Bovine tuberculosis (bTB) is a disease of significant economic importance and is a persistent animal health problem with implications for public health worldwide. Control of bTB in the UK has relied on diagnosis through the single intradermal comparative cervical test (SICCT). However, limitations in the sensitivity of this test hinder successful eradication and the control of bTB remains a major challenge. Genetic selection for cattle that are more resistant to bTB infection can assist in bTB control. The aim of this study was to conduct a quantitative genetic analysis of SICCT measurements collected during bTB herd testing. Genetic selection for bTB resistance will be partially informed by SICCT-based diagnosis; therefore it is important to know whether, in addition to increasing bTB resistance, this might also alter genetically the epidemiological characteristics of SICCT.P > 0.05). These emerging results demonstrate that genetic selection for bTB resistance is unlikely to change the probability of correctly identifying non-infected animals, i.e. the test’s specificity, while reducing the overall number of cases.Our main findings are that: (1) the SICCT test is robust at the genetic level, since its hierarchy and comparative nature provide substantial protection against random genetic changes that arise from genetic drift and from correlated responses among its components due to either natural or artificial selection; (2) the comparative nature of SICCT provides effective control for initial skin thickness and age-dependent differences; and (3) continuous variation in SICCT is only lowly heritable and has a weak correlation with SICCT positivity among healthy animals which was not significantly different from zero  contains supplementary material, which is available to authorized users. M. bovis-purified protein derivative (PPD) and M. avium sb sp. avium-PPD antigens, also referred to as tuberculins. By comparing the magnitude of the responses to the two tuberculins, and applying pre-determined thresholds to their difference, SICCT can differentiate most cases of true bTB infection from cross-reactions due to exposure to other mycobacteria in the environment [Sp; Sp > 99%) [Se), with estimates ranging from 55 to 91% [BTB diagnosis in the UK continues to rely on the single intradermal comparative cervical test (SICCT)  and currironment . SICCT ip > 99%) , 3, its 5 to 91% , 4, withPrevious genetic studies on confirmed bTB infection have demonstrated the feasibility of selection for bTB resistance in cattle, (a) by showing that there is heritable genetic variation for this trait –6, and  for bTB resistance in cattle are derived from the genetic evaluation of observed cases and survivors within a completed herd breakdown (a discrete epidemic contained within a herd), following the model of [The aim of this study was to conduct a quantitative genetic analysis of the continuous variation in the SICCT test using the extensive available field data collected during bTB herd testing. The genetic variances and covariances in response to the test inoculations are estimated for SICCT and its components, and the genetic basis of the hierarchical comparative structure of SICCT is explored. The study develops a quantitative genetic model to overcome some of the limitations of using field data, which arise from the uncertainty in identifying infected and healthy individuals unambiguously. This model is used to infer the potential magnitude of the genetic correlation between bTB resistance and the liability for positivity in healthy animals , which is related to the individual model of . The conn = 112,116). The age of the animals tested ranged from 43 to 6605 days, with a mean of 1397 days. Repeated measurements were available for 11,910 animals: 10,678 animals with two records, 1104 with three records, and 128 with four records.The data comprised 117,356 Holstein–Friesian female cattle, all with known sires, with 130,626 SICCT records originating from 646 herds undergoing bTB breakdowns. A breakdown is a period of strict control for an entire herd, starting when a case of bTB is identified in the herd and continuing until the herd is free of bTB as determined by the testing protocols. All animals had been tested over a period of 9 years (2002–2010), from herds undergoing their 1st, 2nd, 3rd or 4th bTB breakdown, with the majority of animals being tested during the 1st breakdown within a herd (a1) and 3 days post-inoculation (a2); at the site of bovine tuberculin injection before inoculation (b1) and 3 days post-inoculation (b2). A histogram of these measures is in Fig. da = a2 − a1, which captures the responsiveness to M. avium inoculation; (2) db = b2 − b1 which captures the responsiveness to M. bovis inoculation; and (3) the test measurement for decision-making which is defined as dc = db − da. Individuals are categorised as non-reactors (NR), inconclusive reactors (IR), or positive reactors (R), following the standard or severe interpretations [dc < 1 mm → NR, 1 ≤ dc ≤ 4 mm → IR, and dc > 4 mm → R. Individuals classified as NR and IR are treated as healthy, however, these may also include some misclassified bTB cases  due to the imperfect Se of SICCT. This binary classification is decribed as SICCT positivity in this study, defined as 1 if R, or 0 if NR or IR under the standard interpretation. Those classified as R are considered to be cases and are culled, but in UK only a proportion of these cases undergo a confirmation process involving the culture of M. bovis, and some uncertainty remains over whether the remaining R are true or false positives. Since R are culled, the 11,910 repeated measurements are all NR or IR up to their final measurement.SICCT involves four skin thickness measurements (mm): taken at the site of avian tuberculin injection before inoculation a and 3 daetations . For thea2 and b2 measurements of 77 and 99, respectively (considered to be wrong entries) were removed. Furthermore, the data indicated that 12 animals were retested although diagnosed as R, and the 25 records from these animals were removed. This left, 130,599 records for 117,342 animals that were retained for subsequent analyses. Assessment of the individual a1, a2, b1, and b2 measurements indicated that 11 herds with no standard R and less than 2 IR involving 418 records, but these were retained in the data. A detailed description of the quantitative data used in the analysis is in Table Two animals with extreme Q0 = Q1 = 1, Q2 = 3, Q3 = 9, and Q4 = 2028, i.e. the median was equal to 3  [db but not for da  on age was conducted on  2.15.2) . There wdc. This further reduction left 88,932 records in the analysis, containing 1261 standard R cases, with apparent prevalence p′ = 1.4%. Additional file Se and Sp.The detailed analysis was conducted on a dataset that was further reduced to contain only the first known test date within each of the 728 breakdowns and ignoring later tests. The justification for this is given in the “dc measurement in healthy animals and SICCT positivity; and (d) exploration of the impact of age on the estimated heritabilities.Linear mixed models were fitted using ASReml . Analysea1, b1, da, db, and dc as follows:dc > 4.These were fitted in ASReml for the response variables a1 and b1, a1 and a2, b1 and b2, da and db to obtain phenotypic, genetic and environmental correlations. Furthermore, the phenotypic and genetic regressions of b1 on a1, a2 on a1, b2 on b1, and db on da were calculated since they underlie key assumptions of the comparative test. The models fitted were bivariate extensions of the univariate models (1):A series of bivariate analyses were conducted following the hierarchy of components of SICCT between dc measurement (mm) in the animals that are assumed healthy, i.e. NR and IR, and the SICCT positivity, which was defined as 1 if R, or 0 if NR or IR under the standard interpretation. The analysis was conducted on the observed scales for both traits. In this analysis, the test measurements for animals classified as reactors were treated as missing.A bivariate analysis using model (2) was conducted between the dc, da and db, within each of the three age-groups. In addition, a multivariate analysis was conducted among the three age-groups using a tri-variate extension to model (2), to obtain genetic correlations across different ages.Three age groups were defined as follows: Group 1: age ≤ 750 days, Group 2: 750 days < age ≤ 1100 days, and Group 3: age > 1100 days Table ” sectiondc increased by 4 units of magnitude. This result indicated a cryptic structure that arises from repeated test dates within breakdowns and justified the restriction to first tests within breakdowns only.The results from fitting models that account for multiple-test days within a breakdown are in Fig. a1 and b1 resulted in similar estimated heritabilities of ~0.49 (SE = 0.021), which demonstrates that skin thickness is moderately heritable. Estimated heritabilities for da and db were very low and similar. Nevertheless, these estimated heritabilities for da and db were greater than that for dc which was estimated as 0.010 (SE = 0.003) and statistically significant (P < 0.001). The genetic variance observed for db (0.257) was greater than that for da (0.038), although the increase in phenotypic variance for db resulted in a heritability very close to that for da. The large genetic variance for db is anticipated to include the variance that comes from the expression of genetic variation in resistance to bTB, amongst other sources. The magnitude of the genetic variances in da, db and dc are biologically relevant although the heritabilities may be small: the genetic standard deviations of da, db, dc were equal to 0.19, 0.50 and 0.14 mm, respectively, which implies that the upper and lower 5% in the population will differ by 0.63 mm in da, 0.85 mm in db and 0.47 mm in dc due to genetic factors. However, it is anticipated that the values for db and dc in particular would vary with prevalence.Estimates of heritability after retaining only the first known test within each breakdown and first breakdowns per herd are in Table a1, b1, or da. The estimated heritability for dc was slightly increased, while that for db was slightly reduced compared to the analyses including the R cases, but these differences were within the range of the standard errors of the estimates. The truncation on dc caused by removing the R cases had a notable impact on reducing the genetic variance in db.Estimates of heritability obtained from the univariate analyses after removing the R cases under the standard interpretation, are in Table a1 and b1, a1 and a2, b1 and b2, and da and db, are in Table a1 and b1 were highly genetically correlated (>0.99) with both genetic and phenotypic regressions of b1 on a1 close to 1. This was consistent with expectations since a1 and b1 are measurements of the skin thickness on the same animal, thus they measure biologically the same trait on the same scale. a2 was found to be highly genetically correlated with the initial skin thickness measurement a1 , and similarly b2 was highly genetically correlated with b1 . The genetic and phenotypic regressions for both a2 on a1 and b2 on b1 were close to but marginally greater than 1, and their standard errors indicated that this difference was statistically significant.The results from the bivariate analyses between da and db was positive and large  but it was statistically different from 1, with a genetic regression of db on da close to 1 . The phenotypic regression of db on da was notably less than 1 , and much closer to the environmental regression of db on da i.e. 0.620 (SE = 0.004), since the genetic variance forms only a small part of the total variance in db and da. The genetic correlations of dc with da and db were equal to 0.077 (SE = 0.141) and 0.500 (SE = 0.107) respectively, with a value close to 0 for the correlation between da and dc, which is a consequence of the genetic regression of db on da being close to 1 and of dc = db − da. The small magnitude of the genetic correlation of dc with da is consistent with the observation that removing the standard R cases, hence truncating the values on dc, had only a small impact on the genetic variance in da  and 0.657 (SE = 0.002), respectively. The implications of these results are discussed in detail in the “The genetic correlation between the derived traits dc. Removing the R cases had only a small impact on the genetic correlations but did increase their values towards 1. Post-truncation, the genetic regression of db on da was less than 1 (0.607 SE = 0.037). As indicated above, truncation on dc had a large impact on the genetic variance in db and only a small impact on the genetic variation in da and post-truncation genetic correlations of dc with da and db were equal to −0.840 (SE = 0.045) and −0.566 (SE = 0.107), respectively.The estimated parameters obtained from the bivariate analyses after removing the R cases under the standard interpretation are in Table dc measurement (mm) in the animals classified as healthy and SICCT positivity (i.e. passing the threshold of being a standard R or not) provided a genetic correlation of −0.01 (SE = 0.14). This value provides evidence of the magnitude of the genetic covariance between SICCT responses in healthy animals and resistance to bTB, but is interpreted in more detail in Additional file dc < −4) were also removed from the data. The genetic correlation between the dc measurement and SICCT positivity remained very small .The bivariate analysis between the a1 and b1 is particularly evident at young ages while heritability decreases to very low values in age-groups 2 and 3. The genetic variances for da, db and dc are more consistent across age-groups, with all estimated heritabilities for dc being less than 0.02 , by testing this model against a null hypothesis that all correlations were 0, and a further null hypothesis that all genetic correlations were 0.9: in neither case did the test criterion exceed the 5% significance threshold for rA = 0, Δ = 3.98, and for rA = 0.9, Δ = 6.36.The genetic correlations obtained from the multivariate analysis across the three age-groups are in Table Se to SICCT, is incompatible with the published evidence . Therefore the finding and documentation of genetic variation in the biology of SICCT and its components are important, and the potential consequences need to be considered, with or without artificial selection for resistance to infection. In cattle, there is strong evidence for genetic variation in resistance to bTB infection , 5 and aSp—this phrasing is determined by the definition of specificity as the probability of SICCT indicating uninfected given the individual is uninfected); and (3) the liability for positivity in the SICCT test when infected with bTB . Information on the first of these traits comes from observing cases and survivors over the full course of a breakdown and it is not proposed that the continuous variation of SICCT that is studied here adds significantly to this information. In contrast, genetic variation in the continuous variation in SICCT is the direct measure for the underlying liabilities for an individual Sp in healthy animals, as dc is the continuous trait upon which the threshold for culling is applied. Interpreting the continuous variation in SICCT in infected animals as a liability for individual Se is more complex as the true liability for positivity will depend on the time since infection which is unknown among other factors.There are (at least) three traits for which genetic variation may influence bTB control: (1) the liability to become infected with bTB when exposed to the pathogen; (2) the liability of avoiding positivity (i.e. pass or fail) in the SICCT when not infected with bTB . However this data also has important weaknesses: (1) it is not possible to infer the disease status of individuals unambiguously so that some individuals may be wrongly considered as healthy, and some may be wrongly considered as infected; and 2) the quality of the recorded data has been questioned , 15, and the quala1 and b1 are both measurements of skin thickness in a well-defined location taken prior to inoculation and it is anticipated that the scales of variation would be similar, as was found, and that the genetic correlation would be equal to 1 since they measure the same biological trait. In the data, the observed estimate of this genetic correlation could not be distinguished from 1 despite the significant power of the data. This correspondence was also observed in the phenotypic variation although the phenotypic regression was marginally less than 1, which was expected since some environmental variation, e.g. measurement and recording error, in a1 will be independent of b1 and vice versa. However, it is expected that a2 and b2 are more prone to poor recording practices with recording by the observer based on the perceived NR/IR/R outcome rather than the measurement itself. Therefore, while the results are an important part of the knowledge base, and as such will influence decision making, they require careful interpretation.The data in the present analysis do pass some tests on data quality in that All the models presented were sire models combined with pedigree information. The sire model offers further protection against suboptimal data quality. In the sire model, information is based on variation between sire means which are modified averages of half-sibs, and as a result of the Central Limit Theorem, this model is less vulnerable to the underlying distribution of the variables. Further modelling challenges arise from the structure of the herds and breakdowns in the data . Given the information available, the exact sources of such a structure are unknown and so the breakdown was fitted as a fixed effect, which recovered information from within herds and avoided any inference of genetic variance from between breakdowns.dc: , to , to dc itself.The primary findings of this study show that the SICCT is genetically robust in that its design provides substantial protection against random genetic changes that arise from genetic drift and from correlated responses among its components due to either natural or artificial selection. Whereas robustness was the intention of a comparative test , this waa1 and b1 represent skin thickness measurements at the site of observation and they show substantial genetic variation particularly at young ages within the Holstein–Friesian gene pool (and breed variation will add to this in the wider population). The results show that this genetic variation in skin thickness contributes directly to genetic variation in a2 and b2 with high genetic correlations, so that any assessment of b2 or a2 as a single measure of bTB or avian infection will be flawed. Such an assessment would be open to substantial genetic drift, irrespective of selection, would likely vary by breed and sub-population, and diagnostic ranges would depend on age. The use of da and db controls both these risks associated with skin thickness. This was demonstrated through the regressions of a2 on a1, and b2 on b1 which were found to be close to 1, indicating that by using da and db the test becomes near-independent of skin thicknesses, a1 and b1, i.e. da and db are not a function of a1 and b1 as da = a2–1.a1 and similarly db = b2–1.b1. These findings were also found across ages, showing that this control for skin-thickness was not age-dependent although the genetic variance for a1 and b1 varied with age. Therefore, including a1 and b1 and calculating da and db make SICCT more stable with respect to the genetics underlying the initial skin thickness.The two measurements db on da is 1 prior to culling decisions, and the estimated value of 1.03 found here (SE = 0.03) is therefore reassuring. If this regression were different from 1 then correlated responses in SICCT could arise due to changes in specific components. The magnitude of the genetic correlation between da and db is less important and is not expected to be 1, since the additional genetic variance in resistance to infection, and other possible sources of variation in db which are independent of da, and in da which are independent of db will reduce it. Nevertheless, a high genetic correlation might be expected even in the presence of genetic variance in resistance to infection since the correlation will be inversely related to prevalence  and in this sample the prevalence, while uncertain, is unlikely to be greater than 3% given a fraction of 2.3% of R animals even under the severe interpretation. The implication of the genetic regression of 1 is that if Adb and Ada are breeding values for db and da respectively, with phenotypes db = Adb + εdb and correspondingly da = Ada + εda where ε represents deviations of environmental origin, then dc is independent of the additive genetic variation in da since Adb = Ada + δ where δ represents genetic terms independent of Ada. These results were also evident across age groups, which indicates the robustness of SICCT that arises from the hierarchy. In summary, the observation that there is genetic variance in da, with an estimated heritability of ~0.05, and its high genetic correlation with variation in db indicate the possibility of relatively weak random genetic drift due to non-specific responsiveness to environmental mycobacteria in non-comparative tests based on db alone, but this is effectively removed by the comparative nature of SICCT. A corollary of this finding is that SICCT itself is unlikely to drift across generations through genetic changes in da as was proposed [Similarly, it is important that the genetic regression of proposed . Althougdc = db − 1.da removes the confounding responses to environmental mycobacteria relies on the regression coefficient of 1: while this was true for the genetic variance, it was not the case for at least some of the non-genetic sources of variance in dc as the estimated phenotypic regression was much less than 1, namely 0.63. The hypothesis that arises from this estimate is that the use of dc is over-correcting for this non-genetic variance and that animals with a large positive da originating from a non-genetic source have a lower risk of being declared as R, which was the implication of the negative phenotypic correlation observed between dc and da. Such biases have no implications for genetic change. The non-genetic sources of variance encompass measurement and recording errors but this study has no information on which to decompose all the sources of non-genetic variance. The second implication concerns the use of human TB tests for detecting significant quantitative trait loci (QTL) [db, is interpreted as resistance. The use of non-comparative tests is open to confounding with background genetic variation in response to environmental mycobacteria, as shown in this study. The comparative nature of SICCT removes such ambiguities in cattle.These concepts have implications beyond the genetic variation in bovine SICCT components, i.e. first in the wider conduct of SICCT testing, and second in the search for genetic risks to human TB. The concept that ci (QTL) –20. Thesci (QTL)  and in tSp and Se leading to changes in population-wide Sp and Se. Despite the inevitable uncertainty in the classification of animals in this study into infected and uninfected, it is possible to draw some conclusions for two reasons which underpin the quantitative model in Additional file Sp [Se than a single SICCT measurement. In the UK, the breakdown in a herd, with its associated management restrictions, continues until two consecutive tests over a minimum period of 60 days are clear across the herd. The purpose of this process is to identify and remove all infected animals, so that the process has a Se of 1, or close to it.The more challenging question is what impact selection on predictions of disease resistance, which are made by using models analogous to the ultimate fate models of , may hav file Sp  in this Sp is expected to be weak and slow, and negligible over a small number of generations. The current estimates suggest that increasing resistance is likely to slightly reduce specificity .These results do provide evidence that suggest that any response due to cross-reactions in healthy animals that reduces population-wide Sp, or in the individual Se. In the latter case, a scenario with reduced prevalence but a greater proportion of non-responding infected individuals cannot be excluded. Such risks can be further reduced by developing more accurate genomic predictors for bTB resistance by expanding the training sets of confirmed cases and survivors of breakdowns [However, some theoretical risks cannot be excluded and would remain even if selection did not take place. The Holstein cattle breed has a small effective population size due to the widespread use of popular sires and if the genetic variation in these bTB and SICCT related traits were large enough to display significant selection response, direct or correlated, they can also exhibit population changes that arise from genetic drift, which is caused by the unrecognised genetic merits of popular sires with respect to bTB. Both the risks associated with selection and with drift can be managed by continued monitoring of key population data, e.g. SICCT measurements as studied here, the outcome of the confirmation process of cases observed with or without SICCT positivity. With such monitoring, implementing genetic selection for bTB resistance is unlikely to compromise the integrity of the test in the short to medium term. Conceivably over longer time periods, without intervention, some response to selection might occur in the individual eakdowns , since tSp of the test.The hierarchy of SICCT was shown to be important at the genetic level, which makes it a genetically robust test for the purposes of genetic selection for reduced susceptibility to bTB. The continuous variation in SICCT is only lowly heritable and the SICCT outcome in the healthy animals is very weakly genetically correlated with the SICCT positivity, which indicate that selection for individuals more resistant to bTB infection is not likely to change the probability of correctly identifying non-infected animals, i.e. the


Saccharina latissima s. l. Using molecular data  we confirm that S. latissima comprises also the NE Pacific S. cichorioides complex and is composed of divergent lineages with limited range overlap and genetic admixture. Only a few genetic hybrids were detected throughout a Canadian Arctic/NW Greenland contact zone. The degree of genetic differentiation and sympatric isolation of phylogroups suggest that S. latissima s. l. represents a complex of incipient species. Phylogroup distributions compared with paleo-environmental reconstructions of the cryosphere further suggest that diversification within S. latissima results from chronic glacial isolation in disjunct persistence areas intercalated with ephemeral interglacial poleward expansions and admixture at high-latitude (Arctic) contact zones. This study thus supports a role for glaciations not just in redistributing pre-existing marine lineages but also as a speciation pump across multi-glacial cycles for marine organisms otherwise exhibiting cosmopolite amphi-boreal distributions.Glacial vicariance is regarded as one of the most prevalent drivers of phylogeographic structure and speciation among high-latitude organisms, but direct links between ice advances and range fragmentation have been more difficult to establish in marine than in terrestrial systems. Here we investigate the evolution of largely disjunct  phylogeographic lineages within the amphi-boreal kelp  Genetic differences accumulate in isolated gene-pools and thus may eventually lead to new genomic recombinations in subsequent contact events or reproductive isolation. High-latitude species have been particularly prone to undergo such processes as a consequence of the cyclic growth and decay of the ice-sheets during the Quaternary ice-age (2.5 Ma ago to present)2. Continental-scale changes in regional temperature and oceanographic circulation regimes, among others, have resulted in important latitudinal shifts in the distribution and connectivity of biodiversity. For most high-latitude species in the northern hemisphere, glacial maxima, such as the last glacial maximum 3, would have been characterized by more southerly, and often more fragmented, geographical distributions, such as the disconnected southern European peninsular refugia4. Likewise, with each ice advance and retreat, shallow coastal areas were also alternately exposed and submerged5, opening and closing dispersal corridors for marine and terrestrial biota such as the glacial Beringia land bridge (currently Bering Strait).Glacial vicariance, the geographical separation of species into regional populations associated with glacial conditions, is one of the most pervasive and recurrent drivers of phylogeographic structure and speciation10. Even when species ranges become more continuous, gene-flow can continue restricted beyond secondary contact/hybrid zones, so that pre-existing genetic differences are preserved, at least in original refugial areas. This can be due to limited dispersal and/or density-barrier effects10, or evolving post-zygotic (e.g. Dobzhansky-Muller) incompatibilities11. Such a composite mode of divergence and speciation, characterized by cyclic regional isolation and accumulation of genomic differences that are conserved beyond narrow and often geologically temporary interglacial contact/ hybrid zones, now seems pervasive among low-dispersal terrestrial taxa13.Speciation, the evolution of reproductive isolation leading to the formation of new biological species, can be an extended process spanning multiple climatic shifts. Diverging gene pools thus only become evolutionarily relevant from a speciation perspective if they survive and retain their integrity (i.e. do not fully merge) in the longer-term. Low-dispersal species are often subdivided in phylogeographic groups that are largely parapatric and presumably formed when isolated subpopulations expanded and contacted, a common process post-glacially16, where often dispersal barriers are elusive, detailed fossil records unknown, and glacial histories obscured by intrinsic high rates of dispersal. Still, approaches including phylogeography and niche modelling have revealed effects of past climatic shifts and changes in the geographic distribution of marine organisms and/or of their lineages. Northern extirpations, climatic refugia (including northern refugia) and post-glacial contact/hybrid zones have been identified across the world oceans, and several pathways of poleward post-glacial expansions established22. However, explicit biogeographical settings of marine glacial vicariance, and how they might have actually contributed to generate new marine biodiversity in the long-term, have seldom been identified (but see23).Links between glacial/sea-level dynamics, allopatric divergence and speciation have been more challenging to document in the marine realmSaccharina latissima sensu lato (s.l.) was recently shown to be composed of three lineages of uncertain biological status, morphologically cryptic but genetically distinct , so that admixed populations are among those more prone to be erased in subsequent glacial advances. On the contrary, pure, southern populations will tend to persist across climatic shifts.Lineages B and C are certainly native to the Atlantic, i.e. evolved and split there, possibly after a much older (pre-LGM) colonization event from the diversification centre of NW) Fig. . ConsequS. latissima s. l. in the northern hemisphere, where the species currently exhibits a rather continuous cosmopolitan distribution that might suggest that genetic admixture opportunities are prevalent over long-term isolation and differentiation. There are many other amphi-boreal, spatially-structured species complexes in the northern Hemisphere, although in contrast with kelps these are mostly high-dispersal fish and invertebrates, e.g.60). All this evidence from marine organisms with distinct dispersal means suggests that episodic waves of trans-Arctic and trans-Atlantic migration and chronic glacial vicariance probably represent rather general drivers of marine diversification.A scenario of glacial vicariance intercalated with ephemeral interglacial admixture would have cumulative genetic and ultimately reproductive effects across multiple glacial cycles. It represents, in our view, the best explanation for the remarkable diversification and modern phylogeographic structure of S. japonica and S. latissima/S. cichorioides illustrates well the potential problems arising from the absence of clear and consensual diagnostic taxonomic characters in widespread plastic taxa, and the importance of sensibly analysing and interpreting genetic data available from public databases, especially when the underlying taxonomy of the studied group is to some extent unreliable and/or field identifications (even by experts) potentially incorrect . The COI barcode provides a standardized, cost-effective first step for species assignments in Saccharina spp. and new and more robust insights are likely to emerge as data from additional species and populations accumulate. Future molecular surveys should prioritize the NW Pacific diversity hotspot, where many putative endemic species occur, and where the diversity and distribution of genetically confirmed S. latissima s. l. remains basically unassessed. Finally, fine-scale studies focusing on modern contact zones – e.g. integrating morphologic, spatial and genetic (microsatellite) data – are required to further evaluate the nature and strength of the putative isolating mechanisms at play in this complex.The over-classification of S. latissima s. l. are fascinating models to investigate marine demographic responses, resilience and diversification associated with the Pleistocene climatic shifts. We attributed the clear genomic and biogeographic differences detected within this cosmopolitan species to prolonged glacial isolation in disjunct persistence areas intercalated with interglacial expansions and limited admixture in ephemeral, high-latitude contact zones. It remains to be evaluated whether present biodiversity coincides with relevant functional differences that could be important from a conservation or cultivation perspective, particularly in a scenario of ongoing climatic change and ever increasing demand for marine products. More generally, this study shows that glaciations are not just relevant in terms of the redistribution of pre-existing marine lineages but may actually function as an allopatric speciation pump, across multi-glacial cycles, for widespread amphi-oceanic organisms.Cold-adapted species like Saccharina latissima (Linnaeus) C.E. Lane, C. Mayes, Druehl & G.W. Saunders (=Laminaria saccharina (L.) Lamouroux) is an edible, short-lived perennial, canopy-forming brown seaweed  widely distributed  in shallow-water habitats throughout much of the Northern Hemisphere. Its documented but only partially confirmed range extends from the pack-ice border in the high Arctic to cold-temperate latitudes on both sides of the Atlantic and the Pacific oceans31. This vast distribution is accompanied by extensive ecological, morphologic and physiological plasticity, as well as substantial phylogeographic structure64.The sugar kelp Saccharina were reconstructed with Bayesian and Maximum Likelihood (ML) inference methods using the barcoding COI mitochondrial gene. COI data included all published sequences of adequate range available (Table S. latissima s. l. from both sides of the Atlantic and the Arctic (primers in32). PCRs were performed in 20 μL total volume containing ±10 ng of template DNA, 1x buffer, 2 mM of MgCl2, 0.5 mM of dNTPs, 0.5 μM of each primer and 1 U of GoTaq G2 Flexi DNA Polymerase (Promega). PCRs were run in a ABI 2720 Thermal Cycler (Applied Biosystems) with an initial denaturation step , 35 cycles of 95 °C for 30 s, 50 °C for 45 s and 72 °C for 1 min, and a final extension step . Sequences were aligned, proofread and edited in GENEIOUS 4.8 . Nucleotide substitution models (using 3 substitution schemes) were compared with jModelTest 265 and best-fit models selected based on the Akaike information criterion. Bayesian analyses were performed using MrBayes 366. Two parallel Metropolis-coupled Markov chain Monte Carlo searches, each with four chains (3 ‘heated’), were run for 2 × 106 generations, sampling trees and parameters every 100 generations. The number of substitution rates, among-site rate variation, and base frequency priors were set according to the substitution model selected, leaving the remaining options as default. Run length sufficiency was confirmed by inspecting the average standard deviation of split frequencies between runs and cold chains Log-likelihood stationarity. Based on the latter, 2 × 105 generations (2000 trees) were discarded as burn-in. The remaining 18000 trees sampled were used to produce 50% majority-rule consensus trees and to calculate branch posterior probabilities. Maximum likelihood analyses were performed with PhyML 367 using the ATGC bioinformatics platform (http://www.atgc-montpellier.fr/phyml/). Nodal support was calculated using 1000 bootstraps, and trees were rooted with S. sculpera .Phylogenetic relationships within le Table , as wellS. latissima s. l. were further illustrated with a network calculated with Network 4.6 68, using the median-joining (MJ) algorithm. Divergence within and between recovered taxa were estimated in MEGA 769 using Kimura’s two-parameter 70 sequence distances.The genealogic relationships within Saccharina latissima s. l. were collected throughout both sides of the Atlantic, from northwest Iberia to Svalbard and northeast Greenland (NE) and Quebec to northwest Greenland (NW) (Table 2) from a variable number of individuals (8–30) were collected haphazardly, safeguarding at least one meter between consecutive samples. These were preserved dried in silica-gel crystals and genomic DNA was extracted using the Nucleospin 96 Plant II kit . This sampling was complemented with individuals from the NW Atlantic and NE Pacific (British Columbia) previously analysed by33. The latter were collected at multiple sites and dates, but for simplicity individuals were pooled in artificial “populations” matching their general geographical origin  at CCMAR, Portugal. Microsatellite alleles were manually scored in STRAND  using the 500 LIZ size standard (Applied Biosystems) and binned using the R package MsatAllele72.Populations of W) Table . At eachn), Nei’s gene diversity (HE), observed heterozygosity (HO) and inbreeding coefficients (FIS), were calculated with GENETIX 4.0573. Population structure within north Atlantic S. latissima s. l., including its concordance with recovered mtDNA phylogroups, was assessed using genotype-based approaches. Genotypic structure was assessed with a factorial correspondence analysis (FCA) implemented in Genetix 4.0573 and with admixture analyses implemented with the R package LEA74. The latter performs Structure-like analyses75 but uses a much faster algorithm. Individuals were analysed without any a priori population assignments. Each number of assumed populations  was run 20 times with default parameter values, to the exception of the alpha that was set to 100. The best subdivisions of Atlantic S. latissima were determined by inspecting the minimal cross-entropy curve and the inter-run stability of genetic clusters produced for each K.Summary statistics of the microsatellite genetic diversity, including allele frequencies, mean allelic richness (A), standardized number of private alleles (AS. latissima s. l. was further reconstructed using Approximate Bayesian Computations (ABC) as implemented in DIYABC76. We compared competing scenarios in two hierarchical levels77 in order to (i) identify the putative topology of ancestry and timing of divergence between the main phylogeographic groups identified . Effective population sizes and parameters for the Generalized Stepwise Mutation model were set to the program default78, while time of population divergence (in generations) were defined as uniform within pre- and post-LGM periods. Prior distributions for admixture rates were set between 0.001 and 0.999 (see Tables 79. Confidence in selected scenarios was evaluated by a Monte Carlo estimates of true and false allocations78.The evolutionary and demographic history of Atlantic phylogroups of https://figshare.com/s/5a8fae0032a0148211a3.The single new COI sequence was deposited in Genbank under accession number MF288586. Genotypic data were deposited in Figshare repository@ Supplementary files


Eurycea quadridigitata; and Southern Leopard frogs, Lithobates sphenocephalus) to understand how habitat characteristics and spatial scale affect genetic diversity across a landscape. Samples were collected from wetlands on a longleaf pine reserve in Georgia. We genotyped microsatellite loci for both species to assess population structures and determine which habitat features were most closely associated with observed heterozygosity and rarefied allelic richness. Both species exhibited significant population genetic structure; however, structure in Southern Leopard frogs was driven primarily by one outlier site. Dwarf salamander allelic richness was greater at sites with less surrounding road area within 0.5 km and more wetland area within 1.0 and 2.5 km, and heterozygosity was greater at sites with more wetland area within 0.5 km. In contrast, neither measure of Southern Leopard frog genetic diversity was associated with any habitat features at any scale we evaluated. Genetic diversity in the Dwarf salamander was strongly associated with land cover variables up to 2.5 km away from breeding wetlands, and/or results suggest that minimizing roads in wetland buffers may be beneficial to the maintenance of population genetic diversity. This study suggests that patterns of genetic differentiation and genetic diversity have associations with different habitat features across different spatial scales for two syntopic pond‐breeding amphibian species.Population genetic diversity is widely accepted as important to the conservation and management of wildlife. However, habitat features may differentially affect evolutionary processes that facilitate population genetic diversity among sympatric species. We measured genetic diversity for two pond‐breeding amphibian species (Dwarf salamanders,  Genetic diversity is important for enabling populations to face environmental challenges. Variable populations have a broader repertoire of potential responses to ambient changes, and subpopulations acting as refuges may make populations more resilient to local extinctions. Additionally, variable populations have a reduced risk of inbreeding depression caused by an increased frequency of deleterious alleles in the population. Effective maintenance of genetically diverse populations requires understanding the evolutionary processes responsible for determining gains or losses of genetic diversity. Over an ecological time frame, genetic diversity in populations is gained by gene flow from other populations and lost through genetic drift , and Dwarf salamander genetic diversity was expected to be more closely associated with habitat features at smaller spatial scales relative to Southern Leopard frogs  reserve containing numerous isolated wetlands that vary in size (0.2–76.4 ha), hydroperiod , and vegetation type . The properties surrounding Ichauway are composed almost entirely of center‐pivot agricultural fields  were located at the Jones Ecological Research Center at Ichauway . Attempts were made to collect at least 30 larval Dwarf salamanders from nine wetlands and at least 30 larval Southern Leopard frogs from 10 wetlands Table  across tTo collect representative samples of individuals from each wetland, dipnet sweeps were distributed equally around each wetland perimeter and interior shallow microhabitats (<0.5 m deep). When larvae were sufficiently abundant, a maximum of one individual per sweep was collected to avoid collecting full siblings. All Southern Leopard frog samples were from larval specimens, and Southern Leopard frogs were collected from all three wetlands types. Dipnet sampling was supplemented with funnel trapping and active searches for adult salamanders around wetland perimeters. Traps were distributed around the perimeter of wetlands and in shallow microhabitats and checked daily. In cases where tadpole species identification was questionable, the individuals were collected and reared in the laboratory to metamorphosis to confirm species identity. Captured individuals were euthanized in 0.5%, pH neutral‐buffered MS‐222. Tissue samples were preserved in 95% EtOH at −20°C.2.3genemapper v4.0  under Hardy–Weinberg equilibrium (HWE), observed heterozygosity (Ho), the fixation index (F), and mean number of alleles per locus (Na) were calculated for each locus in genalex v6.503  was calculated in fstat v2.9.3.2, and Ho and F were calculated in genalex v6.503. For both species, one of the sites (PSK) had significantly lower rg compared to the other sites ; Slatkin, genepop v4.2 using a Mantel test with 10,000 permutations.Genetic distances between populations were measured with 2.5arcmap 9  within the buffers. Given the rural location of the study site, devel was a general indicator of road density. Genetic diversity parameters for both focal species were expected to be negatively associated with devel, which may be partial barriers to dispersal and a source of mortality .Habitat and land cover variables were characterized for each sample site. p 9 ESRI  was usedp 9 ESRI . Radii sp 9 ESRI ; . Neutral genetic theory suggests that populations at larger wetlands should be larger and more genetically diverse as a result of greater carrying capacity  and observed heterozygosity (Ho). Spatial autocorrelation of predictor variables similarly violates the assumptions of statistical independence. Moran's I was used to examine the spatial autocorrelation of predictor variables. Multiple linear regression was performed in sam v4.0  was selected based on the lowest Akaike's Information Criteria value corrected for small sample size , we used condition number (CN) to determine the degree to which multicollinearity was an issue within models  were calculated for the models in the confidence set containing that variable. Model‐averaged predictor variable estimates were calculated by multiplying the predictor variable estimates by the associated Wi, and summing the weighted estimates for each variable  ≤ 2 from the top model still have substantial support and were therefore included in the confidence set , and Southern Leopard frog Ho ranged from 0.67 to 0.78 (SD = 0.03). Low variance in the genetic diversity parameter estimates makes it difficult to investigate associations between habitat features and genetic diversity parameter estimates as there is little variance in parameter estimates to partition.Both species had a surprising lack of variance in both genetic diversity parameters, with the exception of ns Table . When PS3.2FST for three Southern Leopard frog pairwise comparisons, which indicates biased estimation or low statistical power. Negative FST values were converted to zero as they were uninterpretable from a biological perspective. As expected, Dwarf salamanders exhibited greater population structure than Southern Leopard frogs. Pairwise FST values with PSK ranged from 0.002 to 0.156  for Dwarf salamanders, and 0–0.128  for Southern Leopard frogs. When PSK was removed, the range and mean of pairwise FST values dropped slightly for Dwarf salamanders, ranging from 0.002 to 0.118 . However, removing PSK for Southern Leopard frogs resulted in a large decrease in the range and mean of pairwise FST values .Results from the analysis of genetic differentiation and isolation by distance yielded negative values of R2 value decreased from .797 (p = .005) to .217 (p = .041) after PSK was removed , more isolated , and had longer hydroperiods . When PSK was not included, most predictor variables were spatially autocorrelated at Southern Leopard frog sites, but were not spatially autocorrelated at Dwarf salamander sites  with habitat variables than the top models of Southern Leopard frog genetic diversity , and both models were more strongly correlated with the respective diversity parameter than top models of Southern Leopard frog genetic diversity . Top models of Dwarf salamander genetic diversity suggested stronger associations  were more strongly associated with Dwarf salamander genetic diversity than were local features . Similar results were found for spotted salamander abundance  egg mass abundance after a period of drought, compared to the stronger relationship between landscape‐scale variables and egg mass abundance during normal weather conditions. Walls, Barichivich, Brown, Scott, and Hossack  occupancy rates of ponds decreased by more than 50% and local extinction rates increased in association with a 2‐year drought, potentially due to drought‐induced pond drying. Because drought rendered a number of potential study sites dry or unoccupied, the wetlands included in this study were inherently larger or had significantly longer hydroperiods than wetlands that were not included, which may have influenced the statistical relationship between genetic diversity and variables associated with wetland size and hydroperiod, as well as other habitat associations. Partially filled wetlands may have reduced the number of individuals that bred in them, thereby increasing the chance of full siblings being collected.Below‐average rainfall in southwest Georgia in 2006, 2007, and 2008 (total rainfall deviated from average −10.2, −43.2, and −7.6 cm, respectively, in Baker County, Georgia (University of Georgia  Hossack  found th4.4The maintenance of population genetic diversity is widely accepted as important to the conservation and management of wildlife; however, it is an often‐overlooked component in biodiversity conservation management (Taberlet et al., Though studies have called for an integrated management of wetland complexes and intervening terrestrial habitats to conserve amphibians (Semlitsch, None declared.Microsatellite data for this manuscript are archived in the online Supplementary Information. Click here for additional data file. Click here for additional data file.


Sorghum bicolor (L.) Moench] is a type of cultivated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass accumulation, making this crop an important feedstock for bioenergy production. Sweet sorghum breeding programs that focus on bioenergy have two main goals: to improve quantity and quality of sugars in the juicy stem and to increase fresh biomass productivity. Genetic diversity studies are very important for the success of a breeding program, especially in the early stages, where understanding the genetic relationship between accessions is essential to identify superior parents for the development of improved breeding lines. The objectives of this study were: to perform phenotypic and molecular characterization of 100 sweet sorghum accessions from the germplasm bank of the Embrapa Maize and Sorghum breeding program; to examine the relationship between the phenotypic and the molecular diversity matrices; and to infer about the population structure in the sweet sorghum accessions. Morphological and agro-industrial traits related to sugar and biomass production were used for phenotypic characterization, and single nucleotide polymorphisms (SNPs) were used for molecular diversity analysis. Both phenotypic and molecular characterizations revealed the existence of considerable genetic diversity among the 100 sweet sorghum accessions. The correlation between the phenotypic and the molecular diversity matrices was low (0.35), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. Furthermore, the clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis. The low correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program.Sweet sorghum [ Sorghum bicolor (L.) Moench] is a type of domesticated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass production, making this crop an important alternative for bioethanol production, and cogeneration of energy . Swe. Swebiconsistent . Geneticnsistent ,27,29–32Several strategies have been used to access genetic diversity in many crop species –37 basedThe use of molecular markers in genetic diversity analyses has some advantages over the phenotypic characterization, since molecular markers are not influenced by the environment and allow identification of differences in the DNA level that would be imperceptible via phenotyping –48. DiffThe aims of this study were: i) to perform phenotypic and molecular characterization of 100 sweet sorghum lines from the germplasm bank of the Embrapa Maize and Sorghum breeding program, using morpho-agronomic traits and SNP markers obtained via GBS; ii) to examine the relationship between the phenotypic and molecular diversity matrices; iii) and to infer about the population structure in the sorghum accessions.One hundred sweet sorghum accessions  from theSorghum bicolor descriptors for cultivar registration purposes, based on the "Instructions for the Execution of Distinctness Tests, Homogeneity and Stability of Sorghum Cultivars" [-1), weighing all plants from the effective plot area; juice extraction , using hydraulic press, from five to eight plants sampled randomly per plot, without panicles; total soluble solids  in the extracted juice, using hydraulic press, with the use of a digital automatic refractometer; sucrose concentration in the juice , which is the measure of the amount of sucrose in the sugar mixture; reducing sugars in the juice , in which the weight of juice was calculated through the equation adapted from CONSECANA [For phenotypic characterization, morphological traits related to the plant architecture, stem, leaf, panicle and caryopsis, and agro-industrial traits related to the production of sugars and biomass were used. The morphological traits were selected according to the list of ltivars" , which rONSECANA ; lignin ONSECANA , using sONSECANA .® Plant Mini Kit . The quality and quantity of extracted DNA was checked in agarose gel and NanoDrop® ND-1000 Spectrophotometer. Genotyping-by-sequencing (GBS) [Sorghum bicolor reference genome [Leaf samples were collected from five plants per accession and the DNA extraction was performed using the Dneasyng (GBS)  was perfe genome , 63 verse genome . SNPs wee genome . SubsequFor the morphological traits, a correlation analysis was performed based on the Pearson′s correlation coefficient  to identyijk is the random phenotypic effect of the genotype i at block j, in replication k; μ is the general mean; dik is the number of days to flowering for the genotype i, in replication k, and β is the corresponding fixed effect; hik is the plant height for the genotype i, in replication k, and γ is the corresponding fixed effect; rk is the fixed effect of replication k; bjk is the random effect of block j, in replication k, with bjk~ gi is the random effect of genotype i, with gi ~ εijk is a random non-genetic effect, with εijk ~ N. FLOW and PH have direct effect on the phenotype of other agro-industrial traits related to the production of sugar and biomass. The correction for these phenological covariables was necessary for identify accessions that have favorable phenotypes for the production of sugar and biomass independently of their flowering time and plant height. Thus, this correction was considered when fitting the model for: fresh biomass yield (FBY), juice extraction (EXT), total soluble solids (TSS), sucrose concentration in the juice (POL), reducing sugars in the juice (RSJ), lignin (LIG), hemicellulose (HEM) and cellulose (CEL). Random and fixed effects in the model were tested using the likelihood ratio test (LRT) [r is the number of replications. All mixed models analyses were performed using the software GenStat v15 [Hmisc (R Core Team 2015).Agro-industrial phenotypic data were analyzed using the following mixed model, in which the number of days to flowering (FLOW) and the plant height (PH) were used as covariables:st (LRT)  and the st (LRT) , respectst (LRT) ,70. Varist (LRT) ,72. HeriStat v15 . Then, p2), according to the method proposed by Singh [Genetic diversity analyses in the sweet sorghum accessions were conducted separately using the phenotypic and the molecular data. Initially, for the phenotypic data, all morphological and agro-industrial variables were standardized. Then, the dissimilarity matrix between lines was calculated using the Average Euclidean distance . The relby Singh , using tby Singh . Subsequby Singh  in the sby Singh  using thby Singh . Differeby Singh  was perfby Singh  at a 5% by Singh  was perfby Singh , availabby Singh .After correlation analysis performed for the 44 morphological traits, 11 variables that showed high correlation with another variable, were not included in the diversity analysis (r > 0.80). The remaining 33 descriptors used in the diversity analysis are listed in the Genetic variances were significant, using the likelihood ratio test at a 1% significance level, for all agro-industrial traits , indicatAfter raw GBS sequence data processing, a total of 403,433 SNP markers distributed along the ten sorghum chromosomes were obtained, varying from 21,823 to 71,557 SNPs for the chromosomes 8 and 1, respectively . Then, SBased on the morphological and the agro-industrial traits, the Neighbor-Joining method resulted in the identification of five major clusters of sweet sorghum lines . The cluThe Neighbor-Joining method, using the SNP markers data, resulted in 6 major clusters of sweet sorghum lines . The cluThe phenotypic and the molecular diversity matrices exhibited a low correlation coefficient  obtained via the Mantel Test, which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. The clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis.The population structure revealed by the principal component analysis (PCA) based on the SNP markers data was also consistent with the genealogy and the historic background of the sweet sorghum lines . In this-1 of fresh biomass yield, 60.37 and 63.06% of juice extraction, 13.99 and 14.30 °Brix of total soluble solids, 9.08 and 8.30% of sucrose concentration, 1.44 and 1.56% of reducing sugars in the juice were observed for the clusters II-M and VI-M, respectively. On the other hand, based on the phenotypic diversity analysis, the clusters III-P and V-P showed satisfactory consistency with the lines genealogy and the historic background and also interesting averages for several agro-industrial traits. For example, averages of 44.74 and 51.36 t.ha-1 of fresh biomass yield, 57.57 and 65.49% of juice extraction, 14.31 and 16.01 °Brix of total soluble solids, 9.45 and 9.03% of sucrose concentration, 1.69 and 1.46% of reducing sugars in the juice were observed for the clusters III-P and V-P, respectively  between the phenotypic and the molecular diversity matrices. These authors stated that it is possible to obtain a relevant minimum subset of markers that can be used in combination with morphological data to better classify genotypes. Furthermore, their study indicated that, although the phenotypic characterization is time-consuming and greatly influenced by the environment, in general, it is a significant and practical way to make progress in the evaluation of sorghum germplasm. Wang et al. [p < 0.01) between molecular and phenotypic diversity matrices, concluding that the clusters of accessions formed based on the SSR markers data did not coincide with the clusters based on the phenotypic data, suggesting that the molecular diversity analysis provided better results. According to Geleta et al. [The low correlation between the phenotypic and the molecular diversity matrices should not be considered as a limitation to access the genetic diversity but as an indicative of the complementarity of these tools ,48,85. Msterilis . In sorgsterilis ,51. For g et al.  conductea et al. , the besBreeding populations exhibiting high genetic variability are required for the success in selecting individuals with favorable genotypes for a given trait . The knoExpressive contribution of the morphological traits was observed (53.1%) to the phenotypic diversity analysis, and these traits represent a simple way of measuring genetic diversity while studying genotype performance under normal growing conditions . MoreoveSome sweet sorghum accessions used in this study were also previously used in other genetic diversity studies , 30. ForAccessing the genetic diversity of potential parental lines by phenotypic and molecular characterization can provide valuable information in order to help breeders to identify promising crosses in a commercial hybrid breeding program . Sweet sPhenotypic and molecular characterization revealed the existence of considerable genetic variability between the sweet sorghum accessions from the Embrapa Maize and Sorghum breeding program. The clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters identified in the phenotypic diversity analysis. The population structure revealed by the PCA based on the SNP markers data was consistent with the genealogy, the historic background of the sweet sorghum lines, and, as expected, with the clusters obtained by the Neighbor-Joining method using the SNP markers data. A low correlation was observed between the molecular and the phenotypic diversity matrices, which highlight the complementarity between the molecular and the phenotypic characterization to assist a breeding program.S1 TableAdditional information is provided for each accession according to the source, pedigree, place of origin and registration year. Accessions were classified as LIS (Landrace World Collection—ICRISAT sorghum collection), LMN (Landrace Meridian Mississippi—USDA sorghum collection), LSSM (Landrace Sorghum Seed Montpelier—CIRAD sorghum collection), ML (Modern Line), ML—EMBRAPA (Modern Line EMBRAPA) and HL .(DOCX)Click here for additional data file.S2 TableSorghum bicolor descriptors for cultivar registration purposes, based on the “Instructions for the Execution of Distinctness Tests, Homogeneity and Stability of Sorghum Cultivars”.Morphological descriptors were selected according to the list of (DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableNumber of SNPs before and after data filtering for a minor allele frequency (MAF) of 5% and a maximum of 5% of missing genotypes per locus, final chromosome coverage and final marker density.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


The Internet currently enables unprecedented ease of access for direct-to-consumer (DTC) genetic testing, with saliva collection kits posted directly to consumer homes from anywhere in the world. This poses new challenges for local jurisdictions in regulating genetic testing, traditionally a tightly-regulated industry. Some Internet-based genetic tests have the capacity to cause significant confusion or harm to consumers who are unaware of the risks or potential variability in quality. The emergence of some online products of questionable content, unsupported by adequate scientific evidence, is a cause for concern. Proliferation of such products in the absence of regulation has the potential to damage public trust in accredited and established clinical genetic testing during a critical period of evidence generation for genomics. Here, we explore the challenges arising from the emergence of Internet-based DTC genetic testing. In particular, there are challenges in regulating unaccredited or potentially harmful Internet-based DTC genetic testing products. In Australia, challenges exist for the Therapeutic Goods Administration, which oversees regulation of the genetic testing sector. Concerns and challenges faced in Australia are likely to reflect those of other comparable non-US jurisdictions. Here, we summarize current Australian regulation, highlight concerns, and offer recommendations on how Australia and other comparable jurisdictions might be more proactive in addressing this emerging public health issue. Sample collection kits can be posted directly to the consumer without involvement from any health professional. Internet-based DTC genetic tests vary in price, quality, and genetic content measured, ranging from “recreational” testing (A direct-to-consumer (DTC) genetic test is any DNA test for a medical or non-medical trait that provides interpretation or communication of test results directly to a consumer, rather than via a health professional. DTC genetic tests are often accessed  testing  to retur testing . Online  testing , 3. Ther testing –6 and arOnline DTC genetic tests are generally delivered in the absence of genetic counseling or medical oversight. Some consumers with DTC test results are now looking to general practitioners or clinical genetic services for assistance with interpretation or management of DTC genetic findings, posing an emerging challenge for the medical community , 7.via the Internet from any country, essentially by-passing local testing regulations in non-US countries. Some online DTC tests, if sold locally in non-US jurisdictions, would be in violation of local guidelines for genetic testing. However, direct access via a global online marketplace creates challenges for non-US authorities in enforcing local regulations on Internet-based products.Many online DTC genetic tests originate in the USA, where the Food and Drug Administration (FDA) has ongoing challenges in maintaining regulatory oversight . Online How local jurisdictions, such as Australia, the UK, and Europe, should approach regulation and quality control of Internet-based genetic testing is uncertain –12. The via the Internet, but are not subject to any Australian regulation.Under current Australian regulation, there is a strict regulatory regime governing the registration and provision of human genetic tests offered by Australian companies –17. FurtConsumers may have difficulty distinguishing between locally accredited Australian products and unaccredited, offshore products marketed online. The inability of local authorities such as the Therapeutic Goods Administration (TGA) to regulate online DTC genetic testing and advertising leads to a multitude of regulatory, medical, and ethical concerns, which are set out below and summarized in Table Although stringent standards apply to genetic testing conducted in Australia, the TGA and other regulators are not empowered to prevent access to or regulate the quality of Internet-based DTC genetic tests conducted overseas. Similar issues are faced by other international regulators , with isThere is evidence consumers of Internet-based genetic tests are increasingly seeking the advice of general practitioners or clinical genetics services for interpretation of results . This riFurthermore, consumers may have difficulty in distinguishing between established locally accredited clinical genetic testing services , versus cheaper online options not subject to the same quality measures. This has the potential to confuse consumers and may compromise long-standing efforts of local genetic services .Consumers purchasing DTC genetic tests may be motivated by curiosity, ancestry, or recreational motivations rather than medical reasons. However, they may uncover serious medical risk factors, non-paternity, or other unexpected genetic information in the process of testing, without having considered the implications beforehand , 26. In Genetics services providing clinical testing in Australia follow international guidelines regarding the evidence required to substantiate medical risk information before it is provided to the consumer . Model gSome DTC tests return genetic risk information for untreatable conditions prior to symptom onset, such as the APOEe4 risk allele of Alzheimer’s disease . AlthougThe increasing number of consumers providing DNA samples to online companies also raises concerns around the privacy of genetic data. Recent studies have shown that many online DTC companies do not consistently meet international guidelines regarding data use and privacy , and conGiven the growing fascination with genetic testing, it is inevitable consumers will continue to seek Internet-based DTC products. The demand for cheap, Internet-based DTC genetic testing may also be fueled by the lack of access to, and cost of, locally accredited clinical genetic testing options in some countries, especially those with publicly funded health systems .There is currently no international association tasked with regulating the online DTC market. The Global Alliance for Genomics and Health  is develUnless governments take steps to inform consumers of the dangers of some online DTC genetic testing products, or provide alternative testing pathways, it is likely that consumers will continually have difficulty distinguishing between quality  and non-quality (unaccredited) online products. Many consumers may choose low-priced, low-quality tests and therefore be vulnerable to many of the medical, ethical, and privacy concerns. The potential for confusion, unexpected outcomes, and harm will increase and could threaten the public perception of genomics at a critical time. It is vital that public faith and engagement are safeguarded during the ongoing period of evidence generation for implementation of genomic medicine.In the future, the concept of governments or public health systems providing access to universal, population-wide genomic screening for disease prevention needs to be considered. This would provide an alternative testing pathway to unregulated Internet-based DTC testing accessed through the private sector. It would ensure stronger quality control, appropriate informed consent, and implementation of evidence-based prevention following national screening principles . A recomPromote education of the public regarding DTC genetic testing, including publicizing warnings in prominent and widely accessed media about risks of unaccredited online DTC genetic testing products.Publicly endorse any local or international companies whose genetic tests meet local accreditation standards, though an easily recognizable accreditation icon, so that consumers can readily identify valid and approved tests.Amend current regulations so that personal importation of unaccredited genetic tests is not sanctioned.Prohibit Internet advertising of non-accredited offshore tests and engage with overseas regulators regarding strategies for regulating advertising of, and access to, online tests.Implement compulsory guidelines requiring the application of evidence requirements for interpretation of genetic tests before the return of results to consumers.Consider a proof-of-concept study to pilot the development of a low-cost, publicly funded, population genomic screening program for young adults, linked with the health system, accompanied by education, focused on the delivery of evidence-based, medically useful risk information for those who seek it.We recommend the Australian government and comparable jurisdictions take the following steps:The implementation of these recommendations would require significant allocation of resources by the government, both toward regulation of online tests and steps toward building a health system capable of undertaking population genomic screening, including scaling of genetic counseling and other medical services. Significant feasibility studies and health-economic modeling will be required before this can become a reality.via the online marketplace regardless, especially for recreational purposes such as ancestry testing, which have limited potential for harm. However, for medical risk information, there are more complexities to consider.The future landscape of genetic testing in countries with strong public health systems, such as Australia, remains uncertain. Many individuals will continue to seek DTC testing The prospect of a national genomic screening program in Australia to identify actionable genetic risk in consenting adults could be considered. This could potentially identify preventable disease risk early, which if linked to public health system services, could enable closer and more appropriate medical, scientific, and ethical oversight for mainstreaming of genomic testing. A public health screening strategy would ensure those genuinely at-risk are identified and offered appropriate clinical genetic services when needed. Under this model, only established actionable genetic findings, supported by clinical guidelines and standard-of-care for preventable disease, would be disclosed . This may make interpretation of genomic results and subsequent medical risk assessments more achievable.Screening could be accompanied by national education and genomic literacy programs. These efforts may deter people from seeking unaccredited DTC testing products online for medical disease risk assessment and encourage appropriate management for those genuinely at risk. The prospect of genomic population screening linked to a public health system would require significant bolstering of Australian clinical genetic services, far beyond the current scope. This would need substantive increases in public funding and infrastructure. Steps in this direction will need to be considered as the wave of consumers turning to DTC testing will continue to rise in coming years.The Internet, combined with an increasing public fascination in genomics, is currently resulting in an unprecedented access to genetic testing. This will continue to rise and present new challenges for nations in regulating testing and interpretation services. It is likely a pro-active and forward-thinking approach to regulation will be required.JT drafted the initial manuscript. JT and PL contributed to each draft of the 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. JT provides legal consulting services to genetic testing companies.


Agronomic management of plants is a powerful evolutionary force acting on their populations. The management of cultivated plants is carried out by the traditional process of human selection or plant breeding and, more recently, by the technologies used in genetic engineering (GE). Even though crop modification through GE is aimed at specific traits, it is possible that other non-target traits can be affected by genetic modification due to the complex regulatory processes of plant metabolism and development. In this study, we conducted a meta-analysis profiling the phenotypic consequences of plant breeding and GE, and compared modified cultivars with wild relatives in five crops of global economic and cultural importance: rice, maize, canola, sunflower, and pumpkin. For these five species, we analyzed the literature with documentation of phenotypic traits that are potentially related to fitness for the same species in comparable conditions. The information was analyzed to evaluate whether the different processes of modification had influenced the phenotype in such a way as to cause statistical differences in the state of specific phenotypic traits or grouping of the organisms depending on their genetic origin . In addition, we tested the hypothesis that, given that transgenic plants are a construct designed to impact, in many cases, a single trait of the plant , the phenotypic differences between domGE and domNGE would be either less (or inexistent) than between the wild and domesticated relatives (either domGE or domNGE). We conclude that (1) genetic modification (either by selective breeding or GE) can be traced phenotypically when comparing wild relatives with their domesticated relatives (domGE and domNGE) and (2) the existence and the magnitude of the phenotypic differences between domGE and domNGE of the same crop suggest consequences of genetic modification beyond the target trait(s). Bacillus thuringiensis (Bt crops) (i) . The effectiveness of traditional practices, or human selection, is possible because the selected traits have a genetic basis that are phenotypically expressed in particular agroecological and cultural environments . Consequt crops)  and otheops) (i) . Thus, tops) (i) .Solanum tuberosum), soybean (Glycine max), and wheat (Triticum aestivum) , or through genetic engineering (domGE) have phenotypic effects that may not correspond, in magnitude, to the associated genetic changes . Some ofestivum) . Some ofestivum) .Oryza sativa), canola (Brassica napus), sunflower (Helianthus annuus), pumpkin (Cucurbita pepo), and maize (Zea mays)  consequences are of particular relevance if we consider that most of the modifications are done in economically important crops. As such, unintended changes in phenotypes have been observed in crops that are key for global food production, such as rice (ea mays) . Moreoveea mays) , and repea mays) .in situ conservation of agrobiodiversity is of key importance, including not only phenotypic and genetic diversity, but also the accompanying management practices and the environmental context that allows future adaptation (including wild relatives)  . Therefolatives) .Although the phenomena of the unintended effects of genetic modification have been widely reported, these observations are the product of many individual studies. Thus, we propose a meta-analysis profiling approach in order to perform an unbiased analysis with high statistical power. Meta-analysis profiling allows for the integration of large quantities of data in order to identify patterns among different studies that share a common theoretical framework, but that have been conducted independently . This ap®, GoogleScholar®, and UNAM theses databases. For Scopus® and GoogleScholar® databases, we employed Boolean operators for each crop, such as “Cucurbita [AND] wild [OR] domesticated [OR] GMO [AND] fitness.”In order to determine whether genetic modifications have unintended phenotypic consequences in plants, we identified suitable studies for our analysis by looking for articles published in agricultural and ecological journals, as well as in the thesis database for the National Autonomous University of Mexico (UNAM) for the case of maize. We focused on five of world’s most economically important species: rice, canola, sunflower, pumpkin, and maize. We searched for information in the Scopus1 and were obtained from the National Autonomous University of Mexico theses database. We applied these criteria rigorously, rejecting hundreds of comparisons that did not satisfy all of them.To be included in the database, all publications had to satisfy three selection criteria: (1) An estimate of plant fitness between wild relatives and domesticated varieties with and without GE must have been measured; (2) Tests must have been performed under conditions in which the agent of selection was absent; and (3) The genetic background must have been controlled to minimize differences affecting the fitness traits being measured. In cases where experiments included extreme biotic and/or abiotic conditions , only the data of the controls were used, since we considered these treatments as perturbations and not as natural environmental variation. In the case of maize, we also used information from thesis reports in which a yield comparison between wild relatives and domesticated relatives was performed. All thesis reports had gone through a peer review process Table 1. The data reported in the articles were collected for the period 1990–2017, representing the timeframe of the first release of genetically modified organisms to date. Although the available literature sometimes reports more phenotypic traits, only six were chosen in the analysis presented here: height (cm), number of flowers, days to flowering, number of seeds, pollen viability (%), and number of fruits. Those traits were chosen because they are functional traits that have potential impacts in survival and reproduction of the plants  as a post hoc test in the R Multcomp package (R Fsmb package (R MASS package (R program (version 1.17.15) . In the cases where the  package . A graph package . In addi package  using th package . All the1.17.15)  and all The results of the 990 comparisons show significant phenotypic differences among the three categories  for almost all of the analyzed crops and the majority of the traits. With regard to outlier management, the number of points that lie outside the normal distribution was significantly less than the total number of comparisons for each crop. In the case of canola, the outliers represent 2% of the total comparisons, for sunflower 1.8%, for rice 4.3%, and for maize 12%. In the case of pumpkin, we did not find any outliers.Figure 1 and Supplementary Figure The differences between wild relatives and the domesticated categories (either domNGE or domGE) were expected, but unexpected differentiation between the domesticated categories (domNGE and domGE) was also observed in the analyzed traits (which were not the target of selection or genetic modification). Since the proportion of outliers within the dataset is relatively small, this general pattern observed in the results is maintained regardless the outlier treatment, with only some specific differences per crop  panels]. These three groups are different in size, position, and/or direction along the axes of the DA. In some cases, the overlapping of the groups is larger than in others. For instance, in canola, although the groups can be differentiated, the overlapping of the three groups is the largest compared with the other analyzed crops  = 1.541, p = 0.166), while in maize  = 8.5571, p = 1.058e-07) and rice  = 11.284, p = 2.868e-11) the overlapping is the smallest of all, with pumpkin  = 13.357, p = 1.066e-08) and sunflower  = 4.1348, p = 0.00081) in an intermediate range of overlapping . Moreover, the percentage of variation explained by the discriminant axes varies considerably among crops, with the most extreme cases being maize and sunflower. For maize, the total phenotypic variation is distributed in the two discriminant axes , while in sunflower and canola, the variation is mainly explained by LD1 . A more detailed analysis of the DA results shows that the dispersion of the phenotypic variants within groups is, in most cases, larger in wild groups than in domesticated ones (domNGE and domGE) . The only case where the phenotypic variation found in the wild group was less than that found in the domGE groups was in sunflower.Through the DA of the phenotypic traits of all crops , we found a clear distinction of phenotypic variation in three groups, which correspond with the wild, domNGE, and domGE categories . This observation implies that the traits that define the phenotypic variation within each group are different, at least between wild and domesticated categories . In fact, in almost all the cases, the phenotypic variation of the domesticated groups goes in the same direction, while the wild group is almost orthogonal, and more evenly distributed between the two axes. This observation holds for all of the five analyzed crops.The DA results show a change in the direction of variation between wild and domesticated (domNGE and domGE) categories .The GLM analysis identifies the traits that explain pairwise differences in phenotypic variation among groups and the results are shown in the (c) panels of Figure 1, panels (a) and (c), respectively], we find evidence that, overall, supports these expectations for the comparison of wild and domesticated categories (domNGE and domGE), but that do not hold for the comparisons between domesticated categories (domNGE vs. domGE). A graphical representation of these results, showing only mean values for all traits and populations, is found on Figure 1, (b) panels.The normal sequence of reduction of genetic  variation in the process of domestication and human interventions suggests that wild relative populations represent the largest pool of diversity, which is then reduced during domestication and genetic modification through GE . MoreoveFigure 1, (c) panels], which investigates pairwise differences between wild and domesticated groups (Wild-DomPp), show that rice, pumpkin, and maize have statistically significant differences for almost all traits.Regarding the comparison between the wild and domesticated (domNGE and domGE) categories, we observe that only canola fits the expectation of subgrouping. In contrast, regarding the reduced phenotypic variation of domesticated groups compared with their wild relatives, 4/5 analyzed crops fit the expectation (sunflower was the exception). These four crops  show that, although the phenotypic variation is reduced in the domesticated groups, this is not a subgroup within the wild group. The exceptional case, of sunflower, shows that domGE groups have increased phenotypic variation compared with both domNGE and wild relative groups. The results of the GLM . For rice, we found differences between domesticated groups in “pollen viability” and “height;” for canola we found differences in “pollen viability;” for pumpkin the differences were found in “number of fruits” and “flowers;” finally, in maize we found statistical differences in “days to flowering,” “height,” and “number of seeds.” Overall, these results suggest unintended phenotypic effects, and no consistency in the specific traits that change due to human interventions in wild populations, either through domestication or GE modifications.Regarding the comparison of domNGE vs. domGE, we observe that, on the one hand, rice and canola are cases in which the results show some subgrouping of domGE within domNGE populations. On the other hand, maize, sunflower, and pumpkin represent almost the opposite scenario, with almost no overlap, nor subgrouping of the domGE within domNGE populations. Regarding the expectation of reduced phenotypic variation in domNGE, we observe a case of increased phenotypic variation, and specifically we found that domGE groups of sunflower have more variation than their domNGE relatives. Moreover, we also found statistically significant differences in the pairwise comparisons between domNGE and domGE groups in almost all crops and traits . NonethThe differentiation of wild and domesticated populations was expected due to the genetic changes that occur in the evolutionary process of domestication. The genetic changes can be the result of genome level modifications , but also can be the result of more localized effects associated with genetic linkage of selected regions  . The pheZYMV) , and that the modifications are allegedly directed to modify specific phenotypic traits not included in the present analysis, it was expected that the phenotypic variation of the domGE would be a subgroup of that in the domNGE group. This expectation is based on the premise that GE works usually with foreign DNA in order to introduce traits that are not present in the species, and this is performed in isogenic hybrid lines; thus, theoretically, the only differences between a Genetically Modified Organism (GMO) and its isogenic line will be the added trait(s) . HoweverGenerally, the intended effects of a genetic modification refer to a specific phenotype. But the transgene may also impart a range of phenotypes that constitute the unintended effects of the transgene. These new (unintended) phenotypes can appear due to the interaction of the transgenes with another genes (pleiotropic effects)  or by poIn addition to pleiotropy, other phenomena related to the genetic modification, such as position effects, that result from non-directed insertions of DNA fragments  in the target genomes can occur . These pIn maize, phenotypic variation of domGE is reduced and shifted along LD1 compared with domNGE, which is worth noting given the potential implications of introgression (gene flow) with domesticated non-transgenic populations  that in Figure 1, panels (a) and (b)], are not all statistically significant in the GLM pairwise comparisons between groups . In particular, for sunflower, there is no statistically significant result for the GLM analysis, although differences among populations can be observed in both DA and spider chart analyses. This apparent inconsistency could be rooted in some fundamental reflections about evolutionary processes. For instance, it is known that genetic variation, even if not reflected as statistically significant differences between populations, can be of evolutionary significance (Gossypium hirsutum) . However, it is worth noting that these differences, although evident through DA and spider charts . AlthouIn addition, the results presented here show how human interventions in plant populations have different consequences in reducing and changing the direction of phenotypic variation to produce food. Historically, these strategies have proven to be effective, but it is worth reflecting on the unintended effects that some interventions can have in these natural resources that might reduce our options to adapt in the future. The reflection on the strategies to follow in this adaptation to future climate change conditions must include a revision of the regulations of crop technologies given the major consequences that this can have in global food security . For exaFinally, and in the context of climate change, there is an undeniable urgency to adapt to future uncertain conditions . HoweverThe results presented show how human interventions in plant populations have different consequences in reducing and changing the direction of phenotypic variation to produce food. In particular, we found that (1) genetic modification (either by selective breeding or GE) can be traced phenotypically when comparing wild relatives with their domesticated ones (GE and NGE), and (2) the existence and magnitude of the phenotypic differences between domGE and domNGE of the same crop suggest consequences of genetic modification beyond the target trait(s). Further studies documenting phenotypic changes in human modified crops must include as many traits as possible, preferably non-target traits, to design interventions that do not compromise the decision spectrum in the face of trade-offs for adaptation to current versus future conditions.AH-T: designed the research, did the analysis, and wrote the manuscript. AW: designed the research and wrote the manuscript. MB: designed the research and revised previous versions of the manuscript. RL: designed the research. AE: designed the research and wrote the 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.