I. communis had a high frequency than I. robusta in the present study, more haplotypes were observed in I. robusta than I. communis. It suggested that sexual reproduction may accelerate the genetic differentiation of I. robusta.Sexual reproduction should be another indispensable factor significantly affecting genetic differentiation. The teleomorph has been observed in vitro on  robusta ,11 but uhis3 gene sequence as a genetic marker in studying the population structure of CLF. Rich sequence data of his3 in the GenBank allowed us to compare the worldwide populations of I. robusta and I. mors-panacis with our isolates. A partial sequence of the his3 gene with 468 bp generated strong differentiation and a high level of genetic diversity in 169 isolates, suggesting the his3 gene is informative for population genetic studies in CLF. This is consistent with previous phylogenetic research that the his3 gene has the highest resolution among the multi loci [his3 marker is not only essential to delineate CLF isolates at species level but also generate finer resolutions of the population within a species. It is common in genetic diversity studies of plants and animals to use COI gene, but it is rare to use one gene only in fungal genetic diversity research [his3 gene sequences in taxonomy and phylogenetic studies of CLF. This method is worthy of application in the genetic study of CLF.This is the first time of using the lti loci , which plti loci ,30,31. Tresearch ,33. We sP. ginseng and P. quinquefolius were determined in Northeast China. Genetic differentiation of CLF was positively correlated with geographic distance. Geographic populations were found to be in two clusters, which were discriminated by Changbai Mountain. This result will advance the study of population genetics of CLF in order to better understand the impacts of geo-climate and hosts, as well as facilitate the development of effective disease management strategies for the pathogen complex.Population structure and genetic diversity of CLF causing root diseases in


ST = 0.373), which were clearly distinguished based on phylogenetic analysis. Further nucleotide diversity (Pi) analysis showed that the variety ‘Changjiang No.2’ had the best intra-variety consistency among 11 tested varieties. Our findings suggest that the RAD-seq could be an effectively alternative method for the variety distinction of Italian ryegrass, as well as a potential tool for open-pollinated varieties (OPVs) of other allogamous species.The primary approach for variety distinction in Italian ryegrass is currently the DUS  test based on phenotypic traits. Considering the diverse genetic background within the population and the complexity of the environment, however, it is challenging to accurately distinguish varieties based on DUS criteria alone. In this study, we proposed the application of high-throughput RAD-seq to distinguish 11 Italian ryegrass varieties with three bulks of 50 individuals per variety. Our findings revealed significant differences among the 11 tested varieties. The PCA, DAPC and STRUCTURE analysis indicated a heterogeneous genetic background for all of them, and the AMOVA analysis also showed large genetic variance among these varieties (ΦThe online version contains supplementary material available at 10.1186/s12870-022-03617-6. Lolium multiflorum Lam., also known as Italian ryegrass, is one of the most important cool-season annual bunch grasses with numerous advantages, such as leafiness, multiple tillers, high herbage yield, palatability, digestibility, rapid seed establishment and weed suppression [https://www.upov.int/edocs/tgdocs/en/tg004.pdf), in which 22 morphological traits (except for ploidy level) under field conditions are required to evaluate in at least 60 spaced plant and 8 m of row plot trials. With the continually rising number of new varieties released onto the market, discrimination on the basis of morpho-physiological characteristics becomes increasingly difficult and limited, due to a requirement to include larger numbers of similar varieties in the trial along with high genetic heterogeneity within cultivated varieties, thus leading to high cost, complexity and poor capacity [pression , 2. Italpression . The Intcapacity . Moreovecapacity . In thiscapacity . In genecapacity . CompareAt present, research on the implementation of molecular marker technologies assisting DUS testing in a broad range of agricultural plant species has proven widely successful, especially for cultivars with a unique pure genetic background (a single genotype). This is due to the fact that a DNA marker system could provide a much higher number of polymorphic markers compared to the number of DUS morphological traits with their strong environmental dependence, and also has the potential to include many more samples in genotypic assays , 5. HoweLolium perenne and Medicago sativa by bulked samples, based on genome-wide allele frequency within varietal populations [Compared to traditional molecular markers, single nucleotide polymorphism (SNP) is more common within plant genomes, making it a better tool for variety identification in modern plant breeding programs. In particular, owing to the rapid development of next-generation sequencing (NGS) techniques, especially simplified reduced-representation approaches including the two most common methods, i.e., genotyping-by-sequencing (GBS) and restriction site associated DNA sequencing (RAD-seq), the acquisition of SNP markers has recently become cheaper and more efficient. Furthermore, implementation of GBS-SNPs has been used to distinguish varieties of outbreeding species including ulations , 16. Forulations ; Additioulations . Thus, REleven Italian ryegrass varieties were selected, with their detailed information summarized in Table L. multiflorum has been noticed [L. multiflorum varieties) with the parameter -k 32 -M, and -M is an option used to mark shorter split alignment hits as secondary alignments [http://sourceforge.net/projects/picard/). Finally, variant calling of SNPs was performed for all samples using the Unified Genotyper of GATK [RAD-seq libraries were constructed using a protocol adapted from Baird et al. . Briefly noticed . But thi noticed , which a noticed . That is noticed  against ignments . The ali of GATK . The SNPhttp://www.ualberta.ca/~fyeh/popgene). The larger parameter value of Ne, Na, Ho, He, Nei and Shi indicated that the varieties had the higher genetic variability among three bulked samples. In addition, POPGENE was used to calculate gene flow (Nm) between different varieties. The distinctive capacity was evaluated based on the fixation index (Fst) between pairwise varieties [Fst in the range of 0 to 1, there was no genetic differentiation between varieties when Fst was 0, and the highest genetic differentiation occurred when Fst was 1 [Fst value between 11 Italian ryegrass varieties was calculated by the Arlequin software [The SNPs after screening were used to calculate the effective number of alleles (Ne), the mean number of alleles (Na), the observed heterozygosity (Ho), the expected heterozygosity (He), and the Molecular Variance Analysis (AMOVA) by GeneAlex ; Nei’s garieties . Essentist was 1 . In thissoftware . In addisoftware  softwaresoftware .adegenet [adegenet package in R 3.5.3 [Lolium multiflorum varieties using the STRUCTURE program version 2.3 [L. multiflorum varieties using the the modal value of ∆K (K = 2–11) [https://www.megasoftware.net/),, and the kinship relatedness matrix was calculated based on the VanRaden algorithm in GAPIT [Principal component analysis (PCA) is one of the most widely used statistical multivariate methods, which transforms the intricate correlated variables into simple correlated variables to perform the correlations among the varieties by multifactor dimensionality reduction . Herein,adegenet , 32. Mea R 3.5.3 . The Gen R 3.5.3 . In thission 2.3 . This ansion 2.3 . The bat = 2–11) . Further = 2–11)  , a total of 18,558,247 SNPs were detected from the 11 Italian ryegrass varieties, and several genetic diversity indexes were calculated. For all of these varieties, the Ne ranged from 1.2640 to 1.3237 with an average value of 1.2947. The Ho and He varied from 0.1546 to 0.1842 and 0.1505 to 0.1847 with an average of 0.1722 and 0.1861, respectively  were detected between the ‘YC’ and ‘TG’ varieties, which showed that these had the lowest genetic differentiation and differences. Conversely, the highest Fst values (0.2480) were presented between the ‘CJT’ and ‘AGS’ varieties, which had the highest genetic differentiation and differences.The mean 80 Table , which rThe nucleotide diversity (Pi) analysis within the tested Italian ryegrass varieties was able to reflect the variety consistency at the molecular level. The results showed that the ‘CJT’ had the lowest Pi value Fig. , indicatST = 0.373) also supported the varieties’ divergence based on Shannon’s information index , and the FST values between pairs of varieties  based on GBS  [Traditional molecular markers are basically limited to dozens of pairs of primers, of which only a few hundred bands can be amplified based on PCR amplification , 8, 12.  needed) . It may In addition to optimal high-throughput sequencing, a suitable sampling strategy was also essential for the success of variety identification of OPVs. Previous studies , 12, 42 www.upov.int) argues against the widespread use of molecular marker-based DUS testing to protect Plant breeders' rights (PBR) for any species. Recently, a proposal named “vmDUS”  test was implemented to require that the candidate cultivar has a clearly improved VCU  trait when compared to a genetically similar registered cultivar [Despite the present limitations, the current Plant breeders' rights (PBR) protect or plant variety protection (PVP) of registering candidate varieties still relies on DUS testing, which applies morpho-physiological traits examination to indicate whether candidates are distinct (uniform and stable) from all existing registered ones . Howevercultivar .L. multiflorum varieties proved to be efficient in their distinction, thus is considered a valuable assistant measure for DUS testing. For certain varieties with similar genetic relationship that are therefore difficult to identify, the combination of RAD-seq and phenotypic evaluation is the recommended approach. Our findings can assist variety registration with proposing regulatory changes to incorporate the higher established variety distinction potential for L. multiflorum.In this study, the RAD-seq technique performed on bulked samples of 11 Additional file 1:Table S1. Gene flow among different varieties.


However, when only regular consumers of fermented clams were assumed as the population, the probability of HAV foodborne illness increased to 8.11 × 10−8/person/day. These results suggest that, while there is a low likelihood of HAV foodborne illness from consuming fermented clams across the country, regular consumers should be aware of the possibility of foodborne illness.This study estimated the risk of hepatitis A virus (HAV) foodborne illness outbreaks through the consumption of fermented clams in South Korea. HAV prevalence in fermented clams was obtained from the Ministry of Food and Drug Safety Report, 2019. Fermented clam samples (2 g) were inoculated with HAV and stored at −20–25 °C. Based on the HAV titer (determined using plaque assay) in fermented clams according to storage, the Baranyi predictive models provided by Combase were applied to describe the kinetic behavior of HAV in fermented clams. The initial estimated HAV contamination level was −3.7 Log PFU/g. The developed predictive models revealed that, when the temperature increased, the number of HAV plaques decreased. The Beta-Poisson model was chosen for determining the dose–response of HAV, and the simulation revealed that there was a 6.56 × 10 Hepatovirus within the family Picornaviridae. HAV infects the blood through the intestinal tract and causes liver inflammation. There are seven HAV genotypes , of which IA and IB genotypes mainly infect humans  ,30. The To describe the changes in the HAV contamination level in the fermented clams according to the changes in temperature and time, predictive models were developed. The generated curves of primary predictive models for the number of HAV with the Baranyi model based on temperature and time changes revealed that viral titers at 25 °C decreased slightly over time. At −20 °C and 4 °C, the HAV concentration barely changed . 2R value of the developed secondary model was not high, the validation results showed that the developed predictive models were applicable to depict the fates of HAV in fermented clams. The RMSE value was 0.116, which was close to 0, and fA and fB were 1.00 and 1.03, respectively, which were close to 1. Accordingly, the shoulder period of HAV for fermented clams was 70 h at −20 °C, but the shoulder period was 5.4 h at 25 °C . The salThe changes in the HAV contamination level in the fermented clams simulated by the developed predictive models with the probabilistic distributions of temperature and time during transport and storage are presented in Nine participants stated they ate ‘fermented clam ’ out of a total of 7064 who took the KNHANES in 2018. Accordingly, 0.13% of South Koreans consumed fermented clams, and they consumed 9.77 g on average. The optimal probability distribution for the consumption amount was the Exponential distribution [Expon)] .β)α−] with α = 0.373 and β = 186.4 was cited because this dose–response model was most frequently used in other studies [In this study, the Beta Poisson model [P = 1 − . Compared to other foods, fermented clams had a lower consumption ratio (0.13%) and average intake (9.77 g), which had an impact on the risk calculation results. Thus, the risk was estimated to be slightly lower. However, when assuming regular consumers of fermented clams, the probability of HAV foodborne illness increased to 8.11 × 10−8 on average, with a maximum of 7.12 × 10−5. After analyzing the correlation between the variables and risk, the consumption ratio showed the greatest correlation with risk (To assess the risk of HAV foodborne illness occurring per person per day when fermented clams were ingested, a simulation model was developed using the data gathered in this investigation . The resith risk . As a re−11/person/day for the entire population, but only for those who consumed the fermented clams, the probability of HAV foodborne illness increased to 8.11 × 10−8/person/day. In conclusion, the risk of HAV foodborne illness caused by fermented clams is considered minor throughout the country, but those who regularly consume fermented clams should be aware of this foodborne illness. The consumption ratio might be a highly related factor, which is also shown by the correlation coefficients. Therefore, risk management should be applied to control consumption ratios.The risk of HAV foodborne illness from fermented clams in South Korea was 6.56 × 10


Genetics education can be integrated into general care medicine through primary care residency programs. A study of primary care residents was done to evaluate quality, satisfaction, and barriers in genetics education in residency training programs. Thus, providing more evidence for the necessity for its development and progress.A cross-sectional descriptive self-administered questionnaire survey was delivered to four primary care West Virginia University (WVU) residency training programs in 2020–2021. The anonymous 14-item survey included the following questionnaire domains: general data, genetics training satisfaction, and genetics education barriers.The survey response rate was 52% (70/123) and 59 participants completed the survey. Overall, respondents viewed genetic education as critical to their chosen specialty (90%). Trainees at all educational levels obtained their education mostly from class based educational curricula . The majority of survey respondents indicated insufficient experience with genetic patient care . The percentage of residents who were satisfied with genetic topics were as follows: basic genetics (57%), capturing family history (82%), initiating basic genetic workup (15%), a basic understanding of the genetic report (23%), basic management surveillance in the genetic patient (18%), understanding the genetic referral and explaining it to a patient (47%).Residents reported barriers to genetic interest included complexity of the field (87%), followed by limited utility of genetics testing (41%). The most common suggestions for improving the genetic education component were to provide more lectures (61%), followed by enhanced advertisement of genetic education resources specifically rotations in the genetics department (22%). Other suggestions include the integration of genetic education in inpatient learning (20%) and providing research experience (7%).Primary care residents were satisfied with their genetic knowledge in the classroom and stated a clear need for enhanced hands-on clinical skills and research experience in their current residency training. The survey suggestions for improvement can enhance primary care residents’ genetic training that can lead to advances in rare disease recognition, precision medicine, and improve access to genetics testing.The online version contains supplementary material available at 10.1186/s12875-022-01765-0. As the science of genetics expands, the demand for genetic specialists has outpaced the availability of genetic professionals Fig. . A recenThe American Association of Family Practice (AAFP) define a Primary Care Physician (PCP) as a physician who provides definitive care to the undifferentiated patient at the point of first contact and takes continuing responsibility for providing the patient's comprehensive care . These pGenetic testing and service are no longer restricted to rare diseases; they are increasingly essential in the diagnosis and management of medical disorders such as congenital malformations, developmental delay evaluations, cancer, prenatal care, and neurological problems , 11. TheRecent research has revealed significant challenges in offering genetic education in medicine . PCPs laGenetics education may be integrated into general care medicine through primary care residency programs. Yet our understanding of satisfactions and barriers of current genetics education in primary-care training has not been elucidated.The purpose of this study was to assess residents' genetics education, satisfaction, as part of their residency training programs, as well as to identify potential barriers, and make recommendations for future paths in enhancing genetics education.A self-administered anonymous questionnaire with 14 survey items was issued to four primary care residency program trainees (2020–2021) from WVU Medicine in Morgantown, West Virginia: Pediatrics, Internal Medicine, Medicine-Pediatrics, and Family Medicine. Program directors and the office of medical education circulated the electronic survey through email. A multidisciplinary team that included a geneticist, a pediatrician, and a graduate medical education leader created the survey. The questions covered the following topics: demographic information (six questions), genetics interest and experience (five questions), training satisfaction , and genetics education barriers (one multiple-choice question with open-ended questions) and recommendations (one question). Supplement 1 contains the survey questions. How were the survey questions agreed upon?Data from completed questionnaires were gathered and analyzed using Qualtrics and Microsoft Excel. The five-point Likert scales were condensed into binary data by combining: "satisfied /very satisfied" for those who were satisfied with their genetics education and "not satisfied /very unsatisfied" for those who were dissatisfied. The survey was recirculated four times with at least two weeks interval. The first 50 participants received a $10 gift card as appreciation, once a completed questionnaire was received. This study was approved by WVU Institutional Review Board Committee (Protocol number 2103261670).n = 135) . The majority received their genetic experience through a classroom-based education . The majority received no or limited hands-on clinical experience, 34% had a genetic inpatient ward experience, 5% has a genetic clinic experience, none of the respondents had completed a genetic rotation Fig. –3.Fig. 2The following is the percent of residents who were satisfied in the surveyed genetic domains: basic genetics (57%), capturing family history 82%), initiating basic genetic workup (15%), a basic understanding of the genetic report (23%), basic management surveillance in the genetic patient (18%), understand the genetic referral and explaining it to a patient 47%). Figure 7%. Figur%, initiaResidents reported the complexity of the field as a barrier to genetic education (87%), followed by the limited utility of genetic testing (41%), and 11% found genetics of rare disease, not an area of interest. More lectures  were suggested as a way to improve the genetic education component, while 13 residents (22%) suggested rotation in the genetic division . There were 12 residents (20%) who proposed incorporating genetic education into inpatient learning. Four residents (7%) proposed learning genetics through research experience.Assessing genetic education as part of a primary care training program allows for the identification of knowledge gaps and the implementation of training-level improvement strategies. Although trainees believed that genetics was important for their future practice, there were significant gaps in hands-on clinical experience during their clinical training. The majority of residents received their genetic education in the classroom rather than through hands-on clinical experience. This could be owing to our institute's small genetics division and lack of a genetics fellowship program, both of which could help residents interact more in genetic education. However, some training programs do not have genetics divisions or genetics specialist, which may exacerbate the situation. Offering genetic electives at another school or providing training through telegenetics could be one approach.The findings of this study add to the growing body of evidence as our PCPs in training recognize that genetic knowledge is required for practice. Harding et al. in a survey of PCPs who endorsed a responsibility to integrate genetics into their practices and expected advances in genetic medicine to expand, despite the fact that knowledge deficiency remains a problem . A multiIn addition, our findings corroborate previously published documented knowledge gaps in the literature regarding genetic education at the PCP level. A survey of American Academy of Pediatrics members on genetic testing yielded similar results: because of the complexity 72%, of respondents refer patients to genetic services   , 20. OurExpanding of genetic testing and genetic diseases Fig.  necessitTo our knowledge this is the first study aimed at examining genetics education perception during residency training in a large tertiary healthcare system. The study was limited by a small sample size and a low response rate, which raises concerns about the generalizability of the findings. The lower response rate could be attributed to those who did not respond ‘lack of interest’ in genetics education. We used a web-based questionnaire to collect useful information at a low cost. The use of a questionnaire requires self-reporting. Future studies should focus on examining knowledge of genetic education during residency training through testing  and clinical practices.Residents’ perceptions of genetic education in primary care residency programs enabled us to identify gaps in training and the potential to integrate solutions. A larger multicenter study can provide a foundation for concrete recommendations on what changes in education should occur in response to advancements in the field. This study adds to a growing body of evidence demonstrating the need for greater genetic education in residency and continuing medical education to keep physicians current on the constantly changing field of genetic testing and its application in personalized care. Additional strategies are required to increase medical students' exposure and education, as well as to remove barriers and ease primary care providers' exposure to the complexities of rare genetic diseases.Additional file 1.