Randomized controlled trials (RCTs) are typically designed for a single specific primary outcome, and their reporting focuses on this outcome. Large-scale trials, prevention studies in particular, could yield more valuable and relevant evidence by focusing on the benefit–harm balance. These results would be more helpful for informing guideline development, health policy and individual treatment decisions. Currently, RCTs are often inconclusive and sometimes misleading with respect to the balance of benefits (i.e. efficacy outcomes) and harms (e.g. side effects or treatment burden) of interventions, which is ultimately of interest to decision-makers . Little of the recent progress in methods to evaluate the balance of benefits and harms of interventions has been applied to improve the design and reporting of RCTs. There is still a lack of consensus on how to design RCTs specifically to address the benefit–harm balance and contribute better evidence for decision-making. We propose three fundamental changes to the current practice to maximize how much RCTs, in particular prevention trials, can increase the certainty when estimating the benefit–harm balance of interventions. the common practice of selecting single benefit outcomes as primary outcomes generally emphasizes benefits and reduces the RCTs’ value for interpreting the benefit–harm balance. For example, after an expert panel decided that a top priority question was whether a lower systolic blood pressure target reduces cardiovascular events more than a standard target in people with hypertension and without diabetes, cardiovascular benefits were designated as the primary aim. Accordingly, the Systolic Blood Pressure Intervention Trial (SPRINT) was designed as an efficacy trial and was stopped early when the primary outcome, cardiovascular events, was significantly reduced with the lower target. As a large, high-quality, definitive study with a unique comparison of blood pressure targets, SPRINT was well-positioned to directly inform guidelines and trigger guideline updates. But a debate arose around the clinical relevance and a potentially inappropriate focus on benefits. Finally, guideline developers disagreed whether benefits outweigh increased rates of adverse events like acute kidney injury or increased treatment burden, ultimately resulting in two conflicting US guidelines.4First, we call for defining the benefit–harm balance as the primary aim of large-scale prevention RCTs. Since the primary outcome sets the focus in how the results should be interpreted, Rather than just showing a debatable benefit–harm balance of less myocardial infarctions at the cost of more gastrointestinal bleeds, ASPREE demonstrated that aspirin did not prolong disability-free survival. If such composite outcomes are the primary outcome, they need to fulfill fundamental criteria to be interpretable. They should also be highly relevant to the target population, such as disability-free survival for the elderly.Specifying a primary outcome that more directly informs the benefit–harm balance reduces the risk of multiple testing and misleading interpretation of study results. For example, the Aspirin in Reducing Events in the Elderly (ASPREE) trial used disability-free survival as a primary outcome. but is otherwise non-specific. It is typically neither feasible nor necessary to power the study for all patient-important outcomes, or (often) for all outcomes in a core outcome set. Instead, we propose that sample size calculations should aim at estimating a metric for the benefit–harm balance  as precisely as needed for decision-making, based on expected treatment effects on both benefits and harms and taking into account both baseline risks and the relative importance of outcomes.Second, we call for large-scale RCTs to have sufficient statistical power to explicitly measure clinically relevant differences in the benefit–harm balance. Current guidance proposes that studies should be powered for multiple patient-important outcomes to ensure that benefit–harm balance can be assessed,79 To inform RCT design, such benefit–harm metrics should be sensitive to key patient-important benefits and harms and be responsive to the intervention. The duration of RCTs should reflect time-frames relevant to stakeholders in which both benefits and harms occur, as treatments or preventive and screening interventions may cause some benefits or harms earlier than others. Powering for the benefit–harm balance will require a larger sample size than powering for the composite of all benefit outcomes, as is often done. However, if there is more than one benefit outcome, compared to powering for a single benefit outcome only, the sample size could both decrease or increase when powering for the benefit–harm balance instead. By powering RCTs for a benefit–harm metric, RCTs will generate more valid and precise evidence for the benefit–harm balance. This will also avoid stopping large supposedly definitive RCTs like SPRINT based on benefit alone, and instead allow formulating explicit stopping rules for net benefit, net harm or futility.Benefit–harm metrics are useful to compare multiple outcomes on a common scale and to model the impact of additional evidence on the benefit–harm balance.Third, we advocate for nested patient preference surveys, as preference surveys could strongly guide the interpretation of results and impact guideline development and policymaking. Patient preferences can inform the choice of the outcomes and their relative importance. Decision-makers need to consider the patients’ perspective to balance benefits against harms and determine clinical relevance. In the absence of such evidence, guidelines can contradict each other, as in the blood pressure target example above. Although some evidence on patient preferences may be available, it is unlikely that any preference survey designed and performed independently of an RCT will include all outcomes  of interest and that outcome descriptions in the survey match outcome definitions of the RCT. Furthermore, respondents of surveys performed separately may not represent the trial or target population well. We could show that there is large variation in preferences between individuals, and that individual preferences can shift the benefit–harm balance of blood pressure targets. Guideline developers highly valued this result and suggested shared decision-making would be appropriate. We also found that patient preference surveys are difficult to perform for guideline developers: funding may frequently be lacking, and contacting the right patients may only be feasible through collaboration with care delivery groups with learning health systems, which can identify and contact a representative sample of members of the target population. Therefore, nested surveys in RCTs will likely provide the most applicable, valid and precise evidence on preferences to many guideline panels.Moreover, for large definitive RCTs like SPRINT, it is feasible to perform sufficiently large nested preference surveys to additionally determine the impact of variation in preferences between individuals. In contrast, guideline panels do not typically have the resources to perform large, applicable preference surveys. In a recent research project, we performed our own preference survey in patients with hypertension using best-worst scaling, a ranking exercise where the respondent repeatedly chooses the best and the worst outcome in different combinations. Furthermore, the ethics committee and data and safety monitoring boards would need to accept the benefit–harm metric as valid to determine equipoise. Given the disproportionate focus of RCTs on benefits and often lacking evidence on harms, it is not surprising that guideline developers frequently come to conflicting conclusions although often based on the same RCTs. Since RCTs are the main source of information for guideline development, policies and ultimately individual decision-making, a design and reporting that focuses more on the benefit–harm balance will help RCTs to provide high-quality, actionable evidence.In summary, we propose a major change in the culture for large-scale prevention RCTs to primarily aim to increase the certainty in the benefit–harm balance rather than in single benefit outcomes. In particular, definitive RCTs should aim to establish net benefit, net harm or equivalence. This approach requires thorough stakeholder engagement, in particular to ensure all patient-important outcomes are considered.


We apply this to the experimental evolution of a hypermutator strain of Salmonella in response to antibiotic stress (ciprofloxacin). By analysing sequence polymorphisms in individual cells from populations we identified the presence and prevalence of sub-populations which have acquired polymorphisms in genes previously demonstrated to be associated with ciprofloxacin susceptibility. We were also able to identify that the population exposed to antibiotic stress was able to develop resistance whilst maintaining diversity. This population structure could not be resolved from bulk sequence data, and our results show how high-throughput single-cell sequencing can enhance experimental studies of bacterial evolution.Single-cell DNA sequencing has the potential to reveal detailed hierarchical structures in evolving populations of cells. Single cell approaches are increasingly used to study clonal evolution in human ageing and cancer but have not yet been deployed to study evolving clonal microbial populations. Here, we present an approach for single bacterial genomic analysis for  The authors confirm all supporting data and methodologies have been provided within the article or through supplementary data files. Raw sequencing data for this work is available from NCBI under the accession PRJEB40189.in vitro evolution of populations of Salmonella under weak antibiotic stress and show that even under weak selection bacteria were able to evolve antimicrobial resistance. Furthermore, these populations exhibited increased population structure compared to antibiotic free cultures. This suggests small antibiotic concentrations could not only promote the emergence of resistance but also increase population diversity, which would have implications for adaptation to other stressors and environments. Our work demonstrates the power of single-cell genomics for the study of bacterial populations.Recent advances in genomic technologies have highlighted a previously unobserved level of microbial diversity. Measuring this diversity at single-cell resolution, both within and between populations, is a potentially powerful tool to explore the mechanisms of bacterial disease and antimicrobial resistance. In this study we applied single-cell whole genome sequencing and E. coli has emphasised the insights that can be gained from the study of evolutionary processes in bacteria in vitro    and its http://www.eucast.org/ast_of_bacteria/).Susceptibility testing used the broth and agar dilution methods with Mueller-Hinton media following the guidelines and interpretation criteria provided by EUCAST  was added to the aqueous phase, and the tube was mixed gently by inverting until the content turned milky. The tube was centrifuged at 3 220 g for 5 min and the aqueous (top) phase was recovered once again with a 1 ml cut-end tip and transferred to a new tube. To precipitate the DNA, 1/10 volumes of 3 M sodium acetate pH 5.2 and 2.5 volumes of ice-cold absolute ethanol were added to the tube before mixing gently by inverting until DNA precipitates. A Pasteur pipette hook was used to spool the DNA pellet and transfer it in a 2 ml tube containing 80% ethanol for a 5 min wash. The DNA pellet was then air dried for 5 min on the hook before being resuspended in 200 µl of ultrapure water, by incubation at room temperature overnight.For DNA purification, 1 vol of phenol:chloroform:isoamyl alcohol mixture (25 : 24 : 1) was added to 1 vol of cell lysate and centrifuged at 3 220 mutS lineages (exposed and control) and the MICs of ciprofloxacin and nalidixic acid were determined to identify the prevalence of resistance within the population. This confirmed selection of resistance in the exposed lineage, but also identified heterogeneity within the population with 13.6% of individual isolates being resistant (MIC >0.06 mg l−1) to ciprofloxacin in the exposed lineage , compared to none in the control. This data confirmed there was heterogeneity in the population and therefore the samples were a suitable test set for analysis by single cell sequencing.To examine the heterogeneity of susceptibility within a population, 96 individual colonies were isolated from Ten reference samples were sequenced on the Illumina NovaSeq platform with a PCR-free library preparation and generating 150 base paired end reads. Genomes were sequenced to an average depth of around 2700×.Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 (AM933172) [The chromosomal sequence of M933172)  annotateE. coli evolution experiment. This pipeline uses bowtie2 [breseq pipeline also calculated various mapping and coverage statistics.Read mapping to the reference and variant calling was performed using the breseq pipeline , develop bowtie2  for mappRead mapping to the reference and variant calling was performed using the breseq pipeline, as above. Polymorphisms present in at least one percent of reads with a read coverage of 10 on each strand at the variant position were called. Variants only seen on one strand were removed.breseq bam files using breseq. Each SNP location could then be classified as SNP, reference or low-coverage for each single cell genome. Genomes with more than the mean plus one standard deviation positions out of the 315 candidate sites lacking sufficient coverage for SNP calling were excluded for phylogenetic reconstruction and population structure leading to 177 single cell genomes from all populations remaining for phylogenetic reconstruction.Single cell genomes exhibiting variation (above) were considered. The non-unique fixed SNP sites (SNPs present at a frequency greater than 0.9 and seen in more than one single cell genome) in the single-cell genomes were determined leading to 3452 SNPs at 357 unique locations and ultimately 315 SNP locations within genes. The alignment coverage at each of these positions was determined from the breseq bam files using Samtools-1.9. Low coverage genomes were removed from the alignment as above leading to 216 single cell genomes from all populations remaining.SNP sites including intergenic SNPs present at a frequency >0.9 were selected. G-A or C-T transitions seen in more than one single cell were retained resulting in 1807 variant sites. The alignment coverage at each of these positions was determined from the A sequence alignment of variant sites from above was used to generate a maximum likelihood phylogenetic tree, for each culture, with RAxML using the GTRCAT model implemented with an extended majority-rule consensus tree criterion . RHierBaPCA was performed using R package factoextra. The sequence alignment used for phylogeny reconstruction for each case was converted to a three-level numeric matrix with values of 100, 1 and 0 for SNP, non-variant, and missing-data respectively, and then converted to a Euclidean distance matrix for which the principal components were determined. The fviz_pca_ind function was used to generate a PCA plot.To estimate the number of SNPs that would have been observed at smaller single cell sample numbers, the following subsampling strategy was used: synthetic collections of cells were generated by sampling at random without replacement from the observed single cell SNP profiles. The number of unique SNPs in these subsampled collections were then counted. This process was repeated 1000 times for every collection size between one and the actual single cell sample number. The median and 95% confidence intervals in the subsampled SNP numbers were then calculated. To estimate the total SNP diversity we fitted Michaelis-Menten saturating curves to the median estimates using non-linear least squares as implemented in the R nls function. The total SNP diversity as the Michaelis-Menten curve prediction at infinite sample size was estimated. This procedure was repeated for all four treatments using the filtered SNPs although we failed to fit a Michaelis-Menten curve for the WT strain in the presence of ciprofloxacin.E. coli and S. enterica serotype Enteritidis PT4 isolate P125109 (S. Enteritidis) into a 96 well plate, followed by whole genome amplification and sequencing. Individual cells predominantly had reads mapping to either one or the other genome, indicating that individual bacteria can be sorted from a mixture with a low rate of doublet cells , or its isogenic hypermutator strain with an internal and in-frame mutS deletion mutant (mutS). This specific mutS mutation was previously identified in a strain that caused a chronic infection in an immunocompromised patient spanning more than a decade and that evaded antimicrobial therapy leading to selection of antimicrobial resistant sub-populations [mutS strains at the end of the exposure series. We expected to see a difference in diversity between drug-exposed and naive populations in these conditions with selection in the former potentially imposing a bottleneck on drug-sensitive lineages.To demonstrate the ability of this approach to detect genomic heterogeneity and study population structure, we generated test populations founded by a wild-type (WT) ulations . Both stulations . We samp−1) to ciprofloxacin in the exposed lineage .Measurement of the MICs of ciprofloxacin against the final populations revealed no change in ciprofloxacin susceptibility in untreated lineages but emergence of resistant mutants in all populations exposed to the antibiotic (Table S1). Isolation and susceptibility testing of randomly selected individual colonies from the final populations confirmed the emergence of resistant variants in the exposed lineages. This analysis also allowed us to determine the heterogeneity of ciprofloxacin susceptibility within the population with 13.6% of isolates being resistant , as well as mini-bulk (100 cells) and empty wells for each experimental group. In parallel, we performed high-coverage Polymerase Chain Reaction (PCR) free sequencing of the cultured populations to determine high-confidence variant calls  than in the WT strain (mean proportion=0.23) . This pattern is expected due to the mutS deletion in the hypermutator and was previously seen in the mutS hypermutator strain of S. Enteritidis that evolved within an immunocompromised patient [In many of the single-cell samples, over 40% of bases of the le cells . The mea patient .In the corresponding single cell genome data, 115 286 raw SNPs were called at 99 617 unique genome sites. SNPs were divided into 12 categories  to assess possible error profiles due to single cell genome amplification and sequencing. In this work we decided to concentrate on the use of SNPs only to infer sub-populations. While insertions and deletions could also have been used, they comprised less than ten percent of total variants and their inclusion would have necessitated a revised variant filtering approach. SNP profiles were similar for all four samples and distinct to those seen in bulk sequencing. Single-cell SNPs were dominated by G-A and C-T transitions (mean proportion 0.42) and C-A and G-T transversions (mean proportion 0.38) . The prevalence of these transitions and transversions in multiple displacement amplification (MDA) has been previously described and attributed to deamination (transitions) or alkylation (transversions) during cell lysis .We filtered the single-cell SNP data to remove as many false positives as possible. As true single-cell SNPs should be monoallelic, single cell SNPs present at frequencies below 0.9 were then removed , comprising 105 684 SNPs  at 93 239 positions, leaving 9602 SNPs at 6687 positions remaining. Inspection of the remaining SNP profiles showed that there was still an increased signal from G-A and C-T transitions when compared to the profile from the bulk sequencing - 4981 more G-A and C-T SNPs than A-G and T-C SNPs in the single-cell samples. Assuming the true number of transition types should be approximately the same as seen in the bulk data, we inferred that many of these additional SNPs were false positives. To remove potential false positives from further analysis, G-A and C-T SNPs seen in only one single-cell genome were removed, leaving 4448 SNPs at 1536 positions. The vast majority of SNPs present in our samples are expected to be rare, having arisen in the last generation of the experiment  and the mutS strain in the absence and presence of ciprofloxacin , acrR T12A (frequency 0.06), envC G347S (frequency 0.014) and ompX A9V (frequency 0.011). In the corresponding single-cell data the gyrA D87G mutation was seen in 11 out of 88 (a frequency of 0.125) mutS cells grown in the presence of ciprofloxacin (acrR T12A SNP was seen in three single-cell genomes (a frequency of 0.034) (envC and ompX mutations seen in the pooled data at minor frequencies (~0.01) were not observed in the single-cell genomes, likely due to their low frequency.Since one of the experimental conditions was the presence of ciprofloxacin, we expected to observe SNPs in genes previously demonstrated to be associated with ciprofloxacin resistance. Examining the variants seen in the bulk sequencing data in the hypermutator strain (recovered from the population passaged with a 1 : 1000 dilution rate), SNPs were present in floxacin . These ff 0.034) . The envgryA mutation. In the context of this experiment this suggests that the common SNPs could be due to adaptation to culturing and indeed a closer examination of the shared SNPs highlights variation in genes associated with cell maintenance and metabolism (Table S2). The phylogenetic and population level analysis was also performed using 1807 putative variant sites in coding and non-coding regions . More uniquely ciprofloxacin positive than negative populations were again identified, consistent with an unexpected increase in diversity under selection. Ciprofloxacin has been shown to have a mild mutagenic effect which may impact the mutant accumulation seen here although ciprofloxacin induced mutations have been demonstrated to largely be short insertions and deletions rather than SNPs [mutS strain grown with antibiotic, and sub-clones of gyrA D87G and acrR T12A mutant cells were present as distinct clades on the phylogenetic tree , [The population structure analysis of the 315 high confidence variant sites within all four experimental conditions determined seven first-level clades . Of theshan SNPs . This grtic tree . Mutantsf acrR),  our datain vitro evolution experiments can be used to resolve population structure in evolving populations. This method allows greater understanding of population dynamics and will help predict evolutionary trajectories for populations exposed to stresses of interest. A similar S. Enteritidis hypermutator strain to that used in these analyses was able to evade antimicrobial therapy over more than a decade of intensive treatment [Our approach enables the high-throughput and reliable determination of SNPs from individual bacteria within a population, which when combined with reatment . Our datde novo mutation associated with adaptation to sub-inhibitory concentrations of ciprofloxacin we saw several mutations potentially associated with adaptation to the general evolution experiment environment (Table S2). For example, a mutation was found in frsA, encoding an esterase. Disruption of this gene has been shown to increase cellular respiration on glucose and several other sugars in E. coli and was hypothesised to be part of switch between respiration and fermentation [yaiW. Mutations in this gene have been described in E. coli as off target mutations, in response to co-exposure to streptomycin and pesticides [sbmA, mutations in which have been proposed to alter cellular permeability and affect uptake of external molecules [csgB, disruption of this region is associated with impaired biofilm formation in Salmonella Typhimurium [Beyond the entation . Mutatiosticides . This geolecules . A mutathimurium . It is thimurium .In summary, we have demonstrated that single-cell isolation, whole genome amplification, SNP detection and evolutionary analyses from bacterial genomes can be readily performed on hundreds of cells derived from complex liquid cultures. Although here we have applied the method to unlabelled cells, the use of FACS would enable the separation of individual cells on the basis of differentially expressed reporter genes or molecular probe-based detection. Which when coupled with advances in liquid handling and microfluidic technology could enable the rapid scaling of such approaches to incorporate analysis of thousands of individual bacteria. Furthermore, the development of multi-omic approaches such as G&T-seq  to be coClick here for additional data file.Click here for additional data file.


Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method.r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy.We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model. The simultaneous estimation of the genomic effect on quantitative traits using all available variants, named genomic prediction , has revA meta-analysis refers to the inference of more robust outcomes by combining the results of multiple studies in a single analysis, in other words, the analysis of analyses. The term “meta-analysis” was first proposed by Glass . Multi-tTo estimate the substitution effect of single nucleotide polymorphisms (SNPs) when raw data are not available, different meta-based models were proposed regardless of whether the variants were fitted independently as in genome-wide association studies (GWAS) or jointly as in genomic prediction studies –16. AlthThe objective of the present study was to develop a meta-analysis (named MetaGS) that mimics the multiple-trait best linear unbiased prediction (mBLUP) model using raw data with limited approximation. Our model assumes that each population would benefit from its correlation with other populations to gain more accurate population-specific SNP effects. Participant data holders are expected to share the LD matrices, the frequencies, and the effects of their SNPs as well as the variance of the direct genomic value and error variance in their populations. The model is sufficiently flexible to allow for imputing missing variants and their effects in different populations using the previous summary statistics without accessing the individual-based data. The model was tested on Australian Holstein and Jersey cattle populations for milk, fat and protein yields and it showed that it can duplicate the joint analysis that uses the raw data with no approximation.c), the SNP effects are Our multi-trait BLUP model assumes that the effects of a SNP in population 1 is a vector of 1s; AA, AB or BB, respectively [The input to the meta-analysis are SNP effects estimated within each population excluding foreign data. We assume that the input individual estimates of SNP effects for population UP model  that wouectively , \documeistensen  showed tUnder the SNP BLUP model , SNP effDGV represents the sum of all SNP effects:For the multi-trait BLUP model, SNP effects from different populations have the following (co)variance matrix:Similar to the definition of matrix The (co)variance matrix of the population SNP effects, Eq. , becomesThe effects of the meta-analysis model are estimated using the following mixed model equations:A in the population; and (4) the variance of direct genomic values. All the participating data holders must code the two SNP alleles A and B in the same way , so they end with equivalent All the terms in Eq.  can be dIn the present paper, we assumed that there was no residual polygenic (RPG) effect. However, if an RPG effect exists, it can be fitted during the estimation of SNP effects for each individual population following Liu et al.  without It is not necessary for data holders to submit multiple If reliabilities across traits were different, data holders should submit multiple Here, we propose a method to account for different SNP datasets used by different populations. In this method, we expand the list of SNPs to include all SNPs used by any of the participating populations. Equation  shows hoThe previous assumption requires a set of animals with both the missing and non-missing SNPs recorded. This could be done within population Then, the RHS for the missing SNPs can be calculated by:This gives all the necessary inputs for Eq. . TherefoAfter the complete equations have been solved and yield prediction equations for each population based on the complete SNP set, the solutions for the original SNP set of population The data involved Australian Holstein (4627 bulls) and Jersey (1178 bulls) populations genotyped with the 50k SNP chip. After filtering for minor allele frequency, the dataset included 40,850 SNPs. Three traits were considered, milk yield, milk fat and milk protein yields. The reference population included 1071 Jersey and 4105 Holstein bulls born before 2010. The validation set contained 107 Jersey and 522 Holstein bulls born after 2010. The raw data were analysed using MTG2 [To test the accuracy of rescaling the mentclasspt{minimaTo test the accuracy of imputing the mentclass2pt{minimrix (Eq. ). Another = 1) when comparing the GEBV for all traits in both HOL and JER populations  was compared to the single trait (ST) analysis as well as the joint analysis (mBLUP) that exploits raw data for both Australian Holstein (HOL) and Jersey (JER) cattle breeds for three traits. Compared to the ST model, the average correlation across traits between SNP effects produced by MetaGS and ST was 0.97 for HOL and 0.76 for JER (data not shown). MetaGS performed almost the same as mBLUP and they produced highly correlated SNP effects with correlation coefficient values ranging from 0.98 to 0.99. The correlation was even higher  unlike the Jersey population which had an average correlation coefficient of 0.76. This is mainly due to the large reference population for the Holstein breed (4105 bulls) compared to the Jersey breed (1071 bulls), which limited the effect of the Jersey’s input on the Holstein SNP effects. On the other hand, the smaller Jersey population benefits more from its correlation with the larger Holstein population.We demonstrated that the MetaGS model can duplicate the estimation of SNP effects and prediction results of the mBLUP model. The outcomes of both models were highly correlated in all tested scenarios. For the Holstein population, SNP effects of both models were highly correlated with the ST model  and almost the exact genomic estimated breeding values (r > 0.997) were obtained when applying both MetaGS and mBLUP on fat, protein and milk yields of Australian Holstein and Jersey data. The method was extended to synchronize the variants among different populations using a shared reference population. This was achieved by imputing missing variants from the shared summary statistics without the need to apply imputation on the individual-level data. We also developed a method to facilitate the sharing of summary statistics to avoid sharing multiple large files for different traits. MetaGS is not restricted to bull’s data and can be applied to any organism and can consider the variation in genetic correlation between participant populations. For example, in plant breeding, e.g., wheat, different breeding companies targeting distant environments belonging to the same mega environment could benefit from sharing their results within the analytical framework of MetaGS [We developed a meta-genomic prediction method (MetaGS) that accurately duplicated the results of the standard multi-trait best linear unbiased prediction (mBLUP) method without directly analyzing the raw data. Highly correlated SNP effects (f MetaGS . However


Hvmlh3) containing a mutation in the DNA mismatch repair gene HvMLH3. We have previously shown that Hvmlh3 decreases both plant fertility and recombination by ~50%. We compare our results to those from previously published traditional genetic analysis of F3 families derived from multiple F2 lines containing WT or mutant alleles of HvMLH3, revealing a high level of correspondence between analyses. We discuss possible applications of the approach in streamlining the assessment of recombination in plant meiosis research.We report a novel approach for establishing the number and position of CO events in individual homozygous inbred plants by combining low level EMS mutagenesis, speed breeding, whole genome shotgun sequencing and sliding window analysis of the induced molecular variant data. We demonstrate the approach by exploring CO frequency and distribution in self-fertilised progeny of the inbred barley cultivar Bowman and compare these observations to similar data obtained from a Bowman nearly isogenic line (BW230  Meiotic recombination in plants is a key element in the drive to increase genetic diversity in plant breeding and genetics. During meiosis homologous chromosomes pair and DNA double stranded breaks (DSB), introduced throughout the genome by the topo-isomerase-related enzyme SPO11, define the possible spectrum of crossover (CO) positions within the genome . HoweverTo establish the impact that either genetic perturbations e.g.,  or imposHowever, all of these approaches rely first on the generation of hybrids between suitably diverse parental genotypes where divergent sequences at either the nucleotide or structural level  may influence the observed outcomes. While they successfully catalogue and contextualise CO events at high resolution, they are constrained to chromosomal mosaics observed in the surviving genotypes of a population of progeny plants. For example, Given we work on barley, a large genome (4.3Gb) inbreeding crop plant with a six-month generation time, we were motivated to explore a more cost and time-effective approach for monitoring productive recombination events. We wanted to take advantage of recently available reference genome information , new seqHvmlh3) seeds were treated with 25 mM Ethyl methanesulfonate EMS exactly as described previously  as per manufacturer’s instructions. Between 1.5 and 2 g of frozen leaf tissue was used per sample. DNA was sent on dry ice to Novogene for library preparation and whole genome shotgun sequencing. All samples were sequenced at Novogene using Illumina short reads 2x150bp on a NovaSeq 6,000. The sequencing depth was adjusted during the experiment. The two ‘parental’ M2 plants were sequenced to a depth of 15x coverage. Initially, 13 M3 plants derived from each M2 genotype were sequenced to a depth of 2x coverage. Subsequently, 12 different M3 plants from each M2 genotype were sequenced to a depth of 4x coverage.https://github.com/SchreiberM/Measuring-frequency-and-distribution-of-meiotic-cross-overs. The raw data has been deposited at the European Nucleotide Archive (ENA): PRJEB52593 (https://www.ebi.ac.uk/ena/browser/view/PRJEB52593).All scripts are available through the following GiHub repository: Reads were mapped against Morex V2  with defHvMLH3 on 5H and a short introgression at the end of 4H identified previously from genotypic analysis of BW230 and Bowman. For statistical analysis and identification of the effect of background mutations we included SNPs which had homozygous alternative alleles in the M2 but homozygous wildtype background in Bowman. SNPeff  only high confidence SNPs were kept. In addition, we used very specific filtering steps reflecting the experimental setup. Firstly: we only kept SNPs that were heterozygous in the M2 but homozygous in the Bowman wildtype; secondly: we filtered for most likely EMS-induced mutations G - > A or C - > T; thirdly: we removed the previously defined introgressed region around . SNPeff  and PROV. SNPeff  were useThe heterozygous SNPs were further filtered for equal distribution and same number of SNPs with PLINK . OverrepFor mapping the M3 sequencing reads, the same parameters as described above were used. This time variant calling was carried out with Freebayes, using the previously identified and filtered heterozygous positions as an input file and only calling variants at those locations in the genome. For the individuals sequenced to a 2x coverage the SNPs were filtered for a read depth of 2 while for the individuals sequenced to a 4x coverage the SNPs were filtered for a read depth of 4 before processing further.We used a sliding window approach to identify clear switches from heterozygosity to homozygosity along each chromosome. To take account of the fact that each barley chromosome varies in length and distribution of genomic features , a Bowman near isogenic line carrying a mutation in the HvMLH3 gene, with EMS. The resulting M1 plants were expected to be chimeric, with a different suite of mutations in different progenitor cell files. From the treated M1 seeds 71.43% germinated for Bowman and produced M2 seeds and 62.86% germinated for BW230 and produced M2 seeds. Barley has been previously determined to have an average Genetically Effective Cell Number (GECN) of six , which will lead to an expected segregation ratio for each EMS induced SNP of 21 WT: 2 heterozygous: 1 homozygous mutant  in a population of M2 plants . Initially, multiple M2 seeds of each genotype were chosen and grown under speed breeding conditions. Leaf material was harvested from all M2 plants which were grown to maturity while monitoring plant health. All M2 plants were indistinguishable from their parental lines. One individual M2 plant was then chosen from each genotype (Bowman and BW230) and seeds harvested. DNA was extracted from the leaf material previously collected from these two plants and used for whole genome shotgun (WGS) sequencing to a depth of 15x coverage in order to construct a reference variant file and provide sequence context of SNPs introduced by the EMS treatment. Focusing only on the heterozygous SNPs in a single M2 plant then allowed us to determine changes in SNP phase  in the M3 families and therefore pinpoint CO positions  were not considered for the final variant dataset in BW230.As there was a difference in total number of heterozygous SNPs between Bowman.M2 and BW230.M2 the dataset was first thinned by using Plink to maintain 1 SNP every 10 kb and achieve a more equal distribution along the genome. We further reduced the whole dataset to 20,000 heterozygous SNPs of similar depth and distribution between the two genotypes. The distribution of the SNPs across the genome is shown in 2 genotype  and initially sequenced 13 from each to a depth of 2×. After mapping and variant calling the SNPs were filtered for those already identified in the M2. In a first approach we plotted the unfiltered SNP zygosity along the chromosomes  and a WT (Mlh3/Mlh3) as parents.Here we developed and tested an approach that maintains the biological constraints of observing recombination in viable gametes while also allowing analysis of recombination in essentially homozygous inbred lines. We assumed that the resulting data would provide more biologically accurate measures of recombination than those obtained by either high throughput sequence variation analysis of collections of individual gametic cells , scoring progeny . This alThe barley genome is relatively large at ~4.3Gb and has been previously characterised both cytogenetically and using molecular markers as having extensive non-recombining pericentromeric regions covering roughly half of each chromosome . In addimlh3/mlh3 mutant seed with a low level of EMS (25 mM), which we had previously established induced few morphological or developmental phenotypes . Given the relatively frequent occurrence of small inversions or rearrangements in the barley genome this could potentially result in an inflated assignment of physically close double recombinants, but this was not explicitly tested here. A reference standard genome assembly of cv. Bowman will be required to explore this further.Making direct comparisons between our data and that from tetrad, fluorescent reporter or pollen-based sequencing assays are more problematic , 2017. CAn obvious question about the approach we describe here is ‘when would it be attractive to use?’ We give two examples. First, and continuing on the theme of using barley, large collections of phenotypically characterised homozygous ‘semi-fertile’ (or ‘desynaptic’) mutants are stored in international genebanks or local collections . As ‘semi-fertility’ is often associated with mutations in genes affecting meiosis, an obvious question for us is whether CO frequency or distribution is increased or decreased in some of these lines, and whether this will have practical value. Second, we know that different stresses or treatments  can affect recombination and exploring these effects at the genetic level is a routine question . While tHvMLH3. We found that the outputs of our analyses were directly comparable to those obtained using routine genetic analysis of segregating F3 families. The major advantage is that we achieved this both quickly and logistically simply, and in the absence of potentially complicating biological  or genotypic  effects that in certain scenarios could influence our overall conclusions.We show that introducing a low level of polymorphisms into the genomes of diploid homozygous inbred barley lines using EMS combined with low coverage whole genome shotgun sequencing and sliding window data analysis of induced variants in M3 populations can be used to determine CO numbers and recombination rate in essentially inbred lines. While we analysed a relatively small number of chromosomes  in both WT and meiotic mutant plants, this was sufficient to demonstrate CO frequency and distribution and the impact of a homozygous mutation in 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 in the article.MS and Y-YC conducted the experimental and bioinformatics analysis. RW and LR designed the experimental setup. MS, LR, and RW wrote the manuscript. All authors contributed to the article and approved the submitted version.MS and Y-YC were supported by the ERC Advanced Grant SHUFFLE (Project ID: 669182) to RW. MS, LR, and RW were in addition funded from the Scottish Governments Rural and Environment Science and Analytical Services Division Theme 2 Work Program 2.1.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In addition, qRE4 and qRRE9 were identical. Furthermore, we found eight candidate genes that are involved in abiotic and biotic stress, immunity, cell expansion, and phosphate transport in the loci of qRRE8, qRRE9, and qRRE15. Moreover, four candidate genes, i.e., Os01g0200700, Os06g0621900, Os06g0493600, and Os06g0622700, were verified correlating to Zn tolerance in rice by quantitative real time-PCR (qRT-PCR). Taken together, these results provide significant insight into the genetic basis for Zn toxicity tolerance and tolerant germplasm for developing rice tolerance to Zn toxicity and improving rice production in Zn-contaminated soils.Zinc (Zn) is an essential micronutrient for rice, but it is toxic at a high concentration, especially in acid soils. It is yet unknown which genes regulate Zn tolerance in rice. In the present study, a genome-wide association study (GWAS) was performed for Zn tolerance in rice at the seedling stage within a rice core collection, named Ting’s core collection, which showed extensive phenotypic variations in Zn toxicity with high-density single-nucleotide polymorphisms (SNPs). A total of 7 and 19 quantitative trait loci (QTL) were detected using root elongation (RE) and relative root elongation (RRE) under high Zn toxicity, respectively. Among them, 24 QTL were novel, and  Zinc (Zn) is an essential micronutrient for rice growth and development, as it is involved in numerous physiological and biochemical processes . HoweverDeciphering the genetic mechanism for Zn tolerance in rice is the premise of improving rice Zn tolerance. Plants have developed resistant mechanisms responding to Zn toxicity, which include translocation from root to shoot, sequestration into vacuoles, and homeostasis with iron ,9,10,11.qZNT-1 on chromosome 1, shows an effect with an LOD value of 6.0 and explains 21.9% of the total phenotypic variation using leaf discoloring index [qZnRL11 from chromosome 11 [qSFW3b for shoot fresh weight and qSDW3b for shoot dry weight [qZNT-3 [A total of 74 QTL for Zn tolerance in rice have been reported in previous research via linkage mapping and GWAS ,14,15,16ng index . qZnRL11 [qZNT-3 . We summArabidopsis, root length growing under heavy metal treatment is frequently used to evaluate heavy metal tolerance, and some tolerant and sensitive accessions were identified by using root length under heavy metal toxicity. Furthermore, well-known genes such as FRO2, GSNOR, and SRF3 were located through GWAS [Phenotyping is a prerequisite for evaluating heavy metal tolerance in plants. In ugh GWAS ,18,19,20A variety of plants from highly contrasting environments showed extensive natural variation and increased the probability of identifying causal variants . Rice laIn the present study, we performed a GWAS with more than 4.3 million high-quality SNPs by using root elongation and relative root elongation (root elongation in high Zn conditions/root elongation in control conditions) under high Zn toxicity at the seedling stage within Ting’s core collection. This study aims to (1) identify the candidate genes corresponding to Zn toxicity and (2) evaluate the landraces with strong Zn tolerance in Ting’s core collection. This study may provide information for improving rice Zn tolerance in potentially Zn-contaminated soils.We made use of Ting’s core collection and assessed the phenotypic variations in root elongation under Zn toxicity. The root elongation (RE) and relative root elongation  of Ting’s core collection were measured before and after Zn exposure. Extensive phenotypic variations in root elongation in response to Zn toxicity were observed . The aveThe data of genetic analysis referenced from our previous studies, in which genetic analysis included the linkage disequilibrium (LD) decay distance, population structure, and kinship value of Ting’s core collection required for GWAS, were uncovered well.p < 0.94 × 10−5) associated with root elongation under high Zn toxicity by using the EMMAX model  alleles was significantly smaller than those with alternative alleles in Ting’s core collection, whereby the most important SNPs of qRE3 and qRE6  associated with root elongation under high Zn toxicity by using the EMMAX model  alleles were significantly more sensitive than those with alternative alleles in Ting’s core collection, which were grouped by the most significant SNPs of qRRE3, qRRE5, qRRE8, qRRE9, qRRE10, qRRE13, qRRE14, qRRE15, qRRE17, qRRE18, and qRRE19  was an iron (III)–deoxymugineic acid transporter (OsYSL15), which is responsible for iron uptake and the phloem transport of iron [qRRE1 on chromosome 1, there were 29 candidate genes in the region 5.38–5.59 Mb. One candidate was Os01g0200700 (OsMTI-3a), which is involved in the detoxification of Zn, Ni, and Cd [qRRE4 on chromosome 3, there was only one significant SNP and 17 candidate genes in the region 13.65–13.85 Mb  of all these loci was located on chromosome 6 around the top SNP at position 25,053,430. The candidate region was from 24.92 Mb to 25.12 Mb (200 kb) by LD calculation and contained twenty-two candidate genes, which were covered by 35 significantly associated SNPs , which plays a role in the uptake and the long-distance transport of Pi from roots to shoots, [qRRE15, there were sixteen candidate genes in the region 8.88–9.08 Mb, which were covered by eight significant SNPs.On the basis of the above reasons, the information about candidate genes is detailed as follows: For  of iron . For qRR, and Cd . For qRR13.85 Mb a. For qRant SNPs b. One cated SNPs c. For qRFurthermore, eight candidate genes detected by GWAS were selected to verify by qRT-PCR. Among the eight candidate genes, the expression of three genes was significantly different in the roots between Nagabo (Zn tolerant) and You zhan hong (Zn sensitive) a,d,f undTing’s core collection may be a potential resource of elite genes/alleles for Zn tolerance in rice due to abundant and useful natural variations for agronomic traits, aluminum tolerance, as well as rice sheath blight resistance, which have been reported within this core collection in our previous studies ,23,24,25The concentrations of Zn toxicity used in previous studies were diverse due to different populations and methods, e.g., 300 mg/L and 200 mg/L were used in the studies of Meng et al. (2017)  and ZhanqRRE4, qRRE8, and qRRE9) using relative root elongation under Zn toxicity exceeded the supposedly stringent significance threshold (−log10(1/n) = 7.64, qRRE4 and qRRE8, there were only one and four SNPs, respectively, within a candidate region of 200 kb distance surrounding the most significant SNP. For qRRE9, there were thirty-five SNPs surrounding the most significant SNP at position 25,053,430 on chromosome 6  detected by relative shoot dry weight and relative total dry weight using reciprocal introgression populations [qZnRL11) detected by root length using MAGIC populations [qRE4/qRRE9 and qRRE15 may be the causal loci involved in Zn toxicity tolerance.Furthermore, in the present study, the same QTL were detected by using different phenotypes, as well as identified in previous studies. When comparing QTL detected by root elongation and relative root elongation under high Zn toxicity, we found that as qRRE9 . When coas qRRE9  with preulations  and a QTAlthough many QTL associated with Zn toxicity tolerance in rice have been detected using different traits ,14,15,16AtFRD3, which encodes a member of the MATE efflux family, has been demonstrated to play a role in cross homeostasis between Fe and Zn tolerance in Arabidopsis [OsMT1a accumulate more Zn than wild type [OsYSL15 located in the region of qRE2, OsMTI-3a in the region of qRRE1, OsPHO1;3 and two MATE efflux-related genes in the region of qRRE8, and OsPT6, as well as a MATE efflux gene, in the region of qRRE12. In addition, qRT-PCR was performed to support the candidate genes identified in the present study; we will deeply study four candidate genes, i.e., Os01g0200700, Os06g0621900, Os06g0493600, and Os06g0622700, by using technology such as CRISPR-Cas9 and over-expression. Therefore, the above results suggest that these genes might be the causal genes that respond to Zn toxicity. However, the molecular mechanism of tolerance under Zn toxicity is complicated; hence, further studies are necessary to confirm the candidate genes and reveal the molecular mechanisms of rice adaptation to Zn toxicity.There is already vast knowledge on the molecular mechanisms of Zn homeostasis in plants, which was summarized in a review of Kaur and Garg 2021) . Previou021 . Prebidopsis . Yellow bidopsis . Transgeild type . Thus, wMoreover, Zn-tolerant  and Zn-sensitive varieties (You zhan hong) are suitable genetic resources for improving the Zn tolerance of rice cultivars. Therefore, the tolerant elite alleles in these varieties can be pyramided by marker-assisted breeding or edited directly by CRISPR-Cas9 in an elite, modern cultivar background.Ting’s core collection, which consisted of 150 rice landraces, was collected from China, Korea, Japan, the Philippines, Java, Oceania, Vietnam, and Brazil  Table S.2 (pH = 5.6). After 1 day, the seedlings were subjected to a solution of 0.5 mM CaCl2 containing 0 (control) and 120 μM ZnSO4 for 24 h. The experiment was conducted using a randomized complete block design with three replicates and 6 uniformly germinated seeds. The root length (RE) was measured by a ruler before and after the Zn treatment. Relative root elongation (RRE) was calculated based on (root elongation in high Zn/ root elongation in control conditions) × 100 [The seeds of Ting’s core collection were harvested from the field of China National Rice Research Institute, Hangzhou in 2020. The seeds were soaked in water for 2 days at 30 °C in the dark, transferred to a 1 L pot with 96-well PCR plates (8 × 12) in a solution containing 0.5 mM CaCls) × 100 . The scrTM 4000 with 6~7-fold of genome coverage; detailed information was described in our previous study [The 4,395,003 SNPs used for GWAS were based on re-sequencing performed by Illumina HiSequs study ,24. The p-value threshold for identifying the significant marker–trait associations in our study was 2.27 × 10−7 , which is a rough Bonferroni correction corresponding to −log10(p) = 7.64. However, when there was no peak detected, another significance threshold was calculated, i.e., a minimum Bayes factor (mBF). The mBF was calculated using the following formula: mBF = −e*p*ln(p) [We performed GWAS to detect the trait–SNP associations for Zn tolerance using 4,395,003 SNPs and the mean value and tolerance index of root elongation at high Zn. Two mixed linear models were used, the EMMAX and LMM methods. The critical *p*ln(p) , thus, t2 solution (pH = 5.6) for 1 day after germination, and then subjected to a 0.5 mM CaCl2 solution containing 0 μM (control) and 120 μM (high Zn) Zn concentrations for 12 h. Whole roots were harvested and immediately frozen in liquid nitrogen. Total RNA was extracted using the FastPure Universal Plant Total RNA Isolation Kit . First-strand cDNA was synthesized using the ABScrip III RT Master Mix . qRT-PCR reaction was prepared using the THUNDERBIRD SYBR qPCR Mix . Three biological replicates and two technical replicates were analyzed for each gene. Relative expression of all genes was normalized using OsUBQ1 (Os03g0234350) as an internal reference. The primers used for qRT-PCR are shown in The seedlings were grown in 0.5 mM CaClqRE4, which was detected at high Zn concentrations, was located in the same region as qRRE9. qRRE15 was located in the same region of three previously reported QTL . These results indicate that there may be causal genes regulating Zn tolerance in the above-mentioned QTL. Furthermore, a number of candidate genes were detected in QTL in the present study, including the genes involved in Fe homeostasis, metal chelators, and Pi transporters. Moreover, eight candidate genes were verified through qRT-PCR. Three varieties  had elite alleles identified in the QTL associated with high Zn tolerance. Taken together, our findings provide significant insight into the genetic basis for Zn toxicity tolerance and will be very useful for developing rice varieties that are tolerant to Zn toxicity and improving rice production in Zn-contaminated soils.In the present study, a total of 7 and 19 QTL were identified as significantly associated with Zn tolerance using root elongation (RE) and relative root elongation (RRE) under high Zn toxicity. Among them,


B. taurus and B. indicus ancestry and proven resistance to ticks, diseases and other harsh conditions of the African geographical landscape. The multi-coloured Nguni coats have found a niche market in the leather industry leading to breeding objectives towards the promotion of such diversity. However, there is limited studies on the genomic architecture underlying the coat colour and patterns hampering any potential breeding and improvement of such trait. This study investigated the genetics of base coat colour, colour-sidedness and the white forehead stripe in Nguni cattle using coat colour phenotyped Nguni cattle and Illumina Bovine HD (770K) genotypes. Base coat colour phenotypes were categorised into eumelanin (n = 45) and pheomelanin (n = 19). Animals were categorised into either colour-sided (n = 46) or non-colour-sided (n = 94) and similarly into presence (n = 15) or absence (n = 67) of white forehead stripe. Genome-wide association tests were conducted using 622,103 quality controlled SNPs and the Efficient Mixed Model Association eXpedited method (EMMAX) implemented in Golden Helix SNP Variation Suite. The genome-wide association studies for base coat colour (eumelanin vs. pheomelanin) resulted into four indicative SNPs on BTA18 and a well-known gene, MC1R, was observed within 1 MB from the indicative SNPs (p < 0.00001) and found to play a role in the melanogenesis (core pathway for melanin production) and the MAPK signalling pathway. GWAS for colour-sidedness resulted in four indicative SNPs, none of which were in close proximity to the KIT candidate gene known for colour-sidedness. GWAS for the white forehead stripe resulted in 17 indicative SNPs on BTA6. Four genes MAPK10, EFNA5, PPP2R3C and PAK1 were found to be associated with the white forehead stripe and were part of the MAPK, adrenergic and Wnt signalling pathways that are synergistically associated with the synthesis of melanin. Overall, our results prove prior knowledge of the role of MC1R in base coat colours in cattle and suggested a different genetic mechanism for forehead stripe phenotypes in Nguni cattle.Nguni cattle are a Sanga type breed with mixed  The Nguni cattle are a Sanga breed native to South Africa  and farmThe unique and diverse Nguni cattle coat colours are of cultural significance particularly to the Nguni ethnic people . BecauseThe mechanism of coat colouration (melanogenesis) is determined by the melanin found within the melanosomes in the melanocytes  and can The Illumina HD bovine Beadchip  was developed by a multi-institutional consortium, Bovine HapMap, and contains 777,962 SNP markers. The taurine, zebu and composite breeds were used in the development of the array  and the Despite its socio-cultural and economic importance, very little is known about the genetics of coat colour in Nguni cattle, absence of this information of which hinder efforts for genetic improvement for such traits. The aim of this study was therefore to investigate genomic regions (SNPs and candidate genes) and genetic mechanisms that are associated with the coat colours and patterns in South African Nguni cattle using the Illumina Bovine HD genotypes and Nguni cattle phenotyped for base coat colour, colour-sidedness and presence and absence of white forehead stripes. The Nguni cattle used in this study were from two research populations of Bartlow Combine and Kokstadt in KwaZulu-Natal province and consisted of unrelated animals representative of the Nguni cattle diversity.n = 78) and Kokstadt (n = 66) in the KwaZulu-Natal region. The Bartlow Combine and Kokstadt research stations are located at the North and Southern parts of KwaZulu-Natal province of South Africa respectively. These two research stations are conservation farms for the Nguni cattle diversity and were chosen as a representative sample of the total Nguni cattle diversity. Using photo images, the coat colour patterns were phenotyped into 1) black, brown or red base coat colours and 2) colour-sidedness and the 3) white forehead stripe patterns as illustrated in n = 39) and brown (n = 6) animals and 2) pheomelanin with the red (n = 19) animals. Of the 64 cattle used in the base colour analysis, 35 were from the Bartlow Combine station and 29 were from the Kokstadt station. Forty-six (46) colour-sided and 94 non-colour-sided Nguni cattle were phenotyped and used in the colour-sidedness trait analysis. A total of 82 Nguni cattle were phenotyped for the white forehead stripe trait of which 15 animals had the white forehead stripe and 67 did not have the forehead stripe.A total of 142 Nguni cattle were sampled from two research stations of the Bartlow Combine . The concentration of the extracted DNA was measured using the qubit fluorometer  and concentrations greater than 25 ng/μL were accepted for further analysis. DNA integrity was verified on a 1% agarose gel. High-quality DNA was genotyped using the Illumina Bovine HD BeadChip Array kit  and in accordance with the Infinium HD Assay Super Manual Protocol at the Agricultural Research Council Biotechnology Platform, South Africa.n = 847) were also removed and 622,103 SNPs remained for downstream analysis.SNP quality control was conducted using PLINK version 1.9 . A genotO), expected heterozygosity (HE) and inbreeding coefficients (FIS) all of which were analysed using PLINK version 1.9. Genetic relationships between the Nguni cattle from the Bartlow Combine and Kokstadt conservation stations were also analysed using Principal Component Analysis (PCA) and genetic relationship matrix (GRM) on Golden Helix SNP Variation Suite (SVS) version 8.5.0 . PCA and GRM were also conducted for the animals within each coat colour phenotype  in order to investigate the genetic relationships between cases and controls for each GWAS.Genetic variation within populations was estimated using four measurements of minor allele frequency (MAF), observed heterozygosity , α was regarded as the vector of the fixed effect (genotype) while K was the relative kinship matrix, α/K . Manhattan plots were generated using the Bonferroni adjusted log of the p-values on Golden Helix SNP Variation Suite (SVS) version 8.5.0 . Per marker pairwise FST analysis was conducted using Golden Helix SNP Variation Suite (SVS) version 8.5.0  to investigate loci highly differentiating cases from controls.Genome-wide association tests for the base coat colours and patterns were done using Golden Helix SVS version 8.5.0  using the Efficient Mixed Model Association eXpedited analysis (EMMAX) . The eumr2) values were estimated between adjacent SNPs for each autosome and the genome-wide LD over all autosomes in each Nguni herd using Plink v1.09 . LD decay LD decay was then analyzed for three maximum distances between SNP pairs, ≥10 kb, ≥100 kb, and ≥1,000 and for each distance, SNP comparisons were binned applying bin sizes of 1 kb, 10 kb, and 100 kb, respectively. The average r2 for each bin was estimated and plotted against the inter-marker distance.In order to guide gene annotation, pair-wise LD (λ), defined as the median of the resulting chi-squared test statistics divided by the expected median of the chi-squared distribution drawn from the EMMAX analysis.Furthermore, a quantile-quantile (Q-Q) plot was generated using Golden Helix SNP Variation Suite (SVS) version 8.5.0  in order to assess the influence of population stratification on the GWAS. In addition the average genomic inflation factors (Candidate genes in close proximity (upto 1 MB downstream or upstream from the significant SNPs (p < 0.00001) were identified using the Golden Helix SVS GenomeBrowse using the BTAU5.0.1 (November 2015) reference genome. Pathways for the identified genes ±1 MB from the indicative SNP for each trait  were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and their association with coat colour patterns inferred.O) and expected (HE) heterozygosity for the Bartlow cattle were 0.361 ± 0.148 and 0.348 ± 0.135 respectively, which were similar to those observed in Kokstadt cattle . The observed heterozygosity for Bartlow Combine and Kokstadt populations were greater than the expected heterozygosity (HO > HE), indicating the possibility of previously isolated populations coming together to form a single population. As a result, slightly negative inbreeding coefficients were observed for both Bartlow (−0.038 ± 0.037) and Kokstadt (−0.033 ± 0.060) cattle.Minor allele frequencies of 0.260 ± 0.137 and 0.265 ± 0.135 were reported for Bartlow and Kokstadt herds respectively . The obs–12 ± 0.013) kinship was observed between pairs of animals from the two herds (–13), presence and absence of forehead stripe (4.05 × 10–15) and colour sided (2.02 × 10–12) phenotypes respectively  of 0.413 ± 0.219 for Bartlow and 0.402 ± 0.209 for Kokstad was observed. LD decay analysis demonstrated that LD stayed just above 0.2 upto 600 kb and decayed to just about 0.1 at 1000 kb window .λ) for the three phenotypes were close to 1 . Both the QQ plots and λ suggested little or no residual population structure effects on the test statistic inflation. Despite the small sample size, the results of GWAS are therefore reasonable and worth further investigation.The quantile-quantile plots (Q-Q plots) show that the influence of population stratification was negligible . MoreoveThe GWAS for base coat colour (eumelanin vs. pheomelanin) resulted in two and four significant (p < 0.000001) SNPs on BTA10 and BTA18 respectively. . The twoThe GWAS for colour-sided versus non-colour-sided animals resulted in four significant SNPs on BTA6 (3 SNPs) and on BTA7 (1 SNP) as illustrated in ST for the different phenotypes generated similar genomic regions as GWAS analysis for forehead stripe on chromosome 6 and 21 and for colour-sidedness phenotype on chromosome 6 as illustrated in Per marker pairwise FCoat colour and patterns have been widely used to differentiate cattle breeds , identifE of 0.28 reported in a genetic diversity study on six South African cattle breeds  of the bovine extension (E) locus, that make the MC1R, were characterized. The MC1R gene was further reported to be associated with the dark coloured coats of Gir cattle  yielded indicative SNPs on BTA10 and BTA18. The SNPs on BTA10 (BovineHD1000021741 and BovineHD1000021742) had no gene within 1 Mb region known to be associated with coat colour. The well-known MC1R gene was however found in the vicinity of the indicative SNPs on BTA18. The MC1R gene harboured by chromosome 18 was first reported to be responsible for the black and brown pigmentations of cattle coat colours in 1995 . In thisr cattle . The obsr cattle  and the r cattle . The stur cattle . The stur cattle .ASIP has been regarded as a regulatory protein for the melanin production because when it binds to the MC1R, it suppresses the formation of eumelanin and activates the production of pheomelanin . The ASIl-DOPA) that ultimately produces melanin after a few reactions. The PPP2R5E gene found on BTA10 in the study has role in the PI3K-Akt signalling pathway that regulates the MAPK in B16F10 mouse melanoma cells (Two major pathways associated with base coat colour were observed 1) the MAPK and 2) melanogenesis pathways. The MAPK pathway is one of the core pathways that lead to the pigmentation of mammals . In melama cells . The AICma cells . A studyma cells . The HSPma cells .Cs), is inherited in its dominant state and the expression of the gene in the heterozygous form brings about variations in those animals. Colour-sidedness trait has been observed in Belgian Blue and Brown Swiss cattle (In our study, the KIT gene was surprisingly not observed in the vicinity of the indicative SNPs for colour-sided Nguni cattle. According to , the gens cattle . Durkin s cattle . Anothers cattle . A recens cattle  and anots cattle . SNPs onBesides the possibility of other genetic mechanisms other than those impacted by KIT gene, confounding known and unknown population structures implicate GWAS results and when not accounted for may lead to false positives and false negatives . Also, pWhen melanogenesis is disrupted, leucism can occur. Leucism is a condition where there is lack of pigmentation due to the absence of melanocytes on the animals’ skin and/or coat colours and results in what is seen as white spots or patches on a solid base colour. These phenotypes include the white forehead stripe on the cattle’s heads and white patterns observed on the animals’ body and head. White phenotypes can also be desirable traits to some farmers or breeders in hot humid environments because animals can tolerate heat and function at their optimal. Nguni cattle have been reported to be an optimal breed for the production of beef and have immunity against diseases . It is tOverall the GWAS on presence of white forehead stripe in Nguni cattle demonstrated role of different genes at play that impact pathways known to have an influence on melanogenesis and therefore pigmentation in vertebrates. This could imply either genetic heterogeneity of coat colour and patterns phenotype or complexity of coat colour phenotypes in Nguni cattle leading to confounding effects. A more comprehensive study with intensive phenotyping of white forehead stripe is therefore required.Overall, the GWAS for coat colour patterns in the South African Nguni cattle shed light into the genetics of coat colour patterns in this breed. The GWAS for base coat colour reported SNPs, genes and pathways similar to those from other studies, with the observation of MC1R gene, which is well known to be associated with coat colour in cattle and other species. This gene was also observed to be linked to the well-known MAPK and melanogenesis pathways. Although, the GWAS for colour-sidedness and white forehead stripe did not report the well-known KIT gene, the significant genes such as MAPK10, EFNA5, PPP2R3C and PAK1 are synergistically involved in the synthesis of melanin. Another gene, TECRL, on BTA6 observed to be associated with white forehead stripe phenotype in this study has been reported to be associated with pigmentation patterns in Fleckvieh cattle. Because of the complexity of coat colour patterns in the Nguni cattle, this study recommends comprehensive sampling and phenotyping particularly of leucism traits for future GWAS studies.


As one of the most utilized commercial composite boar lines, Duroc pigs have been introduced to China and undergone strongly human-induced selection over the past decades. However, the efficiencies and limitations of previous breeding of Chinese Duroc pigs are largely understudied. The objective of this study was to uncover directional polygenic selection in the Duroc pig genome, and investigate points overlooked in the past breeding process.Here, we utilized the Generation Proxy Selection Mapping (GPSM) on a dataset of 1067 Duroc pigs with 8,766,074 imputed SNPs. GPSM detected a total of 5649 putative SNPs actively under selection in the Chinese Duroc pig population, and the potential functions of the selection regions were mainly related to production, meat and carcass traits. Meanwhile, we observed that the allele frequency of variants related to teat number (NT) relevant traits was also changed, which might be influenced by genes that had pleiotropic effects. First, we identified the direction of selection on NT traits by The selected loci that we identified across methods displayed the past breeding process of Chinese Duroc pigs, and our findings could be used to inform future breeding decision.The online version contains supplementary material available at 10.1186/s40104-022-00751-x. As seleex (FST) . Most apex (FST) . Indeed,Recently, some methods have been developed to detect polygenic selection, e.g., the Generation Proxy Selection Mapping (GPSM) allows us to observe how complex polygenic selection alters the genome over short timescales in a trait-agnostic manner  and \docDuroc pig, as an older breed of domestic pig, was developed in America and formed after a long period of artificial selection. In modern pig industry, Duroc pig is one of the most utilized commercial composite boar lines, and well-known for its growth, feed conversion efficiency, carcass and meat quality traits . BecauseFST and θπ ratio) to identify the potential large-effect NT trait-related genomic regions. Further, we conducted a comprehensive analysis to identify the putative pleiotropic genomic regions. The results of this study uncover the genetic improvement of Chinese Duroc pig population over the past decade and will be used to inform future breeding decision.Herein, we first used two methods  with approval number SCAU#2013-10, and the experiments were performed according to the regulations and guidelines established by this committee.A total of 1067 animals consisted of 984 females and 83 uncastrated males from a Duroc pig population managed in Fujian, China, were used in this study. These animals were born between 2009 and 2017. All phenotypic records were extracted from the Herdsman swine management platform . The number of left teats, right teats were recorded by simple counting. The number of teats was the sum of the teat number at both sides. In addition, the number of left teats, right teats, and teats of each individual was counted at birth and the malformed teats were not recorded. Furthermore, we obtained the phenotypic (litter size at weaning (LSW), litter weight at weaning (LWW), days to 100 kg (D100), backfat thickness at 100 kg (B100), and loin muscle area at 100 kg (L100)) data from our previous studies .N > 10% of one read) were removed using Trim Galore version 0.6.1 to produce the clean reads. Further, the clean data were aligned to the Sus scrofa 11.1 reference genome using Burrows-Wheeler Aligner (BWA) version 0.7.15 [2 ≥ 0.8; (2) the SNPs with a call rate ≥ 0.9 or MAF ≥ 0.01 or significant deviations from Hardy-Weinberg equilibrium ; (3) retaining sites with a mean depth < 3. Hence, a total of 1067 individuals with 8,766,074 independent SNPs were eligible for inclusion in the following analyses , then checked using agarose gel electrophoresis and quantified with a NanoDrop 2000 . Either the Illumina PorcineSNP60 BeadChip  comprising 63,480 SNPs or the GeneSeek GGP-Porcine chip  comprising 51,558 SNPs were used to genotype the individuals. The common SNPs contained 33,359 SNPs between two chips were retained. Among the Duroc pigs used in the current study, we selected 50 key individuals using the marker-based genetic relationship matrix to maximize the expected genetic relationship between the key individuals and the remaining population, as in Ye et al. . These in 0.7.15 . The genn 0.7.15  was usedn 0.7.15 , followiAn animal’s age as of December 21, 2017 was used as the generation proxy in GPSM, and we fit a univariate genome-wide linear mixed model as follows:Y is an individual’s generation proxy; β is the estimated effect size for each SNPs; u is polygenic term and is set as G is the genomic relationship matrix. X and Z are incidence matrices for β and u; e is the random residuals and is set as I is an identity matrix. We used FDR corrected q-values to control for multiple-testing and SNPs with q-value < 0.1 was deemed to be significant variants [Where variants .Δj is the change in allele frequency of SNP j from 2009 to 2017, αj is the allele effect of SNP j, and m is the total number of SNPs. Later, we permuted SNP allele effects with 1000 times to generate We estimated a composite statistic We conducted ROH detection to identify regions of homozygosity using PLINK version 1.09  by a slit-test and listed in Table Sπ ratio and FST were used to detect signatures of selection in this phenotypic differential population pair with the use of a sliding window method (50 kb window and 10 kb step). The θπ ratio between LT group and ST group was calculated as ln(θπ|LT/θπ|ST). In addition, the 1% of windows with the highest θπ ratio and FST values was considered as the potential selection regions.The pairwise difference between the individuals born on each year were tested using a Welch Two Sample Y is the number of left teats, right teats, and total teats of the individuals; α is a vector of fixed effect, including the year and season of individuals at birth; β is the substitution effect of the SNPs; u is the random effect and is set as G is the genomic relationship matrix; W, X, and Z are incidence matrices for α, β, and u; e is the random residuals and is set as I is an identity matrix. The analyses were performed using GEMMA software [P-values lower or equal than 0.05/N , were regarded as genome-wide significant SNPs. SNPs with a P-value higher than 0.05/N but lower than 1/N were considered as genome-wide suggestive significant SNPs. Then, quantile-quantile (Q-Q) plots were drawn and the inflation factors (λ) were calculated to check the population stratification.GWAS was performed using a mixed linear model, as follows:software . The BonIn order to uncover pleiotropic effects of the intron mutation (rs346331089) and its potential effect on the phenotype in Duroc pigs, we utilized our previous reported data sets of economic traits, followed by implementing the association analyses between rs346331089 and economic traits using PLINK with “-linear” parameter.We downloaded a gene expression matrix of Duroc pig tissues  from publicly available datasets . As in ZP-value based on multiple tests less than 0.05 were retained. Furthermore, the genes located in putative selection regions were identified using R package GALLO [Sus scrofa 11.1 reference genome assembly. We used R package clusterProfiler [P-value < 0.05 were selected.The Animal QTL Database  was usedge GALLO . Then, tProfiler  to condun = 50) of a factory-farmed Duroc pig population was re-sequenced with 150 bp paired-end reads using the Illumina HiSeq 3000 platform. A total of 19,754,293 SNPs was found, of which 6.13% were novel in comparison with the latest pig SNP database . In addition, transition-to-transversion (TS/TV) ratios of SNPs was 2.33. With the use of Variant Effect Predictor (VEP) to determine the effect of the variants, we obtained location of the variants and the most severe consequence of the variants on the protein sequence. The most SNPs  were located in intron regions, followed by intergenic (19.94%), downstream genic (8.71%), and upstream genic regions (8.37%). Moreover, the plot showed that SNPs were evenly distributed across porcine autosomes  > 0.8. The proportion of each consequence types was broadly similar with reference panel, and the distribution of SNPs was shown in Fig. SAfter imputation with key individuals as reference panel, we obtained a large-scale genotyping dataset contained 8,766,074 high-quality SNPs with dosage R-squared DR > 0.8. Tq-values < 0.1) were significantly associated with birth date in the Chinese Duroc pig population  3 . Moreover, we conducted P-value = 0.0002, Fig. SP-value = 0.0003, Fig. SP-value = 0.0009, Fig. STo further estimate the direction of selection on NT relevant traits, we summarized the phenotypic records and found that there were significant differences in the number of teats between pigs born on 2009 and 2017 . Genotype effects of rs346331089 on NT traits showed similar trends in its effects on D100 and B100 traits, whereas its effects had opposite patterns for LSW, LWW, and L100 traits. Overall, human-induced selection for the genetic improvement  at the genome regions might lead to the decreasing number of teats.We further found that rs346331089 has pleiotropic gene action on NT and economic traits, e.g., LSW, LWW, D100, B100, and L100 traits  were reported to be associated with reproduction traits [Based on the results of the GPSM analyses, we infer that the Chinese Duroc pig population was mainly subjected to high-intensity artificial selection on production and meat quality traits, in line with the breeding goals of Duroc pigs . Furtherat level . We obsen muscle . FASN is in pigs . GAS7 wa in pigs . Moreove chicken . In addin traits , 32.MAD2L1 and SEC24D) that were putatively under selection in American and Canadian Duroc pigs have been previously reported [In addition, we detected ROH hotspots to explore the selection landscape of the studied population. A set of candidate genes  overlapped with traits-specific selection signatures have underlying correlations with economically significant traits. ASPH are involved with tissue morphology, skeletal and muscle development, and fat deposition [CYP46A1, PNLIP, and DPP10 have been identified as regulators of lipid metabolism [EPHA4, which was detected in the endometrium during embryo implantation in pigs, was found to have relationships with reproduction traits [PLCB4 was implicated in growth and stature traits, and has been identified as genes under directional selection between Duroc and Duroc synthetic pig populations [KCNK10, as a member of tandem pore domain potassium channel family, involves in stabilizing the negative resting membrane potential and counterbalancing depolarization. KCNK10 has been reported that it is a regulator of mitotic clonal expansion during the adipocyte differentiation [FOXN3 could be considered as a candidate gene for the hairless phenotype in pigs [RND3 was identified as a candidate gene for residual feed intake in pigs [RIF1 was one of putative regulatory factors that contribute to the molecular mechanisms that underlie fat content and energy balance in muscle [During the rapid improvement of the performance of economic traits, the number of teats has decreased in the Chinese Duroc pig population. We hypothesized that genetic correlations between NT and economic traits. Traits-specific selection signatures were high enriched in both teat number relevant traits and fat-related traits, which confirmed that the hypothesis was correct. Moreover, several candidate genes  and functional classification (B) of the detected single-nucleotide polymorphisms (SNPs).Additional file 2: Fig. S2. The distribution (A) and functional classification (B) of the Imputed single-nucleotide polymorphisms (SNPs).Additional file 3: Fig. S3. The LD block in significant chromosome regions located in Sus scrofa chromosome (SSC) 3 (A) and 8 (B).Additional file 4: Fig. S4. Ghat analysis with phenotypic records of the number of total teats (A), left teats (B), and right teats (C).Additional file 5: Fig. S5. Detection of ROH in the Chinese Duroc pig population. (A) Manhattan plot of the occurrence (%) of each SNP in ROHs of Duroc pigs. The dashed line corresponds to the significance threshold. (B) QTL enrichment analyses with ROH hotspots. The richness factor was obtained by the ratio of the number of QTLs annotated in the candidate regions and the total number of each QTL.Additional file 6: Fig. S6. The Manhattan plots of GWAS for the number of left and right teats traits. The Y-axis of Manhattan plots displayed the -log10(P) of each SNP in the genome wide association analysis for the number of left (A) and right teats (B) traits, the X-axis represented the position of SNPs for chromosomes. (C) QTL enrichment results of GWAS on teat number relevant traits.Additional file 7: Fig. S7. Regional association plots around rs322980623. (A) GWAS result around rs322980623. (B) pCADD values around rs322980623.Additional file 8: Fig. S8. Regional association plots around rs324534752. (A) GWAS result around rs324534752. (B) pCADD values around rs324534752.Additional file 9: Fig. S9. QTL enrichment results of the potential selection regions around rs346331089 (A), rs322980623 (B), and rs324534752 (C).Additional file 10: Table S1. The pairwise difference of teats number between each birth year using a Welch Two Sample t-test.Additional file 11: Table S2. The significant Generation Proxy Selection Mapping (GPSM) signals.Additional file 12: Table S3. Candidate genes that overlapped with ROH hotspots.Additional file 13: Table S4. Candidate genes that overlapped with teat number relevant traits-specific selection signature.


Colletotrichum sublineola is a destructive fungal pathogen that causes anthracnose in sorghum. Senegalese sorghum germplasm is currently being considered as an option of sources for genetic resistance. In a recent study, Senegalese sorghum accessions were evaluated for response to a mixture of Texas isolates of C. sublineola at the 8-leaf stage in the greenhouse. As a comparison, 159 Senegalese sorghum accessions at the 1-leaf developmental stage were evaluated against a single Texas isolate of C. sublineola (FSP53) using an excised-leaf assay. A genome-wide association study (GWAS) was conducted based on the phenotypic data acquired to discover genetic variation associated with response to C. sublineola using 193,727 single nucleotide polymorphisms (SNPs) throughout the genome. Sorghum seedlings tended to be more resistant when compared with sorghum plants inoculated at the 8-leaf stage in the greenhouse in previous experiments. Based on the highest score evaluated in the 1-leaf developmental stage excised leaf assay for each accession, 16 accessions were labeled as susceptible. GWAS identified the SNP locus S01_72868925 that is associated with protein kinase domain // Leucine rich repeat N-terminal domain at a level of confidence that surpassed Bonferroni correction. Along with the SNP locus S01_72868925, other top SNP loci were also associated with genes that are known to play critical roles in plant defense or plant stress responses. Sorghum bicolor (L.) Moench] is a multipurpose food crop which is ranked among the top five cereal crops in the world1. Like other important cereal crops, sorghum is consistently challenged by biotic and abiotic stresses that constrain overall productivity2. Sorghum anthracnose caused by Colletotrichum sublineola Henn. ex Sacc. & Trotter 1913 is an important disease of cultivated sorghum worldwide3. In recent years, genome-wide association studies (GWAS) have identified potentially important defense-related genes against C. sublineola in various sorghum germplasms6. As examples, genes that encode an F-box, a protein tyrosine kinase, a leucine rich repeat and a peroxidase were identified as top candidates for anthracnose resistance in the sorghum association panel (SAP) lines4. Similar studies identified genes whose products include motifs such as pentatricopeptide repeat and a leucine-rich repeat (LRR) as top candidates in the same SAP lines5.Sorghum , a wild relative of sorghum, were reported to be more resistant in young plants compared to fully grown plants when inoculated to a sorghum isolate of C. sublineola13. As Table 8. Majority of accessions were resistant in seedling stage, but they become more susceptible at the 8-leaf stage. In contrast, 11 cultivars showed completely opposite results; susceptible seedlings were detected, but acervuli formation was not observable in 8-leaf stage plants in the previous study 8. Both of these changes infer different resistance mechanisms operate in juvenile versus older plants. If dhurrin is inhibitory, these plants may simply be those with naturally low levels. The single isolate used in these studies was selected due to its pathogenicity on BTx635 at both stages of growth whereas a mix of isolates known to represent different pathotypes was used in in the previous tests. Nevertheless, a recent study showed that following inoculation with single or a mixture of two isolates of C. sublineola, had minimal to no effect, in that infection rates were more characteristic for the virulent isolate in mixed inoculum14. The use of the single isolate may contribute the decrease in number of high scores, but as FSP53 strain has a strong and broad virulence pattern to sorghum cultivars11, the effect is assumed to be minimal. The results of screening sorghum to C. sublineola can also be altered by environmental effects. For example, screening results in Texas and Puerto Rico differed in a study8. Similarly, SAP lines responded differently to anthracnose based on inoculum and field locations4. PI514284, PI514293 and PI514473 were susceptible at both seedling and 8-leaf stage8. It is speculated that change of phytoalexin levels may vary from seedling to 8-leaf stage in individual sorghum accessions.Sorghum seedlings are known to be more resistant compared to later growth stages. It has been assumed this may be because they contain the preformed cyanogenic glycoside dhurrin, which may play a role in seedling protection8. Although not the same SNP, GWAS analysis based on identical accessions after inoculation at the 8-leaf stage showed the SNP locus S06_60609133 as the top candidate SNP, and the SNP locus S06_60609133 tagged leucine rich repeat/protein tyrosine kinase (Sobic.006G274866)8. In other studies, sorghum mini core collection and SAP lines, regarding anthracnose and head smut, leucine rich repeat containing proteins were commonly listed as top candidate genes6, but chromosomal locations differed in each study.The SNP locus S01_72868925 is located in protein kinase domain // Leucine rich repeat N-terminal domain (Sobic.001G451800). LRRs are a feature of nearly all cloned resistance genes and are widely known for roles in plant host defense15. Transgenic plants with reduced PARP levels have broad-spectrum stress-resistant phenotypes16.The SNP locus S08_7370058 is 16202 bp away from a poly (ADP-ribose) polymerase (PARP), catalytic domain. PARP domains have also been implicated as factors in stress responses of plants17. CHS expression causes accumulation of flavonoid and isoflavonoid phytoalexins and is involved in the salicylic acid defense pathway17.The SNP locus S09_51943886 is located within the gene coding for flavonoid 3'-monooxygenase. Chalcone synthase (CHS) is a key enzyme of the flavonoid/isoflavonoid biosynthesis pathway9, it was expected to see majority of 1st leaves to be scored as 1. Sorghum pathologists often label an accession as susceptible when even a single sorghum plant among a number exposed to inoculum can be infected. An example is head smut caused by Sporisorium reilianum (Kühn) Langdon & Fullerton. Similarly, a GWAS analysis based on the highest score of the 1st leaves based on the excised-leaf assay in each accession identified candidate genes associated with SNP loci supported by strong statistical power. As sorghum responses differed at the 1-leaf stage versus the 8-leaf stage, it is not surprising to identify novel SNP loci, including the SNP locus Sobic.001G451800 that are associated with seedling defense, but it will be essential to explore the response more deeply in the future. It is quite possible that different SNPs are highly associated with sorghum defense in seedling stage only or throughout the whole growth stages. The candidate genes identified in this study can be tested for further analysis such as real-time quantitative reverse transcription PCR  or RNA sequencing analysis (RNA-Seq) to confirm that different genes are highly expressed in seedling stages. Gene editing technology can also be applied to verify the defensive roles of the candidate genes. Lastly, level of cyanogenic glycosides can be measured at seedling and 8-leaf stages to find associations with phenotypes and the identified genes.As sorghum seedlings typically contain the preformed cyanogenic glycoside dhurrinC. sublineola with an excised-leaf assay and verified that the excised-leaf assay can be useful to study seedlings, but compared to 8-leaf stage inoculation methods, a greater number of seedlings should be screened to determine susceptibility in each accession as the results can easily be skewed.In sum, this study explored sorghum seedling responses to C. sublineola interactions; it can be rapidly conducted within laboratory setting.However, the excised-leaf assay applied to 1-leaf stage seedlings is expected to promote studies associated with sorghum seedlings and C. sublineola) were obtained from the USDA-ARS Plant Genetic Resources Conservation Unit. BTx623, TAM428, and PI609251 (susceptible) and SC748-5 (resistant) were used as positive and negative controls18.The 159 accessions  plate and grown in an incubator for 2 weeks. FSP53 is one of the most virulent isolates based on the response of susceptible checks BTx623, TAM428, and RTx430 in field evaluation5, and the identical Senegalese sorghum accessions’ response to a mixture of Texas isolates including FSP53 were screened at the 8-leaf stage in a greenhouse recently8. Approximately 50 ml of sterile water was added to the plate, and a sterile spatula was used to scrape and remove colony growth, and the suspension mixture was screened through four layers of cheesecloth to obtain conidia19. Conidia concentrations were adjusted to a f 106 conidia/ml19. For the excised-leaf assay, whole leaves of each cultivar were placed, adaxial side up, on a half strength PDA plate, and 5 μl of the spore suspension was inoculated on the leaf blade19. In each trial, three 1st leaves were inoculated for each accession, and 3 trials of the excised-leaf assay were conducted . Excised 1st leaves were inspected under an Olympus BX60 microscope  with 10 × magnification at 96 h post-inoculation, and scored for seedling responses to C. sublineola were recorded by using a 1–5 scale where 1 = fully resistant without visible fungal infection; 2 = fungal germ tube formed; 3 = fungal bed formed with some imperfectly formed acervuli; 4 = 1–5 acervuli perfectly formed and 5 = more than 5 acervuli perfectly formed19. Among 9 observed 1st leaves, the highest score was used to determine the symptom types to be categorized into two reaction types: Ratings 1 or 2 as resistant; ratings 3–5 as susceptible19. All raw scores are listed in Supplemental Information.Plug flats with 40 Square Cells (L x W x H≈ 5 cm × 5 cm × 7 cm for each cell)  were filled with Metro Mix 200 , and sorghum accessions were grown at 23 °C with 65% humidity under LED light for approximately 12 h a day. When seedlings reached at l-leaf stage, an excised-leaf assay described by Prom et al.8 were used. Originally, SNP data were extracted from an integrated sorghum SNPs dataset based on sorghum reference genome version 3.1.1 and originally genotyped using GBS23, and missing data were imputed using Beagle 4.124.SNP data from a recent study12 version 5.2.80 was used to conduct a mixed linear model (MLM) association analysis based on the highest score for disease ratings in each accession. False associations were reduced by removing SNPs with greater than 20% unknown alleles. Furthermore, SNPs with minor allele frequency (MAF) below 5% were removed as well. SNPs contributing to seedling responses to C. sublineola were tracked to the specific chromosome location by using the sorghum genome sequence, version 3.1.1 available at the JGI Phytozome 13 website25. To verify statistical significance, the mean disease rating score for all Senegalese accessions with either of the two prevalent bases was determined and verified to differ significantly (P < 0.05) based on Student’s T-test by using JMP Pro 15 5. Any top candidate SNP with (P ≥ 0.05) were removed from the results reported in Table TASSELSupplementary Information.