The African livestock sector plays a key role in improving the livelihoods of people through the supply of food, improved nutrition and consequently health. However, its impact on the economy of the people and contribution to national GDP is highly variable and generally below its potential. This study was conducted to assess the current state of livestock phenomics and genetic evaluation methods being used across the continent, the main challenges, and to demonstrate the effects of various genetic models on the accuracy and rate of genetic gain that could be achieved. An online survey of livestock experts, academics, scientists, national focal points for animal genetic resources, policymakers, extension agents and animal breeding industry was conducted in 38 African countries. The results revealed 1) limited national livestock identification and data recording systems, 2) limited data on livestock production and health traits and genomic information, 3) mass selection was the common method used for genetic improvement with very limited application of genetic and genomic-based selection and evaluation, 4) limited human capacity, infrastructure, and funding for livestock genetic improvement programmes, as well as enabling animal breeding policies. A joint genetic evaluation of Holstein-Friesian using pooled data from Kenya and South Africa was piloted. The pilot analysis yielded higher accuracy of prediction of breeding values, pointing to possibility of higher genetic gains that could be achieved and demonstrating the potential power of multi-country evaluations: Kenya benefited on the 305-days milk yield and the age at first calving and South Africa on the age at first calving and the first calving interval. The findings from this study will help in developing harmonized protocols for animal identification, livestock data recording, and genetic evaluations  as well as in designing subsequent capacity building and training programmes for animal breeders and livestock farmers in Africa. National governments need to put in place enabling policies, the necessary infrastructure and funding for national and across country collaborations for a joint genetic evaluation which will revolutionize the livestock genetic improvement in Africa. African livestock sector plays an important role by contributing to the livelihoods of households as well as food, nutrition security and health. According to FAOSTAT , Africa’Phenotypes play an important role in understanding the genetic basis of livestock performance and are essential in informing and ensuring effective herd and flock management . PhenomiStudies have shown that accuracy of predicted genetic merits of animals, and hence the genetic gains that could be realized, depend to some extent on different genetic models used for estimation . PhenomiHowever, in most African countries, phenotyping or performance recording has always been a major challenge due limited investments in on-farm recording . LivestoThe aim of this study was therefore to assess the current status of phenomics and other data systems and recording, livestock genetic evaluation approaches in Africa and to demonstrate the effects of various genetic models on the accuracy and rate of genetic gain that could be achieved in livestock.A survey was carried out in 2020 using an online questionnaire, telephone and skype interviews. The questionnaire recipients included: 1) current Food and Agriculture Organization of the United Nations (FAO) national focal points for animal genetic resources, 2) individuals identified from lists of participants of scientific conferences and workshops on livestock production and genetics held in Africa from 2000 to 2019. The conferences and workshops were randomly selected and all the names on the delegate lists were contacted as respondents. The survey questionnaire was sent to 501 respondents from the 54 African countries. The contacted delegates were predominantly scientists and other professionals in animal/veterinary science, animal breeders and geneticists affiliated with government livestock ministries, research institutes, universities, farmers associations and NGO’s . The e-survey was active for a period of 68 days. A reminder e-mail was sent every 15 days after first dispatch. The survey questions were a combination of open-ended, close-ended, structured and unstructured questions . The maiThe data used for joint genetic evaluation was reported by the respondents. Thus, performance and pedigree data were obtained from the Agricultural Research Council (ARC) in Pretoria, South Africa and the Kenya Livestock Breeders Association (KLBA), Kenya. In summary, the data comprised 305-day milk yield (MY305) records from 2,333 and 25,208 first lactation of Holstein-Friesian cows in Kenya (1979–2014) and South Africa (1997–2014), respectively. The pedigree data comprised 103 and 505 sires with daughter performance records in Kenya and South Africa, respectively. The common sires between both countries were 40. The reproduction traits were age at first calving  and the interval between first and second calving .Survey data were analysed by country and region using descriptive statistics tools of the R software package . Proport:In the joint-genetic analysis, a multi-trait animal model was used in Blupf90  to analyYijk = an observation of MY305, AFC or CI1; Hj = fixed effect of herd j in which animal i was born; HYSk = fixed effect of the kth herd-year-season of production; age (cov) = age as covariate; ai = random additive genetic effect of animal i; and eijk = random error term. The calving age was not included in the analysis of AFC. The across-country analysis was implemented by pooling data from the two countries using the model similar to Eq. Where: Where R: Response to selection or predicted genetic gain per generation, i: Selection intensity, ρ: Accuracy of selection (square root of reliability of sire EBVs), σ g: genetic standard deviation of studied traits. The R was based on sire selection only within- and across country . Differep < 0.05) followed by branding (17.6%), ear notching (16.3%), ear tattooing and number tagging (11%) while the tribal signs (0.4%) and hindquarter tattooing (0.4%) were the least frequent animal identification methods. Only 18.5% of the respondents mentioned existence of national animal identification system (NAIS) in their respective countries . No NAIS was mentioned in Central Africa. However, most of the mentioned NAISs are still rudimentary based on ear tags. Furthermore, it was reported that farmers are not interested to participate in the national animal identification system. The NAISs reported by the respondents are described below.At the herd level, Ear tagging (33.1%) was the most used animal identification method (in vivo) and in National Gene Bank (in vitro). In Morocco, the electronic chips identification for goat and sheep is managed by the ministry of agriculture. All goat and sheep farmers are recommended to use E-chips for identification of their animals. Horses are ear-tagged. In Tunisia, two NAISs were reported: the identification system for cattle, sheep, goat and camel based on ear tagging and managed by “Office de l' Elevage et des Pâturages (OEP)” and the identification system for horses based on electronic chips and managed by The National Foundation for the Improvement of The Horses Breed (FNARC). In Nigeria, the animal identification system is not well developed. However, there is a recent Livestock24 programme (https://livestock247.com/) that tracks animals from farm, livestock market and slaughter houses in Nigeria. In Tanzania, the Tanzania National Animal Identification and Traceability System  has been recently developed. Thus, ear tagging or branding of unique national ID has been introduced but is yet to be practiced by all farmers. TANLITS is a web-based platform developed to drive the animal identification, registration and traceability Act Chapter 184 of the Tanzania laws and its regulations. The purposes of TANLITS include controlling livestock theft and animal diseases, to regulate movement of livestock, enhancing food safety assurance and promote access to livestock markets. In South Africa, systematic animal identification of cattle was reported by respondents. The Department of Agriculture, Land and Rural Development manages the South African NAIS. Farmers are given a branding criterion and from age of 7 months any cattle born in South Africa needs to be branded. The South Africa Studbook Association also manage animal identification through the registration and recording of the birth and ownership information of purebred animals and continuously update these animals’ pedigree information. In Mozambique, a national ear tag identification system was reported where a combination of letters (indicating year) and numbers indicating order of birth in the group are used to tag animals. Some units also use tattoos. However, the majority of farmers in Mozambique do not identify their animals. In Zimbabwe, a NAIS exists for Tuli cattle and is managed by the Livestock Identification Trust (LIT). Farms pay for tag and the LIT generates tag numbers. Tags give traceability and are specific to each farm. Each region with its own brand and each farm within a region with its own ID. The reported livestock data in Africa and the custodians are described below.In Egypt, animal identification is limited to state organisations and farms. This is part of the preservation of the purebred and developed chickens in some governmental institutional stations and projects (p < 0.01). The available data per species and regions are shown in From the survey, 58.7% of respondents mentioned ongoing livestock genetic improvement programmes or projects with performance and pedigree and/or genotypic data available in their respective countries . Out of the positive responses, Eastern Africa (76.2%) and Southern Africa (75%) had the highest proportions of ongoing genetic improvement initiatives followed by Central Africa (60%), Western Africa (52.6%) and the Northern Africa (45.4%) had the lowest (p < 0.001). The most recorded traits in Western Africa were growth traits (23.7%) followed by reproduction traits (19.7%) and pedigree data (17.1%). In Eastern Africa, milk traits (19.2%), growth traits (19.2%), reproduction data (16.7%) and pedigrees (15.4%) were the most reported. In Northern Africa, reproduction data (27.8%), pedigree (22.2%) and Growth traits (16.7%) were the most reported while pedigree data (29.8%), growth traits (19.1%) and reproduction data (12.8%) were the most reported in Southern Africa. In Central Africa, milk traits (25%), reproduction data (16.7%), health data (16.7%) and economic data (16.7%) were the most reported.The reported data were recorded mostly on dairy cattle (22.9%) followed by beef cattle (19.1%), chicken (16.8%), sheep (16.8%), goats (16%), pigs (5.3%), guinea fowl (2.3%) and horses (0.8%). In Western Africa, chicken was the most reported specie with production and pedigree data while dairy cattle, beef cattle and chicken were the most reported in Central Africa. Beef cattle was the most reported in Southern Africa. Dairy cattle and sheep were the most reported in Eastern and Northern Africa respectively. As shown in p < 0.001) as shown in The most important custodians of the reported data in Africa were research institutes (48.4%) followed by government (26.6%), universities (7.8%), NGOs (6.3%), breeders/farmers association (6.3%), industries (3.1%) and individual farmers (1.6%), with significant differences across custodians (p < 0.001). A similar trend was observed within each region, except in Central Africa where selection was based only on phenotypic performance data. Most respondents (90.7%) who have reported availability of livestock data believed that it is a good idea to share such data. Out of this number, 74.1% opined that the custodians of the reported data would be willing to be supported to use the same data to undertake better genetic evaluations. However, some (25.9%) of the respondents believe that the custodians may not be willing to be supported.The livestock genetic evaluation methods being used in Africa are shown in Only 13% (12 out 92) of the respondents mentioned ongoing national animal ranking systems (NARS) in their respective countries  as presented in The potential for multi-country genetic evaluation in Africa was assessed through the survey. Most of the respondents (92.4%) believe that across-country genetic evaluation will have some mileage in improving livestock production in Africa. According to these respondents, the reasons for a potential success in across-country genetic evaluation included 1) sharing of resources and benefits (42.4%), 2) existence of transboundary breeds across countries and regions (34.1%), 3) large reference population will give high accurate predictions making genomic selection possible (15.3%), 4) similar environmental conditions and breeding challenges across countries (8.2%), 5) high and fast genetic gain (4.7%), 6) existing high livestock genetic diversity in Africa (4.7%), 7) capacity of African diaspora already involved in across country genetic evaluation (3.5%) and 8) willingness to implementing multi-country genetic evaluation in Africa (1.2%) were expressed. However, some of the respondents (7.6%) mentioned that multi-country genetic evaluation would not work in the African livestock production systems, mainly because of: 1) limited genetic material exchange and hence lack of herd connectedness between countries (42.9%), 2) lack of functional national animal identification, data recording and evaluation systems (42.9%), 3) lack of required infrastructure for across country genetic evaluation (28.6%), 4) limited international collaboration  in Africa (14.3%), 5) lack of human capacity (14.3%), 6) lack of enabling breeding policies for across country genetic evaluation (14.3%) and 7) scepticism and concerns about data sharing (14.3%). Two respondents mentioned that multi-country genetic evaluation for poultry, pigs and small ruminants can be challenging, costly and not feasible.Using the reported dairy performance and pedigree data from Holstein-Friesian in South Africa and Kenya, we assessed the impact of different genetic evaluation approaches and models, on accuracy and the rate of genetic progress that could be achieved for 305-day milk yield (MY305), age at first calving (AFC) and first calving interval (CI1) , 3. The p < 0.05). The lack of human capacity in livestock genetic evaluation was the main challenge in Eastern Africa (85.7%), Southern Africa (66.7%) and Western Africa (60.5%). The major challenge in Northern Africa and Central Africa were poor animal identification and data recording (54.5%) and lack of government funding for livestock data recording and genetic evaluation (60%), respectively. The respondents mentioned the following challenges to potential implementation of multi-country genetic evaluation in Africa: 1) human capacity for multi-country genetic evaluation and management (28.4%) 2) differences in national breeding policies (26.1%), 3) lack of framework for joint funding for such an initiative (21.6%),4) required infrastructure for across country genetic evaluation (20.5%), 5) absence of standardization of data collection tools and methods (17%),6) differences in breeding goals/traits of interests among countries (15.9%) and 7) data access and benefits sharing issues across countries (14.8%), among others.Respondents identified the main challenges affecting livestock data recording and genetic evaluation in Africa. The challenges varied across countries and regions. At the continental level, the following challenges were cited: 1) lack and/or inadequate human capacity and skills in livestock genetic evaluation (60.9%),2) lack of required infrastructure for livestock data recording and genetic evaluation (42.4%), 3) inadequate and lack of governmental funding for livestock genetic evaluation (39.1%), 4) lack of enabling animal breeding policies (32.6%), 5) poor animal identification and data recording (23.9%), and 6) lack of systematic animal performance and pedigree data recording (22.8%) among others. There was significant differences in the reported challenges across the five African regions , and the CBBP for goats in Tanzania, Uganda and Malawi. The successful data recording by ADGG and the CBBP programmes were due to innovative use of mobile technologies and ICT tools as reviewed by As observed in the present study, South Africa has a well-established cattle data recording and ranking system. It has been previously reported that South Africa is the only sub-Saharan Africa country having a sustainable national animal identification and performance recording scheme, as well as routine genetic evaluation programmes . South Ahttp://animalbreeding-africa.org/) could also build on previous efforts to address the human capacity issue through annual and purposely designed short term training courses in animal breeding, quantitative genetics, genomics and bioinformatics. The tailor-made short courses will emphasize theoretical concepts, problem-solving and hands-on training. It is envisaged that, in addition to strengthening of the capacity of scientists AABNet will work with strategically selected individual countries to support the development of long-term genetic improvement programmes, including performance recording, pedigree information, genotyping to support genetic analysis, genomic prediction, inter alia. The case farms identified in target countries as part of the initiative will be supported by the trained scientists who will help them to select and disseminate genetically superior animals during implementation phase of breeding programmes. Systems for animal performance recording that provide feedbacks to farmers need to be developed with supportive policies to enable their large-scale adoption . In addition to solving the inadequacy of human capacity, across country evaluation will enable more rigour and higher reliabilities of the genetic predictions. For example, the current study showed that Kenya benefitted more from selecting sires across different countries than using only its own national sires for genetic evaluation of MY305 and AFC while South Africa benefitted for AFC and CI1. The benefits from across country genetic evaluations in cattle have been well demonstrated in developed countries . In a reAs alluded to above, a multi-country breeding programme based on joint genetic evaluation would be possible when there are genetic links across countries, and would provide a platform for accelerated genetic gains through selection and germplasm exchange between sub-Saharan African countries . HoweverApplication of ICT and mobile devices to record performance data on-farm in dairy cattle and small ruminants is already happening in Africa . In SoutThere is also scope for inclusion of georeferencing of herds to enable connection of collected performance data with related global meta-weather data to estimate the effects of climatic conditions on productivity and hence integrate climate resilience in livestock breeding objectives. In addition, a combination of mobile telephony and collection of e GPS coordinates of farms can provide for a powerful spatial modelling to identify superior animals in smallholder farms , thus ovAvailability of appropriate technical capacity is crucial for functioning genetic improvement programmes. Many African University curricular have not been responsive to the rapid developments in technologies and the opportunities these provide for animal genetic evaluations and overall design and execution of breeding programmes. As alluded to above, the mission of AABNet places it in good place to facilitate the retooling of African animal geneticists and breeders as well as private sector players, including farmers, to equip them with what they need to drive genetic improvement programmes using state-of-the art technologies and tools. One tool approach in this regard could be through organisation of massive open online courses (MOOCs) and annual summer classes. The key topics of the training sessions will focus on definition of breeding goals adapted to African livestock production system systems, hands-on training on inclusion of performance, pedigree, and genomic information in the estimation of genetic merit of animals, breeding programme design, optimisation and simulation. This will help to develop breeding programmes that are sustainable and adapted to the various livestock production systems in the continent. AABNet has recently organised a 3-week training (14 February—11 March 2022) to support 48 participants from 30 African countries. The training mode used included classroom lectures, and hands-on lab sessions covering various topics. A big emphasis on definition of standard and harmonisation animal identification and data recording systems will be needed in future trainings. A lesson learnt from the previous trainings organised in Africa is non-availability of real data from the attendees as well as the short duration of the trainings limiting the acquisition of hands-on skills. To address this challenge, AABNet could invite the participants from the institutions of the custodians of available livestock data (as reported in this study). The custodians of data could nominate participants from their institutions who would bring specific data sets with them to analyse as part of the training. This approach can potentially deliver huge benefits by contributing to the transfer of the acquired skills to the home organisations. After the national genetic evaluation, the across country genetic evaluation could be carried out under the coordination and technical support by the AABNet members—both in the continent and Diaspora—and the results shared with participating countries through processes which also include capacity development to enhance effective use of the results. For across country evaluation, emphasis should initially be on dairy cattle due to the evidence of use of international sires for artificial insemination in different African countries. As also mentioned by This study has highlighted the current status of livestock phenomics and genetic evaluation approaches in Africa, with a focus on the main challenges at the national, sub-regional and continental levels, and possible solutions to these challenges. The challenges identified are related to animal identification and data recording systems, availability of digital tools and ICT not relying on internet, human capacity, infrastructure, funding and animal breeding policies. It is evident that the lack of a robust animal identification and data recording systems as well as human capacity has greatly influenced the choice of selection method, and explains the predominance of mass selection as the method currently being applied by most countries. A case example done as part of this study of joint genetic evaluation of Holstein-Friesian cattle data from Kenya and South Africa resulted in higher accuracy of prediction and genetic gains, demonstrating the benefits of such an approach. Results showed that Kenya benefited from the joint evaluation on the 305-days milk yield and the age at first calving, while South Africa got benefits on the age at first calving and the first calving interval. In addition to demonstrating these benefits, the findings of this study have identified issues around harmonized protocols for animal identification, livestock data recording and genetic evaluations  as well as capacity building and training programmes for animal breeders and livestock farmers in Africa. Other needed enablers identified include policies, appropriate infrastructure and funding. It is concluded that the development of a joint genetic evaluation across African countries could revolutionize livestock genetic improvement in the continent.


C. perfringens within their gut microbiomes compared with individuals who are healthy controls (HCs). Isolates derived from patients with MS produced functional ETX and had a genetic architecture typical of highly conjugative plasmids. In the active immunization model of experimental autoimmune encephalomyelitis (EAE), where pertussis toxin (PTX) is used to overcome CNS immune privilege, ETX can substitute for PTX. In contrast to PTX-induced EAE, where inflammatory demyelination is largely restricted to the spinal cord, ETX-induced EAE caused demyelination in the corpus callosum, thalamus, cerebellum, brainstem, and spinal cord, more akin to the neuroanatomical lesion distribution seen in MS. CNS endothelial cell transcriptional profiles revealed ETX-induced genes that are known to play a role in overcoming CNS immune privilege. Together, these findings suggest that ETX-producing C. perfringens strains are biologically plausible pathogens in MS that trigger inflammatory demyelination in the context of circulating myelin autoreactive lymphocytes.Multiple sclerosis (MS) is a complex disease of the CNS thought to require an environmental trigger. Gut dysbiosis is common in MS, but specific causative species are unknown. To address this knowledge gap, we used sensitive and quantitative PCR detection to show that people with MS were more likely to harbor and show a greater abundance of epsilon toxin–producing (ETX-producing) strains of   C. perfringens is a unique candidate environmental trigger for MS because this bloodborne neurotoxin specifically targets CNS endothelial cells, leading to disruption of BBB integrity (Multiple sclerosis (MS) lesions are centered on postcapillary venules , 2, and ntegrity .perfringens is a gram-positive anaerobe categorized into 7 toxinotypes based on production of 1 or more of 6 major toxins (etx)  . In the e growth –30. Huma mammals –43. As apressure –46. Follpressure .ETX is a member of the aerolysin-family of pore-forming toxins –50. ETX C. perfringens can function as environmental triggers for MS. To characterize the possible role of ETX in MS, we (a) identified an association between ETX-producing strains of C. perfringens and MS in clinical samples; (b) found that people with MS have increased abundance of ETX-producing strains of C. perfringens in their gut microbiome compared with healthy controls; (c) showed that ETX was sufficient to induce multifocal, inflammatory demyelination in the context of active immunization with MOG35-55; (d) demonstrated that ETX-induced inflammatory demyelination in active EAE more closely resembled the lesion distribution observed in MS when compared with the traditional PTX model; and (e) found that, in CNS endothelial cells, ETX induced expression of genes known to overcome CNS immune privilege.In this report, we present evidence that ETX-producing strains of https://doi.org/10.1172/JCI163239DS1). Inclusion into the MS arm of the study required a diagnosis of confirmed MS based on the 2010 revised McDonald criteria , 62 participants were recruited under the IRB protocol: Harboring the Initial Trigger of Multiple Sclerosis (HITMS), consented, and received instruction on self-collection of fecal samples. Participants who were healthy controls (HCs) and those with MS were matched for age, gender, BMI, location of residence at time of fecal sample donation, and ancestry or ethnicity , and, in particular the class Clostridia , with P = 0.0002  was associated with etx status (etx– or etx+), independent of sex, with or without stratification by treatment status (treated or not treated). We found that the disease status was independently associated with etx status, even when adjusting for sex. In addition, the disease status (MS versus HC), with or without treatment, was independently associated with etx status, adjusting for sex  and age-matched controls (16%)  . This suetx in fecal microbiota harvested from people with MS and HC. To do this, we performed quantitative analysis of etx abundance relative to universal 16S by TaqMan real-time PCR using a modified 2–ΔCt method  are unlikely to be involved in MS. TaqMan real-time PCR showed a significant increase in cpa abundance for people with MS compared with people who were HCs  compared with the HC group   with fewer type B  strains detected (C. perfringens enterotoxin (cpe), which is the main agent of C. perfringens–mediated human food poisoning, was not detected. Notably, unlike the more sensitive singleplex PCR  and type B/D strains .The 2018 revised classification scheme for nd netB) . We perfulations  27). Th. ThC. pedetected . C. perfplex PCR , etx andetx-positivity in the individuals who were HCs. Our multiplex PCR result suggested that there was a distinct lack of or weakened etx band in the group of 4 etx+ HC participants  over non-etx–producing strains  from etx+ MS and HC groups. The 2–ΔCt value  culture was used as a calibrator  in the bacterial community. Our results revealed significantly increased 2–ΔΔCt values for the etx/cpa ratio in people with MS compared with people who were HCs , we then obtained a maximum estimate of the average percentage of etx-harboring strains in the C. perfringens community for people with MS or people who were HCs. Our analysis showed that participants with MS who were etx+ had 32% etx-harboring strains and 68% non-etxC. perfringens strains  and an IS1151 transposase upstream of etx , several environmental isolates, including type D strains isolated from ruminants, and our laboratory type B strain. We successfully sequenced a closed 54.5 kb plasmid derived from isolate pSHDS0050 . This plm of etx , but hadly after . The plajugation  and the g system . The plaSAM gene . InteresSAM gene , which sSAM gene . AlthougNCTC8533  80). pS. pSetx lWe next compared the circularized chromosomes of the type D strains CN3842, NCTC8346, and FU17, and the type B strains ATCC3626 and NCTC3110 to ensure that patient strain SHDS0050 was not a collection strain contamination. A GView BLAST atlas plot revealed that the patient-derived strain had multiple unique regions within its chromosome, distinguishing it from the other isolates .C. perfringens type D strain derived from a patient with MS possessed typical type D plasmid architecture, was competent to produce functional ETX, and conceivably could have arisen from a ruminant source.To evaluate production of epsilon protoxin (proETX) and pathogenicity, we cultured SHDS0050, the ATCC type B strain, and a ATCC type F strain in TGY broth under anaerobic conditions and assessed supernatants for proETX by Western blot. The strain derived from a patient with MS produced an approximately 32.9 kDa proETX protein of similar mobility to the ATCC type B strain . The ATCC. perfringens genome (etx genes with coding DNA sequences (CDS) available through the National Center for Biotechnology Information (NCBI) database and performed a comparative alignment analysis. The SNP at site 762 carried the least conserved SNP among the 6 that we identified and was the only SNP present in the coding sequence of activated ETX. The minor allele at the 762 site was a substitution of guanine for adenine (762G), regardless of C. perfringens toxinotype  carried the minor allele at site 762, 762G , ETX at 50 ng/kg body weight (approximately 1 ng per mouse), or ETX at 500 ng/kg body weight (approximately 10 ng per mouse), all delivered i.p.Knowing that bloodborne ETX specifically targets CNS endothelial cells , we want35-55) . Onset, temporal course, and peak clinical deficits were similar for ETX- and PTX-induced EAE when using the classical EAE scoring scale  . In the ng scale . The magng scale . These rETX-EAE groups displayed a wider array of behavioral deficits when compared with PTX-EAE, including ataxia, head tilt, imbalance, axial rotation, and left/right leaning, as captured by the atypical EAE scoring scale . Demyeli+ lymphocytes. While both models induced similar demyelination in the spinal cord, PTX-EAE showed significantly more infiltrating CD4+ T cells in the spinal cord compared with ETX  mice into recipient WT B6 mice 24 hours before induction of active EAE by s.c. immunization with MOG35-55 in CFA. Immunized mice then received either PBS, 10 μg/kg PTX, or 500 ng/kg ETX immediately after immunization and again 48 hours later . Of these DEGs, 595 changed in response to both ETX and PTX treatments , or PTX (5 μg/kg BW) on 2 consecutive days followed by isolation of CNS endothelial cells from spinal cord for bulk RNA-Seq. CNS endothelial cells were isolated from spinal cords, as previously described , 16 houreatments . Comparieatments . Consideeatments . In Figueatments . The oveeatments . Furthereatments .C. perfringens types B or D enter log-phase growth and, thus, is tied to increased abundance of these strains in the gut microbiome  has been proposed as an environmental trigger for MS , potentiC. perfringens was significantly elevated in the gut microbiome of patients with MS. We provided a biologically plausible mechanism by which ETX functions in the multistep process of CNS autoimmunity. Ultimate proof of this hypothesis, as would be the case for any environmental factor, will require a clinical trial to neutralize ETX or to eliminate C. perfringens type B or D in the human host.In summary, we found a strong clinical association between a specific bacterium, its toxin, and a diagnosis of MS. In addition, we found that the abundance of ETX-producing strains of Details on participant recruitment, reagents, and experimental methods are found in supplemental materials.C. perfringens chromosome and plasmid sequences were deposited into GenBank under the following accession numbers : C. perfringens type D CN3842 , C. perfringens type D NCTC8346 , C. perfringens type D FU17 , C. perfringens type D SHDS0050 , C. perfringens type B NCTC3110 , and C. perfringens type B ATCC3626 .t test (for Gaussian distribution) or Mann–Whitney test (for non-Gaussian distribution) were used to compare 2 data sets for statistical significance. For multiple data sets, 1-way ANOVA followed by post hoc Tukey’s multiple comparisons test (for Gaussian distribution), Kruskal–Wallis test followed by post hoc Dunn’s multiple comparisons test (for non-Gaussian distribution), or 2-way ANOVA with Šidák’s multiple comparisons test were carried out to determine statistical significance as indicated. For EAE time course analysis, nonparametric Friedman’s test followed by post hoc Dunn’s test was performed. Pearson’s correlation analysis was performed to examine whether the density of CNS infiltrating lymphocytes correlated with the extent of demyelination. One phase decay model was employed for curve fitting. Multivariable logistic regression was used to test the difference while adjusting for covariates. P values are stated in figures and legends.Statistical analysis was performed using GraphPad Prism  and Microsoft Excel . Data of Gaussian distribution are represented as mean ± SEM or SD, as indicated. Data of non-Gaussian distribution are represented as median ± range. Unpaired 2-tailed The RNA-Seq data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus  and are The HITMS Study, protocol # 1003010940, was approved by the Weill Cornell Medicine Institutional Review Board. All study participants received, reviewed, and signed a written informed consent.The Weill Cornell Medicine IACUC approved all experimental procedures with animals, which were also consistent with the ethical guidelines of the NIH.C. perfringens isolates, and created the plasmid maps for the patient and environmental isolates, as well as having edited the manuscript. JRL generated the antibodies used in the detection of ETX and assisted with the design of the study and analysis of etx detection in fecal samples from MS and HC study participants. She also assisted with obtaining the MS participant isolate, conducted the analysis of CHO cell cytotoxicity, isolated endothelial cells and purified RNA from mice used in the analysis of differential gene expression, assisted with writing the IRB and IACUC protocols, and edited the manuscript. SH assisted in the analysis of fecal samples and purification of isolates. BZ performed standard and TaqMan real-time PCR analyses of fecal samples, and assisted with daily monitoring and scoring of EAE mice in all experiments involving the model. JBG designed, conducted, and analyzed the EAE antigen recall and flow cytometry experiments. PZ conducted the analysis of endothelial cell differential gene expression, and constructed the principal component analysis plot, gene ontogeny plot, and heat maps for endothelial cell differential gene expression. FD assisted in the design, analysis, and strategy for endothelial cell differential gene expression. DB performed Ilumina shotgun sequencing and Nanopore long reads on purified bacterial isolates and edited the manuscript. CPP advised and assisted with CNS endothelial cell isolation and preparation of RNA for bulk RNA seq experiments. KT edited the manuscript. PNW performed Western blot analysis on supernatants from clinical isolates. KRR assisted with design of the study, interpretation of data, and edited the manuscript. SAG assisted with design of the study, developed the RedCap database for entering secure demographic and clinical information on recruited subjects, and edited the manuscript. VAF assisted with the design of the study, interpretation of results, and edited the manuscript. BAM assisted with the analysis and interpretation of the plasmid maps for patient derived strains and environmental strains and edited the manuscript. FAU provided the C. perfringens types B and D environmental isolates., provided background on C. perfringens type D in ruminants, and edited the manuscript. LZ recruited patients for the study and maintained the database. MM assisted with Western blot experiments and figure preparation. RR edited the manuscript. EB edited the manuscript and assisted with antigen recall experiments. DD assisted with plasmid assemblies. JP recruited patients for the study. UK recruited patients for the study. NN recruited patients for the study. ILB edited the manuscript. ZC reviewed the power calculation for the analysis of etx in human fecal samples, multivariable logistic regression, and reviewed all statistical methods. JZX assisted with RNAseq strategy, RNA quality check, library generation, and sequencing. DB supervised and designed the bioinformatics approach for endothelial cell differential gene expression. RD assisted with design of the transcriptional profiling experiments and interpretation of the RNA-Seq data sets and designed the strategy for isolation of CNS endothelial cells from whole animals for bulk RNA-seq. GFS conceptualized, supervised, and interpreted the EAE experiments. CEM designed the sequencing strategy for C. perfringens strains. TV conceptualized and supervised the overall study, conceptualized and designed the analysis of fecal samples for etx detection, conceptualized the EAE experiments, conceptualized and designed the CNS endothelial cell RNA-Seq experiments, analyzed and interpreted data from the etx detection, EAE, and CNS endothelial cell RNA-Seq experiments. They were also responsible for writing and adhering to the IRB and IACUC protocols, and wrote the manuscript. The order of the co–first authors was based on the time and effort devoted to this project.YM conceptualized, designed, and carried out EAE experiments and performed histological and ultrastructural analyses. He conceptualized quantification methods and carried out the analyses of fecal samples by standard and TaqMan real-time PCR. He also wrote the IACUC protocol and edited the manuscript. DS conceptualized, designed, and carried out the purification of bacteria from fecal samples using the Nycodenz method, performed the genomic assembly of the human


Pagellus bogaraveo, Brünnich 1768) inhabits a large geographical range, a condition that might contribute to high resilience to fishing activity. The biology of the species has been patchily investigated in past years, and to date a complete picture of its connectivity across its distribution area (Eastern Atlantic Ocean and Mediterranean Sea) has not been available. We investigated the species’ genetic variability and differentiation at a very large geographical scale by analysing nuclear DNA markers. The absence of genetic population structuring over such a wide area was found, strengthening the hypothesis that egg and larval dispersal are fundamental in sustaining the genetic connectivity of the Blackspot Seabream.The response of marine fish species to external pressures highly depends on their intrinsic bio-ecological traits. Among those species of commercial interest, the deep-water Blackspot Seabream  is a commercially valuable deep-water fish highly exploited over past decades. Considering its exploitation status, deepening the knowledge of intraspecific variability, genetic diversity, and differentiation using high-performing molecular markers is considered an important step for a more effective stock assessment and fishery management. With one of the largest efforts conceived of and completed by countries overlooking the Atlantic and Mediterranean coasts in recent years, a total of 320 individuals were collected from different fishing grounds in the Mediterranean Sea and Atlantic Ocean and analysed using 29 microsatellite loci. We applied multiple statistical approaches to investigate the species’ connectivity and population structure across most of its described distribution area. Considering the incomplete knowledge regarding the migratory behaviour of adults, here we suggest the importance of egg and larval dispersal in sustaining the observed genetic connectivity on such a large geographical scale.Investigations of population structuring in wild species are fundamental to complete the bigger picture defining their ecological and biological roles in the marine realm, to estimate their recovery capacity triggered by human disturbance and implement more efficient management strategies for fishery resources. The Blackspot Seabream ( Genetic structuring in marine populations has been demonstrated to be the result of historical and contemporary interactions among complex scenarios involving bio-ecological, demographic, behavioural, evolutionary, and oceanographic processes ,2,3. ThePagellus bogaraveo, Brünnich 1768) is a large-sized, benthopelagic species common in the Eastern Atlantic Ocean from Norway and Sweden, to Cape Blanc (Morocco), Madeira, Canary, and Azores Islands, as well as in the western and central Mediterranean Sea and the Aegean Sea  calculated on the dataset thus defined corresponded to 3.1% and did not concern any specific location.Significant deviations from HWE, after Bonferroni correction, were observed in all locations  and in 1MICROCHECKER software indicated the presence of Null Alleles (NA), as reflected in the departure from HWE observed across loci. The errors for stuttering (ST) were detected only in PbMS20 for STD, POR, ANB, MLT, and ION locations, in PbMS18 for STD, POR, and TNG locations, and in Pb-OVI-D114 for the KSR location. No evidence of large allele dropout was detected.The number of individuals for each location, the expected (Hexp) and observed heterozygosity (Hobs), mean values of the number of alleles, allelic richness, FIS values, and HWE results for geographical location are summarised in After testing for relatedness between individuals captured in the same location, 92.92% of the individuals were unrelated, while 6.38% and 0.05% resulted in Half and Full Sibling relationships, respectively, but regardless of the geographical origin. No Parent–Offspring correlation was observed across the dataset.The mean value of allelic richness was normalised using the minimum number of individuals within a location . Sample SPA-19 showed the highest value of mean allelic richness (8.87), while TNG showed the lowest (7.95). However, the results indicated high levels of genetic diversity, with an average of 22.20 alleles per locus. The mean number of alleles per locus estimated in each location fluctuated between 15.80 and 8.70 in SPA-19 and GHZ locations, respectively. The results appeared to be related to the sample size, since allelic richness and expected heterozygosity were similar for all locations .ST = 0.005 or higher values, with a probability close to one, for sample sizes and allele frequencies obtained from the samples in this study. At low levels of divergence, 20 microsatellites are more powerful for detecting structuring than 14 microsatellite loci  and that using 14 loci (see below) detected Fite loci . The chiHowever, considering the results of descriptive analyses of genetic diversity, six markers were excluded since NA and deviations from the HWE characterised more than 50% of the 14 sampling locations. Thus, the following analyses focussing on population differentiation and demography were performed on a reduced dataset including 309 individuals and 14 microsatellite loci.ST values between sampling locations, low estimates of this index were highlighted across the dataset (FST ranging between 0.000 and 0.050). Nevertheless, a pattern of genetic differentiation was mainly observed when comparing ANB with STD, KSR, SPA-19, TBK, MZR, and ION  percentage of variation was observed among groups in AMOVA performed for scenario 3. The percentage of variation among locations within groups ranged from 0.10 to 5.17 and was significant for both scenarios 1 and 2. The largest variation was observed within individuals and ranged from 94.48 to 94.64 and was significant in all tested groupings .The results from DAPC analyses were obtained involving fourteen locations and six macro-areas  trend to evaluate the best K according to STRUCTURE HARVESTER. The plot did not show the expected plateau usually displayed when a population structure is present . The plop-value: 0.05) for tail 1, which showed a deficit of heterozygosity.BOTTLENECK outcomes  showed nPagellus bogaraveo, by applying both complete and selected panels of microsatellite loci to geographical samples collected in the main fishing areas of the Atlantic Ocean and the Mediterranean Sea. Here, the choice of the nuclear markers to be applied underwent an experimental workflow that highlighted the failure of cross-amplification of nuclear loci originally isolated from P. erythrinus on P. bogaraveo. This failure may be linked to rapid divergence between congeneric species, leading to erroneous amplification of DNA fragments [Along with conservation and management purposes, the identification of fish stock units is also a major topic in exploited populations, and the inconsistency between the biological and management scale of the units’ boundaries can hamper effective fisheries management . In thisragments ,70. On tragments ,72.The chosen methods for the assessment of Null Alleles (NA) frequencies showed that NA are present in the dataset overall, especially associated with loci isolated and characterised from individuals collected around the Azores Islands . As a maThe genetic diversity of Blackspot Seabream populations in terms of the number of alleles per locus and per population, and the expected heterozygosity across loci, was consistent with that found in previous studies ,37, althIS values (0.122–0.263) over all loci, HWE deviations were not persistent, and the relatedness test conducted across sampling locations did not highlight sibling relations. Most of all, it confirmed the total absence of co-occurrence of parents and offspring in the analysed samples. A low percentage of HS and FS confirmed the lack of inbreeding inside the tested populations.Despite high FST = 0.005 or higher was considered, leading to the conclusion that no informative loci were excluded from the dataset when using the restricted dataset (14 microsatellite loci) in the analyses of genetic differentiation.Many of the markers included in this study have already proved to be prone to NA flaws but, to date, there is no consensus about the appropriate limit of NA to be allowed across loci and geographical samples ,74,75. FST values  and adults’ reproductive strategies  remain poorly investigated. Indeed, studies of adults’ residency and migratory behaviour of young specimens at a regional level could identify feeding grounds for sub-adult fish and spawning grounds adopted by mature individuals. Evidence produced to date highlighted that Blackspot Seabream individuals live in small shoals mainly composed of adults dwelling near the sea-bottom, where they are prevalently resident [Individual-based approaches revealed a homogeneous distribution of genetic components across sampling locations, confirming the lack of distinct clusters for resident ,16,17,19resident ,26,28,74resident ,77.Although a general agreement exists regarding the Strait of Gibraltar as the main spawning area for the Eastern Atlantic Ocean–Western Mediterranean region ,33,78, sThe potential mobility of adults related to spawning events and the segregation between the typical habitat of juveniles and adult specimens has also been demonstrated through the morphological and microchemical variations in the otoliths of Hidalgo et al. ,80. WhenP. bogaraveo and the isolation of the Azores archipelago as a glacial refugia [P. bogaraveo gene flow is present between Mediterranean and Atlantic areas, it should be sought by intensifying the sampling effort along the eastern Atlantic Ocean across a larger investigation scale . ribution ,86,87,88ribution  and Singribution , new sturibution ,40,82 anP. bogaraveo, and showed the species’ overall genetic homogeneity, which is likely related to its dispersal potential across different life stages. Because of the limitations related to the applied genetic markers, the testing of more informative genome-wide markers is recommended to identify hidden genetic structures and update effective management measures for the responsible use of this important and appreciated fishery resource.Based on an extensive sampling effort targeting most of the geographical distribution of the Blackspot Seabream, the species’ genetic variability and its population connectivity were estimated using available, powerful markers and multiple analytical approaches. The results described here contribute to the bigger picture of the overall variability, characterising even distant population samples of


Lutjanus guttatus is a species distributed in the TEP for which previous studies using mitochondrial data recovered a panmictic pattern along its distributional range. In this study, we used SNP data of L. guttatus individuals sampled along its range to evaluate population genetic structure and investigate whether oceanographic factors influence the species’ genetic architecture. Finally, we assessed the role of adaptive selection by evaluating the contribution of outlier and neutral loci to genetic divergence.The lack of barriers in the marine environment has promoted the idea of panmixia in marine organisms. However, oceanographic conditions and habitat characteristics have recently been linked to genetic structure in marine species. The Tropical Eastern Pacific (TEP) is characterized by dynamic current systems and heterogeneous oceanographic conditions. The Gulf of Panama  is influenced by a complex current system and heterogeneous environment, which has been shown to limit the gene flow for shoreline species. Next Generation Sequencing (NGS) has contributed to detect genetic differences in previously reported panmictic species by the assessment of loci associated with selection and to understand how selection acts affects marine populations. L. guttatus covering nearly all its distributional area. Genetic variation was assessed using both spatial and non-spatial methods by comparing three different data sets: (i) a Combined Loci (CL dataset = 2003 SNPs); a search for putative loci under selection allowed the evaluation of (ii) Neutral Loci (NL dataset = 1858 SNPs) and (iii) Outlier Loci (OL dataset = 145 SNPs). We used the estimating effective migration surface (EEMS) approach to detect possible barriers to gene flow.The RADcap method was used to obtain 24 million paired reads for 123 individuals of Lutjanus guttatus. A migration corridor was detected that coincides with the Costa Rica Coastal Current that flows from Central America to the Gulf of California, allowing the homogenization of the northern population. In the Southern cluster, a migration corridor was observed with the OL from Panama to Colombia, which could be associated with the currents found in the Gulf of Panama. Genetic variation found in the OL of Lutjanus guttatus highlights the usefulness of NGS data in evaluating the role of selection in population differentiation.Genetic differences were found in the OL dataset, showing two clusters (Northern and Southern), whereas NL showed no differences. This result may be related to the Selection-Migration balance model. The limit between the Northern and Southern groups was in the Gulf of Panama, which has been previously identified as a barrier to gene flow for other species, mainly due to its heterogeneous oceanographic conditions. The results suggest that selection plays an important role in generating genetic differences in  The apparent absence of physical barriers to gene flow in the ocean realm has prompted the idea that most marine species are panmictic . Large ee.g., from Panama to Colombia, as observed in Elacatinus puncticulatus (redhead goby)  nearshore currents and the strong oceanographic gradients make this area highly complex. Within the Equatorial section, the region known as the Gulf of Panama, which stretches west from the Panama coast to Ecuador (close to 81°W), has shown to be an important area promoting the genetic differentiation of populations. The circulation pattern in the Gulf of Panama is influenced by The Intertropical Convergence Zone (ITCZ) , generatad goby) ). The abLutjanus campechanus  that promote local adaptation of populations, is of relevance to understanding how adaptive selection affects the gene pool of species  distributed in the Galapagos Islands, where the oceanographic conditions have promoted an adaptive process  . This pa process . Althoug process .Lutjanus guttatus is a demersal fish species displaying high dispersal capability, widely distributed in the TEP, from the Gulf of California to northern Peru, including the Galapagos Islands  in the “least concern” category, despite its high exploitation rate.The spotted rose snapper  Islands . The Spo Islands . Adult i Islands , while j Islands , oceanic Islands . LutjanuL. guttatus and its sister species L. peru along the shoreline in the TEP using the mitochondrial D-loop region concluded that these species are genetically homogenous throughout their ranges, with high genetic diversity and signals of recent expansion  and oceanographic factors were responsible of the genetic structure observed  was used to index individuals for downstream filtering of PCR duplicates. After cleaning the PCR products with a 1.2:1 volume ratio of SpeedBeads:DNA, three limited-cycle PCR replicates were performed to increase the concentration . From this initial set of data, we retained only individuals that had at least 1000 reads and at least 4x of read depth , and with a maximum observed heterozygosity higher than 70% (h = 70) to exclude potential paralogues . Markers were considered to be under selection if they had a false recovery rate (FDR) correction of 0.01 in PCAdapt v.4.0.2 , outlier loci (OL), and all loci (combined [CL]). Sequences from the OL were blasted in the NCBI dataset (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the blastn option. Additionally, we scanned the OL dataset with the genome from L. erythropterus to evaluate if loci under selection were related with a protein (BioProjects number: PRJNA662638).We identified outlier loci using two different approaches. The first, BayeScan v.2.1 , is a Ba v.4.0.2 . We applbasic.stats and allelic.richness from the R package hierfstat v.0.04.22 (He) and observed (HO) heterozygosity, inbreeding coefficient (FIS) and allelic richness (Ar). Confidence intervals for FIS values were calculated with 1000 bootstraps in the boot.ppfis function on hierfstat. Paired FST were calculated following the Weir and Cockerham method (1984) in Arlequin version 3.5.2.2 (α) was adjusted by the α by the sum of the numbers of tests.The functions .0.04.22  were use 3.5.2.2 . The sigfind.cluster function was used to identify the K value. The best-supported number of clusters was identified by comparison of the Bayesian Information Criterion (BIC) for the different values of K. After running the DAPC, 70 PCs, which accounted for approximately 80% of the total variation in the data set, were retained using the dapc function. Finally, the DAPC scatterplots were obtained using the ggplot2 package  in SAMOVA 2.0  consider package  in R. To package . This soFST/1-FST values and the geographic distance among localities ). Geographic distances were calculated with marmap v1.0.4 package (Isolation by distance (IBD) is widely used to explain differences among populations in species with a broad geographic range . To test package  in R. Th package , were usv. 4.0.5 (To test isolation by environment (IBE) and isolation by resistance (IBR), we obtained 13 oceanographic variables from Bio-ORACLE . The IBEv. 4.0.5 . The cirv. 4.0.5 . Finallybed2diffs module from EEMS (L. guttatus and obtained with the polyline method in the Google Maps API v3 tool (http://www.birdtheme.org/useful/v3tool.html). For the EEMS model we used three independent runs considering 200, 300, and 450 demes, a burn-in of 1,000,000 steps and MCMC length of 5,000,000 iterations. The convergence of runs and the spatial surface (maps of effective migration rate (m)) and effective diversity (q) was visualized using the Reemsplots function in R.We evaluated the spatial population structure as a function of potential barriers to gene flow using EEMS (estimated effective migration surface) software . This aprom EEMS . The disStacks using the baits as pseudo-genome, , which correspond to the combined loci dataset . The two approaches used to detect loci putatively under selection obtained as a consensus panel of 145 candidate outlier SNPs, which were separated into the OL dataset, while another 1858 SNPs remained as neutral loci dataset (NL). Contrasting results obtained with the OL and NL datasets will be shown in the main manuscript, while results for CL are reported in We obtained 24 million reads with the RADcap protocol. After filtering data and removing duplicates, 10 million paired reads were retained. The first evaluation of the quantity of reads per sample left 154 samples, which recovered 10814 SNPs with 39.64% missing data. This dataset was filtered for the missing data, generating a final dataset consisting of 123 individuals from 17 localities . From thp = 0.433 and p = 0.84 respectively)  had a negative value . Confidence intervals of FIS were above zero (FIS (HO) values were similar between the NL dataset (0.047–0.081) and the OL (0.053–0.078) (He), NL had lower values (0.047–0.127) than OL (0.033–0.175) (The ia indicate a lack of disequilibrium linkage in NL and OL (ctively)  Values ove value . For theove zero , suggestove zero . For LPAero (FIS . The obs3–0.078) . For exp3–0.175) . Allele 3–0.175) .FCT . The first group included all of the localities for the northern distribution of the species , and the second cluster recovered three localities from the Southern distribution   .FST estimates using the NL dataset were not significant for almost all the comparisons (p = 0.015), only the comparison between SLV vs PAN was significant . In contrast, the OL dataset presented higher estimations of FST, resulting in highly significant differences involving the three locations from the Southern group , CLB  and ECU . None of the comparisons between these southern locations and OAX were significant, although they showed high pairwise FST; this is likely due to the fact that the OAX location consisted of a single individual.Pairwise-sample parisons . After arn group : PAN . In contrast, the AMOVA using the OL dataset showed significant genetic differences for variance among Northern and Southern groups   .K = 2    and currents velocity (IBR) (ctively)  and 4C. = 0.002)  and 4D.L. guttatus in the Gulf of California (LPA and LOR) and between NAY and COL . Levels of overall genetic diversity observed in the localities using the NL and OL datasets   and L. argentiventris  (He = 0.097 for NL and He = 0.102 for OL). Notwithstanding the weaknesses of the SNPs for the assessment of genetic diversity, we cannot discard inbreeding to explain the deficit of heterozygotes observed in the present work as has been stated in the brook charr Salvenilus fontinalis  , Symphodcellatus , and forx lucius . A prope= 0.757) , as wellntinalis . Howeverd effect .Lutjanus guttatus is a representative species of the Lutjanidae family, known as snappers, which includes a large number of commercially important fishes  (t fishes . Genetict fishes . Hernándechanus) . Neverthechanus) .L. guttatus are consistent with the hypothesis that the genomic data, particularly polymorphic loci as SNPs, are better for resolving genetic questions in L. guttatus where a model of selection under migration was recovered. Loci showing adaptive selection clearly separated the populations into Northern and Southern clusters , where neutral loci detected three genetic clusters in the Mediterranean, “but this variation was not clear and low supported”, while by using outlier SNPs there were revealed two additional populations in the eastern Mediterranean related with adaptations to environmental factors  (Lutjanus such as the red (L. guttatus) and yellow (L. argentiventris) snappers  , and in snappers . Correlasnappers  are congL. guttatus . The ITCZ is located north of the equator (between 4–8°N), representing the limit between the northern and southern eastern Pacific areas, and the effect of the temperature as one of the variables that explain the genetic variation found with the OL in guttatus  corroborguttatus , which iguttatus . South oguttatus . These cguttatus . In thisguttatus . Limitedguttatus .L. guttatus.The effect of the currents were also found with EEMS results, which recovered a barrier for the gene flow located in front of the Mexican coast , AlthougL. guttatus populations using either NL or OL, so IBD is not contributing to any pattern of genetic structure in L. guttatus , is important information for the conservation of this important fishery resource. The use of SNPs highlights the importance of selecting adequate markers for evaluating genetic structure in snapper, and particularly in Lutjanus guttatus using the OL dataset, which indicates that selection is playing an important role in delineating genetic differentiation within the species. Although the search for genes associated with selection failed, correlations found between the genetic distance and the environment reinforce the hypothesis that oceanographic conditions in the Gulf of Panama have an effect on L. guttatus, as has been observed in other species. On the other hand, results for the NL dataset did not distinguish two groups, which indicates that migration allowed the homogenization of neutral loci. The contrasting results between neutral and selected loci is consistent with a Selection-Migration balance and is a step toward understanding how the oceanographic conditions affect marine populations in the presence of constant gene flow. Our analysis of effective migration surface with both datasets identified a corridor that coincided with the Costa Rica Coastal Current. For the first time, genetic structure for L. guttatus was found, highlighting the importance for choosing the appropriate marker. This leads us to propose the use of more genomic markers with the aim of evaluating whether other localities in the TEP are influenced by the oceanographic conditions . Finally, assessment of environment variables by separated in future works for the species could help to identify which factors are most important for selection and if there are future risks for the species. This information may help to improve management programs, which is an important step toward the conservation of this important fishery resource.Two well-differentiated groups were found among populations of 10.7717/peerj.15029/supp-1Figure S1Index of Association calculated for (A) NL and (B) OL datasets .Click here for additional data file.10.7717/peerj.15029/supp-2Figure S2Confidence values for the inbreeding coefficient at 95% confidence intervals for each locality.Click here for additional data file.10.7717/peerj.15029/supp-3Figure S3Evaluation of the number of clusters obtained with ADMIXTURE using (A) NL and (B) OL datasets .Click here for additional data file.10.7717/peerj.15029/supp-4Figure S4Lutjanus guttatus using (A) NL and (B) OL datasets . Geographic distances were transformed to natural logarithm (ln).Mantel test for Click here for additional data file.10.7717/peerj.15029/supp-5Figure S5Lutjanus guttatus using NL dataset (A) and (C) 1858 SNPs and OL dataset (B) and (D) 145 SNPs.Mantel test for Northern and Southern groups for Click here for additional data file.10.7717/peerj.15029/supp-6Figure S6http://oceancurrents.rsmas.miami.edu/. Scale bar is in °C; values shown represent long term average temperature records for April.Map of the oceanographic currents of the tropical Eastern pacific (TEP). Map is based on Mariano, A.J. and E.H. Ryan, 2018. Click here for additional data file.10.7717/peerj.15029/supp-7Figure S7q), obtained with estimating effective migration surfaces (EEMS) analysis using (A) NL and (B) OL datasets . Higher values than average genetic diversity are presented in blue, while lower values are depicted in brown.Results for the posterior mean diversity rates .Click here for additional data file.10.7717/peerj.15029/supp-9Table S2Results of SAMOVA for the NL and OL datasets . The best representation of the observed clusters was obtained without previously defined groups.Click here for additional data file.10.7717/peerj.15029/supp-10File S1Identification of polymorphic loci and SR-Snapper-Bait design and synthesis.Click here for additional data file.10.7717/peerj.15029/supp-11File S2Results for the 2003 SNPs corresponding to the combined loci (CL) dataset.Click here for additional data file.


Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright’s F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program. The maize Iowa Stiff Stalk Synthetic (BSSS) population has undergone recurrent selection since 1939. This population was developed by intermating 16 inbred lines selected for superior stalk quality . The C0 exp). Expected heterozygosity (He) is defined as the probability that any two alleles at a single locus, chosen randomly from the population, are different from each other (Molecular markers like single nucleotide polymorphisms (SNPs) have proven to be valuable for the characterization of maize germplasm and their application becoming more feasible over the past two decades due to the availability of new, high density and affordable genotyping technologies . Useful ch other .ST) is a standard measure for the degree of genetic differentiation among subpopulations  segment refers to DNA segments descended from common ancestors and could be useful to estimate the genetic relationships in a population. IBD occurs when identical alleles are inherited from a common ancestor and constitutes a measure of the degree of relationship between individuals . The estIn this study of the BSSS, we propose to determine how much of the genomic variation in C0 has been lost during the selection process. C0 may be a reservoir of untapped favorable genetic diversity for previously unselected traits. Developing DH lines from earlier cycles of selection could be an alternative approach to conventional breeding for introduction of diversity into related elite lines. Genetic heterogeneity and high genetic load present in C0 could be overcome by production of DH lines  to unlocOur overall question in this and a companion paper Three synthetic populations BSSS, BSSS(R)C17, and BSSS/BSSS(R)C17 representing different cycles of selection in the reciprocal recurrent selection program with BSSS, and the Iowa Corn Borer Synthetic number 1 (BSCB1) were used to develop DH lines. The synthetic BSSS corresponds to the unselected base population (C0) formed by intermating 16 inbred lines selected for above average stalk quality in 1934 . The C0 R-nj marker gene in the endosperm, but not in the embryo, were classified as haploid kernels. The haploid seed was germinated in plug trays in the Department of Agronomy greenhouse. Once seedlings developed 2-3 leaves, a colchicine treatment was applied following the protocol used by the DH Facility at ISU C0 (C0_DHL), 185 DH lines from BSSS(R)17 (C17_ DHL), and 170 DH lines from BSSS(R)C0/BSSS(R)17 (C0/C17_DHL) were obtained. The DH lines were developed by the DH Facility at ISU (http://www.plantbreeding.iastate.edu/DHF/DHF.htm ).Randomly selected individuals within each population were pollinated with a maternal haploid inducer BHI301  in an isy at ISU . Two dayGenomic DNA was extracted from DH line seedlings established in a greenhouse. Leaf tissue samples from three plants per DH line were collected at the 3-4 leaf developmental stage, and DNA extraction was done using the standard International Maize and Wheat Improvement Center (CIMMYT) laboratory protocol . GenotypThe inbred line B73 was used as technical control and was repeated in seven separate plates to verify assay reproducibility. The resulting SNP core set was 24,885 SNP markers corresponding to 487 DH lines  and 15 progenitors). The progenitor CI 187-2 was omitted because of heterozygosity greater than 8.8% (not expected in inbred lines) and was removed from further analyses. After this point, only 15 progenitors with low heterozygosity were used in the study.exp) was calculated to quantify the genetic variation in the maize lines sampled. The expected heterozygosity is defined as the probability that two alleles randomly chosen from the test sample are different  on a per locus basis using the methodology described by ST. The FST values can range from zero to one, where high FST values showed a considerable difference in the allele frequency among two populations.To assess the degree of genetic differentiation between the groups of DH lines and the progenitors, we used the Wright’s F-statistics C0, BSSS(R)C17, BSSS(R)C0/C17 and the progenitor groups, respectively. To examine the overall population structure across all lines, we performed a principal component analysis (PCA). PCA analysis allows the classification of individuals into genetically similar groups. PCA relies on reducing dimensionality by using principal components to maximize genetic variability . Each pr2) among alleles at two loci, for all possible combinations of alleles, and then weighting them according to the allele frequency. P-values were determined by a two-sided Fishers Exact test (2 among adjacent sites (The average linkage disequilibrium (LD) decay among SNP markers for each chromosome was determined in each group of DH lines using the squared Pearson correlation coefficient  segments. An IBD segment refers to DNA segments descended from common ancestors. IBD occurs when identical alleles are inherited from a common ancestor and could be used to estimate the genetic contribution. Estimation of IBD segments with genotypic data allows the quantification of the proportion of the covered genome descended from each progenitor. For the genetic contribution and the average LD decay among SNP marker analysis, a different filtering process of the genotypic data was conducted to have the most reliable SNP markers and ensure genotype concordance. From the 32,929 SNP markers successfully called within the B73 RefGen_v4 . SNP marilibrium . IBD segilibrium .The initial number of SNP markers in the DArT-seq data set was 51,418. A total of 32,929 SNP markers were successfully called within the B73 RefGen_v4 . After rexp = 0.28), followed by the C0_DHL group with Hexp = 0.21  were according with the expectation and were predictable values since we knew the parent populations (C0_DHL and C17_DHL) values.The 24,885 SNP markers were polymorphic with a MAF greater than zero (The greatest genetic distance was observed between the progenitor group and the C17_DHL group (0.18) and the smallest genetic distance was observed between C17_DHL and C0/C17_DHL groups . The highest value was observed between progenitors and C17_DHL  plus 15 progenitors of the BSSS maize population. The rationale of using un-imputed data without filtering for MAF was that the BSSS maize population came from 16 founder genotypes. For some SNP markers, an allele was provided by only one founder. The expected frequency would in such a case be ~6.2%. If genetic drift occurred, the actual frequency in C0 can be even lower. C0 seed used in this research came from subsequent cycles of seed multiplication for maintenance, increasing the chance of genetic drift to occur.When dividing the number of SNP with heterozygous loci by the total number of SNPs, we observed that our DH lines presented a very low rate of heterozygous loci (less than 3%). Therefore, our DH lines attained an appreciable level of homozygosity, and the DH technology was efficient to fix the loci without requiring further generations of purification. Higher MAF and expected heterozygosity values of the progenitor and C0_DHL groups . AdditioThe high expected heterozygosity values found in the C0_DHL group were an indication for the presence of more rare alleles in C0. This could be an important source for new functional alleles of desirable traits, which have been lost during multiple generations of recurrent selection. Potential reduction in genetic diversity in advanced cycles were consistent with previous studies of the BSSS maize population in different cycles of the recurrent selection program , where gImprovement of plant characteristics like flag leaf angle, anthesis-silking interval, plant height, tassel branch number, total number of leaves and grain yield has been observed when advancing cycles in the BSSS recurrent selection program . These cST) used to measure population substructure and the overall genetic divergence among the different groups showed that the degree of differentiation was higher between the progenitor inbred lines and the C17_DHL group compared to C0_DHL and C0/C17_DHL groups as expected since the two groups share fewer alleles. Lower FST values indicate limited differentiation between groups of DH lines. When we compare the FST values of C0_DHL versus C17_DHL, we observe a clear genetic differentiation among these two groups. These results can be confirmed with the wider genetic distance found among them, reflecting the uniqueness of most lines within these groups. Similar results were found by The Wright’s F-statistics  is used to reveal genetic divergence among populations . In thisKinship coefficients are defined by pedigree and can be estimated based on molecular information. Thus, it is possible to find hidden relationships. We found that most of the DH lines in the entire panel were distantly related to each other. Therefore, this shows us a low relationship between DH lines of the C17_DHL and C0_DHL. The estimation of the degree of the relationship depends on the description of an ancestral population, which by definition, is assumed to be the base from where the past ancestry is no longer accounted . Thus, tLinkage disequilibrium (LD) refers to the non-random co-segregation of alleles at two loci. Recombination events shuffle genetic material during meiosis among homologous chromosomes and cause LD to decay with increasing distance. Multiple factors are affecting LD in crops. Generally, LD decays faster in cross-pollinated crops, diverse populations, but also, different genes and genomic regions in the same crop can exhibit different rates of LD decay. It is expected in maize, for genome regions to decay at distances around 1 kb for exotic landraces, as described by . In the On average, the mean genetic contribution of the BSSS progenitor lines estimated using high-resolution detection of IBD segments changed in the different groups of DH lines. The progenitors had the highest genetic contribution in the C0_DHL group, due to their use in obtaining the population, and the lowest contribution in the C17_DHL group in relation to the other groups. This suggests that relationships caused by more recent ancestry had the most significant contribution in the IBD segments among individuals. Additionally, 17 cycles of recurrent selection have changed the allele frequencies in the C17_DHL group, because only individuals with superior performance for the selected traits contributed alleles to the next generation.In the identification of regions in the genome inherited from the progenitors, we found prevalence of small to medium sized segments, where 50.4% of the segments were between 2.4 to 4.1 Mb. And, 28.2% of the segments ranged from 4.1 to 8.1 Mb inherited from the progenitor inbred lines. The number of segments decreased with increased length segments. IBD segment sizes from the progenitors changed across groups of DH lines. We found that some progenitors showed longer IBD segments in the C0_DHL group and others longer in the C17_DHL. Large, preserved regions in the genome could be associated with selection processes, resulting in long DNA segments inherited as a block from the parents. Therefore, under positive selection favoring a phenotype, a slight increase in LD surrounding the favored alleles will be produced. In these cases, the length of the IBD segment surrounding the alleles subject to selection will increase, experiencing less recombination at the population level . AlbrechST observed between the progenitors group and the C17_DHL group demonstrated a clear genetic differentiation among groups caused by the loss of different alleles during the recurrent selection program in the BSSS maize population, reflecting the uniqueness of most lines within these groups of DH lines. Thus, these DH lines can be evaluated in replicated trials, and genomic selection can be applied for the estimation of the breeding value for each DH line. Additionally, DH lines derived from the BSSS maize population could be ideal for association mapping due to the low population structure. Thus, we could identify genes or regions in the genome associated with a particular trait. Using genome-based data and DH technology was a powerful tool for access to the genetic diversity available in C0_DHL or C0/C17_DHL groups, which would be beneficial to incorporate in BSSS(R)17 to broaden its genetic variation while minimizing yield or other penalties. Thus, the results of this research will also help maize breeders to explore useful genetic variation for further improvement in a breeding program.In this study, we measured the genetic diversity among different sets of DH lines derived from the BSSS maize population and our results confirmed the separation from BSSS(R)C0 to BSSS(R)17 through the recurrent selection process. The selection process and the effective population size applied to the BSSS maize population have reduced the genetic variability. Consistent with previous studies . Althoughttps://doi.org/10.25380/iastate.22893878.v1.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Iowa State University DataShare, accession 22893878. DOI: TL, AL, JE, UF and SH conceived and designed the experiments. AL analyzed the genotypic data and conducted the molecular characterization. AL and FA conducted the IBD analysis. AL, AU and TL wrote the manuscript, with contributions from all the other authors. All authors contributed to the article and submitted and approved the submitted section.


The user is able to interconnect the physical and genetic map for a whole chromosome or a specific chromosomal region and can inspect a landscape of recombination hotspots. Moreover, the user can investigate which of the frequently used genetic-map functions locally fits best. We further provide auxiliary information about markers being putatively misplaced in the ARS-UCD1.2 release. The corresponding output tables and figures can be downloaded in various formats. By ongoing data integration from different breeds, the app also facilitates comparison of different genome features, providing a valuable tool for education and research purposes.The arrangement of markers on the genome can be defined in either physical or linkage terms. While a physical map represents the inter-marker distances in base pairs, a genetic (or linkage) map pictures the recombination rate between pairs of markers. High-resolution genetic maps are key elements for genomic research, such as fine-mapping of quantitative trait loci, but they are also needed for creating and updating chromosome-level assemblies of whole-genome sequences. Based on published results on a large pedigree of German Holstein cattle and newly obtained results with German/Austrian Fleckvieh cattle, we aim at providing a platform that allows users to interactively explore the bovine genetic and physical map. We developed the R Shiny app CLARITY available online at  Genomic research involving gene mapping of economically important traits, population-specific genetic structure and evolutionary history relies heavily on genetic maps built on the extent of linkage disequilibrium (LD) between genomic markers e.g., . For exaGiven the value of cattle in sustaining the world food security, the bovine genome is subject of vast amount of ongoing research. We recently updated the genetic map of German Holstein breed  and compThe app provides access to linkage maps built based on the 50K genotypes of a large pedigree of German Holstein cattle  as well as German/Austrian Fleckvieh cattle  and based on estimates of recombination rate between intra-chromosomal marker pairs. In what follows, we briefly describe the workflow towards genetic coordinates as depicted in Step 1:Genotype data were filtered for minor allele frequency >1% and for Mendelian inconsistencies both on marker and individual level using PLINK v1.9  with recStep 2:deterministic approach developed by likelihood-based approach, as implemented in the R package hsrecombi v0.3.4 :As a novel contribution, the relationship between recombination rate and genetic distance between all intra-chromosomal marker pairs was investigated for a set of commonly used genetic-map functions . Given ta. Rao’s system of mapping function requires We solved this optimisation problem using the R function optim with “Brent” option allowing to specify restrictions on For the Binomial map function of The CLARITY app is an R Shiny web GUI for various operating systems. It relies on several R packages to enable the outcome and visualisation functionalities. CLARITY was implemented in R v4.1.3  with helWe optimized the app following recommendations for best practice with lighthouse . FiguresThe structure of the app relies on modules, which eases a clear and concise organisation. The use of modules and corresponding interfaces to the main shiny ui and shiny server enables a straightforward maintenance of the software. Furthermore, since each module is an independent app with its own interface and server , future The app has three sidebar menus: “Information”, “Breed analysis” and “Breed comparison” which are described in the following.The first sidebar menu comprises two subitems: (1) general information about the project and contact options as well as (2) details of resources used. More specifically, subitem (2) contains a brief data description for each breed and outlines the methodology used for data analysis and parameter estimation. This subitem also provides auxiliary information about candidate markers identified as being putatively misplaced and/or residing in problematic regions of the ARS-UCD1.2 release . These mUnder the second sidebar menu “Breed analysis”, all tabulated and graphical outcomes are presented for the available breeds. So far, options “Holstein” and “Fleckvieh” are available. The user can select a single chromosome or all chromosomes to interconnect the physical and linkage map. The results are divided into different tabs (implemented as separate modules) within the main panel: general information, genetic map, hotspot detection and genetic-map functions. Generally, outputs including tables and figures for a certain interval or for the entire data can also be downloaded for being locally stored. The properties of each tab are explained in more detail below.versus genetic length per chromosome.For all chromosomes, summary statistics are provided about the number of markers considered, total number of recombination events, length of physical and both genetic maps from  in tabulPhysical and genetic map coordinates are listed for all markers in table or graphical format. If a specific chromosome is selected on the sidebar menu, the user can zoom into relevant chromosomal regions . In eachThe CLARITY app offers a landscape of putative recombination hotspots, in which marker intervals with an elevated recombination rate are colour-marked across the bovine genome or a selected chromosome . The defThe user can investigate the suitability of frequently used genetic-map functions and their overall and local fit to the observed recombination activity . The parversus genetic distance for computational reasons. Especially for chromosomes with The fitted genetic-map functions are illustrated together with a reduced scatterplot of recombination rate The third sidebar menu “Breed comparison” contains comparative analyses between breeds separated into the same tabs as described above. In addition, a Venn diagram summarises numbers of breed-specific and shared SNPs on a selected chromosome or over the entire autosome as well as in hotspot intervals. In the tabs “genetic map” and “hotspot detection”, the Venn diagram is interactively linked with the corresponding table—this allows the user to retrieve particular information of a selected Venn set. The Venn diagram also dynamically adapts to a user defined range and threshold, respectively. Furthermore, since the fit of genetic-map functions might differ between breeds and chromosomes, a barplot displays counts of “best” genetic-map functions in each breed if the option “all chromosomes” is chosen.Particularly, a comparison of Holstein and Fleckvieh cattle suggested similar recombination activity genome-wide. As an example, an inspection of hotspot intervals on chromosome 3  underlinThe CLARITY app provides an environment to interactively explore the physical and genetic map in selected cattle breeds. Importantly, processing of genotype data and presenting results via Shiny app rely on the current bovine genome assembly ARS-UCD1.2. In case of new assembly releases, Further work on the integration of data from other breeds  is underway and will facilitate complex comparative analyses of map features  in different genomes. Our Shiny app will be extended accordingly, certainly increasing its value for educational and research purposes.