Collective team collapse occurs when multiple players of a sport team experience a sudden and extreme underperformance within a game. To date, minimal research has been conducted on the causes of collective team collapse. Thus, goals of this study were to explore perceived causes of collective team collapse in different sports and to define team collapse in contrast to negative momentum. To investigate factors causing and maintaining collective sport team collapse, an inductive, exploratory qualitative analysis of individual interviews was conducted. Semi-structured interviews were carried out with 10 athletes of professional German teams of various sports playing in between first and fourth division. Participants were interviewed about a team collapse event they had experienced with their team during the past year. Data were collected and analyzed using a grounded theory methodology. Collective team collapse appeared to be induced by a temporal cascade of causes rather than by single triggers. This cascade included antecedents, which represent factors that make the occurrence of a team collapse more likely; critical events, which include specific events within the game that trigger a team collapse; as well as affective, cognitive, and behavioral outcomes that foster a maintenance of the collapse. Within this theoretical framework, social factors, such as decreased performance contagion or emotional contagion, played crucial roles in causing a team collapse. These results illustrate that collective team collapse is more than the sum of individual choking of multiple players at the same time. In conclusion, a new definition, differentiating team collapse from negative momentum, is introduced. Furthermore, a process model of causes of collective team collapse is proposed. The results provide first insights into causes of collective collapse in a variety of team sports. The developed model is supposed to help future research to better connect to practice and to support athletes, coaches, and sport psychologists. Most team sport athletes are familiar with the following phenomenon: Their team’s game is going well and suddenly, from one moment to the other, performance drops so considerably that nothing seems to work anymore for the team. The 2017 Super Bowl describes one of the most significant examples of such a sport team collapse in recent years: The Atlanta Falcons led 28-3 against the New England Patriots during the second half and had a 98.9% statistically calculated chance to win the game, but unexpectedly lost 28-34 in overtime . The FalMomentum constitutes a phenomenon that is closely related to collective team collapse , but exiSeveral factors influencing momentum  and possWhile empirical research on causes of momentum, psychological momentum, and collective team collapse is still developing, many studies have investigated the causes of individual performance failure . Choking can be defined as “performance decrements under circumstances that increase the importance of good or improved performance” . VariousWhile individual choking is well explored already, research related to the collective collapse of an entire team is still limited. It further appears that existing research applies two different terms, namely collective team collapse, and negative momentum, to describe this phenomenon without differentiating between the terms. Momentum is even used to describe both the individual and collective underperformance of athletes. Thus, the current qualitative study does not only aim to investigate athletes’ perception of causes of collective sport team collapse, but also attempts to define team collapse in contrast to the seemingly similar construct of negative momentum. In order to overcome limitations of One purpose of the study was to gain an understanding of athletes’ lived experiences and perceptions of collective team collapse in their specific team sport environment, and a second to develop an inductive theory that displays the phenomenon in these specific situations. Considering this purpose and the limited empirical research exploring the causes of team collapse, a constructivist–interpretivist grounded theory methodology  was usedM = 24.30, SD = 3.74). Participants’ criterion for inclusion was 10 years or more of experience in the sport and varied between 10 and 20 years . The time they were a member of their current team varied between 0.5 and 14 years . All participants were playing in between first and fourth division in Germany and therefore either on a national or the highest regional level. The experience level of the athletes was required to ensure that a lack of skills and abilities was not the reason for the experienced team collapse.Participants consisted of 10 athletes  from different teams within various sports . Athletes’ ages ranged from 19 to 30 years  be members of a team sport consisting of more than two players, (b) play in between first and fourth division, (c) have experience in playing the sport for 10 years or more, (d) have experienced a team collapse event with their current team, and (e) be willing to talk about the team collapse event. Athletes who fulfilled these criteria were recruited for the study and the purpose of the research was explained upon arrival for the interview. After implementation of the initial interviews, theoretical sampling was applied to recruit further participants. First results showed that key players and their performance seemed to play a crucial role in the team collapse process and that athletes attributed more responsibility to key players, who fulfill a leadership role in the team. Although there are several formal and informal leadership roles present within teams, and even though team captains do not necessarily fulfill a principal informal leadership role in their team, it is undeniable that captains hold a formal leadership role. Therefore, in order to include the perception of causes of team collapse of players with a leadership role in the team, team captains were predominantly recruited during theoretical sampling. The retrospective semi-structured interviews were carried out by seven different interviewers, whereby the first author interviewed three athletes and the second author interviewed one athlete. The additional five trained assistant interviewers were involved in interviewing due to organizational issues, such as the implementation of interviews in different parts of Germany at the same time. Furthermore, it was important for the interviewers to be knowledgeable in the sport to fully engage in the interview. All interviewers were trained professionally on semi-structured interview conduction for 12 h prior to carrying out any interviews. To further ensure consistency among the different interviewers, they tested the interview guide with two athletes each as a pilot testing phase, while being supervised by the first and second authors. Specific feedback on the interview conduction was provided before interviewers completed actual interviews with study participants. This procedure was repeated twice and occurring differences in the application of the interview guide were discussed between interviewers, and first and second author until consensus about the interviewing process was reached.M = 31.14; SD = 6.97). Interviews were carried out in German, which was the native language of all participants and interviewers. All participants were informed that participation was voluntary and that goals of the study were to identify underlying factors and mechanisms of the phenomenon of team collapse. They were assured the right to withdraw from the interview at any time without penalty. Participants were further informed that audio records would be used for research purposes only and that recorded data would be treated confidentially. Additionally, they were asked to sign a declaration of consent, stating that they had been informed about the purpose of the study and agreed with audiotaping of the interview. All participants gave written informed consent in accordance with the Declaration of Helsinki. The study did not involve any invasive or potentially dangerous methods and therefore, in accordance with the German Research Foundation (DFG) and in accordance with the guidelines of the Department of Sport and Health Sciences at the Technical University of Munich, did not require a formal ethics approval.The duration of the interviews ranged from 20 to 40 min  described that his teammates were mentally absent and how this lack of concentration transferred to other players: “I have the feeling that we went on the court and it was like we missed the start of the game. Maybe one or two [players] were there a little, but the rest was ‘sleeping’ and you infected the others with it.” While Athlete 1 reported an absence of concentration at the beginning of the match, other athletes described that their perception of the opponent as weak was what caused a lapse in concentration. For example, Athlete 2  described that the attentional focus of her team was minimal because everything went smoothly and was possibly too easy for them: “A team collapse happens if everything seems to work on its own, if the opponent makes mistakes and the concentration decreases. You have to keep concentration up, no matter if you score or not. If everything goes too smooth, you lose focus. You have to continue playing as precisely as possible, as if you had to give everything.” This preexisting lack of concentration seems to increase the likelihood of unforced errors and therefore team collapse occurrence, which is a well-known cause of individual choking  said: “There was this pressure on us. It was the last game of the season and you wanna win the title. And the audience that was there and the atmosphere and you know it’s the last game. The whole season, the practice, what you invested. This is the final game … and I believe that performance wouldn’t have decreased so significantly in another game. There was so much external pressure in this game … and I’m someone who then becomes relatively anxious.” This finding is in line with Another factor mentioned to be present prior to a team collapse was overconfidence to be important prior to a team collapse. Athletes described that their team had already perceived a “win” in their heads, because they had a big lead or because the opponent was not playing well, before the team collapse occurred. Athlete 9 (soccer) stated: “We were in the lead 3-0 at half time and we were quite euphoric. The atmosphere was – it was a really good day, nothing could go wrong anymore. Then we even scored the 4-0 after half time. Actually, there wasn’t much that could happen anymore. And then the first goal of the opponent was okay but from the second on there was a jolt going through the team. Because you don’t wanna ruin it.” It seems that the team was very confident about winning the game and thus surprised when the opponent scored, whereby their confidence of winning shifted to a fear of losing the game. Athlete 5 (soccer) even described his team being arrogant due to their ranking and dominance in the game, which likely led to mistakes of the players due to their careless style of playing: “I would say it [the reason for the collapse] was extreme arrogance on our side. We were ranked first in the league and we thought we would easily win … It [the collapse] happens when you feel overly secure and you try difficult passes, play extremely careless and thoughtless, and make mistakes.” Participants also perceived age and experience of the players on the court to be important before a team collapse event. Athletes suggested that a larger number of younger players on the court with less experience in difficult game situations increased the likelihood of the team to experience a collapse. For example, Athlete 6  explained the cause of the team collapse as follows: “The team was set up newly with many younger players, who hadn’t experienced such a situation before. You could see that they were, well not actually desperate, but they didn’t know how to handle the situation at all and then everything became hectic …And the coach started substituting players way too late”. The athlete argues that younger and unexperienced players were more affected by team collapse situations than older players were. This is similar to what Participants also found the composition of physical exhaustion to be an antecedent for team collapse. Athlete 10  explained that his team was exhausted from a previous game and therefore not able to show its regular performance in the team collapse game: “Maybe [the collapse happened] because we were a little tired. Because we had semifinals the day before, where we played really well and then we were a little tired the next day …And then there was this extreme collapse.” Fatigue has been found to cause individual choking  explains: “I think it [the collapse] came from half-time, when we didn’t warm up enough. You have a break of 10–15 min and after talking to others and relaxing a little, you should focus during the break and warm up for 8–10 min to prepare, to get back in the game, but we didn’t do that, were not focused, and couldn’t play well.” In addition, insufficient practice before the game seemed to increase the risk of a team collapse, as Athlete 8 (field hockey) described: “I believe 2 weeks before, the basis for this event was founded because we didn’t practice and the effort and intensity of practice decreased, which is why we couldn’t follow the speed of the opponent during the first minutes.” Within choking research, Furthermore, participants reported unforced error accumulation, meaning that several individual players produced errors within the game at the same time or in succession. For example, Athlete 6  stated: “After the first set was finished, nothing worked anymore at all in the second set. I believe we lost that set by 12 or 13 and this was not related to any individual person, it was the whole team. The individual players started to make mistakes they didn’t make before, meaning serving, mainly during attacking, many balls just hit the block and then there was this pressure and tension in the team that didn’t vanish during the whole game anymore.” The accumulation of individual errors is likely a key factor that may lead to an underperformance of the whole team and to perceived pressure and tension. Athlete 8 (field hockey) expanded on this statement. The athlete proposed that several unforced individual mistakes led to a collapse of the tactical approach of the team and therefore to the actual collapse: “The collapse happened early in the game, where both teams were still scanning each other and several simple mistakes happened, which the opponent took advantage of. It was relatively simple individual mistakes of single players that eventually undermined the whole play system of the team and therefore made it very easy for the opponent to score.” The inability to maintain the play system due to the mistakes of the players appears to allow the opponent to score and cause the team to fall behind. Mistakes have been shown to play a crucial role in relation to a team’s underperformance but tend to be classified as a symptom or outcome of team collapse  stated: “I have the feeling that it was two or three [players] at the same time, but important players, who reacted too slowly and then there was a bad pass caused by two, but somehow the third, who could get the ball, didn’t do anything either.” Athlete 7  further explained: “We got a key player, who is very stable in reception, and she suddenly couldn’t manage anything anymore and that continued within the team …That was what caught my eye, that nothing worked anymore for her and shortly after that the same thing happened to the other players.” Somehow, the team seems to be strongly oriented toward key players and their underperformance appears to immediately cause an underperformance in other players. Some players further described that the poor performance of key players had a particular impact on the team and that scoring of the opponent to have a crucial impact on performance of their own team, triggering the team collapse. Athlete 3  stated: “We were in the lead with three points in the first quarter and then in the second quarter we lost 9-33. We really did score only nine points. It started when the opponent scored all their throws and nothing worked anymore for us, we lost the ball, produced turnovers.” The opposing team scoring points during this time seemed to disturb their team’s course of play. This happened even if the own team was playing well before, as Athlete 2  explained: “So we have a run, meaning everything works, the interplay and everything; and then there’s this momentum when the opponent surprisingly scores and then we lost five points in a row because we couldn’t play the ball anymore.” Scoring of one team might lead to negative momentum for the other team demonstrating a momentum shift for both teams  described that the team complaining about the referee’s decision was what caused the team collapse: “There was this harmless duel in the middle [of the playing field]; we knew it was a foul, but we complained and stopped and they continued playing and were running up to our goal. They didn’t score but they gained back hope and played euphorically and induced a lot of pressure.” Athletes also reported that a perceived wrong All athletes further described that the critical team collapse event seemed to have changed certain processes within their team that led to aggravation and maintenance of the team collapse. These changes included emotional, cognitive, and behavioral factors and appeared to be mutually dependent upon each other. Several of the athletes’ statements reported especially emotional and cognitive outcomes to be so interrelated that it was impossible to identify a causal relationship in their perception . Factors were identified as emotional factors, if they represented one of the six basic emotions , which cnegative emotional contagion within their team due to the collapse situation. Athlete 7  described how the mood of her team changed during the collapse: “When we lost the first few points, everything was okay, we were like ‘okay, we’re gonna do this!’ Because in volleyball, it happens that you lose two or three points. But we lost more and more points and even if we scored in between, it [our mood] became more negative on the field and no one wanted to take the ball anymore.” Player 6  explained how negative emotions increased within the team and led to despair: “Some players became aggressive, others went quiet. I believe that emotions play a crucial role in volleyball and we didn’t lift ourselves up on our own points anymore and desperation became bigger and bigger, like a vortex.” Many studies have reported an association between negative emotions and individual underperformance  explained: “We were 2-7 behind and then we received this run against us and then you start to panic, and that panic doesn’t help at all and that’s how the collapse remains. I believe if we would have been more relaxed and would have kept cool, we may have been able to manage the collapse.” Athletes also specified that they experienced a fear of making mistakes, which led to cautious play and caused even more errors. Athlete 6  described it as follows: “The quality of our play got worse in every possible way. We knew we could do better but no one performed anymore, especially in attacking, many balls were hit into the block blindly, serves weren’t hard anymore, because you had fear of failure. It’s hard to say but this also went along with a more hectic way of playing.” Especially fear of losing the game, fear of negative evaluation, and panic about the collapse itself seemed to be causes of a hectic way of playing and thus of a remaining underperformance of the team. This supports findings reported by Athletes also described that anger as another affective outcome. They explained how they themselves or other players within their team expressed their anger on the court due to their dissatisfaction with their team’s or their own performance. Athlete 10  said: “Suddenly nothing works anymore and people get angry and yell at each other because of a mistake.” Athlete 9 (soccer) described how mistakes of teammates can lead to anger in individual players: “If your neighbor starts doing weird things, it has an indirect impact on you, too. Because it annoys you and makes you angry and then you are not as focused as you should be during the next action …and if a player causes four or five failures in a row, the team becomes uneasy. It can happen that two start to yell at each other and then the next yells at them.” This increasing anger in the team seems to prevent the team from finding their way back to a regular performance, possibly, as Athlete 9 describes, because they are busy dealing with their emotions and cannot focus on their actions or the game anymore. One participant in Participants described pressure resulting from the team collapse event hindered them from returning to an effective play. Athlete 7  described how pressure developed from the feeling of falling behind and the necessity to score in order to end the collapse: “During the game, nervousness increases because you think ‘Okay, you HAVE [emphasis added] to score now’. And that blocks the head even MORE because it’s always about HAVING to do something.” It seems that, as an outcome of the perceived pressure, the player experienced her thought processes as being blocked. Pressure being an outcome of the team collapse event itself besides being an antecedent present before the occurrence of team collapse has not been reported in other studies so far. It is assumed though, that underperformance caused by individuals or several team members, further increases pressure, which then again maintains the team collapse as kind of a vicious cycle.Cognitive outcomes were perceptions, thoughts, and thought processes about the team collapse that maintained the team’s underperformance. Participants perceived that insecurity in players. Some athletes explained that this was what caused them making even more mistakes. Athlete 9 (soccer) stated: “Then there’s insecurity, I’d say. If things don’t work and you don’t have a good day, you don’t trust in yourself and do more things that you normally wouldn’t do.” Athlete 7  further described how insecurity spread within the team during a team collapse and how it increased even more, if the key player did not perform well: “If you’re insecure on the court already, you look at the key player and if she plays good you think: it’ll work out somehow. And if she collapses you think: Okay, if she can’t do it, how am I supposed to?” The choking of a key player in this case seems to further increase insecurity in other players, which made them underperform as well. Increased insecurity caused by a team collapse event has also been reported by The critical team collapse event as well as the pressure resulting from it often seemed to cause perceived lack of accountability of the individual players. Athlete 5 (soccer) described how, especially in difficult situations, responsibility was handed over from one player to another: “You realized that this is a team sport and you can easily shift responsibility to the next one, meaning players are passing and then they hide a little.” Athlete 7  explained that this was also related to insecurity and anxiety associated with potential failure, meaning that players handed responsibility to their teammates so they themselves would not be responsible for the next mistake: “Everyone thought ‘oh god I don’t wanna take the ball anymore, you take it, you do the mistake.” This shift in responsibility has not been reported in team collapse literature so far. This perception of insecurity was closely related to the perception that the team suffered from a despair within the team during the collapse. Athlete 6  described that helplessness and despair within his team increased like a vortex due to the experience of team collapse: “Some players became aggressive, others went quiet. I believe that emotions play a crucial role in volleyball and we didn’t lift ourselves up on our own points anymore and desperation became bigger and bigger, like a vortex …You could sense a certain helplessness; no one knew why that happened or what actually happened in that situation.” Athlete 6 describes how the despair and helplessness perceived by his team originated from negative emotions on the court. It seems that emotions became so negative that the team felt desperate and hopeless about the game, did not know what was going on anymore, and ultimately resigned since they did not believe to be able to change anything anymore. Furthermore, athletes stated to have experienced increased decreased performance contagion to be occurring within their team, meaning that decreased performance of individual players transferred to other players of the team. Athlete 8 (field hockey), for example, described how the bad performance of individual players caused other players of the team to fail as well: “The collapse was triggered by individual mistakes and that led to collective failure. I would say that the individual mistakes caused an insecurity in the whole team and mistakes increased even more.” This contagion effect seems to be especially strong, if the player making initial mistakes is a key player. Athlete 7  specified that the key player “infected” the team with her bad performance and caused collective collapse: “We have a key player who is very stable in reception and she suddenly didn’t get her act together anymore in reception and shortly after that the same thing happened to the others.” Behavioral outcomes were behaviors that athletes showed as a reaction to the team collapse event. Participants reported cautious play to be present within their team during a collapse as a result of insecurity and anxiety to fail. Athlete 4  said: “The setter doesn’t dare to play the right pass, the more difficult long way anymore, but focuses on simple passes, but then you can’t make the game complex anymore and it’s easy for the opponent to read the game. And that makes it harder for the attackers, which is why the error rate increases even more and that continues.” Some players also found hectic rush and made the players rush their actions in order to overcome the collapse and score. Athlete 6  described how a hectic rush, in order to end the collapse, made scoring even harder: “It’s the task of the setter to make the game calm but due to the hectic rush, setting became imprecise and due to that it was more difficult to make a point. It’s a vicious circle.” Contrary to cautious play, athletes described how team collapse and the pressure created a limited communication was important during the team collapse situation and prohibited the team’s recovery from the collapse. Athlete 2  explained: “We usually communicate a lot on the court; you realize the collapse when it gets quiet. No one calls for the ball anymore.” Some players even indicated that others pulled them down by communicating less after failure, like Athlete 7 : “There was a lack of communication on the court. Two, three players had a down and pulled the others down, too. We should have talked to each other more to get out of this again.” While Players further found that blaming others after failure also played a role during team collapse and fostered maintenance of the collapse. Athlete 10 , for example, admitted to show this behavior himself due to his increased nervousness: “[In team collapse situations], I become extremely nervous and I’m someone who doesn’t try to find the failures in his own play, but in others’ [play] instead.” This negative handling of each other’s failures within critical events seems to foster maintenance of team collapse. Athletes said that The main goals of this study were to gain an understanding of athletes’ perspectives on causes of collective team collapse and to define team collapse in contrast to negative momentum. The definition that developed from the interviews was: “We define collective team collapse as a sudden, collective, and extreme underperformance of a team within a competition, which is triggered by a critical situation that interferes with the team’s interplay, a loss of control of the game, and ultimately the inability of the team to regain their previous performance level within the game.” Our results show that the collectivity of the collapse manifests itself in the transfer of negative affect, cognition, and behavior within the team. These transfer processes within the team illustrate that collective team collapse is more than merely the sum of individual choking of multiple players at the same time. Our definition adds two valuable new aspects compared to Researchers have examined similar constructs to collective team collapse, such as the choking of individuals in a team setting ; howeverRegarding social or group-related causes of team collapse, unforced error accumulation, key player collapse, emotional contagion, decreased performance contagion, lack of accountability, limited communication, and blaming other players for mistakes were identified as social factors that cause and notably maintain team collapse in various situations. These factors differed from individual factors influencing team collapse , as they necessarily involved an interaction with other players. While insecurity, cautious play, or anger could also be expressed by an individual as an individual reaction to a bad game situation, social factors, such as a lack of accountability or blaming others for mistakes, are directly dependent on and necessarily involve the interaction of individuals on the court.Results also showed that factors causing team collapse seemed to be interconnected within a temporal process that leads to team collapse and maintenance of this collapse. The model proposed by It is noticeable, although athletes from interactive ball team sports were included in the study in order to identify overall causes of team collapse relevant in a variety of team sports, that participants named a wide range of causes of collective team collapse. This variety of antecedents, critical events, and outcomes maintaining team collapse on the one hand illustrates the complexity of the phenomenon and the many factors that can, through combined appearance, trigger a team collapse. On the other hand, this variety may be an indicator for causes being of varying importance in evoking a team collapse depending on the type of team sport. Future research could therefore further investigate differences in causes of collective team collapse in different types of team sport.As mentioned earlier, existing research does not sufficiently distinguish between the terms negative momentum and collective team collapse. Researchers have argued that individual athletes who choke differ from those who underperform ; specifiThere are some limitations to the study. First, the interviews were conducted by seven different interviewers due to organizational settings of the study. Although one can argue that several interviewers are less subjective than one, this procedure does not pursue a strict interpretation of grounded theory, since themes developed during initial interviews may not shape later interviews in the same way as if conducted by the same interviewer. Besides that, the interview length of 20–40 min, due to the limited availability of professional athletes, has to be acknowledged as a further limitation. It cannot be ruled out that longer interviews may have created additional information. Another factor to be mentioned is that athletes may have been influenced in defining team collapse by the short colloquial description of the phenomenon that they received prior to the interview in order to make sure they knew what kind of phenomenon we were addressing. Moreover, the study only included athletes from elite sport levels. We are aware that team collapse situations can occur in amateur sports as well, which is why they should be included in future research. Another point to be raised is that the current study applied only one method to assess athletes’ perceptions in a single setting, using only one theoretical pathway to interpret data. Future studies should include multi-modal methods to capture participants’ perceptions across different settings and use more than one theoretical interpretation of the data. Furthermore, the study only revealed insights into athletes’ inner perceptions of antecedents, critical events, and outcomes as causes and maintaining factors of team collapse. In order to get a more global view of the phenomenon, outer perceptions of observers  of team collapse should be considered as well. We suggest for future studies to include coaching staff’s perceptions of causes of team collapse within specific team sport settings. Future research should also engage in investigating the relationship between affective, cognitive, and behavioral consequences of critical team collapse events to gain a better understanding of possible starting points for future interventions. Besides that, sport-specific triggers of collective team collapse could be investigated.The findings of the current study allow for the derivation of several applied implications for sport teams to prevent a team collapse, which also need to be investigated and tested for effectiveness in future research. Such applied implications could for example include the prevention of antecedents, which tend to increase the probability of a team to suffer from collective team collapse. Especially poor preparation or physical exhaustion from practices before the game could be prohibited by coaches and players. Furthermore, coaches and sport psychologists could help the team to react differently to a critical team collapse situation. Emotional regulation strategies have been shown to help prevent negative emotion and improve team performance e.g., . AnotherIn this study, we explored athletes’ perceptions of causes of collective sport team collapse in various team sport disciplines. Major causes included emotional contagion, decreased performance contagion, lack of accountability, limited communication, and blaming other players for mistakes. These causes appeared to be interconnected in a temporal order consisting of antecedents, critical events, and outcomes that foster a maintenance of the team collapse. Based on these findings, we proposed a process model of causes of collective sport team collapse and a definition of team collapse to distinguish it from the related concept of negative momentum. The results provide first insights into causes of collective team collapse in a variety of sports and sport teams and found a basis for understanding what team collapse is and how it divides from other individual or group phenomena related to performance decreases.The original research is part of the Ph.D. thesis of VW, supervised by JB. VW, ZZ, CM, and JB contributed to the conception and design of the study. ZZ and VW performed data collection and analysis. VW wrote the first draft of the manuscript. CM and JB wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cardioglossa schioetzi and Leptodactylodon bicolor (both in the Arthroleptidae), in and around Ngel Nyaki Forest Reserve on the Mambilla Plateau in eastern Nigeria. The landscape comprises continuous forest on steep slopes and small riparian forest fragments in a grassland matrix. While increased fragmentation is well documented for these and other forests in the mountains of Cameroon and Nigeria over the past century, there are no previous assessments of the impact of forest fragmentation on montane amphibian populations in this region. Our estimates of genetic diversity are similar across populations within each species with levels of heterozygosity values consistent with local population declines. Except for a pair of populations (C. schioetzi) we did not observe genetic differentiation between forest and riparian forest fragment populations, nor across sites within continuous forest (L. bicolor). Our results demonstrate recent gene flow between forest fragments and the adjacent protected forests and suggest that small forest corridors connecting these may lessen the genetic consequences of at least 30 years of intense and severe fragmentation in Ngel Nyaki.Amphibians are the vertebrate group with the highest number of species threatened with extinction, and habitat loss and fragmentation are considered to be among the leading causes of their declines and extinctions. Little is known of the population biology of amphibian species inhabiting montane forests in Central and West Africa, where anthropogenic activities such as farming and cattle raising are major threats to native biodiversity. We used Amplified Fragment Length Polymorphisms (AFLPs) to assess the population genetic structure of two poorly known species,  Globally, amphibians face the most extreme population declines of all major vertebrate groups –3. ContrAs is the case with many other taxa, habitat fragmentation likely has long-term effects on the genetic viability of amphibian populations because of the combined effects of reduced population size and increased isolation . IncreasTo begin to redress the paucity of population genetics studies of African amphibians, we focused on populations of frogs within the forests of the Nigerian Highlands of the Cameroon Volcanic Line . This isCardioglossa schioetzi and Leptodactylodon bicolor, are endemic to the montane forests of Cameroon and Nigeria, they are small (<30 mm snout–vent length), have stream-adapted tadpoles, and live in leaf litter and rocky areas . Our results suggest that despite considerable habitat degradation  gene flow is still occurring (or occurred recently) among forest patches in two Afromontane frog species. This illustrates the importance of these degraded riparian forest fragments to amphibian communities.In Nigeria and all along the Cameroon Volcanic Line ,89, as eS1 FigK based on the Ln(K) K = 5, whereas graph B) and bar plot D) show the better K based on Evanno‘s method ΔK = 2. Each vertical bar represents an individual for which is shown the proportional genetic assignment to each cluster.Graph A) and bar plot C) depict the optimal (TIF)Click here for additional data file.S2 FigK based on the Ln(K) K = 7, whereas graph (B) and bar plot (D) show the better K based on Evanno‘s method ΔK = 2. Each vertical bar represents an individual for which is shown the proportional genetic assignment to each cluster.Graph (A) and bar plot (C) depict the optimal (TIF)Click here for additional data file.S1 TableTogether, the two preferred primer combinations ESP1B/MSP3 and ESP1B/MSP6 yielded 275 loci for the 198 samples representing both species.(XLSX)Click here for additional data file.


Phytophthora infestans isolates collected from six locations representing a range of thermal variation and cropping systems in China. We found high genetic variation in the Avr3a gene resulting from diverse mechanisms extending beyond point mutations, frameshift, and defeated start and stop codons to intragenic recombination. A total of 51 nucleotide haplotypes encoding 38 amino acid isoforms were detected in the 96 full sequences with nucleotide diversity in the pathogen populations ranging from 0.007 to 0.023 (mean = 0.017). Although haplotype and nucleotide diversity were high, the effector gene was dominated by only three haplotypes. Evidence for a selective sweep was provided by (i) the population genetic differentiation (GST) of haplotypes being lower than the population differentiation (FST) of SSR marker loci; and (ii) negative values of Tajima's D and Fu's FS. Annual mean temperature in the collection sites was negatively correlated with the frequency of the virulent form (Avr3aEM), indicating Avr3a may be regulated by temperature. These results suggest that elevated air temperature due to global warming may hamper the development of pathogenicity traits in P. infestans and further study under confined thermal regimes may be required to confirm the hypothesis.Effectors, a group of small proteins secreted by pathogens, play a critical role in the antagonistic interaction between plant hosts and pathogens through their dual functions in regulating host immune systems and pathogen infection capability. In this study, evolution in effector genes was investigated through population genetic analysis of Avr3a sequences generated from 96  Intragenic recombination can increase genetic variation of effector genes directly through generating new sequences or indirectly by reducing allelic loss associated with random genetic drift and hitchhiking selection. In an ecological context, selection driven by host defenses may be confounded by other biological and environmental factors such as temperature in determining the genetic variation and potential evolutionary landscapes of effector genes. Temperature can exert a critical influence on all aspects of chemical, biological, ecological, and evolutionary processes. It can affect survival, reproduction, and transmission of plants and pathogens both individually and interactively. Indeed, temperature‐mediated rate of spontaneous mutations has been documented in some species  de Bary is a pathogenic oomycete causing late blight diseases of potatoes and tomatoes. It distributes globally and can cost billions of dollars economic losses annually in the potato industry alone  or cell cytoplasm (cytoplasmic effectors). The pathogen genome contains at least 550 effectors all earmarked with a conserved N‐terminal motif of Arginine–X–Leucine–Arginine (RXLR), where X can be one of any 20 amino acids  to determine evolutionary mechanisms generating genetic variation of Avr3a; (ii) to infer the contribution of natural selection to the spatial population genetic dynamics of Avr3a; and (iii) to evaluate the potential impact of temperature on the spatial distribution and evolution of Avr3a.Research on effector genes mushroomed in the last decade. The majority of these studies have focused on the molecular and functional dissection of effector genes. Population genetic analyses of effector genes and their interactions with environmental factors such as temperature are limited but urgently required to properly understand the evolutionary and epidemic behaviors of plant pathogens under a global warming scenario. In this study, we used Avr3a in 22.1Ninety‐six isolates Table  each wit2.2Phytophthora infestans isolates were retrieved from long‐term storage and cultured on rye B agars at 18°C in the dark. Mycelia (~100 mg) were harvested 15 days after inoculation, transferred into sterile, 2‐mL centrifuge tubes, and lyophilized with a vacuum freeze dryer . The lyophilized mycelia were ground to powder with a mixer mill . Total DNA was extracted using a Plant gDNA Miniprep Kit  according to the manufacturer's instructions. The genomic DNA was suspended in 200 μL of ultrapure water and stored at − 20°C until use.™ . Each reaction contained 1× PCR buffer, 100 μM dNTPs, 1 unit of Taq polymerase (TaKaRa Ex Taq®), 0.32 μM of primers and 20 ng of template DNA. PCR amplification was started with an initial denaturation step of 94°C for 4 min, followed by 35 cycles of 94°C for 40 s, 58°C for 1 min, 72°C for 1.5 min, and ended by an extension cycle of 72°C for 5 min. PCR products were isolated by electrophoresis and purified for single direction sequencing according to the manufacturer's instructions (QIAquick® Gel Extraction Kit), ligated into T5 zero cloning vector and transformed into Trans1‐T1 competent cells by heat‐shock at 42°C for 30 s (pEASY®‐T5 Zero Cloning Kit). Three colonies were picked from each transformation and incubated in liquid LB media at 37°C with shaking. One colony was randomly picked and sequenced by GenScript Biological Technology Co., Ltd.  using an ABI3730 automated DNA sequencer .Genomic DNA was amplified with primers  in Avr3a. Haplotype and nucleotide diversities were estimated for each of the six populations as well as the combined population by pooling the sequences for individual populations. SSR data of the isolates were taken from previous publications  using POPGENE 1.32 . The standardized deviation of KST was generated by bootstrapping with 100 replicates and used to compare GST in the Avr3a gene and FST in SSR marker loci by a t test  using the RDP4 suite  to synonymous (dS) substitution  including FEL (fixed‐effects likelihood), IFEL , FUBAR (Fast Unconstrained Bayesian AppRoximation), and MEME (mixed effects model of evolution)  Yang . Only si6 generations and sampled every 100 generations. Chain stationary and convergence of running parameters were checked using TRACER 1.6, and node support was evaluated with posterior probability generated from 105 trees (burn‐in of 2500 trees).A Bayesian phylogenetic tree of Avr3a sequences was reconstructed using MrBayes 3.26 . Annual mean temperature at collection sites was estimated using the air temperature across last 10 years as described previously  and the annual mean temperature at the collection sites was evaluated by Pearson's correlation  different from virulent Avr3aEM. The deduced amino acid isoform of H‐10 was not found previously in the gene.H‐1, H‐3, and H‐10 were the most common haplotypes, accounting for 16.7%, 30.2%, and 3.1% of the combined population, respectively  and eight SSR marker loci were 0.017 and 0.296, respectively. GST was significantly smaller than FST (p < .01) by a two‐tailed t test.The haplotype diversity of nucleotide sequences in the six populations ranged from 0.719 to 0.982 with a grand mean of 0.884, when the 96 sequences from the six populations were combined. Nucleotide diversity of the populations ranged from 0.007 to 0.023 with a grand mean of 0.017  translated to the same isoform. Nucleotide sequences from Gansu, Guizhou, Guangxi, and Fujian populations were found in all three clades with more than half being grouped into Clade 1. Sequences from the Yunnan population were also found in all three clades, but more than half were grouped in Clade 2. Sequences from the Ningxia population were only found in Clade 1 and Clade 2  and its AA residue change alike (Clade 2) in the six populations ranged from 86.7% to 100% with an average of 91.4% . The recombinant was probably generated through interaction between YN3, an Avr3aEM form sequence, and YN8, a new isoform in Clade 3  and −24.967 (p = .000), respectively. Both of these values were significantly lower than that expected under selective neutrality (zero), suggesting the existence of purifying selection acting on Avr3a. The ratio of nonsynonymous to synonymous substitution (dN/dS) was less than one in all variable sites but significance was found only in the 61st (p = .04) and 126th (p = .03) codons.Intragenic recombination was detected in one (YN17) of the sequences from the Yunnan population by four of the seven models with high confidence  to SSR diversity . Avr3a is 1,448 bp in length, and 96 haplotypes were sequenced in the study. Using this information, we estimated that the potential number of artifact sequences in our study was less than one . Furthermore, when isolates with “minor” haplotypes were resequenced with five independent clones, all but one “minor” haplotypes were recaptured in the new amplification.Genetic diversity of Avr3a gene was found to be high in the current study, and nucleotide diversity Table  was posihttp://faostat.fao.org/), thereby possibly hosting the largest P. infestans population in the world. Moreover, epidemiological analysis also suggests that the overall Chinese population of P. infestans has continued to expand in recent decades (unpublished data). Large population size coupled with its continuing expansion creates optimal conditions for the generation and maintenance of genetic variation in the Chinese pathogen population. Interestingly, no shared AA sequences were found between the current and previous studies except the Avr3aEM virulent form  generated by asexual reproduction of parents. This sample error is usually corrected using only one representative of the same genotype in the estimation of genetic variation  representing two nucleotide haplotypes (H‐1 and H‐3) dominated the P. infestans populations. All other isoforms including the new isoforms deduced from H‐10, H‐15, H‐20, H‐23, and H‐27 had a frequency of less than 5%. These results suggest that Avr3a gene might have experienced a selective sweep during its evolutionary history  was significantly lower than genetic differentiation in the SSR marker loci  survived the selective sweep and other haplotypes (isoforms) were derived from the progenitor through mutation and intragenic recombination. The observation of selective sweep indicates Avr3a is an important gene contributing to survival, reproduction, and transmission of P. infestans, consisting with functional analysis of the gene  and amino acid isoforms (38) were detected in the 96 sequences, only a single amino acid form  in isolates from Ningxia and Gansu accounted 100% and 94% of the populations, respectively, which correlated well with the frequency of virulent phenotypes. Isolates from Fujian matched least between genetic and phenotypic data. In this population, Avr3aEM and virulent phenotype accounted for ~90% and ~70%, respectively. The lower phenotypic than genotypic frequency in Fujian and other three populations likely results from recessive nature of Avr3aEM or gain of function mutants , MH043151 , MH043152 , MH043153 , MH043154 , MH043155 , MH043156 , MH043157 , MH043158 , MH043159 , MH043160 , MH043161 , MH043162 , MH043163 , MH043164 , MH043165 , MH043166 , MH043167 , MH043168 , MH043169 , MH043170 , MH043171 , MH043172 , MH043173 , MH043174 , MH043175 , MH043176 , MH043177 , MH043178 GS , MH043179 , MH043180 , MH043181 , MH043182 , MH043183 , MH043184 , MH043185 , MH043186 , MH043187 , MH043188 , MH043189 , MH043190 , MH043191 , MH043192 , MH043193 , MH043194 .


DNA (RAD) sequencing to investigate population structure and assess power to genetically assign Pacific cod to putative populations of origin. Samples were collected across the species range in the eastern Pacific Ocean, from the Salish Sea to the Aleutian Islands. A total of 6,425 putative biallelic single nucleotide polymorphisms were identified from 276 individuals. We found a strong isolation‐by‐distance signal along coastlines that mirrored previous microsatellite results and pronounced genetic differentiation between coastal samples and those from the inland waters of the Salish Sea, with no evidence for hybridization between these two populations. Individual assignment success based on two methods was high overall (≥84%) but decreased from south to north. Assignment to geographic location of origin also was successful, with average distance between capture and assignment location of 220 km. Outlier analyses identified more loci potentially under selection along the coast than between Salish Sea and coastal samples, suggesting more diverse adaptation to latitudinal environmental factors than inshore vs. offshore environments. Our results confirm previous observations of sharp genetic differentiation of the Salish Sea population and isolation by distance along the coast, but also highlight the feasibility of using modern genomic techniques to inform stock boundaries and fisheries management in a low FST marine species.The discernment of populations as management units is a fundamental prerequisite for sustainable exploitation of species. A lack of clear stock boundaries complicates not only the identification of spatial management units, but also the assessment of mixed fisheries by population assignment and mixed stock analysis. Many marine species, such as Pacific cod, are characterized by isolation by distance, showing significant differentiation but no clear stock boundaries. Here, we used restriction‐site‐associated  Library sizes  were estimated with 1% E‐Gel EX agarose gels . DNA concentrations and quality were assessed using Quant‐iT PicoGreen dsDNA Reagent  and a FLx800 Fluorescence Microplate Reader . Libraries were pooled within samples in 10 nM concentrations and sequenced in 100‐bp single‐end reads on a HiSeq2000  at the University of Oregon Genomics and Cell Characterization Core Facility .DNA was extracted from fin clip tissue punches in 96‐well format using a DNeasy 96 Blood & Tissue Kit . RAD libraries were prepared, including d et al.  and Hohed et al. , with mo2.2cstacks subprogram were generated from the five most data‐rich individuals from each sample. Flags  associated with increasing the number of loci, while reducing SNP and allele calling error rates, were set according to Mastretta‐Yanes et al.  were removed to avoid inclusion of erroneous genotypes.Raw data were converted to genotypes using Stacks v1.21 , Hardy–Weinberg equilibrium (HWE) p‐values, and F‐statistics  were estimated with GENEPOP v4.2  of each population was estimated using NeEstimator v2.01 .Locus‐specific allele frequencies, expected heterozygosity  as implemented within R ) was plotted against shortest geographic distance along the continental shelf estimated from Google Earth.The presence and magnitude of global IBD were estimated with Mantel tests (999 permutations) run in GenAlEx v6.5b4  and putative genetic relationships  between individuals were estimated and tested using the maximum‐likelihood framework in ML‐Relate  were first analyzed to examine coastal patterns of selection, and then, a subset of samples  were analyzed to examine differentiation associated with Salish Sea samples versus coastal samples at similar latitude.Outlier tests to identify putative candidate loci under selection were performed using two different methods. First, a Bayesian framework with a differentiation‐based method was employed in BayeScan v2.1  by Hemmer‐Hansen, Therkildsen, Meldrup, and Nielsen (http://www.ensembl.org/Gadus_morhua) using BLASTN , followed by a leave‐one‐out method on an independent test data set to test the assignment  to further resolve population structure and gene flow between the Salish Sea and coastal samples. Individual ancestry was estimated using Structure  ranged from 0.00 to 0.99, with only 19 comparisons showing evidence for a genetic relationship other than unrelated (r = 0.32 ± 0.251). Sixteen pairwise comparisons were at the half‐sibling level (r = 0.22 ± .065) and three at full‐sibling status , strongly suggesting contamination with adjacent sample wells. We therefore excluded all 17 individuals with a multilocus heterozygosity >0.22; half (N = 8) of these individuals were also one member of the contaminated pairs , Hardy–Weinberg equilibrium (HWE) p‐values, locus‐specific F‐statistics , and population pairwise FST values are reported in Table HE averaged 0.195 (SD = 0.0023) across populations , except between the Strait of Juan de Fuca sample (JDF12) and the neighboring coast of Washington  and Hecate Strait . The SS12/13 sample was the most divergent, and pairwise FST estimates with other samples ranged from 0.0148 with JDF12 to 0.0429 with AD06. Finally, Ne estimates averaged 7,955 individuals and ranged from 2,326  for WC05 to 30,750  for HS04  than with RAD sequences (GST″ = 0.02‐0.05).The IBD pattern along the coast was very similar to previous results with microsatellites Figure . Slopes x‐axis) largely reflected range‐wide IBD  evidence for selection by BayeScan  10, 50, 100, 200, 500, 1,000, and all loci, respectively. Some samples were easier to assign to their sample of origin than others. For example, 100% of individuals from the Salish Sea were correctly assigned to their sample of origin using only the top 50 markers. In contrast, only 78% of individuals from AD06 could be assigned to their sample of origin using all loci  and the sample of origin Figure . Median  Figures . In addi Figures , likely p < 0.001; Figures Assignments to geographic location of origin with the SCAT analyses were highly consistent among runs. The median distance between assigned geographic locations of origin in three replicates was 47 km per individual . Assignments for HS04 samples had the greatest precision, with a median difference among runs of 27 km (25% and 75% quantiles = 18 and 45 km), while AD06 had the greatest distance among runs with a median difference of 84 km . Assigned geographic locations of origin (median of three runs per sample) were highly correlated with capture locations  was almost twice as high as FST, suggesting mutation rather than just genetic drift as an important contributor to the observed differentiation , seahorses (Hippocampus spp.), various reef fishes, red snapper (Lutjanus campechanus), and other organisms  and was identified as an outlier by both BayeScan and OutFLANK. This locus aligned to the coding region of a novel protein sequence that is orthologous to genes involved in reproduction and sexual development: steroidogenic factor 1 (SF1) and zona pellucida glycoprotein 3 (ZP3). Second, locus 8253 had the second largest overall FST value (FST = 0.46), was identified as an outlier in both coastal analyses, and aligned to an intron of the folliculogenesis‐specific bHLH transcription factor, which may regulate the expression of the reproductively important zona pellucida genes , and seven were identified among HS04, WC05, and SS12/13. The identification of these loci by two different approaches supports the notion that they are truly under selection, although some of the observed patterns may still be the result of IBD . The higher assignment success of RAD sequencing is likely due to both the greater number of loci in this study and the inclusion of outlier loci with high levels of differentiation.Poor population assignment rates are often expected for species exhibiting IBD  in this study (median = 220 km) were greater than generational dispersal from IBD patterns  as well a small geographic scales , thus providing important information for the definition of management units. Potentially, more importantly, we established the utility of assignment to both population and location of origin and applied that latter approach for the first time in a marine species. SNP‐based genetic stock identification has the potential to provide deeper resolution of population structure and understanding of seasonal migrations in a species where spatial dynamics are not well characterized . Scripts and genotype data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.402sb71Sequence data for this study have been deposited at the Short Read Archive of NCBI, accession number SRP137021 ( Click here for additional data file. Click here for additional data file.


Invasive candidiasis (IC) is the most common invasive fungal infection. The epidemiology of IC in hospitalized patients has been widely investigated in many metropolitan cities; however, little information from medium and small cities is known.Candida infection in a regional tertiary teaching hospital in Southwest China.A 5-year retrospective study was carried out to analyze the prevalence, species distribution, antifungal susceptibility, risk factors and mortality of inpatients with invasive Candida infection during the five-year study period were identified, with a mean annual incidence of 0.41 cases per 1000 admissions and a 30-day mortality rate of 12.3%. The species distributions of Candida albicans, Candida glabrata, Candida tropicalis, Candida krusei, Candida parapsilosis and other Candida species was 45.3, 30.0, 15.2, 4.9, 2.1 and 2.5%, respectively. The total resistance rates of fluconazole (FCA), itraconazole (ITR) and voriconazole (VRC) were 18.6, 23.1 and 18.5%, respectively. Respiratory dysfunction, pulmonary infection, cardiovascular disease, chronic/acute renal failure, mechanical ventilation, abdominal surgery, intensive care in adults, septic shock and IC due to C. albicans were associated with 30-day mortality (P < 0.05) according to the univariate analyses. Respiratory dysfunction  and IC due to C. albicans  were the independent predictors of 30-day mortality.A total of 243 inpatients with invasive This report shows that the incidence and mortality rates are lower and that the resistance rates to azoles are higher in medium and small cities than in large cities and that the species distributions and risk factors in medium and small cities are different from those in large cities in China. It is necessary to conduct epidemiological surveillance in medium and small cities to provide reference data for the surveillance of inpatients with IC infections. Invasive candidiasis (IC) is the most common fungal disease among hospitalized patients worldwide. According to conservative estimates, IC affects more than 250,000 people worldwide every year and is the cause of more than 50,000 deaths . IC is wCandida species and comprises both candidemia and deep-seated tissue candidiasis. Candidemia is the most frequent form of IC. More than 15 Candida species can cause human candidemia  and IC due to C. albicans  were the independent predictors of 30-day mortality.The univariate predictors of poor outcomes due to IC are shown in Table http://www.gov.cn/zhengce/content/2014-11/20/content_9225.htm). The urban population proportion, gross domestic product, income of residents, educational resources, sports resources, medical resources and air pollution in medium and small cities are less than those in large cities [This was a single-center retrospective study in a regional tertiary teaching hospital in southwest China. The inpatients mainly came from Luzhou, Neijiang, Yibin, Zigong, Chishui, and Zhaotong cities, which are prefecture-level medium and small cities  in western China . The mede cities –25; all Candida infection has increased steadily from 0.16 to 0.66 cases per 1000 admissions, which is similar to a metropolitan-based report in Beijing (0.13 to 0.55 cases per 1000 admissions), over the past 5 years [Our data showed that the incidence of invasive  5 years   in the ICU. These results are consistent with a report from a multicenter prospective observational study in China [C. glabrata was higher than that of C. albicans, which was only exhibited in patients with hematological malignancies in the USA [C. albicans species were significantly predominant, C. albicans was the most common isolate, consistent with reports from most other areas in China [C. tropicalis was the most common isolate (28.6%). A possible reason may be related to the type of infection (only candidemia). In our study, we also found that C. glabrata was the second most common Candida species, similar to a report from a hospital in China [Candida infections occurred more frequently in males of advanced age (age > 65), which was similar with those findings in USA [C. glabrata was the most common Candida species; this result was different from a 133 sample-based study in China [In the study, in China . However the USA . Althougin China , 29–31. in China , C. tropin China , and hosin China , Germanyin China  and the in China . Besidess in USA , Denmarks in USA , Pakistas in USA , China , VRC (18.5%) and 5-FC (4.1%). However, none of the isolates in this study were resistant to AMB, and the susceptibility results in our city were higher than those in northern Ireland  [Candida species in the study were higher than those in metropolitan cities, especially Beijing [C. albicans (< 5%) [C. glabrata  [With regard to resistance, resistance to FCA, ITR and VRC were common in , 99.1%) . In Chin, VRC 18.% and 5-F Beijing  and Chon Beijing , which w Beijing . Possibl Beijing . In cont Beijing . Regardi Beijing . The ress (< 5%)  worldwidctively)  were appctively) . The proctively) . In addiy FCA 18.%, VRC . The number of prognostic factors (9 factors) in our city was clearly more than that in other regions of China, such as Beijing [C. albicans were the predictive factors of mortality in the multivariate analysis. The results of some factors in this study agreed with the findings of a multicenter prospective observational study in China [C. haemulonii was the highest , possibly because there were only two cases. The fatal case in our study was a critically ill patient in the ICU with respiratory dysfunction and severe underlying comorbidities. Excluding C. haemulonii, C. albicans was associated with the highest mortality rate (18.2%). In our study, respiratory dysfunction was the major cause of mortality (39.2%) in patients ; Meanwhile, another study also indicated that hematological malignancy was also an independent predictor for 66.7% of death of in-hospital patients [In this study, we analyzed the prognostic factors in patients with IC. Respiratory dysfunction, pulmonary infection, cardiovascular disease, chronic/acute renal failure, mechanical ventilation, abdominal surgery, intensive care in adults, septic shock and IC due to  Beijing , Shangha Beijing , Chongqi Beijing , Nanjing Beijing  and Jina Beijing , and allin China  and a re factors  factors in China ; the cruin China , Mexico  factors  factors in China , Spain . Therefore, our conclusions may not be extrapolated to pediatric patients. Second, due to the technical limitations of the clinical microbiology laboratory and the impact of hospital policies, there are no data on echinocandins in our hospital. Finally, this was a single-center retrospective study. Our data might be influenced by the distribution of the regional population, the level of medical intervention, and the distribution of patient types.This study shows that the incidence and mortality of IC in medium and small cities are lower than those in large cities, that the resistance rate to azoles is higher in medium and small cities than in large cities, and that the species distribution and risk factors in medium and small cities are different from those in large cities in China. This study provides reference data for future epidemiological and susceptibility studies on both antifungal agents and mortality due to IC in our hospital and in other hospitals in medium and small cities.Additional file 1: Table S1. The different of urban population proportion, GDP, income, education resources, sports resources, medical resources and air population between Luzhou city and other cities in 2013Additional file 2: Table S2. The difference of prognostic factors in difference studies about invasive candidiasis


Citrus aurantifolia (Christm.) Swingle) is an important fruit crop, which has high commercial value and is cultivated in 60 out of the 77 districts representing all geographical landscapes of Nepal. A lack of improved high-yielding varieties, infestation with various diseases, and pests, as well as poor management practices might have contributed to its extremely reduced productivity, which necessitates a reliable understanding of genetic diversity in existing cultivars. Hereby, we aim to characterize the genetic diversity of acid lime cultivars cultivated at three different agro-ecological gradients of eastern Nepal, employing PCR-based inter-simple sequence repeat (ISSR) markers. Altogether, 21 polymorphic ISSR markers were used to assess the genetic diversity in 60 acid lime cultivars sampled from different geographical locations. Analysis of binary data matrix was performed on the basis of bands obtained, and principal coordinate analysis and phenogram construction were performed using different computer algorithms. ISSR profiling yielded 234 amplicons, of which 87.18% were polymorphic. The number of amplified fragments ranged from 7–18, with amplicon size ranging from ca. 250–3200 bp. The Numerical Taxonomy and Multivariate System (NTSYS)-based cluster analysis using the unweighted pair group method of arithmetic averages (UPGMA) algorithm and Dice similarity coefficient separated 60 cultivars into two major and three minor clusters. Genetic diversity analysis using Popgene ver. 1.32 revealed the highest percentage of polymorphic bands (PPB), Nei’s genetic diversity (H), and Shannon’s information index (I) for the Terai zone , and the lowest of all three for the high hill zone . Thus, our data indicate that the ISSR marker has been successfully employed for evaluating the genetic diversity of Nepalese acid lime cultivars and has furnished valuable information on intrinsic genetic diversity and the relationship between cultivars that might be useful in acid lime breeding and conservation programs in Nepal.Acid lime ( The matrix estimates the banding characteristics, such as (i) the total number of bands obtained (TNB), (ii) the number of polymorphic bands (NPB), (iii) percentage polymorphism (PP), (iv) polymorphic information content (PIC), (v) band informativeness  were calculated using the strict consensus method of the NTSYS program for each combination of similarity coefficient and UPGMA clustering. CIc measures how resolved the tree is [The genetic diversity in acid lime was computed using Numerical Taxonomy and Multivariate System . Similarity indices were computed by applying a similarity to the qualitative data. From these similarity indices, sequential, agglomerative, hierarchical, and nested (SAHN) clustering was performed using the unweighted pair group method of arithmetic averages (UPGMA) algorithm . Similar tree is . The besIn addition, AMOVA  was performed among and within three agro-ecological zones. The genetic diversity and relationship between the acid lime cultivars of these different agro-ecological zones of Eastern Nepal were also studied through a principal coordinate analysis (PCoA), using the Multivariate Statistical Package , and in terms of percentage of polymorphic bands (PPB), Nei’s genetic diversity (H), and Shannon’s information index (I) using Popgene (version 1.32).Altogether, 234 loci were amplified by 21 ISSR primers across the 60 acid lime cultivars , with an average amplification of 9.72 bands per primer . Among tB) of the 21 ISSR primers ranged from 0.42 (C4) to 1.77 (C1), with an average of 1.12, and the resolving power (RP) ranged from 4.63 (UBC 807) to 23.16 (UBC 857), with an average of 12.03. Here we found that the primers that have the highest PIC value also gave the highest RP score (The polymorphic information content (PIC) value ranged from 0.74 (UBC 807) to 0.93 (UBC 857) with an average of 0.85. The band informativeness (IRP score .p < 0.001) partitioning of the genetic variation . The genetic differentiation (ΦPT) for the three agro-ecological zones was 0.139, suggesting that genetic differentiation of acid lime was comparatively lower than the genetic differentiation of acid lime within agro-ecological zone variation, accounting for the 86% variation , Jaccard’s (J) and Dice’s (D) coefficient, i.e., SM (0.54–0.94), J (0.42–0.90), and D (0.57–0.95), with an average similarity coefficient value of 0.79, 0.69, and 0.81, respectively. The Mantel test (matrix comparison) result of the original matrices showed the correlation value between J and D to be the highest and most significant (0.99710), in comparison to SM and J (0.98143) or SM and D (0.98318) .C value (CIC = 1.00000) was observed for the J and D coefficients (r) between the genetic similarity matrices and cophenetic matrices are presented in r = 0.90356) (The highest CIficients . The cop0.90356) , indicatC. aurantifolia cultivars have been assessed from the pair-wise comparison of the Dice similarity matrix, which revealed the high-hill cultivars LT-20 and LT-21 to be the most genetically similar (0.95), and the Terai cultivars LS-35 and LS-56 to be the most genetically distant (0.571) genotypes. Considering these similarity indices, Terai cultivars were shown to have a wider genetic base, followed by mid-hill and high-hill cultivars.On comparative analysis made for the similarity coefficients, Dice’s coefficient was revealed to be the best, which then subjected to further interpretations of genetic diversity estimates and the relationships between various cultivars of acid lime representing different geographical gradients. Based on the Dice similarity coefficient, genetic similarity within 60 acid lime cultivars ranged from 57–95%, with an average of 81% . The indThe genetic diversity indices of acid lime cultivars from different agro-ecological zones that were assessed based on percentage of polymorphic band (PPB), Nei’s gene diversity (H), and Shannon’s information index (I), using Popgene ver. 1.32, revealed the highest diversity indices in Terai cultivars , followed by mid-hill  and high-hill  .The 60 cultivars of acid lime analyzed were separated into two major clusters (I and II) and three minor clusters  in the phenogram . The culTwo-dimensional plots of the principal coordinate analysis (PCoA) classified the 60 acid lime cultivars based on ISSR allelic variation . The firCitrus spp. is comparable with our present investigation (PP = 87.18%), such as 89.4% in wild Citrus spp. [C. indica [Citrus spp. [Polymorphism reported in rus spp. , 87% in . indica , and 100rus spp. . The totrus spp. . Howeverrus spp. . The PICrus spp. . In our rus spp. , which irus spp. . Howeverrus spp. . Our currus spp. .The clustering based on UPGMA analysis revealed the genetic diversity and relationship among acid lime cultivars of three geographically diverse agro-ecological zones. No specific cluster was formed for cultivars from the different agro-ecological zones under study. Our result is congruent with SSR-based findings as being separated into two major and three minor clusters, apart from the distribution of different cultivars in different clusters . ClusterDice’s similarity matrix-based genetic diversity estimates within each of three agro-ecological zones revealed a wide genetic base in cultivars from the Terai agro-ecological zone (0.57–0.94) in comparison with the mid-hill (0.70–0.94%) and high-hill (0.75–0.95%) zones. However, regarding the genetic base between agro-ecological zones, the highest value was observed between the high-hill and Terai cultivars (57–95%), followed by mid-hill and Terai cultivars (57–94%) . The TerMany qualitative and quantitative agronomic traits, such as high juice content, fruit size, and disease and insect resistance have a genetic basis of inheritance and can be enhanced by the use of molecular markers and marker-assisted selection (MAS) techniques. Selection and improvement of good qualitative and quantitative traits are important steps in the variety of developmental programs. Moreover, breeding of good quality traits requires selection of parents with greater genetic diversity . For thiUsing 60 cultivars of acid lime from three different agro-ecological zones, we investigated the genetic diversity of Nepalese acid lime using ISSR markers. Moderately high genetic polymorphism (87.18%) was detected using 21 primers. The cluster analysis revealed heterogeneous grouping into two major and three minor clusters; however, no clustering based on geographical locations was evident. This high genetic polymorphism indicates that these cultivars might also harbor diverse, agronomically important qualitative and quantitative traits, such as disease and pest resistance, physico-chemical properties, and yield. In-depth studies on these various aspects are still lacking in Nepal. However, a study on physicochemical properties of the same acid lime cultivars has been conducted [ISSR markers have been extensively used as important DNA-based molecular marker tools for the genetic diversity characterization of germplasm and the establishment of the identity of varieties, hybrids, and parental sources in plant breeding, marker trait association, germplasm management, etc. ISSR markers have also been shown to be associated with various agronomically important traits (such as disease resistance) in various crops. Our study revealed the existence of moderately high genetic diversity in acid lime cultivars of Eastern Nepal, indicating availability of various sources of acid lime genes to be exploited in current and future breeding programs. Further research needs to be undertaken to exploit ISSR and other marker systems to generate agronomically important gene- and trait-specific markers and their subsequent utilization in acid lime breeding program.


Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a major agricultural pest that causes economic damages worldwide. In particular, B. tabaci MED (Mediterranean) has resulted in serious economic losses in tomato production of Korea. In this study, 1,145 B. tabaci MED females from 35 tomato greenhouses in different geographic regions were collected from 2016 to 2018  and analyzed to investigate their population genetic structures using eight microsatellite markers. The average number of alleles per population (NA) ranged from 2.000 to 5.875, the expected heterozygosity (HE) ranged from 0.218 to 0.600, the observed heterozygosity (HO) ranged from 0.061 to 0.580, and the fixation index inbreeding coefficient (FIS) ranged from -0.391 to 0.872 over the three years of the study. Some significant correlation (p < 0.05) was present between genetic differentiations (FST) and geographical distance, and a comparatively high proportion of variation was found among the B. tabaci MED populations. The B. tabaci MED populations were divided into two well-differentiated genetic clusters within different geographic regions. Interestingly, its genetic structures converged into one genetic cluster during just one year. The reasons for this genetic change were speculated to arise from different fitness, insecticide resistance, and insect movement by human activities.The sweet potato whitefly,  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a major agricultural insect pest that is distributed worldwide. B. tabaci has an extremely broad host range [B. tabaci is also a vector for more than 100 pathogenic plant viruses [B. tabaci is a complex of 11 well-defined high-level groups consisting of at least 36 putative species identified based on mtCOI  [B. tabaci, MEAM1  and MED , are highly invasive and colonize large areas worldwide [Lonicera japonica)) of the B. tabaci species complex are present in Korea. MEAM1 and MED were first detected in 1998 and 2004 [The sweet potato whitefly, st range  and caus viruses , particu viruses , and a m viruses . B. tabaidase I) , 6. Thesidase I) , 8. Two orldwide . Three pand 2004 . Currentand 2004 , 13.B. tabaci is facilitated [Understanding the population genetic structure of a pest species is important for establishing pest management strategies . Pest poilitated .B. tabaci over relatively large geographic scales.Over the past decades, various molecular genetics tools have considerably extended the boundary of population genetics . DiverseB. tabaci MED from tomato greenhouses were identified and their genetic relationships in Korea were examined.In this study, the population genetic structures and diversities of B. tabaci female adults were collected from 35 commercial tomato greenhouses in Korea using an aspirator during 2016–2018   . The B. B. tabaci is a haplo-diploid species, producing male progeny from unfertilized eggs and female progeny from fertilized eggs [Genomic DNA (gDNA) extraction was performed using a Qiagen Gentra Puregene Tissue Kit  according to the manufacturer’s instructions. Since zed eggs , only adB. tabaci putative species. A fragment of the mtCOI gene was PCR-amplified using the primer pair C1-J-2195 (5’-TTGATTTTTTGGTCATCCAGAAGT-3’) and L2-N-3014 (5’-TCCAATGCACTAATCTGCCATATTA-3’) [2, and 1 unit of Taq DNA polymerase using Accupower PCR PreMix . The reaction conditions included an initial denaturation for 5 min at 94 °C, followed by 34 cycles of 1 min each at 94 °C, 1 min at 52 °C, and 1 min at 72 °C, with a final extension for 5 min at 72 °C [http://blast.ncbi.nlm.nih.gov/Blast.cgi).Two individuals per population were randomly selected in order to identify the ATTA-3’) . All PCRat 72 °C . PCR proB. tabaci MED as templates. PCR amplifications for the microsatellite primers and PCR reactions were performed as previously described [PCR primers were used to amplify microsatellite DNA loci 11, 53 , 68, 145escribed . A totalescribed . The 1 μNA), allele size range, and the observed (HO) and expected heterozygosities (HE), and the inbreeding coefficient (FIS) were computed using GenAlEx v.6.5 [GENEPOP v.4.0  and MicrEx v.6.5  and MicrEx v.6.5 .FST) as a factor of variation among and within the populations. AMOVA computations were performed with 999 permutations to test for significance.AMOVA was performed using GenAlEx v.6.5. AMOVA was used to characterize genetic variation patterns and to estimate variance components. A two-part AMOVA analysis was conducted to check genetic divergence (K) was estimated in STRUCTURE v.2.3.2 with 60,000 Markov Chain Monte Carlo (MCMC) steps and a burn-in period of 600,000. The log-likelihood estimate was run for K = ranges from 1 to 10 with ten replicates each. They were used to determine the number of clusters based on a combination of the mean estimated Ln probability of the data [ΔK) [The number of genetic clusters (the data  and the ata [ΔK) . The Evaata [ΔK) .PCoA was conducted between multi-locus genotypes in all individuals. The codominant-genotypic option of GeneAlex v.6.5 was used to calculate the similarity genetic distance matrix . The PCoadegenet’ package [DAPC was performed in the ‘ package  of R sof package . The numFST/(1-FST) with 1,000 random permutations was conducted using the GenAlEx v.6.5, GENEPOP v.4.0, and ‘ade4’ package [ggpolt2’ package.The Mantel test  was perf package  of R sofThe BOTTLENECK v.1.2.02  was usedFST) value estimates were computed using GENEPOP v.4.0. To correct for null alleles, pairwise estimators of FST values were calculated from each microsatellite dataset that potentially harbored null alleles using the excluding null alleles (ENA) correction method (FST-ENA) following 1,000 bootstrapping permutations over the loci. The ENA correction method was used to obtain unbiased pairwise FST values using FreeNA. To investigate the relationship between the genetic distance revealed by the FST values and geographic distance, an isolation-by-distance analysis was performed using a regression of FST/(1-FST) values against the logarithm of the geographical distance (km) between the populations. Significance of the correlation between the two data matrices was assessed using a Mantel test with 1,000 permutations. This was performed with the ISOLDE program implemented in GENEPOP v.4.0.To assess the level of genetic differentiation between the samples, pair-wise fixation index  species based on representative samples.All B. tabaci MED are shown in NA) ranged from 2.000 (17’JIN) to 5.875 (16’SJ). The expected heterozygosity (HE) ranged from 0.218 (16’JJ) to 0.600 (16’PT), whereas the observed heterozygosity  ranged from 0.061 (16’CW) to 0.580 (16’IS). The value of HE in each population was higher than the value of HO, except for 12 populations that showed negative values for FIS. The estimator of the fixation index inbreeding coefficient (FIS) ranged from -0.391 (17’CC) to 0.872 (16’CW). A positive value for FIS indicates the presence of heterozygotic deficiencies, whereas a negative value indicates the presence of homozygotic deficiencies. The analysis of genetic diversity for all different eight microsatellite loci of B. tabaci MED screened is given in The values of the genetic diversity indexes for the Korea populations of B. tabaci MED populations showed that 48.0% of the total genetic variation was accounted for by variation among the populations and 52.0% of the variation was accounted for by individual variation within the populations  over time , indicating a pattern of isolation by distance  was detected in eight populations under the IAM  and two populations under the TPM , which accounted for 22.8% and 5.7% of the Korea populations  reflects the degree of genetic differentiation among the populations. FST is close to 0 when the genetic variation shows no difference in fixation among the populations. It is close to 1 when genetic differentiation is high. In this study, the FST values ranged from -0.0155 to 0.7501 and the ENA-corrected FST values ranged from -0.0139 to 0.7327 among the populations . The lowest FST value was found between the 17’SJ and 18’SJ populations (-0.0139). In practice, an FST value of 0.00–0.05 indicates low differentiation and FST values > 0.15 indicate a high level of differentiation. Negative FST values are allowed because correlations vary from -1 to +1 [B. tabaci in Korea showed high levels of differentiation.The fixation index (ulations . The hig-1 to +1 . As a reB. tabaci MED (Q1) populations in Korea using eight microsatellite loci. The Korean populations of tomato B. tabaci MED appeared to be classified into two genetic clusters based on STRUCTURE and DAPC analyses, and their genetic structure converged rapidly into one genetic cluster. This phenomenon was reported previously by Dinsdale et al. [B. tabaci rapidly changed even in a period of just four months. The results of this study and those by Dinsdale et al. [B. tabaci MED genetic clusters in Korea might become the dominant species in the future.This study is the first comprehensive genetic structure analysis of e et al.  in Austre et al. , suggestB. tabaci MED genetic clusters in Korea. Although the two B. tabaci MED genetic clusters might have been mixed when they were first introduced in new areas, one genetic cluster would become dominant if there is fitness difference between them. Fitness difference between two genetic clusters could result from different susceptibilities to insecticides. The use of various insecticides, such as neonicotinoids, organophosphates, and carbamates, has been the main control method for B. tabaci MED in Korea. Extensive use of these insecticides has rapidly resulted in high levels of insecticide resistance in B. tabaci MED populations [B. tabaci MED might have different potentials for developing resistance to different insecticides. This differentiation was partially supported by changing the frequencies and diversity caused by chemical control [This phenomenon could be caused by different fitness between the two ulations . The two control , 55. ResB. tabaci MED and this trend was maintained during the past three years. However, in the Pyeongtaek area, the genetic cluster of B. tabaci MED changed every year. The differences in the speed of genetic cluster change could be caused by human-related factors because B. tabaci has a low dispersal ability over long distances [B. tabaci MED populations should not have been affected by other populations because almost all growers produce tomato seedlings themselves and Jeju is isolated because it is an island. On the other hand, the Pyeongtaek tomato growers have purchased tomato seedlings from different nurseries every year. Moreover, the city of Pyeongtaek has one of the most active agricultural trades of all Korean cities. Whitefly populations are generally affected by human activities, such as the movement of infested plants from nurseries, material shipments, and commercial trading, rather than by active flight [However, the speed of this genetic cluster change in Korea could differ by areas. For example, the Jeju populations showed one genetic cluster of istances . In the e flight , 57. ThuB. tabaci in areas where it usually occurs should be useful for efficient management of B. tabaci [B. tabaci MED as an invasive pest in Korea. Thus, the results of this study could be a valuable foundation to develop efficient management strategies for B. tabaci MED in Korea. However, further studies are needed to clearly find the fitness differences between the two B. tabaci MED genetic clusters in Korea.The information on the genetic characteristics of . tabaci –60. The S1 TableaNumber of alleles. bExpected heterozygosity. cObserved heterozygosity. dMean fixation index inbreeding coefficient. eAverage proportion of homozygous for null allele.(XLSX)Click here for additional data file.


Evidence Based Complementary and Alternative Medicine, Vol. 6 (Suppl. 1), 2009, pp. 1–3). The main purposes of the present study are to estimate genetic and environmental influences on SC types, and to explore genetic and environmental correlations that affect phenotypic associations among the SC types. In total, 1,742  twins (mean age = 19.1 ± 3.1 year) completed a Sasang questionnaire. Univariate and multivariate model-fitting analyses were performed. Total (additive and non-additive) genetic influences were 71% for males and 81% for females in TE, 70% for males and 71% for females in SE, and 47% for both sexes in SY. Non-additive genetic effects were substantial, and shared environmental influences were negligible in most SC types. Multivariate model-fitting analysis revealed that non-additive genetic and individual-specific environmental correlations between TE and SE were -0.92  and -0.62 , respectively. The corresponding estimates were -0.55  and -0.44  between TE and SY and 0.19  and -0.40  between SE and SY. These results suggest that the phenotypic associations among SC types may be mediated by pleiotropic mechanism of genes.According to the Sasang theory, humans can be categorized into one of the four Sasang constitution (SC) types. The four SC types are Tae-Yang (TY), Tae-Eum (TE), So-Yang (SY), and So-Eum (SE), which are determined mainly on the basis of anthropometric characteristics, personality, and the balance of the physiological functions of the major organ systems. There is a growing recognition in the complementary and alternative medicine area that SC types have the potential to be a useful scientific tool for the prevention, diagnosis, and treatment of diseases (Cooper,  According to the Sasang constitutional (SC) theory originally developed by Lee Je Ma (1837–1900) who specialized in Korean traditional medicine, there are four constitutional types in humans and one of these four types is dominant for a person from early in life  that the fat mass and obesity-associated (FTO) variant (rs7193144) was significantly inversely related to the SE type after adjusting for body mass index (BMI). This finding was replicated in an independent sample . Twins under 20 years of age were recruited mostly from schools throughout South Korea, while those 20 years of age or older were from Facebook, twin clubs on the internet, and colleges throughout South Korea. Further details of the sample collection are described in Hur et al.  twins who participated in the 2017 survey conducted at the South Korean Twin Registry  registered with the Korea Constitutional Multicenter Bank , non-additive genetic , shared environment , and individual-specific environment plus measurement error .Twin studies make use of the difference in the proportion of genes shared between MZ and DZ twins. Utilizing this difference, the total variance of each SC type is decomposed into following components: additive genetic  and carried out general-sex limitation model-fitting analyses for each SC type to obtain an initial indication on genetic and environmental estimates for each SC type. In the univariate model-fitting analysis we compared the ACE and ADE model for each SC type by using Akaike's information criterion , non-additive genetic (rd), shared environmental (rc), and individual-specific environmental correlations (re) among the three scales were also derived from these variances and covariances. The additive genetic correlation provides a measure of the extent to which two SC types are influenced by the same genes that operate additively. For example, if the additive genetic correlation is estimated at 1.0 between TE and SE, this indicates that TE and SE share all of their additive genetic factors; if this correlation is estimated at zero, then there are no shared additive genetic factors between TE and SE. The same logic applies to non-additive genetic, shared environment, and individual-specific environmental correlations.Cholesky decomposition model divided the phenotypic associations among the three SC types into the proportion of variance associated with additive genetic, non-additive genetic, shared environment, and individual-specific environment factors. The additive genetic, non-additive genetic, shared environment, and individual-specific environment covariance matrices were computed by the product of their respective Cholesky factor loading matrix and its transpose. The additive genetic (p < .05) change in chi-square in the nested submodel indicates that removing the parameters from the full model significantly worsens the model-fit, and therefore the submodel is rejected.We used a raw data option in Mx  and correlations with age for TE, SE, and SY. Age was very modestly correlated with all three types (-0.09 < r < 0.05) in our sample, suggesting that the main effect of age in SC types may be negligible in adolescents and young adults. Males were significantly higher than females in TE . In SE and SY, however, females were significantly higher than males .The distribution of SY was fairly normal with a skewness of -0.11. TE and SE were moderately positively skewed with skewness indices of 0.62 and 0.82, respectively. We thus performed square root transformation of the data for TE and SE, which resulted in skewness indices of 0.05 and 0.02, respectively. r = -0.89 and r = -0.48), indicating the phenotypic overlap. Furthermore, the directions of the phenotypic correlations were consistent with those of the MZ and DZ cross-twin, cross-trait correlations, suggesting that the direction of effect may be the same. Because common shared environmental influences were negligible, the MZ cross-twin, cross-trait correlation represented genetic sources of covariance. Thus, greater MZ than DZ cross-twin, cross-trait correlations in r = 0.15, p < .01). Although the MZ cross-twin, cross-trait correlation for the relationship between SE and SY was slightly greater than the corresponding DZ correlation, both correlations were low, indicating that individual-specific environment may play a role in the relationship between SE and SY.A small but significant phenotypic correlation was found between SE and SY (r = -0.92) and greater than individual-specific environmental correlation (r = -0.62). The non-additive genetic correlation between TE and SY was moderate but still greater than the corresponding individual-specific environmental correlation (r = -0.55 vs. r = -0.44). Taken together, these results provide evidence for significant genetic contributions to the phenotypic associations between TE and SE and between TE and SY.As can be seen in While the non-additive genetic correlation between SE and SY was positive, the individual-specific environmental correlation was negative and larger than the non-additive genetic correlation. As the phenotypic correlation between SE and SY was positive, this result suggested that the effects of common genes may be strong enough to offset common environmental effects on the relationship between SE and SY. One should note that genetic correlations reflect the amount of the genetic influences on two traits that is common to both. This is independent of the heritability of either trait. Therefore, it is possible for a trait to have a small genetic correlation with another trait but to have a high heritability and make a high contribution to the phenotypic association (Plomin et al., To date, little research attention has been given to pleiotropic associations among the SC types. Pleiotropy is the overlap between the genetic architecture of two or more traits due to a variety of shared causal pathways (Solovieff et al., High heritability estimates for the SC types, especially for TE and SE found in this study, pointed to the importance of further identification of novel genetic loci and biological pathways contributing to the heritability. Extreme skewness of the raw distribution can sometimes generate scale-dependent evidence for genetic non-additivity (Jinks & Fulker, Non-additive genetic correlations were significant and large between TE and SE and between TE and SY, indicating that genes that influence TE may affect SE and SY as well. Especially, non-additive genetic correlation of -0.92 between TE and SE suggested that nearly identical genetic factors may influence TE and SE. However, negative signs of the genetic correlations suggest that genes that increase TE may suppress the effects of SE and SY. For better understanding of underlying mechanisms for the phenotypic associations, future molecular genetic studies should test for pleiotropy using SNP-based genetic data.Although individual-specific environmental correlations between TE and SE and between TE and SY were not as large as corresponding genetic correlations, they were significant, suggesting that common sets of environment may also mediate the relationships of these phenotypes. Similar to genetic correlations, the negative signs of individual-specific environmental correlations indicated that environmental sources that enhance TE may reduce SE and SY and vice versa. For example, certain nutrition may help to develop the TE type but suppress the development of the SE type. One should note that correlated measurement errors are confounded with individual-specific environmental correlations. Thus, actual individual-specific environmental correlations may be lower than those estimated in our study.Our study has some limitations. First, non-additive genetic correlations in our study should be interpreted as a sum of additive and non-additive genetic correlations as it is likely that our sample size was not large enough to separate between the two types of correlations. Second, we only used the questionnaire method to measure SC types. Future studies should employ multiple measures and conduct multivariate genetic analyses to determine genetic sources common across measures and those unique to each measure. Third, as the order of the variables in the Cholesky decomposition model is arbitrary, we changed the order of the variables in various ways prior to Cholesky model fitting. However, the results of analyses were the same. That is, Cholesky models with different orders of the variables yielded the same values of the goodness-of-fit statistics, heritability estimates, and genetic and environmental correlations. Finally, our sample was based on South Korean adolescents and young adults. Our results therefore need to be replicated in larger samples with different ethnic and genetic backgrounds.