Staphylococcus aureus (MRSA) transmission in the hospital setting has been a frequent subject of investigation using bacterial genomes, but previous approaches have not yet fully utilised the extra deductive power provided when multiple pathogen samples are acquired from each host. Here, we used a large dataset of MRSA sequences from multiply-sampled patients to reconstruct colonisation of individuals in a high-transmission setting in a hospital in Thailand. We reconstructed transmission trees for MRSA. We also investigated transmission between anatomical sites on the same individual, finding that this either occurs repeatedly or involves a wide transmission bottleneck. We examined the between-subject bottleneck, finding considerable variation in the amount of diversity transmitted. Finally, we compared our approach to the simpler method of identifying transmission pairs using single nucleotide polymorphism (SNP) counts. This suggested that the optimum threshold for identifying a pair is 39 SNPs, if sensitivities and specificities are equally weighted.Methicillin-resistant  Staphylococcus aureus, and in particular methicillin-resistant S. aureus (MRSA) has been a frequent subject, often in a hospital environment where it is responsible for a considerable burden of disease. The focus of previous studies has ranged from identifying bacterial spread between continents (Whole-genome sequencing (WGS) has proved a valuable tool in the investigation of the transmission of infectious agents. ntinents , to recontinents . Severalntinents .In a previous study, phyloscanner , and toRecruitment to the study was described previously . BrieflyThe complete set of subjects screened consisted of 169 adult patients, 98 child patients and 37 HCWs. To the dataset of Tong et al., which comprised up to three sequences from each subject for a total of 76, we added 923 more, for a total of 999. Of these, four were excluded as suspected contaminants, leaving 995, from 55 subjects (compared to 51 in the previous study). Five subjects were HCWs, 21 were surgical ICU patients, and 29 were paediatric ICU patients. The number of sequences per subject ranged from 1 to 239 (mean 18). In addition, 20 sequences (one per patient) for isolates originating from an earlier study in the same hospital  were incThe complete core chromosome sequence alignment was 3,043,210 base pairs in length. Bayesian phylogenetic analysis was conducted using ExaBayes 1.5 . Figure phyloscanner v1.4.2  formed two phylogenetic clades where the median patristic distance between clade MRCAs (across the ExaBayes posterior) was greater than 100 substitutions, sufficiently diverse that they were unlikely to be the product of single-strain colonisation events. The sequences from each of these were separated into two groups and each was subsequently treated independently. We will refer to links in the transmission chain as ‘colonisations’; each isolate belongs to a single colonisation. Numerical codes prefixed with a ‘T’ are used for study subjects and codes with ‘C’ for colonisations. In most cases the numbers after this prefix match; for example C009 is the sole colonisation of subject T009. For the five multiply-colonised subjects, a lowercase letter is used to differentiate them, for example C035a and C035b are the two colonisations of subject T035.This reconstruction was performed with an awareness of the possibility that subjects may experience multiple independent infection or colonisation events, in which case each such event should be treated as a separate entity when investigating transmission. An initial Of 60 identified colonisations from the 55 subjects, cultures for 33 came only from a single positive swab. Those for the remaining 27 were obtained either on at least two time points, from at least two body sites, or both. The median number of available sequences per colonisation was 1, but this rises to 14.5 with these singletons excluded. In 25 of the 33 single-swab colonisations, previous and/or subsequent examinations of the same patient did not identify MRSA belonging to the same colonisation, while in eight only one examination was performed. In what follows we refer to these 33 as trace colonisations.U test p=0.018), suggesting that the nature of trace isolates could be fundamentally different to that of typical isolates from a non-trace colonisation, rather than being simply the product of colonisations of a similar nature subject to sparser sampling. A similar pattern was observed when the median SNP distance was calculated from only those isolates in a non-trace colonisation that were acquired at the time of the first positive swab , and checked whether that transmission was consistent with the timings of hospital stays and sample collection dates. We performed a separate analysis where the set of tips from each subject was randomly down-sampled to a maximum of five. This resulted in considerably more impossible reconstructed events .phyloscanner host relationship diagram, displaying the division of the 27 colonisations into 12 clusters of closely-related infections.  Links between colonisations are made up of three segments, whose colour represents the frequency of a given topological relationship between two colonisations in the ExaBayes posterior. The outer two, which have directional arrows, represent transmission in the direction of the arrow, while the central segment represents the ‘complex’ ancestral relationship  All six had a reconstructed transition in one or both directions in 95% of posterior trees, and, in the subgraphs involved in those transmissions, a posterior median of at least five tips in those belonging to the recipient. The latter condition ensured that a reasonable number of sequences were sampled from the recipient, to prevent the inference of narrow bottlenecks due simply to low sequence counts. These pairs are summarised in Two very long-stay ICU patients, T012 and T126, both appeared to transmit to other patients through bottlenecks of quite variable strength. C012 transmitted to C137 through a relatively narrow bottleneck and C159b through an extremely wide one, and the corresponding patients’ ICU stays each overlapped with T012 for at least four weeks, with both occupying a neighbouring bed to T012 for some  of that time. C126 transmitted a single lineage to C271a in every posterior tree, but showed a wider bottleneck in transmitting to both C234 and C327a. Subject T271 occupied the adjacent bed to subject T126 for 10 days but neither T234 nor T327 ever did.Finally, C183 and C194 had such similar sequences that the reconstruction suggested back-and-forth movement of lineages. However, this may be due to poor phylogenetic resolution rather than the literal truth, especially as subjects T183 and T194 were never simultaneously present in the ICU. A third patient in that ICU, T178, also swabbed positive with a strain identical to some found in both T183 and T194 and one bed was at different times occupied by all three subjects, while a fourth, T249, was also colonised with a closely-related strain and occupied a bed which was later briefly used by T183. We lack the resolution necessary to definitively determine the order of events, but it seems most likely that, if there was no back-and-forth transmission, the transmission bottleneck was wide.23 study subjects had tested positive for MRSA colonising at least two different body sites. In every case, one site was the anterior nares. Two subjects (T178 and T322) provided only a single sequence from each of two sites, which rendered them unsuitable for further investigation as only one phylogenetic topology is possible under that circumstance. To investigate phylogeny-site associations in the remaining 21 subjects, we ran ExaBayes on the sequences from each separately and analysed the results using BaTS , to idenDeviation from the null in the AI statistic indicates the presence of phylogenetic nodes whose descendants are more frequently associated with some sites than would be expected by chance. This is the most sensitive of the three statistics to the existence of an association, but such an association does not imply the existence of the distinct monophyletic clades that would be expected if sites were separately colonised by different lineages, or if a single lineage from one site was transmitted to another. For the PS and the MC statistics, deviation from the null is much more suggestive of site-specific monophyletic clades. However, the PS statistic is invariant if all but one tip in the tree comes from the same site, as is the MC statistic for any site with just a single tip, and hence these statistics are useless in this scenario. If some sites have only one tip and the AI statistic shows significant deviation from the null, then further examination is required to check if, for example, the tip in question is basal. See The remaining four subjects are T009, T126, T232 and T249, where the ExaBayes topologies were analysed for the signals of separate colonisation events and single lineage transmission from one site to another. Consensus trees are displayed in Ultimately, only three of the 21 subjects  had phylogenies consistent with separate colonisation events affecting different body sites or groups of sites. Only T249 exhibited one nested clade from a single site, which would be indicative of a single transmission from another site through a narrow bottleneck, but statistical support for this remains limited.Previous studies have not had access to such rich within-host sampling, and have frequently identified transmission pairs using SNP distance between individual isolates from hosts. As our data admits a more powerful approach based on the topology, we compared our results to those from a hypothetical study where only a single sequence is available from each subject. We used phyloscanner as a gold standard for identification of transmission pairs, and pairs of subjects that were definitively not transmission pairs. For the latter we used posterior support of less than 0.05 for having adjacent subgraphs and a topological relationship implying ancestry. We used two standards for positively identifying pairs. The first was in inverse of the previous, that is support greater than 0.95 for having adjacent subgraphs and a topological relationship implying ancestry. This version, however, has some problems as a gold standard as it does not attempt to eliminate the possibility of unsampled intermediaries in the transmission chain. For a second version where these are much less likely, we used support greater than 0.95 for having adjacent subgraphs and a topological relationship that implied the transmission of multiple lineages. This is the equivalent of the ‘PP’ topology of Romero-Severson et al.  which waFor each subject, we used the samples taken at the first positive swab as potential samples from a single-sequence study. For each pair of these acquired from different subjects, we calculated the raw SNP distance and used these figures to evaluate the performance of SNP distance as means to determine transmission pairs and members of clusters, using the above two measures as gold standards. The results can be seen in For both gold standards, SNP distance has generally good specificity for reasonable thresholds, staying above 0.75 for less than 140 SNPs. Sensitivity reaches 0.75 at a threshold of 16 SNPs for the version which is indifferent to unsampled intermediaries, but this requires 37 SNPs for the strict version.Subfigure 9B shows receiver operating characteristic (ROC) curves for the same investigation. The area under the curve statistic is 0.872 for the first gold standard and 0.836 for the second, while the value of the threshold which minimises the distance to the upper-right corner of the plot  is 39 SNPs for both analyses.In this study, we used a large genetic dataset including within-host diversity to reconstruct MRSA transmission in a hospital setting, utilising new methods that investigate the phylogenetic topology. We found great variation in the size of the transmission bottleneck between study subjects, and that in the majority of cases there was no suggestion of an association between the phylogenetic placement of an isolate and the body site that it was acquired from. Finally, we determined SNP thresholds for identifying transmission pairs with high sensitivity and specificity.33 out of 60 colonisations consisted of a single swab (trace colonisations). If the nature of these was precisely the same as the multiply-isolated colonisations and they were merely sampled less often, one would expect to see no difference in the distribution of genetic distances to isolates already identified in the hospital between the two groups. We did not find this to be the case. One explanation for this would be transient carriage, due to incidental exposure of patients to bacterial lineages already well established in the hospital environment. Indeed, thirteen trace colonisations came from patients who underwent a subsequent screen with negative results . Previous studies have found transient carriage to be quite common in both patients  and HCWsThe concordance of the order of colonisation given by the phylogenetic topology and the known dates of hospital stays was generally good; with the exception of one case (C271a) which plausibly involved contamination, those transmissions that contradicted the reported timings of stays and MRSA screens did not achieve posterior support above 0.76 and a threshold of 0.95 would comfortably eliminate them.The role of variable sample counts per colonisation in an analysis of this sort is complex. The relative number of tips derived from one colonisation is information that the parsimony reconstruction will use in determining the source and recipient in a putative transmission pair; the colonisation with more tips is more likely to be reconstructed as the source in situations of phylogenetic uncertainty. While this may appear at first glance to be a bias, we see here  that remS. aureus . Even for patient T126, where the phylogeny see  shows dier sites , and alser sites . Both obphyloscanner, are to be preferred as they enable reconstruction regardless of the bottleneck size. Short-read deep sequencing, however, is unsuitable for the reconstruction of within-host phylogenies in bacteria as their slow mutation rates do not provide sufficient phylogenetic resolution in segments of the genome of such a short length, while the approach of sequencing multiple colony picks that we employ here may be unsuitable for routine deployment for cost reasons. Ideally, a full bacterial genome could be sequenced without the intermediate step of growing the organism in culture, an approach which could be possible using single-cell genomics.While the bottleneck in colonisation of one body site from another is more often than not wide, that involved in subject-to-subject transmission was very variable amongst the five pairs examined. In one case only a single lineage appeared to be transmitted, while in others it was very wide indeed. This is perhaps unsurprising in a bacterial infection spread by contact, as the circumstances under which colonisation occurs, and the quantity of bacteria displaced, may vary greatly. There did not appear, in this small sample, to be any suggestion of an association between bottleneck width and the proximity of patient beds in an ICU. It appears that the size of the bottleneck can be very variable even in a single setting. This suggests that the shared genomic variant methodologies outlined by n individuals, only n-1 of the n(n-1)/2 pairs are transmission pairs, and for a positive test to be even 50% predictive of a positive result requires a false positive rate of at most 2/(n-2). In large studies picking a threshold to fulfil this may in turn require unacceptably low sensitivity, and it should be noted that this is the best case scenario, in which every transmission pair is sampled. Ideally, sampling of within-host diversity and interrogation of the phylogenetic topology would be preferred to the use of a crude threshold, if this is feasible in a particular setting and using available resources.The SNP cut-off approach to identifying pairs has already been identified as potentially inadvisable; A clear limitation of these results is their applicability to other settings, particularly to hospitals with greater (or poorer) resources for infection control. Better procedures would be expected to decrease the size of within-hospital transmission clusters, and also decrease the size of the bottleneck between both patients and body sites. In addition, our sampling was highly biased towards nasal isolates. We also lacked access to isolates from the general, extra-hospital, population that might have been used for phylogenetic comparison purposes. Finally, the sequencing protocol used here is expensive and replicating it will not be feasible in many settings.This study demonstrates the additional insights into bacterial transmission that may be gleaned when sequencing multiple isolates per host. This is obviously essential when considering questions of bottleneck size and phyloanatomy, but also offers advantages in investigating the direction of between-host transmission. As costs decrease, it may become increasingly feasible to design and conduct studies with sampling frames that accommodate multiple sampling, and also to use it for investigations in clinical practice.DNA was extracted, libraries prepared and 100 bp paired end sequences determined for 2320 isolates on an Illumina HiSeq2000, as previously described .http://www.sanger.ac.uk/resources/software/smalt/) as previously described (http://sanger-pathogens.github.io/gubbins/) using the default parameters (Illumina reads were mapped onto the TW20 (accession number FN433596) reference sequence using SMALT  and also did not use a patristic distance threshold. Edges were drawn between colonisations if they were related in at least 50% of posterior trees, and these edges used to identify the clusters.The cluster diagrams were created by defining two colonisations to be related in a single tree if they had at least one pair of adjacent subgraphs and all subgraphs from the pair were arranged in the ‘ancestry’, ‘multiple ancestry’ or ‘complex’ configurations see . DepartiWhen defining gold standard transmission pairs and non-transmission pairs for the SNP threshold analysis, both versions used posterior support of less than 0.05 for adjacency and a topological relationship of ‘ancestry’, ‘multiple ancestry’ or ‘complex’ to define the latter. The first version used posterior support of 0.95 of the same relationships to define pairs, whereas the second used support of 0.95 for adjacency and either ‘multiple ancestry’ or ‘complex’.The separate BaTS analysis  was perfggtree 1.8.2 ; Taj Azarian (Reviewer #2).Thank you for submitting your article "Improved characterisation of MRSA transmission using within-host bacterial sequence diversity" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The reviewers agreed that this study is a step forward compared to earlier such analyses in that it makes use of multiple sequences from multiple samples per patient, from various body sites, and that it addresses questions that can only be addressed because of this intensive sampling scheme: direction of transmission based on the phylogeny only, width of the bottleneck at transmission, and transmission between body sites within patients. In addition, based on the identified transmission pairs and clusters, the paper investigates the use of a SNP threshold to detect pairs and clusters in settings with fewer sequences available.As a whole, it is a valuable contribution to the field of outbreak analyses with pathogen sequence data. The major strengths of the analysis are:1) The application of a novel analytical approach to an unmatched dataset (995 isolates from 55 patients), which allowed them to move beyond "proof of concept" pilot analyses or simulations.S. aureus genomic epidemiology: (i) the range of transmission bottleneck sizes , (ii) the sensitivity and specificity of using a SNP cut-offs to determine transmission events and (iii) the compartmentalization/ migration of S. aureus to various body sites during carriage.2) In doing so, the paper is able to address (with supporting data/results) three major unknowns about 3) The utility of the approach (and accompanying software) to investigating transmission. This level of dense within-host sampling has never been attempted. Considering this and the results, this study is a significant advance from the original analysis by Tong et al., 2015 as well as the PHYLOSCANNER manuscript by Wymant et al., 2017.4) The characterisation of aspects of MRSA hospital dynamics that were previously unknown or only suspected based on other types of evidence, and as a showcase of methods that can be used with such data.5) The manuscript is clearly written, and we very much appreciate the efforts made to conceptually explain some of the methods, notably Figure 3 and Supplementary file 1.Essential revisions:1) "median patristic distance" . How sensitive is the result to this choice? For instance, can't a simple hierarchical clustering algorithm be used? Also, if additional samples are taken from a patient, the distance of these samples to the sequence(s) at infection will increase; consequently, the median distance to another colonisation should increase with larger clusters. Wouldn't it therefore be better to use the minimum distance?2) Subsection “Trace colonisations”: Were the 9 single-swab colonisations with only one examination similar to the 24, e.g. in Figure 2? These could have been true colonisations, after all.3) Figure 2 displays the median number of SNPs, but as explained above in comment 1, just increasing the number of samples is expected to increase this median. That would definitely cause bias in the comparison in Figure 2 and the Mann-Whitney U test.4) Figure 2 is visually a bit misleading because of the 6 samples that are not in the Figure, four of which should be blue. Maybe add them at the right, below a ">100" mark on the X-axis?5) The arguments for the “six trace colonisations” is strong, but there are 27 more. Do the data for the other 27 not suggest the same, or is it impossible to say for the other 27?6) In the end, it seems reasonable to focus on the non-trace colonisations for the cluster and bottleneck analyses. However, for the SNP threshold analysis it should be realised that more than half of the cases were left out, so that identified transmission pairs could very well have been separated by intermediate cases. That affects the "gold standard" transmission pairs set.7) Subsection “Reconstructed transmission events”, last paragraph: The nature of multiple arrows between cases should be explained. From the caption it appears that each arrow is associated with a unique subclade, but it is possible to conclude that multiple arrows between hosts indicate multiple lineages being transmitted, and thus a wide bottleneck?8) Figure 4 legend: One aspect of the analysis that was not clear was if or how the time of transmission was estimated. In at least one instance, it appears that multiple transmission events occurred between two patients. From the text it states that "the timings represent […] the earliest time of sampling of all the sequences involved in the recipient subgraphs." Is this an assumption made for simplicity or is this method able to determine more accurately the timing of the event? This would be good to clarify because it has implications for the generalizability of the method to settings where the exposure period (in this case from admission to discharge) is not as well defined.9) Subsection “Epidemiologically plausible transmissions have high posterior support”: It's difficult to understand why downsampling results in more impossible events. Wouldn't fewer samples per patient results in fewer "ancestry" configurations and more "no ancestry" configurations, so more external sources rather than (incorrect) internal sources? Is it possible to explain this phenomenon in one or two sentences?10) Subsection “Transmission clusters”: A transmission cluster would be a set of cases that is linked by direct transmission, and separated from other clusters by unobserved transmission links. You define transmission clusters with phyloscanner based on "ancestry", "multiple ancestry" and "complex" configurations of the phylogenetic tree, and exclude the "no ancestry" configuration. From this it appears that you would exclude the relation between A and D in Figure 3 but it's unclear why: Figure 3 shows the singleton D is linked to A and not to B and C, which is a transmission link, even more so because transmission links are already defined with the possibility of intermediary patients. The text states "colonisations not closely related", which can't be concluded about A and D in Figure 3. Isn't it possible to connect the clusters of Figure 6 based on being siblings in the phylogenetic tree? Or is the variation between the 100 sampled trees too large to make this useful?The problem is not so much in this part of the paper, which just describes the results following from the definitions. The problem is more in the SNP threshold part, where these defined clusters are used as gold standards for defining clusters with SNP thresholds. For that, it should be made clear why these are indeed separate clusters, and why they are set apart from one another.11) Absence/presence of arrows in Figures 6 and 7: when comparing Figures 6 and 7, absence and presence of arrows is often not the same. For instance, in Figure 6 there are no arrows between C183-C249 and between C194-C249, but in Figure 7 they both point towards C249. In general, Figure 7 seems to have many more directions resolved than Figure 6. Suggestions: first, explain one example to provide intuition for the difference; second, make directions bidirectional (or draw two arrows) if there is evidence for transmission either way (back and forth).12) Subsection "Between-subject transmission bottlenecks are of very variable size":The transmission bottleneck size results are extremely valuable as these results are of great interest to multiple fields. Though, it is hard to judge how generalizable the bottleneck size findings are to other settings. While the authors are cautious in stating this, it would be beneficial to put their findings in the context of previous estimates. Further, they currently present the bottleneck size as the estimated number of transferred lineages, which will depend on the number of sequenced pathogens. Can this also be presented in terms of nucleotide diversity of the transmitted population?13) "narrow bottleneck", but still two lineages at least, so why is that not wide?14) Subsection “Between-subject transmission bottlenecks are of very variable size”: in discussing T183 and T194, the link with T178 is included, but considering Figures 6 and 7, C249 appears even more interesting, as that forms a triangle with both C183 and C194 15) Subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”, last paragraph: subject T232 is mentioned with evidence for separate colonisation events, but in Figure 7 there is an arrow with equal support for the direction as for the link itself. That is not intuitive.16) Subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”, last paragraph: Figure 7: C178 would be expected to have multiple colonisation events, because the nose and throat samples are at completely different parts of the graph. That one missing here (and from Table 2) is also not intuitive.17) Subsection “Identification of transmission pairs from single genomes”: The problem addressed is very relevant, but the gold standard definitions are not convincing.First, transmission pairs (true positives) are defined by a posterior support of 0.95, but what about the set of no-transmission pairs? Is that the complete matrix of all other possible pairs in the dataset? What about the lower-support pairs, and the siblings ? If included into the set of no-transmission pairs, this set will contain transmission pairs as well.Second, transmission pairs from the phyloscanner analysis can be through an intermediary hosts, especially because more than half of the positives (the trace colonisations) are left out. So, a transmission pair may not be a true transmission pairThird, the definition of a cluster, only consisting of proven ancestral relations. As argued above, siblings in the phylogenetic tree may also be related through direct transmission, only without evidence of the direction. In the current analysis, these are treated as separate clusters.18) Regarding the single genome sensitivity and specificity – The authors select one pair of genomes at random from all the possible pairs. It seems more practical to select the first isolate identified during sampling as this would be consistent with practice – most facilities conducting either active or passive surveillance usually don't re-swab after a positive surveillance swab is identified.19) Discussion, eighth paragraph: As you state, it is currently not feasible to sequence 223 isolates from a single individual. In their analysis, they pool the samples from multiple time points and also down-sample to five isolates. It would be of great interest for the field to know what number of isolates is required to achieve this level of transmission/cluster prediction using this approach. What we imagine is presenting the sensitivity/specificity with progressive down-sampling from the entire sample and/or from different epochs. Are five random isolates from a single collection time point sufficient to replicate the accuracy of their analysis? Are multiple isolates longitudinally collected more important? Essential revisions:1) "median patristic distance" .We have added text to the Materials and methods to clarify this (subsection “Phyloscanner”).How sensitive is the result to this choice?This threshold was selected as a number that split up what seemed obvious instances of multiple colonisation by eye, and is not at all sensitive to variations within a reasonable range.For instance, can't a simple hierarchical clustering algorithm be used?phyloscanner and would give similar results to many other simple approaches.Absolutely – this tool was used just because it is already included in Also, if additional samples are taken from a patient, the distance of these samples to the sequence(s) at infection will increase; consequently, the median distance to another colonisation should increase with larger clusters. Wouldn't it therefore be better to use the minimum distance?phyloscanner convention, between the MRCAs of each clade, not between tips. It thus should be at most the minimum distance between tips.  This has been clarified .We apologise; the text was somewhat misleading. The median distance is, as per 2) Subsection “Trace colonisations”: Were the 9 single-swab colonisations with only one examination similar to the 24, e.g. in Figure 2? These could have been true colonisations, after all.p=0.164) or indeed between the 13 trace colonisations with a subsequent negative swab and those without (p=0.327). We acknowledge that some trace colonisations may indeed be genuine and the choice to exclude them all is a cautious one (subsection “Trace colonisations”).The number is actually 8 rather than 9, and we apologise for the typo. There is scant evidence of a difference in distributions between those 8 and the rest according to the metric in Figure 2 (3) Figure 2 displays the median number of SNPs, but as explained above in comment 1, just increasing the number of samples is expected to increase this median. That would definitely cause bias in the comparison in Figure 2 and the Mann-Whitney U test.The reason for the use of the median here is that the null hypothesis is that an isolate from a trace colonisation is no different from a typical isolate from a non-trace colonisation. It would represent a single draw from the same distribution that produces the many samples from the latter. Hence we take the isolates from non-trace colonisations with the median genetic distance as representing “typical” examples. We have clarified the text (subsection “Trace colonisations”).temporal source of bias with this approach. Isolates from trace colonisations cannot be acquired at anything but the first possible opportunity, whereas some from non-trace colonisations may have undergone further days or even weeks of mutation. If instead we take the median distance to the most similar earlier sample from amongst the isolates acquired on the first sample date, we see a similar tendency for shorter distances amongst trace colonisations, but it is not significant at the p=0.05 level (p=0.119). This can be seen in the new Figure 2—figure supplement 1.We do, however, acknowledge that there is a potential 4) Figure 2 is visually a bit misleading because of the 6 samples that are not in the Figure, four of which should be blue. Maybe add them at the right, below a ">100" mark on the X-axis?This figure has been revised to switch to a log scale at distances beyond 50bp.5) The arguments for the “six trace colonisations” is strong, but there are 27 more. Do the data for the other 27 not suggest the same, or is it impossible to say for the other 27?This is not suggested by the data in the other cases. However, while such a pattern implies transient colonisation, not all transient colonisations would be expected to show it; such an event need not happen in the early part of a patient’s ICU stay. The only information we really have that strongly argues against transient colonisation (or indeed contamination) is repeated isolation of the same strain. We choose to emphasise examples where we have this, out of an abundance of caution. We have revised the text somewhat to clarify this point (subsection “Trace colonisations”).6) In the end, it seems reasonable to focus on the non-trace colonisations for the cluster and bottleneck analyses. However, for the SNP threshold analysis it should be realised that more than half of the cases were left out, so that identified transmission pairs could very well have been separated by intermediate cases. That affects the "gold standard" transmission pairs set.The trace colonisations are now present in one of the two new versions of the SNP analysis; they had to be excluded from the other for reasons that are outlined below.7) Subsection “Reconstructed transmission events”, last paragraph: The nature of multiple arrows between cases should be explained. From the caption it appears that each arrow is associated with a unique subclade, but it is possible to conclude that multiple arrows between hosts indicate multiple lineages being transmitted, and thus a wide bottleneck?This configuration does suggest transmission through a wide bottleneck, which we define as also encompassing a scenario involving multiple separate transmission events between the pairs (subsection “Between-subject transmission bottlenecks are of very variable size”). Text has been added to clarify this (subsection “Reconstructed transmission events.”).8) Figure 4 legend: One aspect of the analysis that was not clear was if or how the time of transmission was estimated. In at least one instance, it appears that multiple transmission events occurred between two patients. From the text it states that "the timings represent […] the earliest time of sampling of all the sequences involved in the recipient subgraphs." Is this an assumption made for simplicity or is this method able to determine more accurately the timing of the event? This would be good to clarify because it has implications for the generalizability of the method to settings where the exposure period (in this case from admission to discharge) is not as well defined.It was indeed merely for simplicity as we do not have a means to estimate such timings from the genetic information. This has been clarified .9) Subsection “Epidemiologically plausible transmissions have high posterior support”: It's difficult to understand why downsampling results in more impossible events. Wouldn't fewer samples per patient results in fewer "ancestry" configurations and more "no ancestry" configurations, so more external sources rather than (incorrect) internal sources? Is it possible to explain this phenomenon in one or two sentences?The reason for this is that there is a tendency to infer the more frequently-sampled individual as the source in a likely transmission pair. Equalising counts reduces the topological signal in one direction and makes reconstructions of the opposite direction more frequent in the posterior set. This might seem at first glance to be simply a bias but, as we see here, matters are actually more complex. In our case removing this bias by downsampling results in a reconstruction which is plainly less accurate. High sample counts are not irrelevant information as they indicate something (a long ICU stays here) that in of itself makes an individual a more probable source. A high sample count lies at least partially on the causal pathway from a factor making source status more likely to detection as a source, and a variable lying on the causal pathway is not considered confounding. There is, nonetheless, undoubtedly scope for further investigation of this issue. An explanation of this has been added to the Discussion (fourth paragraph).10) Subsection “Transmission clusters”: A transmission cluster would be a set of cases that is linked by direct transmission, and separated from other clusters by unobserved transmission links. You define transmission clusters with phyloscanner based on "ancestry", "multiple ancestry" and "complex" configurations of the phylogenetic tree, and exclude the "no ancestry" configuration. From this it appears that you would exclude the relation between A and D in Figure 3 but it's unclear why: Figure 3 shows the singleton D is linked to A and not to B and C, which is a transmission link, even more so because transmission links are already defined with the possibility of intermediary patients. The text states "colonisations not closely related", which can't be concluded about A and D in Figure 3. Isn't it possible to connect the clusters of Figure 6 based on being siblings in the phylogenetic tree? Or is the variation between the 100 sampled trees too large to make this useful?The problem is not so much in this part of the paper, which just describes the results following from the definitions. The problem is more in the SNP threshold part, where these defined clusters are used as gold standards for defining clusters with SNP thresholds. For that, it should be made clear why these are indeed separate clusters, and why they are set apart from one another.See below for a full description of the changes to this analysis.11) Absence/presence of arrows in Figures 6 and 7: when comparing Figures 6 and 7, absence and presence of arrows is often not the same. For instance, in Figure 6 there are no arrows between C183-C249 and between C194-C249, but in Figure 7 they both point towards C249. In general, Figure 7 seems to have many more directions resolved than Figure 6. Suggestions: first, explain one example to provide intuition for the difference; second, make directions bidirectional (or draw two arrows) if there is evidence for transmission either way (back and forth).The updated versions of the network figures now have bidirectional arrows, and text has been added to explain the case of C249, which is the most striking example of the improved resolution in Figure 7 (subsection “Transmission clusters”).12) Subsection "Between-subject transmission bottlenecks are of very variable size":The transmission bottleneck size results are extremely valuable as these results are of great interest to multiple fields. Though, it is hard to judge how generalizable the bottleneck size findings are to other settings. While the authors are cautious in stating this, it would be beneficial to put their findings in the context of previous estimates.To our knowledge, there are no previous estimates of transmission bottleneck size between human subjects in the literature.Further, they currently present the bottleneck size as the estimated number of transferred lineages, which will depend on the number of sequenced pathogens. Can this also be presented in terms of nucleotide diversity of the transmitted population?We have added these numbers to Table 1 and discuss them (subsection “Between-subject transmission bottlenecks are of very variable size”).13) "narrow bottleneck", but still two lineages at least, so why is that not wide?We have revised the text here (subsection “Between-subject transmission bottlenecks are of very variable size”) in order to talk about “narrow” and “wide” in more relative terms.14) Subsection “Between-subject transmission bottlenecks are of very variable size”: in discussing T183 and T194, the link with T178 is included, but considering Figures 6 and 7, C249 appears even more interesting, as that forms a triangle with both C183 and C194 .This was overlooked because of the lack of significant overlap with the other patients in the same bed. However, T183 did briefly occupy a bed that had previously been occupied by T249, which we now mention (subsection “Between-subject transmission bottlenecks are of very variable size”).15) Subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”, last paragraph: subject T232 is mentioned with evidence for separate colonisation events, but in Figure 7 there is an arrow with equal support for the direction as for the link itself. That is not intuitive.This part of the manuscript was too cursory and relied entirely on examining the consensus trees. We have changed it to a description of the patterns over the full posterior (subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”). T232 has a quite poorly resolved topology which can support multiple scenarios, and in the main analysis the direction depicted in the original version of Figure 7 was the most well supported by the rules used to construct the diagram.16) Subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”, last paragraph: Figure 7: C178 would be expected to have multiple colonisation events, because the nose and throat samples are at completely different parts of the graph. That one missing here (and from Table 2) is also not intuitive.We neglected to mention that T178, while sampled from two body sites, provided only a single sequence from each, which makes investigation using BATS impossible. The same is, in fact, true of T322. We have added text to this effect (subsection “The majority of subjects showed no evidence of phylogeny/body site correlation”).17) Subsection “Identification of transmission pairs from single genomes”: The problem addressed is very relevant, but the gold standard definitions are not convincing.First, transmission pairs (true positives) are defined by a posterior support of 0.95, but what about the set of no-transmission pairs? Is that the complete matrix of all other possible pairs in the dataset? What about the lower-support pairs, and the siblings ? If included into the set of no-transmission pairs, this set will contain transmission pairs as well.Second, transmission pairs from the phyloscanner analysis can be through an intermediary hosts, especially because more than half of the positives (the trace colonisations) are left out. So, a transmission pair may not be a true transmission pairThird, the definition of a cluster, only consisting of proven ancestral relations. As argued above, siblings in the phylogenetic tree may also be related through direct transmission, only without evidence of the direction. In the current analysis, these are treated as separate clusters.18) Regarding the single genome sensitivity and specificity – The authors select one pair of genomes at random from all the possible pairs. It seems more practical to select the first isolate identified during sampling as this would be consistent with practice – most facilities conducting either active or passive surveillance usually don't re-swab after a positive surveillance swab is identified.For the SNP analysis:1) We have concluded that the definition of a transmission cluster that was used probably cannot be made entirely coherent, and have entirely removed that part of the paper.2) Sequences for SNP analysis are now taken from just the first positive swab for each subject, rather than the pool of all sequences.3) We now use a cut-off of 0.05 posterior support for any ancestry to define non-transmission pairs in the gold standard. Any pairs lying between 0.05 and 0.95 are not used.4) As a result of modification 3, the PPV and NPV statistics become meaningless as these are setting-dependent and the study “population” is no longer even the complete set of pairs in our sample. We have removed these from the results. Nevertheless, the point that in most circumstances most pairs of subjects cannot be transmission pairs, and thus the PPV will be low for even medium-sized study populations unless the false positive rate is also low, is still valid, and we now make it in the Discussion by means of a mathematical sketch .phyloscanner as a gold standard for SNP distance thresholds, we could not use patristic distance thresholds as the reasoning becomes circular. In particular, the relationship between A and D in Figure 3 cannot be established as that of a transmission pair without reference to branch lengths, and branch lengths are themselves obviously very closely related to SNP distance. We used only the topological signal of transmission instead, and sibling clades are not a definitive signal of transmission.5) When using 6) The point about missing intermediaries is valid. To address this, we created a second gold standard set for which membership required posterior support of 0.95 for the “multiple ancestry” or “complex” topological relationships, which are equivalent to the “PP” configuration in Romero-Severson et al. That paper showed that PP tends to imply direct transmission. While the simulations in that paper were designed to be similar to HIV, the reason, that it is much more unlikely that two distinct lineages will be transmitted twice in succession, remains valid here (subsection “Identification of transmission pairs from single genomes”).7) We agree that there is no particular reason to exclude trace colonisations from this analysis, but anything involving a trace colonisation cannot be used in the new gold standard of point 6 because it is impossible to identify multiple lineage transmission to a subject with a single tip in the phylogeny. As a result, we present both – the original version with trace colonisations included, and the new one with them excluded.19) Discussion, eighth paragraph: As you state, it is currently not feasible to sequence 223 isolates from a single individual. In their analysis, they pool the samples from multiple time points and also down-sample to five isolates. It would be of great interest for the field to know what number of isolates is required to achieve this level of transmission/cluster prediction using this approach. What we imagine is presenting the sensitivity/specificity with progressive down-sampling from the entire sample and/or from different epochs. Are five random isolates from a single collection time point sufficient to replicate the accuracy of their analysis? Are multiple isolates longitudinally collected more important?While this would be a very interesting addition, we feel that the paper is quite long as it is and would prefer to leave it for future work.


Transgenic Yellow River carp is characterized by rapid growth rate and high feed-conversion efficiency and exhibits a great application prospect. However, there is still a significant separation of growth traits in the transgenic Yellow River carp family; as such, growth-related genotypes must be screened for molecular marker-assisted selection. In this study, 23 growth-related candidate genes containing 48 SNP markers were screened through bulked segregant analysis (BSA) among transgenic Yellow River carp family members showing significant separation of growth traits. Then, two growth-related genes (Nos. 17 and 14 genes) were identified through combined genome-wide association study (GWAS) of candidate genes and validation of the full-sibling family approach. Nos. 17 and 14 genes encode BR serine/threonine-protein kinase 2 (BRSK2) and eukaryotic translation-initiation factor 2-alpha kinase 3 (Eif2ak3), respectively. The average body weight of three subgroups carrying the genotypes 17GG, 17GG + 14CC, and 17GG + 14TT of these two genes increased by 27.96, 38.28, and 33.72%, respectively, compared with the controls. The proportion of individuals with body weight > 500 g in these subgroups increased by 19.22, 26.82, and 30.92%, respectively. The results showed that appropriate genotype carriers can be selected from the progeny population through BSA sequencing combined with simplified GWAS analysis. Hence, basic population for breeding can be constructed and transgenic Yellow River carp strains with stable production performance and uniform phenotypic properties can be bred.The online version of this article (10.1007/s10142-018-0597-9) contains supplementary material, which is available to authorized users. Since the 1980s, transgenic technology has gained increasing attention for genetic improvement of farmed fish; scholars in the USA, Canada, Britain, Norway, Japan, and South Korea have carried out transgenic fish breeding research on almost all important farmed fish for traits such as growth, nutrition, and disease resistance  gene transgenic carp through microinjection of the fertilized eggs of Yellow River carp  was first used to identify age-related macular degeneration-related genes in the human genome     GH gene (gcGH), whose expression is driven by the β-actin gene promoter of common carp (C. carpio L.)  × Wt female crosses. F2 fish were obtained by crossing F1 transgenic male with Wt female. F3 generation was produced by a similar method. The homozygous transgenic fish TG3 line was obtained by intercrossing F3 transgenic males with females, which were derived from the same strain that possessed a transgene integrated at the same locus in the genome with the same copy number . Ethical approval for the work and field permits for the collection of fish were obtained from Expert Committee of Biomedical Ethics, Institute of Hydrobiology of the Chinese Academy of Sciences. The Reference number obtained was Y119021F01.2 pond for breeding. At the age of 4 months, 442 fish from 5000 individuals were randomly selected. Body weight and length were measured for plotting of frequency distribution map. Based on the peak distribution in the map, the full-sibling group was divided into fast- and slow-growing subgroups.One male (TM) and one female (TF) fish were randomly selected from the 3-year-old TG3 line. Then, one-to-one conventional wet insemination was used to obtain transgenic sibling group fertilized eggs, which were hatched in the mesh. About 5000 1-month-old juveniles were placed in a 600-mThirty individuals were selected from the nearest peak region in fast- and slow-growing subgroups. DNA was extracted from the tail fin by using the phenol–chloroform method. DNA pools were constructed by mixing equal DNA, hereinafter referred to as fast and slow DNA pools. The two DNA pools were used to construct two libraries by using the NEBNext Ultra DNA Library Prep Kit for Illumina  according to the manufacturer’s instructions. The libraries were run in three lanes on Illumina HiSeq 2000 by using 125 base paired-end reads , and the sequencing fragments were 300–400 bp long.http://www.carpbase.org/) by using the BWA software mem algorithm with default parameters. Samtools was used for SNP calling and annotation of the two pools . The difference in allele frequencies was calculated using the population2 software and expressed as Fst value. The Fst value was calculated as follows:Fst = /Pi_totalPi_within = (Pi_population1 + Pi_population2)/2Pi:1−fA2–fT2–fC2–fG2BSA analysis was performed by mapping the clean data of the fast and slow DNA pools to the carp reference genome  in the SNP locus from one group, and Pi_within represents the average Pi value of the two groups considered in the study. Pi_total represents the total Pi of the allele frequencies of the two groups. SNP loci of Fst > 0.5 and coverage > 20× were selected. SNP position information was annotated with the Perl program, and gene function was predicted using the blast2go software to determine candidate genes. Genes with SNPs located in the promoter or exon regions were selected as candidate genes.From each candidate gene, we selected two adjacent SNP loci (one was heterozygous and the other one was homozygous in another parent) to detect the genotype of each individual by re-sequencing. Primers were designed for both sides of the selected SNP site for each candidate gene . The selected SNP sites of these individuals were amplified with the corresponding F- and R-barcode primers. An equivalent amount of PCR products was blended, and the mixed sample was purified with the Gel Extraction Kit . A sequencing library was then constructed with the purified product by using the NEBNext Ultra DNA Library Prep Kit for Illumina  according to the manufacturer’s protocol. The read length was PE150, and R1- and R2-read data were generated.The 9th to 20th bases of all F- and R-barcode fragments were captured and completely mapped with R1- and R2-read by using the BWA software. The read pairs that could match both F and R barcodes were exported. According to the information of the F- and R-barcode sequences of the individuals, the read pairs were split into 442 samples.The read pairs of the 442 samples were aligned using BWA  with SNP amplification regions as reference sequences and indexed with Samtools. SNPs were identified by GATK 3.4. SNPs with minor allele frequency > 0.05 and call rate > 90% were screened. The genetic relationship between the genotype data that met the requirements and the phenotype data was calculated using Tassel5.0, with the parameter of the Kinship method = Centered_IBS, Max Alleles = 6, and genotype data imported using the hapmap format. The association between the genotype and phenotype data was analyzed using the MLM model, with the parameter setting as Compression Level = Optimum Level and Variance Component Estimation = P3D (estimate once). A Manhattan plot was generated using the Manhattan Plot function in the Tassel 5.0 software.p < 0.1 were selected and amplified in the 442 individuals of the full-sibling family by single PCR reactions. The PCR products for all individuals were sequenced by the Sanger method for genotyping. Genotype data were analyzed together with the corresponding body weight and length data. Firstly, SNP loci with p < 0.05 were analyzed at a single SNP site level, and the phenotype data of each genotype were evaluated using one-way analysis of variance (ANOVA) with SPSS19.0. SNP locus genotypes with significantly different growth traits (p < 0.05) were obtained. In addition, some single SNP may not be significantly correlated considering that the growth traits are quantitative traits. Therefore, the p value was amplified to p < 0.1 in the genotype combination analysis. Manual analysis of the two combinations of genotypes in different SNP loci was then performed. Meanwhile, the average weight and the proportion of individuals with body weight > 500 g for each genotype or genotype combination was counted. As a result, we could obtain advantage genotypes or genotype combinations of growth traits.According to the correlation analysis results, SNP sites with A transgenic full-sibling family was obtained by mating of TM and TF from the transgenic TG3 line. The body weight and length of 442 individuals in the full-sibling family were measured at the age of 4 months. The frequency distribution of body weight and length of the 442 individuals did not comply with the normal distribution Fig. a. A signAccording to the BSA classification principle, 30 individuals were selected in the nearest peak area from the fast- and slow-growing subgroups to construct fast and slow DNA pools. About 692,329,016 and 869,508,434 clean reads and 86.54 and 91.03 G clean data were obtained by re-sequencing of the fast and slow DNA pools, respectively. The clean data of the fast and slow DNA pools were mapped to the reference genome of the common carp, with mapped rates of 94.29 and 94.13%, average depths of 47.48× and 37.46×, and 1× coverage values of 98.95 and 99.32%, respectively. This finding confirmed the high quality of the sequencing data and their suitability for further mutation detection and correlation analysis.A total of 18,304,028 and 17,879,410 SNP sites were detected from the fast and slow DNA pools and annotated using Samtools. The Fst value representing the frequency difference of SNPs in the two pools was calculated, and 275 SNPs with Fst > 0.5 and coverage > 20× were obtained. According to the annotated results, 29 SNPs  corresponding to four genes  were obtained. In addition, the genotype combination analysis with the range of p values amplified to p < 0.1 showed that the associated SNP loci increased to 12, which correspond to seven genes .A total of 48 SNP loci from 23 candidate genes were selected to genotype 442 individuals. First, 35 pairs of specific primers . The average lengths of the individuals with GG, TG, and TT genotypes were 316.34, 283.19, and 221.83 mm, respectively, and the average weights were 919.87, 746.91, and 419.12 g, respectively . GWAS was then conducted on these candidate genes to obtain a small number of effective trait-related genes (4–7 genes were found in this study). Cost reduction was mainly reflected in the use of the Pool-Seq method: first, mixing of similar samples for sequencing greatly reduced the number of libraries, and second, overall sequencing volume and workload of data analysis were reduced.However, this method presents certain limitations. For example, when the 35 PCR products of the 48 SNP loci in the 442 individuals were mixed and sequenced, the detection rate of all SNP loci was 77%; the remaining loci with call rate < 90% indicated that the site could not be detected. When the sequencing data were split into 442 samples according to the barcode label, only 67% of the sequencing data were split into 442 samples because of the mixed uniformity of the PCR products and the non-specific amplification of PCR. Schlötterer et al. reported that Pool-Seq requires the mixing of DNA from a large number of samples; as such, different amounts of DNA from individuals in the pool will have a potential negative effect on the results  and eukaryotic translation initiation factor 2-alpha kinase 3 (Eif2ak3), respectively. BRSK2 plays a key role in the polarization of neurons and axonogenesis, cell cycle progression, and insulin secretion Table S2Sequences of the barcodes. (GIF 66 kb)Table S3Barcode combinations of the individuals. (DOCX 21 kb)Fig. S1Amplification gel pictures of locus 23 in 442 individuals. (DOCX 14 kb)High Resolution Image (DOCX 19 kb)Table S4Statistics of genotype/phenotype correlation analysis on the basis of MLM. (TIFF 2584 kb)Fig. S2DNA sequencing map of sites in Nos. 1, 13, and 18 genes. (a) DNA sequencing map of site in the No. 1 gene. (b) DNA sequencing map of site in the No. 13 gene. (c) DNA sequencing map of site in the No. 18 gene. SNP site is indicated by black arrow. (XLSX 39 kb)High Resolution Image (TIFF 11451 kb)


A new rice panel, the Bengal and Assam Aus Panel (BAAP) of 266 aus accessions was generated with ∼2 million informative SNPs obtained using skim sequencing at ∼4× depth. The BAAP was grown in the field in Bangladesh in the ‘boro’ season under both continuously flooded and Alternate Wetting and Drying (AWD) irrigation during 2013 and 2014 in Mymensingh and during 2014 in Madhupur. Heading date, grain mass, straw biomass and harvest index were measured. The majority (94%) of BAAP accessions flowered within a relatively small window of 10 days. The AWD irrigation treatment generally caused an increase in grain mass, but no significant genotype by treatment interactions were detected for this trait. Shoot biomass was the only trait that showed evidence of genotype by treatment interaction. The average LD (Linkage Disequilibrium) decay across the genome was 243 Kbp. Genome wide association mapping revealed 115 quantitative trait loci (QTLs). There was little evidence of QTLs specific to the irrigation treatment, and only a few QTLs co-localized with known genes. However, some QTLs were detected across multiple sites and years. These QTLs should be targets for breeding, and include a region around 2.2 Mbp on chromosome 1, a large region in the middle of chromosome 7 and two regions on chromosome 11 (∼10 Mbp and ∼29 Mbp). The BAAP appears to be a valuable addition to the growing collection of GWA mapping populations of rice.Growing demand for staple crops like rice will need to be achieved predominately through agricultural intensification and more efficient use of inputs. To meet this demand it is essential that the genetic diversity within rice is fully utilized. The  Oryza sativa L.) is one of the world’s most important cereals as it supplies 35–60% of dietary calorie intake for an estimated three billion people  and yield related loci responding to AWD that can be used for breeding. The population was developed using cultivars originally collected from Bangladesh and India. The population was screened for yield traits in the field in Bangladesh at one location over 2 years, and one other location in 1 year. The field trial was conducted using both CF conditions and under AWD. DNA was extracted and sequenced to provide two million SNPs. A large number of loci were identified using GWA mapping of yield component traits, including a number of regions that were stable across treatments, field sites and years, indicating that they are good targets for future study and for plant breeding.The aim of this study was to develop an aus subpopulation by selecting those reported in aus cultivars included were 33 present in the Rice Diversity Panel 1  reference downloaded from Ensembl v212 using bwa version 0.7.5a with parameters mem –M  set using metadata related to subpopulation , while there were eight Bangladeshi japonicas and 10 Bangladeshi indicas. In addition, five aus landraces had sequence depth lower than 1× and were eliminated, while one had high heterozygosity and was eliminated (see Supplementary File S1 for details).From the 298 cultivars, only 260 were used to produce an ) and SNPhylo  was calculated as ng PLINK  with par−1 nitrogen, 20 kg ha−1 phosphorus, 70 kg ha−1 potassium, 15 kg ha−1 sulfur, and 3 kg ha−1 zinc. A further 40 kg ha−1 nitrogen was supplied during the tillering stage (26th March), and another 40 kg ha−1 nitrogen at the flowering stage (6th April). The seedlings were transplanted into the eight plots (each plot was 5 m × 11.4 m) on the 13th of February 2013. They were planted as two plants per hill with a distance of 20 cm between each hill in a row and a 20 cm distance between each row of 4 m length. At total of 276 and 282  rice accessions were planted in single rows, with a check cultivar BRRI Dhan 28 transplanted into each alternate row. After transplanting the plots were flooded. For the four CF plots the surface water was kept at a depth of between 2 cm and 5 cm above the soil surface during the vegetative and reproductive stages (13th April 2013). For the four AWD plots plastic perforated tubes (pani pipe) were placed across the plots to monitor the water depth. The aim was to allow water to drain/percolate naturally from the AWD plots until the average depth of the water was 15 cm below the soil surface. At that point the plots were irrigated to bring the water depth to between 2 cm and 5 cm above the soil surface. Both the AWD and CF plots were kept under the same flooded conditions up until 3rd March when water was withheld from the AWD plots for the start of the first AWD cycle. There were four AWD cycles with the fourth finishing on the 11th April. At this point the rice plants had started flowering and the AWD plots were kept flooded and maintained the same as the CF plots.The population was screened in Bangladesh under both AWD and continuously flooded (CF) conditions in 2013 and 2014 in Mymensingh, and in 2014 in Madhupur. The full details of the field screening is given in Once the cultivars had flowered and the grain matured, the grain and shoots from every cultivar was hand harvested from the six central hills of each row. The grain was then hand threshed and weighed to determine the grain mass per six hills (referred to as grain mass). The shoots were harvested approximately 5 cm above the soil, dried, and then weighed to determine the shoot weight per six hills (referred to as straw biomass).For the field experiment at Mymensingh in 2014 the rice seeds were sown in a nursery bed on 17th December 2013. The field site was prepared as described for 2013, with the rice plants transplanted on 6th February into the eight plots (each plot was 22.7 m × 11.8 m). A total of 254 and 257  accessions were planted out at this field site. The fertilizer regime was as for 2013, with the split application of nitrogen fertilizer applied on 27th February and 27th March. The AWD cycles for the four AWD plots started on the 11th of February with the fourth cycle ending on 10th April. Once the fourth cycle had finished, the AWD and CF plots were maintained under flooded conditions during the flowering stage. Rice cultivars were harvested as described above.For the field experiment at Madhupur in 2014 the rice seeds were sown in a nursery bed at Mymensingh on the 17th December 2013. The field site was prepared as described above for the Mymensingh site. The seedlings were transplanted into the eight plots at Madhupur on the 8th and 9th February. Each plot was 24 m × 10 m. Plants were planted as two plants per hill with a distance of 20 cm between each plant in a row and 20 cm distance per row. A total of 271 and 273  accessions were planted out at this field site. The fertilizer regime was as for 2013, with the split application of nitrogen fertilizer applied on 1st March and 30th March. The AWD cycles for the four AWD plots started on the 3rd March with the fourth cycle ending on 19th April. Once the fourth cycle had finished, the AWD and CF plots were maintained under flooded conditions during the flowering stage. Rice cultivars were harvested as described above.In all the field experiments for each treatment a randomized complete block design was employed with four replicate blocks, but treatments were separated  to pre-process genotype and phenotype data for subsequent analysis by EMMAX  < 4 were examined to group the SNPs into QTL. If two SNPs were closer than the genome average LD decay value of 250 Kbp, they were considered to belong to the same locus.After GWA, SNPs with −logn = 191) as the explanatory variables. For the three-way ANOVA the presence of an interaction between the three explanatory variables was also determined. For correlation analysis a Pearson’s correlation was used.All statistical analyses were performed using the statistical software Minitab v.17  and SigmaPlot v.13  and the significance of ANOVA main effects and interactions reported at alpha < 0.05. Assumptions of normality were tested using the Anderson–Darling test in Minitab. For the plant mass traits two-way ANOVA was conducted with AWD and CF, and cultivar and interaction terms as the explanatory variables for each site. For the 2013 Mymensingh field site there were a total of 221 genotypes in common across both treatments. At Mymensingh in 2014 a total of 232 genotypes were common across both treatments. At Madhupur in 2014 a total of 226 genotypes were common across both treatments. For the plant mass traits three-way ANOVA was conducted with treatment (AWD and CF), site and cultivar  was selected from a larger panel of 511 rice cultivars screened by aus subpopulation. The selected aus cultivars flower within a window of 79–92 days. The 326 SNPs reported in aus cultivars, the panel also contains the OryzaSNP cultivars .A total of 298 cultivars from the BAAP population were sequenced to coverage depths ranging from 0.42 to 37×, with an average of 5.5×. To create the SNP database only 266 accessions from the Figure 1).The average LD decayed across the entire rice genome for these cultivars at 243 Kbp, but was not uniform across chromosomes. The lowest per-chromosome LD decay was observed on chromosome 9 (157 Kbp) and the largest on chromosome 5 (499 Kbp) , consistent with the results obtained using STRUCTURE on the BAAP for 326 SNPs . Similarly, the number of population groups identified by ADMIXTURE using CV  begins to plateau at K = 5 population subgroups . Assuming there are five population subgroups, based on the output of fastSTRUCTURE the 260 cultivars used to make the SNP database fall into group 1 (21 cultivars including DJ 123), group 2 (13 cultivars), group 3 (29 cultivars), group 4 , group 5 (30 cultivars) and admix  . Considered of note is the observation that groups 1, 3, and 5 are predominantly from Bangladesh  while groups 2 and 4 are predominantly from India (92% and 76%). Also considered noteworthy is that of the 21 cultivars which have the term “boro” in their name, 20 belong to group 3. All 20 are from Bangladesh. The cultivar Boro Black is in group 4 and is from India.Analysis of population structure in 326 SNP markers across the entire genome of 511 rice cultivars from which the BAAP was selected revealed four distinct subpopulation groups, these being an s groups . AnalysiFigure 2 and reveal a clear separation of the BAAP cultivars into different population groups consistent with the classification previously used by Figure 2A) and that revealed by fastSTRUCTURE . The five fastSTRUCTURE groups revealed here map onto those revealed by K = 5 is shown in Figure 2C.The first two axes of the PCA of 2,053.863 SNPs are shown in Table 1.The panel was screened in Bangladesh at Mymensingh in 2013 and 2014, as well as Madhupur in 2014, under both CF (Continuously Flooded) and AWD (Alternate Wetting and Drying) irrigation. A summary of the population means for all traits measured is given in Table 1), and in the most extreme scenario (the AWD experiment in year 1), the flowering initiation window was 34 days . Yet, the proportion of cultivars that initiated flowering 5 days either side of the mean for each experiment was between 83.2 and 94.4% of the cultivars.The initial strategy was to have all members of the population to initiate flowering within a 10-day window. However, when grown in the field in Bangladesh, the initiation of flowering from first to last cultivar was longer than this (Table 2). Means of each treatment and the correlations between treatments for each site are presented in Supplementary Figures S4 (grain mass), S5 (straw biomass), S6 (harvest index), and S7 (flowering time). AWD had mostly positive impacts on plant growth and partition traits. For grain mass, treatment explained 1.8–11.9% of variation with AWD producing higher grain mass. There was, however, no genotype by treatment interaction. The impact of AWD was smaller on straw biomass (not significant in Madhupur), but there was a genotype by treatment interaction at two sites explaining approximately 6% of the variation. The impact of AWD was stronger on harvest index. Correlations between the same trait under AWD or CF conditions were generally very strong (and highly significant), with the correlation coefficient ranging from 0.68 to 0.79 for grain mass, 0.73 to 0.86 for straw biomass, 0.46 to 0.59 for HI, 0.65–0.76 for FT . Correlations performed on 191 cultivars for grain mass, straw biomass, and harvest index between years and sites within the same treatment are presented in Supplementary Figures S8–S10. For grain mass in all cases the correlations were highly significant . When plotting the grain mass produced for each cultivar against the AWD responsiveness ratio (ratio of grain mass produced when grown under AWD vs. grain mass produced when grown under CF), it can be observed that the cultivars with the highest ratio  are predominantly the cultivars that produce less grain mass under CF . There was, however, no correlation between sites for this responsiveness to AWD ratio.Overall, grain yield, straw biomass and harvest index was highest in Mymensingh 2013 and lowest in Mymensingh 2014. There was large variation between cultivars for all traits measured. Two-way ANOVA conducted for each site separately revealed strong effects of genotype, generally weaker effects of AWD treatment and little genotype by treatment interactions (Table 3) with site explaining the next highest proportion of the variation and then treatment explaining less variation. There was evidence of significant field site by treatment  and field site by genotype  interaction with the latter explaining 7.6–9.5% of the variation. Only for straw biomass did this analysis reveal genotype by treatment interaction explaining 2.1% of the variation.An import aspect of understanding how AWD impacts upon different rice cultivars compared to CF, is to determine if these responses are variable under different environments irrespective of treatment . A three way ANOVA, which included field site, revealed genotypes explaining the greatest proportion of the variation in grain and straw biomass and harvest index  < 4 with minor allele frequency and effect size, plus a graph for each chromosome. A total of 32 loci were notable where there were either multiple significant SNPs associated with one field in at least one environment, or SNPs associated with multiple fields. These loci were located; one on chromosome 5, two on chromosomes 2, 6, 8, 9, and 12, three on chromosomes 1 and 3, four on chromosomes 4 and 7 and five on chromosome 11 (Table 4). Nine of these putative QTLs were detected in only one environment while 14 were detected in two. Only the QTL around 2.2 Mbp on chromosome 1 was detected in all six environments, while the QTLs detected around 3.45 Mbp on chromosome 3, and in the center of chromosome 7 were detected in five environments. It must be noted that there are two very broad QTLs in the center of chromosome 7 spanning 8.76–11.90 Mbp and 12.30–14.61 Mbp that are reported here as two separate QTLs detected in all environments except Mymensingh year 1 AWD. It may be these represent one very broad QTL.A total of 2,720 SNPs were significantly associated with grain yield in at least one of the six environments tested. Supplementary Figure S11 and Supplementary File S3), revealing 59 notable loci where there were multiple significant SNPs associated with the trait in at least one environment. These loci were located: one on chromosome 10, two on chromosomes 9, three on chromosomes 2, 5, and 12, four on chromosomes 6 and 8, five on chromosome 1, six on chromosome 3 and ten on both chromosomes 7 and 11 (Supplementary Table S1). Twelve of these QTLs were detected in only one environment while 19 were detected in two, 11 were detected in 3, 8 were detected in 4, 7 in 5 leaving 2 QTLs detected in all six environments. These two ubiquitous QTLs were both on chromosome 7 around 15 Mbp and 25.6 Mbp. As with yield, it must be noted that there is a very broad region of QTL activity from 9.35 to 16.93 Mbp where it is not clear how many QTLs are present. Four are reported here, but it might be one very wide one. Eighteen of the straw biomass QTLs detected here overlap with QTLs for yield. Eight of the loci have been previously reported as QTLs for straw biomass from Supplementary Table S1).A total of 8,399 SNPs were associated with straw biomass in at least one of the six environments tested  revealing 35 notable loci. There were nine loci detected on chromosome 11, four on chromosomes 1 and 9, three on chromosomes 2, 3, 6, and 12, two on chromosomes 5 and 8, one on chromosome 10 and none on chromosome 4. No loci were detected in all environments or even in 5/6 but two were detected in four environments being around 20.25 on chromosome 9 and 19.5 on chromosome 11. Four of the harvest index QTLs detected are in the same place as yield QTLs being at 2.2 Mbp on chromosome 1, 3.85 Mbp on chromosome 7 and both 20.7 and 22.8 Mbp on chromosome 11 (Supplementary Table S2). Two of the QTLs detected here were also detected using GWA by APO1) gene identified at 27.5 Mb on chromosome 6 by Supplementary Table S2).A total of 1,853 SNPs were associated with harvest index in at least one environment . A total of 26 loci were detected that either had a number of significant SNPs co-localized from a single experiment or where SNPs from multiple experiments co-localize (Table 5). Of the 26 loci highlighted, in 10 instances significant SNPs were co-localized from two experiments and in only a single instance were SNPs from three experiments co-localized.Across the genome and over the four experiments where flowering time was recorded, a total of 3,515 SNPs were significantly associated with flowering time (Table 6). A conservative estimate of genome wide LD of 200 Kbp was used. Unlike the identification of QTLs for flowering time, where only notable QTLs were reported (Table 5), all significant SNPs were considered when identifying SNPS that were located near known (200 Kbp) flowering time genes. Significant SNPs were identified within the estimated LD for a total of 8 previously identified genes involved in flowering in rice (Table 6), with two of the genes (Hd5 and RCN) having significant SNPs from more than one screen being within the candidate region.In addition to the identification of loci across the genome, the identification of SNPs within LD of known candidate genes involved in flowering time in rice was investigated  around 2.3 Mbp on chromosome 1 yield QTLs were detected in all environments, and harvest index in two of the environments, (ii) between 3.0 and 3.5 Mbp on chromosome 3 where yield QTLs were detected in five environments and biomass in four, (iii) a broad region on chromosome 7 centered on about 15.0 Mbp where there are QTLs for yield and biomass for, respectively, five and six environments, (iv) around 26 Mbp on chromosome 7 where there are biomass QTLs in all environments and yield in three, and (v) around 10.3 on chromosome 11 where there are straw biomass QTLs in five environments, yield in two and harvest index in two. There were three loci where there were multiple yield QTLs which co-localized with flowering time suggesting a role for the timing of flowering in determining yield: 41 Mbp on chromosome 1, 22 Mbp on chromosome 6 and 29 Mbp on chromosome 11. None of these loci are at known flowering time genes.aus used in this study. This is in contrast to previous studies that identified two subgroups within the aus subpopulation  growing season in Bangladesh  and having genetic variation for traits of interest. Structure analysis of the BAAP population identified a number of subgroups (five or more) within the pulation  and presngladesh , this suaus in The average LD decay within the population was 243 Kbp which is similar to the approximate 200 Kbp reported for Table 1). Not only was a high degree of variability in the phenotypes observed, the contribution of genotype to the observed variation was high .While this association population has reduced genetic diversity compared global rice association populations , it hasOne of the aims in the generation of this population was to have a reduced flowering window. During flowering initiation and grain filling environmental factors could have an impact on grain production and grain quality. Therefore if the window of flowering initiation is limited to short period, the cultivars are more likely to be undergoing similar environmental responses during this key developmental stage. A majority (83.2–94.4%) of the cultivars flowered within a 10-day window. This general coincidence of flowering between accessions is better than other association populations, for example the RDP1 which displayed a 20-day window for 90% of the population to flower when grown in Faridpur, Bangladesh or 40 days when grown in Arkansas, United States .Table 1). The highest yielding cultivars under AWD were BJ 1 , and Jagle Boro (the cultivar with the highest yield in Mymensingh year 2 and fifth highest yield in Mymensingh year 2). Other notable high yielding cultivars under AWD include Kasalath and Pachodi 427.As previously identified in a subset of these cultivars ,b, in thA recent meta-analysis on the impacts of AWD on yield has been conducted , the oveFigure 3). The cultivars from the aus subpopulation of rice are a diverse set of cultivars and have been shown to have tolerances to a wide range of environmental scenarios including submergence tolerance  for the high yield Boro cultivar, BRRI dhan28, grown in the same experiment  comparedolerance , phosphoolerance , droughtolerance , and heaolerance . It is ptemperate japonica vs. aus), the degree of potential water stress experience by the plants (−30 kPa at 20 cm soil depth vs. −15 cm water depth), the number of SNPs available (31 K vs. 2 M), and the traits measured . It is important to note that only flowering time can be directly compared across experiments and especially that in their study all traits were negatively affected by the water treatment indicating it was on the severe side of recommended treatments for AWD. Unlike the current study, about half of the QTLs detected by Figure 7 and Table 7). There are a total of 10 loci, where grain mass and/or straw biomass QTLS appear to be co-localized on numerous occasions, many of these QTLs are detected across years and sites. This indicates that these are stable QTLs and ideal targets for plant breeding. Some of the most interesting loci are those at 2.3 Mbp on chromosome 1, the middle of chromosome 7, and at both 10.3 and 28.8 Mbp on chromosome 11.In this study, a large number of QTLs were detected for all the measured traits. Of particular interest are the QTLs for grain mass and straw biomass that are stable, i.e., detected in multiple experiments and conditions . One gene affecting harvest index, APO1 identified by For traits straw biomass and harvest index, relatively few reports of QTL mapping, especially using GWAS are available, and few genes are known. None the less, the co-localization of a small number of the QTLs detected here with previous QTLs were found . While a number of associations co-localize with known genes, a large number of associations do not, and further study of these is needed to ascertain their function. There are a number of reasons that might explain why known flowering time genes were not detected as flowering time QTLs in this population. Most important, the narrow flowering time window imposed on the population will mean that the different genotypes are likely to share similar alleles for the major flowering time genes in rice.While the flowering time was deliberately constrained due to the strategic development of the BAAP, there was still variation that allowed for successful GWA mapping. Since a number of the genes that regulate flowering time have been identified, this trait can be used to assess the effectiveness of the GWA mapping in this population. The GWA mapping of flowering time highlighted a large number of loci that control flowering time. Some of these loci were only detected in a single experiment, while a number were detected in more than one study and a single loci was detected in three of the four environments. As flowering time was measured on only a single replicate in each environment and there are only two experiments in which it was measured (Mymensingh year 1 and 2), it is not possible at this stage to state if any of the loci detected in the AWD experiments are specific to one treatment. Further investigation is needed to support this hypothesis. However, there is some indication that loci could be AWD specific . The ge in rice  with a n in rice , Hd3 (Mo in rice , and DTH in rice ]. For moIn conclusion, the BAAP population is an excellent tool for the determination of yield traits, showing a good range of phenotypic variation and the detection of highly reproducible QTLs. It should prove valuable for other traits where a narrow window of flowering is important such as grain quality traits. There are several QTLs which should be targets for further study due to the detection of the loci in multiple experiments over years and sites.AP and GN selected the cultivars for the population. GN, AP, AT, MS, and MI designed the field experiments. MS and MI supervised the field experiments. AT and AD developed the GWAS pipeline and conducted the GWA mapping. SF and EDPA processed and analyzed the genomic data and produced the SNP database. PR-a conducted the analysis of the flowering QTLs. MN extracted the rice DNA. KM provided support in the development of the SNP database, the selection of cultivars and managed the multiplication and distribution of the seed. AP, AT, and GN interpreted the data. AP and GN wrote the first draft of the manuscript. All authors reviewed the manuscript and approved the content of this manuscript.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.


Mycobacterium tuberculosis complex (MTBC). We present SNPs to Identify TB (SNP-IT), a single-nucleotide polymorphism–based tool to identify all members of MTBC, including animal clades. By applying SNP-IT to a collection of clinical genomes from a UK reference laboratory, we detected an unexpectedly high number of M. orygis isolates. M. orygis is seen at a similar rate to M. bovis, yet M. orygis cases have not been previously described in the United Kingdom. From an international perspective, it is possible that M. orygis is an underestimated zoonosis. Accurate identification will enable study of the clinical phenotype, host range, and transmission mechanisms of all subspecies of MTBC in greater detail.The clinical phenotype of zoonotic tuberculosis and its contribution to the global burden of disease are poorly understood and probably underestimated. This shortcoming is partly because of the inability of currently available laboratory and in silico tools to accurately identify all subspecies of the  Mycobacterium tuberculosis complex (MTBC) encompasses a group of organisms that cause tuberculosis (TB) in humans and animals. TB in humans is caused mainly by M. tuberculosis but also by other members of MTBC, including the less well understood animal-associated subspecies M. bovis, M. caprae, M. pinnipedii, M. suricattae, M. orygis, M. microti, and M. mungi  for all MTBC isolates, we sought to use these data to estimate the burden of animal-associated TB in England. Therefore, we identified a broad panel of SNPs that define each subspecies, lineage, and sublineage within the MTBC and assessed them using a new SNP-based tool, SNPs to Identify TB (SNP-IT).M. suricattae, M. mungi, and the dassie bacillus, which were not present in the Oxford or RIVM collections. The nomenclature we adopted for this study is summarized in We defined a set of isolates (N = 323) from which to identify SNPs associated with subspecies, lineages, and sublineages within the MTBC . We idenhttps://www.illumina.com) to 2 different versions of the H37Rv reference genome. We mapped reads to NC000962.3 with Breseq version 0.28.1 , using a minimum allele frequency of 80% and minimum coverage of 5 times for SNP calls. Separately, we mapped reads again to NC000962.2, for which we used Snippy version 3.1 with default settings  .All newly sequenced genomes are available from the National Center for Biotechnology Information under project accession no. PRJNA418900. The SNP-IT tool, including all relevant reference libraries used for this study, is available as an open-source package online  using clinical isolates sequenced by PHE Birmingham, UK, identified as MTBC and not included in the calibration set . We augmhttps://www.ebi.ac.uk/Tools/sss/fasta) to the catalog of phylogeny-determining SNPs to make predictions for PHE isolates, whereas for isolates downloaded from the nucleotide archive, we created only limited variant calling format files using Snippy . To ensure that genomic loci defining lineage 4 were included, we used a mutated reference genome to create these limited variant calling format files. We compared SNPs in the query sample with reference libraries of lineage-specific SNPs for each clade. We assigned query genomes to particular subspecies or lineages if >10% of lineage- or subspecies-specific SNPs were detected in the strain in question. We assessed all predictions against the maximum-likelihood phylogeny. For M. mungi, we could locate only 1 genome in the public sequence libraries, so we could not validate this subspecies.We compared FASTA files of the whole genome , TB-Profiler (http://tbdr.lshtm.ac.uk), and Mykrobe predictor (http://www.mykrobe.com/products/predictor) on default settings and then after integrating our updated SNP library. To enable our new data to be integrated with published SNP libraries  and the fewest in M. bovis (n = 23). Subspecies that arise from common deep branches, such as M. microti and M. pinnipedii (M. microti and n = 301 for M. pinnipedii) than those that do not, such as M. orygis (n = 781). All predictions made by SNP-IT across all the subspecies, lineages, and sublineages were consistent with the maximum-likelihood phylogeny for all isolates in the validation set  as predictive of taxonomic and phylogenetic groups of interest  Table 3.nnipedii , have lotion set .M. orygis, 3 as M. microti, 34 as M. bovis, and 1 as M. caprae (M. orygis, we further investigated whether there was any genomic signal of possible person-to-person transmission. We identified 2 such instances, 1 in which the pairwise genetic distance between 2 patients was 0 SNPs and a second in which it was 6 SNPs (We retrospectively applied SNP-IT to clinical isolates sequenced as part of the routine PHE diagnostic workflow in Birmingham to estimate the prevalence of the animal subspecies among MTBC samples. Of 3,128 samples from 2,106 patients for which there was a whole-genome sequence available, we identified 24 as . caprae . In the s 6 SNPs  Figure 1M. tuberculosis (Using a previously published list of drug resistance–associated genes for rculosis  Table 4.M. orygis as M. tuberculosis West African lineage and M. pinnipedii as M. microti. KvarQ identified all animal-associated subspecies only as “animal lineage.” TB-Profiler was unable to delineate among animal subspecies, which were all reported as M. bovis/M. tuberculosis West African lineage.Compared with SNP-IT for the clinical set of isolates, Mykrobe predictor reported M. orygis using our new minimal SNP dataset.After we modified the KvarQ, TB-Profiler, and Mykrobe predictor databases with our minimal SNP catalog, all systems agreed on the identity of all the MTBC isolates in the clinical set. SNP-IT was unable to identify 10 samples because <10% of type-specific SNPs were present in these strains. This result was because our pipeline made no call at the lineage informative sites because of the presence of a minor allele, most likely the result of contamination or a mixture of 2 strains in the sample. However, TB-Profiler and Mykrobe predictor were both able to identify 2 of these isolates as mixed Beijing lineage/SNP typing is a powerful method for discriminating among members of MTBC, which are often not discernible by conventional laboratory methods. The SNP databases of Stucki, Coll, and Comas are currently used as the knowledge base for KvarQ, TB-Profiler, and Mykrobe predictor (M. orygis, from humans in the United Kingdom. This recently described member of the complex has a host range that includes waterbucks, gazelles, rhesus monkeys, cows, and rhinoceri (By applying SNP-IT to a clinical dataset, we discovered an unexpectedly high number of animal subspecies among MTBC isolates, particularly M. orygis, with potential implications for TB control both in animals and humans. To recognize the particulars of the clinical phenotype, epidemiology, and optimal management strategy of M. orygis infection, it is first crucial to accurately distinguish these cases from M. tuberculosis West African lineages (,M. orygis isolates that could be compatible with either person-to-person transmission or, possibly, common exposure to the same infected animal. From an international perspective, the role of M. orygis in zoonotic transmission in Africa, Asia, and other high-prevalence settings with extensive animal contact is poorly understood and may warrant further investigation.Given the large amount of resources aimed at controlling bovine TB, it is noteworthy that another zoonosis is seen at a similar rate in this collection of clinical isolates. This finding raises questions about the host range and transmission of All the animal subspecies had phylogenetic SNPs in drug resistance–associated genes. When these genes are not known to be associated with drug resistance, they can be helpfully annotated as such by diagnostic algorithms and excluded for the purpose of predicting susceptibility. An unavoidable weakness of any SNP-based approach is its vulnerability to null-calls as a result of minor alleles at informative positions or a lack of coverage. SNP-IT uses the entire library of subspecies/lineage/sublineage-defining SNPs such that this weakness is not an issue unless it occurs at most of these positions. An additional limitation is that, although we have sought to calibrate SNP-IT using the most diverse collection of samples available to us, it may not be able to correctly identify isolates that originate from deeper phylogenetic branches than those in our calibration set.In conclusion, in this study we demonstrate a higher-than-expected burden of zoonotic TB in a large collection of clinical isolates from the United Kingdom. The SNP-IT tool we have developed will help researchers to examine the epidemiology of zoonotic TB in a global context, as well as optimizing the disease’s clinical management. As more healthcare systems begin to routinely use WGS, there is an opportunity to accurately diagnose the causative subspecies of TB in all cases, which will enable identification of previously underrecognized zoonoses and reverse zoonoses and implementation of control interventions in the interests of One Health.Mycobacterium tuberculosis complex.Additional data about subspecies and associated lineages of