Background: Small ruminant lentiviruses (SRLVs) cause a multisystemic chronic wasting disease in sheep across much of the world. SRLV subtype A2 is prevalent in North America and further classified into multiple subgroups based on variation in the group antigens gene (gag) and envelope (env) genes. In sheep, the ovine transmembrane protein 154 (TMEM154) gene is associated with SRLV susceptibility. Ewes with at least one copy of TMEM154 encoding a full-length protein with glutamate at position 35 , are highly susceptible to SRLV infection while ewes with any combination of TMEM154 haplotypes which encodes lysine , or truncated proteins (haplotypes 4 and 6) are several times less so. A2 subgroups 1 and 2 are associated with host TMEM154 genotypes; subgroup 1 with the K35/K35 genotype and subgroup 2 with the E35/E35 genotype.Methods:  Sequence variation within and among full-length assemblies of SRLV subtype A2 subgroups 1 and 2 was analyzed to identify genome-scale recombination patterns and subgroup-specific variants.Results:  Consensus viral genomes were assembled from 23 infected sheep, including animals of assorted TMEM154 genotypes comprised of haplotypes 1, 2, or 3. Viral genome analysis identified viral subgroups 1 and 2 among the samples, and revealed additional sub-structure within subgroup 2 based on models predicting complex patterns of recombination between the two subgroups in several genomes. Animals with evidence of dual subgroup infection also possessed the most diverse quasi-species and the most highly recombined consensus genomes. After accounting for recombination, 413 subgroup diagnostic single nucleotide polymorphisms (SNPs) were identified.Conclusions:  The viral subgroup framework developed to classify SRLV consensus genomes along a continuum of recombination suggests that animals with the TMEM154 E35/K35 genotype may represent a reservoir for producing viral genomes representing recombination between A2 subgroups 1 and 2. Retroviridae. SRLV infect sheep, goats, and wild ruminants worldwide causing a lifelong persistent infection clinically characterized by wasting, interstitial pneumonia with labored breathing, indurative mastitis, arthritis, and/or more rarely, encephalitis  are a genetically diverse group of lentiviruses belonging to the familyhalitis . Diseasegag,pol, andenv) and three regulatory genes flanked by non-coding long terminal repeat regions (LTRs). Phylogenetic analysis based on partial group antigens gene (gag)and polymerase (pol) gene sequences divides these viruses into five major genotype groups, A-E, which are further divided into different subtypes  gene associates with SRLV susceptibility  and does not produce mRNA encoding functional amino acids downstream of amino acid position 4 of the precursor protein. Consequently, subgroup 1 associated with hemizygous or homozygous K35 genotypes. SLRV A2 subgroup 2 viruses were more likely to infect sheep with one or two copies of either haplotype 2 or 3, and that could also have one copy of haplotype 4 , which were not thought to have critical roles in potential SRLVTMEM154 interactions  obtain full-length consensus genomes for members of SLRV A2 subgroups 1 and 2; 2) identify subgroup 1 and 2 specific variants, while accounting for recombination, and use these variants to estimate levels of intra-host sequence variation; 3) search for subgroup-specific functional viral variants relative to hostTMEM154 E35K genotypes.SRLV A2 subgroups 1 and 2, and their associations withAll animal procedures were reviewed and approved by the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), U.S. Meat Animal Research Center (USMARC) Animal Care and Use Committee prior to their implementation (Experiment Number 96). The source flock’s history of disease surveillance is also relevant when requesting reference samples described in any report. Since first stocking sheep in 1966, USMAC has not had a known case of scrapie. Until 2002, surveillance consisted of monitoring sheep for possible signs of scrapie and submitting brain samples to the USDA Animal and Plant Health Inspection Service (APHIS) National Veterinary Services Laboratory in Ames, IA for testing. All tests have been negative. Since April 2002, USMARC has voluntarily participated in the APHIS Scrapie Flock Certification Program, is in compliance with the National Scrapie Eradication Program, and is certified as scrapie-free. With regards to other transmissible diseases, it is recognized that the USMARC flock of 2000 to 4000 breeding ewes is located in a bluetongue medium incidence area and is known to have some prevalence of contagious ecthyma (sore mouth), foot rot, paratuberculosis (Johne’s disease), ovine progressive pneumonia (visna-maedi), and pseudotuberculosis caseous lymphadenitis.TMEM154 haplotypes  in minimal essential medium . Homogenates were then subjected to two freeze/thaw cycles to further ensure cell lysis. Homogenates were clarified by centrifugation followed by sequential filtration through 0.45 and 0.2-µm syringe filters to remove cellular debris. Clarified samples (250 uL) were treated with 20 U RNase One  and 30 U Turbo DNase  in 1x DNase buffer (Ambion) at 37°C for 90 minutes to degrade unprotected host and environmental nucleic acids. To ensure continuous DNase activity, 10 U of DNase was added to the sample every 30 minutes during the 90-minute incubation. Remaining nucleic acids were isolated using Trizol LS  according to the manufacturer’s instructions. A final DNase treatment was performed to remove final traces of DNA from the RNA preparation.Colostrum (approximately 4 mL) was manually collected from a SRLV seropositive ewe within the first 24 hours after giving birth. Colostrum was diluted 1:2 with cold phosphate-buffered saline and centrifuged at 800 x g for 15 minutes at 4°C. The cream layer was skimmed from the top and 250 µL of milk was treated with nucleases and RNA was extracted as described above.cutadapt 1.9.1 (BBDuk 35.82 (Brian Bushnell within Geneious 11.1.4 (BBMerge 8.9 (Bushnell within Geneious).RNA libraries were prepared as previously described (t 1.9.1  orBBDukThe remaining RNA was used to generate long-read sequencing libraries according to a modified RNA Iso-Seq with poly(A) tails added to the 3’ ends to allow cDNA synthesis of subgenomic fragments. The resulting cDNA was amplified, size fractionated and the largest fragments were used to make SMRTbell templates, which were sequenced on a Pacific Biosciences RSII instrument. SMRT Analysis was used to generate error corrected circular consensus sequences (CCS) from the raw reads and adapters and poly(A) tails were removed with BBDuk.de novo with the Geneious assembler. All reads were then mapped to thede novo assembly, and the resulting consensus sequence was reported. Four strains lacked good quality long-read sequence data . 79 unique genomes were aligned using MUSCLE 3.8.425 , which relate to the biochemical recombination rate (r) according to the formulaerp=2N whereeN is the effective population size.eN is difficult to estimate. Estimates for HIV, a related lentivirus which also produces chronic infections, vary by several orders of magnitude  and fineSTRUCTURE assigned the genomes to populations based on the quantity and lengths of these shared genomic chunks. The following settings were changed from the default in fineSTRUCTURE and/or ChromoPainter to ensure convergence of estimated parameters: ChromoPainter chunks-per-region parameter k=38, ChromoPainter samples s=10, Markov Chain Monte Carlo (MCMC) iterations=1e6 fineSTRUCTURE tree finding maximization steps=1e6 and fineSTRUCTURE independent MCMC runs=10. ChromoPainter iterations i=100 were run for estimating the global switch rate parameter and the global mutation rate. ChromoPainter initializes at the mutation rate ofPopulation assignment of each genome generated in this study was performed in-3 to 10-5 (-3 to 10-8 Morgans-per-base pair) to confirm that diagnostic SNP count did not change when varying input recombination rate by several orders of magnitude. The possibility of a historical recombination event giving rise to subgroup 2a from an introgression of subgroup 1 sequence on a subgroup 2b background was investigated using RDP5  from the fineSTRUCTURE analysis. Population specific mutation rates for the donor specific recombination models were calculated as the mean of the constituent strain-specific mutation rates estimated by ChromoPainter during all vs all painting. Donor specific recombination models were run for 500 iterations to ensure convergence of copy probability. In contrast to all vs all painting, donor specific painting assigns genomic chunks to recipient genomes based on donor populations comprised of multiple genomes. To increase genome-wide assignment probability of subgroup specific SNPs, consensus genomes with evidence of large recombination blocks were iteratively removed from each subgroup pool of donor genomes if average copy probability was increasing. This was done to eliminate the most obvious recombinants from the pools while retaining enough donor genomes to optimize the recombination model. The output donor subgroup-assignments for each SNP were used to identify subgroup-specific SNPs while accounting for recombination. Recombination rates have been estimated for several RNA retroviruses, and most estimates are in the range of 10-6, Minimum Strand-Bias P-value of 10-5 when exceeding 65% bias) relative to their final alignment in Geneious 11.1.4. The percentage of subgroup 1, subgroup 2, and ‘other’ SNPs at each diagnostic locus was calculated for each consensus genome. Subgroup partial dual infections were inferred as contiguous or nearly contiguous SNP blocks bearing both subgroup diagnostic alleles. For visualization relative to the consensus genome, these inferred partial dual infections were limited to genome blocks or scaffolds ≥50 nucleotides long where variants diagnostic for both subgroups co-occurred at a frequency of ≥5% and ≥2 reads. The 5% threshold was chosen as it was a conservative value accounting for sequencing error and mis-mapping when calling quasispecies SNPs. Multiple putative dual infection blocks were extended or scaffolded together when separated by <50 nucleotides and 1 diagnostic SNP. We characterize these as being caused by dual infection with unknown underlying viral haplotypes containing SNPs diagnostic to both subgroups at these regions as this is the most parsimonious explanation. However, quasispeciation in the absence of dual infection is an increasingly possible explanation as the numbers of adjacent subgroup diagnostic SNPs in the characterized regions decrease.The subgroup-specific SNP content was quantified for each strain by extracting intra-host SNPs meeting default statistical restrictions  at the subgroup diagnostic loci were identified by extracting variants from the alignments in Geneious 11.1.5 using the same statistical criteria applied to nucleotides and occurring at a frequency of ≥5%. Highly variable domains inalP-5.0  was usedCoverage of the 23 genomes ranged from 52- to 2661-fold . CompletfineSTRUCTURE was then used to classify the 23 genomes from this study into discrete populations that could be used in recombination analyses. The program first computed the pairwise similarities and then performed clustering to summarize relationships between genomes. Six distinct populations were identified by fineSTRUCTURE , includiExtended data, Supplemental Figure 1 (-67). Additional details of the event are provided inExtended data, Supplemental Table 2  accounted for >88% of the SNPs and were the majority donors to 22 genomes (igure 1 ). These igure 1 . Due to igure 1 , the modigure 1 . This moigure 1 . The putTable 2 . SubgrouTable 2 . All modigure 1 ). Finalligure 1 . This waigure 1  and thisExtended data, Supplemental Table 3 (Subgroup diagnostic SNP inference accounting for recombination between subgroup 1 and subgroup 2 (as fineSTRUCTURE population 2a+2b) resulted in 413 diagnostic SNPs (Table 3 ). The frTable 3 .Extended data, Supplemental Table 4 , averaging 261.6 nucleotides in length (range: 55–1482) and comprising 2%–45% of the genome . A2 subTable 5 ). AnalysTable 5 ). Six suTMEM154 E35K genotypes . The dual infection analysis identified multiple genomic regions featuring modest frequencies of both subgroup diagnostic alleles  and higTable 4 ,Table 2igure 1 ) despiteigure 1 . But theigure 1 . More thTable 2 ). These Table 2 . BecauseTMEM154/A2 association studies chosen was a good starting place for modeling recombination, the addition of larger numbers of geographically diverse SRLV A2 genomes should improve the recombination model(s) substantially due to a larger pool of potential recombinant and parental genomes  have been extensively characterized for HIV . While  ofenv . Subgrou ofenv . The genanimals . Additioenv gene variable region 4 (V4) which was recently identified to contain ’signature patterns’ associated with different clinical status in sheep and goats  can be seen in the downloaded file.Supplemental Table 1. Genbank accession numbers and viral strains for 79 complete or near-complete SRLV genomes. The genomes are listed clockwise in the order they appear in Figure 1 of DickeySupplemental Table 2. Details on Recombination Detection Program 5 (RDP5) recombination event 3.Supplemental Table 3. Four hundred and thirteen diagnostic SNPs distinguishing SRLV A2 subgroups.Supplemental Table 4: Seventy-three genomic regions indicative of dual subgroup infection among 9 SRLV A2 genomes.Supplemental Table 5: Within-host amino acid variability  at 106 subgroup diagnostic loci. Colors according to subgroup 1 vs subgroup 2 recombination model  can be shttps://doi.org/10.6084/m9.figshare.14991975 .Extended data are available under the terms of the In this manuscript, the authors assembled the full-length viral genome sequence of 23 subtype A2 SRLV strains using long-read sequencing technology, and try to identify recombination patterns. The authors compare the 79 unique full-length SRLV genomes. I am reviewing the version 2 of this manuscript that has improved the previous version. The article is well-written, the results of the study are well presented, well-supported, and certainly add to the current knowledge of the topic. Authors could include the prevalence of the different  SRLV subtypes in the breed and the geographical area,  if it is known. The authors state that ewes heterozygous for E35K  may facilitate recombination between subgroups 1 and 2, but they only have 23 full length viral genomes, and this should be confirmed using a bigger experimental design. In my opinion, the manuscript is acceptable for indexing.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Research focused in the application of genomic tools in animal science, mainly in ruminants, including animal breeding, nutrigenomics and nutrigenetics, and also in animal health.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have addressed my concerns. Manuscript is now acceptable for indexing in this revised version.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Genetic and anitigenic characterization of Small Ruminant Lentiviruses (SRLV) in sheep, goats and wildlife ruminants I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this manuscript the authors assembled full length viral genomes and then analyzed specific segments in order to identify recombination patterns by using chromosome painting model. General comments: It’s not clear in the introduction whether this chromosome painting model is used for recombination detection in any other virus? Perhaps any other RNA virus at all? I would highly appreciate if the authors could expand their introduction about this. I would suggest the authors to include RDP software in methods and at least 1 figure in the results to see where the recombination events take place within the genome and compare these results with the chromosome painting one. Currently, these results are as not shown so it’s not very clear whether these results are complementary. My major concern with the recombination events that the authors describe in the manuscript are related with the differentiation between mutation and recombination. They mention in the introduction that RNA viruses have more mutations due to the low fidelity of the reverse transcriptase how did they differentiate these events from true recombination events? Specific comments: Abstract - Methods – The goal. Could the author explain the method rather than the goal? In the first paragraph would be good to read 1 or 2 lines about the importance or impact of this pathogen over sheep population not just the disease that cause the virus. Perhaps include incidence or any statistics about the disease that cause the virus. This would help to put this research in context. As the genome contains more than one non-coding long terminal repeat regions. I wonder how did the authors manage to sequence that specific part as I know they could be problematic to determine sequence identity. Are these regions inverted? Why the authors did not amplify the virus previous to sequencing? Is there any cell culture that could be used for this purposes? Could this help with the four strains that had low quality long read sequence? RDP software is mentioned in the discussion but not in the methods. The split tree software has a tool to measure recombination called PHI test. Is there a reason why the authors did not include this in the manuscript? What does PC stands for in Figure 2?Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Virology; recombination; herpesvirusI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Helicobacter pylori prophages  and Herpes Simplex Virus . This information has been added to the methods. The Yahara et al. reference may be most similar in initial approach. Ours may be the first use of ChromoPainter version 2 ‘donor mode’ to extract subgroup-diagnostic variants in viruses while accounting for recombination.It’s not clear in the introduction whether this chromosome painting model is used for recombination detection in any other virus? Perhaps any other RNA virus at all? I would highly appreciate if the authors could expand their introduction about this. Response: The recombination modeling program utilized in these analyses has been previously used to analyze recombination inI would suggest the authors to include RDP software in methods and at least 1 figure in the results to see where the recombination events take place within the genome and compare these results with the chromosome painting one. Currently, these results are as not shown so it’s not very clear whether these results are complementary. Response: We agree with the reviewer that our use of RDP belongs in the methods. We have attempted to do so in a way that does not distract from the primary focus of the manuscript, which was to identify subgroup-diagnostic variants, rather than recombination events. RDP was only used in this work to confirm a single putative event: the possible introgression of a large chunk of subgroup 1 sequence into a subgroup 2b background to create subgroup 2a . This event  was confirmed by RDP. This information has been added to the results and the details of the RDP event are provided in Extended data, Supplemental Table 2.My major concern with the recombination events that the authors describe in the manuscript are related with the differentiation between mutation and recombination. They mention in the introduction that RNA viruses have more mutations due to the low fidelity of the reverse transcriptase how did they differentiate these events from true recombination events? Response: All recombination analysis programs claim to account for mutation as this is a fundamental issue. ChromoPainter initializes at the mutation rate of Li and Stephens 2003, but we estimated it in our analyses by using the -iM flag according to program developer recommendations. Interestingly, ChromoPainter’s mutation rate estimates are 3-fold higher for subgroup 2 genomes compared to subgroup 1 (Table 2). Population specific mutation rate estimates were obtained by averaging the estimated rates from the fineSTRUCTURE run among members of the assigned population and incorporated into our increasingly refined recombination models using the -m flag. We have added this information and the Li and Stephens reference to the manuscript. Our subgroup-specific-colored blocks in figures 3-5 are best interpreted as regions of sequence assigned by the recombination model to those subgroups, not as events-per-se. We believe regions alternating back-and-forth between subgroups over short genomic spans (e.g. strains labeled k-r in figures 3-5), are sometimes best interpreted as having arisen from multiple recombination events over time. We have attempted to minimize the use of the term ‘event’ in the manuscript in service of this paradigm. Li N, Stephens M (2003) Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165: 2213–2233General comments:Abstract - Methods – The goal. Could the author explain the method rather than the goal? Response: The methods section now reads: Methods: Sequence variation within and among full-length assemblies of SRLV subtype A2 subgroups 1 and 2 was analyzed to identify genome-scale recombination patterns.In the first paragraph would be good to read 1 or 2 lines about the importance or impact of this pathogen over sheep population not just the disease that cause the virus. Perhaps include incidence or any statistics about the disease that cause the virus. This would help to put this research in context. Response: This information has been added to the first paragraph of the introduction.As the genome contains more than one non-coding long terminal repeat regions. I wonder how did the authors manage to sequence that specific part as I know they could be problematic to determine sequence identity. Are these regions inverted? Response: We used PacBio long-read technology to help resolve the ends of the viral genome.  We report some genomes as ‘near-complete’ because we did not obtain the complete LTR sequence for all genomes.Why did the authors not amplify the virus previous to sequencing? Is there any cell culture that could be used for this purposes? Could this help with the four strains that had low quality long read sequence? Response: It is true that the virus can be isolated in culture prior to sequencing; however, there were no commercially available cell lines for the isolation of SRLV at the time we initiated this project. While we could have developed appropriate cell lines, we chose not to isolate the virus first, as this can lead to the selection of a subpopulation of the infecting virus quasispecies. For these reasons, we sequenced the virus directly from the infected tissues.RDP software is mentioned in the discussion but not in the methods. Response: Please see the response to General comment 2.The split tree software has a tool to measure recombination called PHI test. Is there a reason why the authors did not include this in the manuscript? Response: SplitsTree5 does not have this analysis option, though we also remember it from SplitsTree4. We ran a PHI-test in RDP5 and now report it in the results.What does PC stand for in Figure 2? Response: Principal Component (PC), is now incorporated into the legend of Figure 2. Specific comments: In this article, the authors focused mainly on the analysis of recombination of 23 SRLV strains belonging to subtype A2. In this paper a lot of different analyses were performed, but the results and figures are described very poorly. This makes the article very hard to read. For example on Fig. 1 I see only 15 isolates not 23. Lack is information on accesion numbers or isolate names of sequences from GenBank present on the tree. Please indicate to which genotypes they belong. In Figure 2, there are three images that should be labeled as A, B and C and their description should be more detailed (in figure and in results). In the third picture I see three populations not six. I don't understand why all 23 sequences were used in the recombination analysis. The authors write that only subgroubs 2a, 2b and 1 was used in the reombination models but some genomes not belonged to these subgroups . In the discussion the results of the study are repeated.et al., 2013? There are no analyses regarding the occurrence of TMEM154E35K genotypes and susceptibility/resistance of sheep infected with and SRLV A2 subgroups 1 and 2. Did the research confirm previous studies described by SiderIs the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Genetic and anitigenic characterization of Small Ruminant Lentiviruses (SRLV) in sheep, goats and wildlife ruminants I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. In this article, the authors focused mainly on the analysis of recombination of 23 SRLV strains belonging to subtype A2. In this paper a lot of different analyses were performed, but the results and figures are described very poorly. This makes the article very hard to read. For example on Fig. 1 I see only 15 isolates not 23.  Response: Figure 1 is information dense, particularly the very similar subgroup 1 genomes denoted by the lavender-colored oval. However, we agree it is beneficial to list all genomes, so they are now listed in clockwise order in the new Supplemental Table 1.Lacking is information on accession numbers or isolate names of sequences from GenBank present on the tree.  Response: An added benefit to the new supplemental table (see previous comment) is space to put the GenBank accession numbers of all strains in Figure 1. Genbank accession number, isolate name, county of origin, and virus genotype are now listed in Supplemental Table 1.Please indicate to which genotypes they belong. Response: This information is made clearer in the figure by curly brackets, additional annotations, and clarification in the legend.In Figure 2, there are three images that should be labeled as A, B and C and their description should be more detailed (in figure and in results). In the third picture I see three populations not six. Response: The figures sections have been relabeled A, B and C and additional details are provided in the results. We are not certain but think the reviewer may be referring to 3 clusters of strains, not six, since principal components 2 and 3 (PC2 and PC3) do not strongly discriminate subgroups 1 and 2b.  We do not find this problematic since the first principal component strongly discriminates these two populations.I don't understand why all 23 sequences were used in the recombination analysis. The authors write that only subgroups 2a, 2b and 1 was used in the recombination models but some genomes not belonged to these subgroups . Response: Our initial recombination donor model had five donor populations and all 23 genomes as possible recipients . fineSTRUCTURE population 6 was excluded from this analysis on the grounds that singletons could not comprise a population for this type of analysis. The result suggests fineSTRUCTURE populations 4 and 5 are probably not populations either, justifying our continued refinement of recombination models to those with fewer donor populations, which better characterize the subgroups for which there is sufficient evidence, and recombination patterns among them. Our explanation of this is that fineSTRUCTURE assigned ‘population’ status to groups of genomes that would be better described as complex recombinants of already defined subgroups, rather than true populations. Over-splitting of these types of datasets into too many ‘populations’ is a known problem. See: Falush D, van Dorp L, Lawson D, 2016 A tutorial on how (not) to over-interpret STRUCTURE/ADMIXTURE bar plots. bioRxiv 066431; 10.1101/066431. While, fewer potential donor genomes were used in the refined recombination models , all genomes were used as recipients, allowing us to characterize recombination.In the discussion the results of the study are repeated. Response: We believed this was beneficial to organizing points of discussion.There are no analyses regarding the occurrence of TMEM154E35K genotypes and susceptibility/resistance of sheep infected with and SRLV A2 subgroups 1 and 2. Did the research confirm previous studies described by Sider et al., 2013? Response: We generated complete genome sequences from a subset of genomes characterized in the Sider et al., 2013 paper.  The goal was not to confirm or dispute the association, but rather, to generate complete genomes and develop a nucleotide polymorphism typing system for the two major subgroups while accounting for recombination. More complete SRLV sequences are required to identify which viral elements correlate with host adaptation.


CHST11 and SDCCAG8), (2) differentially expressed genes , and (3) biological pathways . These results indicated the potential physiological convergence and enhanced our understanding of the overlapping genetic makeup underlying litter size in goats and sheep.The litter size of domestic goats and sheep is an economically important trait that shows variation within breeds. Strenuous efforts have been made to understand the genetic mechanisms underlying prolificacy in goats and sheep. However, there has been a paucity of research on the genetic convergence of prolificacy between goats and sheep, which likely arose because of similar natural and artificial selection forces. Here, we performed comparative genomic and transcriptomic analyses to identify the genetic convergence of prolificacy between goats and sheep. By combining genomic and transcriptomic data for the first time, we identified this genetic convergence in (1) positively selected genes ( As ruminant livestock, sheep and goats are globally distributed and play important roles in providing meat, milk, and fur to humans. One primary objective of sheep and goat breeding programs is to increase the yield by phenotypic and genetic selection, and female fecundity is a basic and crucial key. Litter size (LS), the lambing number per parturition per ewe or the kidding number per parturition per doe, is an important component of goat and sheep productivity. Importantly and interestingly, variation in individual LS is common within breeds, such as 1–4 in Yunshang black goats, 1–5 Romanov sheep, and 1–6 in Finnsheep . Breeds FecB and FecX) [It is important to elucidate the genetic mechanisms of LS to efficiently utilize these prolific resources. Therefore, numerous studies have been separately performed to enhance our understanding of goat and sheep LS ,4,5,6,7.nd FecX) ,9, in whnd FecX) ,11, thusTherefore, the objective of this study was to identify the genetic convergence of prolificacy between goats and sheep using whole-genome data by examining (1) positively selected genes (PSGs), (2) differentially expressed genes (DEGs), and (3) biological pathways. These results will enhance our knowledge of prolificacy and can be used to improve the molecular design breeding of both goats and sheep.n = 40; Yunshang black goats used ovarian mRNA sequencing (YSR), n = 20) and three sheep populations ; the YSD, FIN and ROM data were obtained from previous studies [p-value < 0.01, Mann–Whitney U test, This study included two Yunshang black goat populations (Yunshang black goats used for DNA sequencing (YSD), To detect PSGs for LS, 20 YSD (LS ≤ 1.5), 9 FIN (LS ≤ 2), and 9 ROM (LS ≤ 1.8) were labeled as controls, and 20 YSD (LS > 2.3), 10 FIN (LS > 3.28), and 10 ROM (LS ≥ 3) were labeled as cases. Likewise, to identify DEGs for LS, 10 YSR (LS ≤ 2) and 6 STH (LS = 1) were selected as controls, and 10 YSR (LS ≥ 3) and 6 STH (LS > 2.66) were selected as cases. For ovary collection, estrus synchronization with controlled internal drug release  for 16 and 12 days was separately performed for YSR and STH. Finally, 10 YSR (5 cases and 5 controls) and 6 STH (3 cases and 3 controls) were slaughtered within 45–48 h of CIDR removal for ovary harvest at follicular phase (FP), and the remaining 10 YSR and 6 STH were slaughtered on days 11 and 9 for ovary harvest at luteal phase (LP).p-value > 10−6.Genomic DNA was extracted from the jugular venous blood of YSD and ear marginal tissues of FIN and ROM following a standard phenol–chloroform method. To obtain genomic SNPs of YSD, whole-genome sequencing of a single library was performed using the Illumina NovaSeq 6000 system , and 150-bp paired-end reads were generated. To obtain clean reads, the following unqualified raw reads were removed: (1) adaptor-polluted reads, (2) reads with over 50% bases with a Phred score <19, and (3) reads with over 5% N bases. Clean reads were mapped to the ASR1 reference genome using a Burrows–Wheeler Aligner v0.7.9a , with anTo explore the genetic relationship of individuals from YSD, FIN and ROM, Plink command “--indep-pairwise 50 5 0.2” was run to generate independent effective SNP sets for principal component analysis .To detect PSGs for LS, the fixation index (Fst), which is a measure of population differentiation, was calculated in VCFtools v0.1.14  using WeTo calculate Fst_RMS, only SNPs shared by FIN and ROM and those with nonnegative Fst values were used. The results were visualized using R package qqman .To identify loci with extremely high Fst values that drive reproductive differences of LG and HG, we ranked the regions/loci in descending order on the basis of Fst(YSD) and Fst_RMS, respectively. The regions with extremely high Fst(YSD)  were treated as candidates for goats, and the SNPs with extremely high Fst_RMS  were selected as candidates for sheep. Finally, PSGs wholly or partially located at these regions or covering candidate SNPs were detected by ANNOVAR v2020-06-07 .HS3ST2 variations and reproductive performance, an additional 544 2–5-year-old female Yunshang black goats were genotyped using a MassARRAY® SNP system. The primer sequences are in To explore the association of three q-value  <0.5, and |Log2FoldChang| ≥ 0.24. Likewise, the total RNA of 12 ovine ovaries for LG and HG at FP and LP  were subjected to the same processing, except they were mapped to the Oar_v3.1 reference genome.The total RNA of hircine ovaries for LG and HG at FP and LP  was extracted and sequenced using the Illumina NovaSeq 6000 platform . After filtering out the adapter reads and unqualified reads, clean reads were mapped to the ASR1 reference genome using HISAT2 , followep-value < 0.01 were considered to be significantly different. Similarly, DEGs were also used to identify the connected gene set for prolificacy between goats and sheep. Therefore, convergent biological pathways were identified as the mutual GO  and KEGG Ontology (ko) terms.To reveal the potential genetic convergence in goat and sheep prolificacy, we focused on (1) PSGs, 2) DEGs, and (3) biological pathways at the level of gene set. Given the availability of PSGs in HG using the number of segregating sites by length (nSL), a haplotype-based statistic with test power to both soft and hard sweeps  DEGs, an, convergThe principal component analysis  demonstrHS3ST2 on chromosome 25 was found to have the strongest selection signature , of which ignature . The var with LS . Coding  of rats . HS3ST5,in sheep . Given tin goats , these rCHST11, SDCCAG8, ATF6, FAM13A, HSP90B1, IBSP, IFNLR1, and STAB2 (CHST11 and SDCCAG8) were also under selection in sheep for which 162 PSGs were identified by Fst_RMS , suggesting that CHST11 and SDCCAG8 are significantly convergent in goat and sheep prolificacy. CHST11 , which encodes carbohydrate sulfotransferase 11, is a target gene of transforming growth factor β (TGFβ)-like growth factors  [Chst11 was found to regulate the proliferation and apoptosis of embryo chondrocytes in mice [CHST11 increases upon the interaction of BMP4 with a phospho-Smad3 binding site in the CHST11 promoter [CHST11 was linked to ovarian cancer [SDCCAG8, is a risk gene for Bardet–Biedl syndrome, which is primarily characterized by postaxial polydactyly, hypogonadotropic hypogonadism, and/or genitourinary malformations, and secondly characterized by polycystic ovary syndrome [SDCCAG8 is a candidate gene for hind limb and rib cage malformations in mice [BMPR1B, a major gene for sheep LS, plays important roles in both chondrogenesis and embryogenesis [SDCCAG8 for SMAD5, a member of the TGFβ signaling pathway, and strong differentiation signals have been observed in SMAD5 of Shaanbei White Cashmere goats with different LS [SDCCAG8 at both LP and FP  and nSL of HG in goats: nd STAB2 . Of thes Fst_RMS . This nuActivin) . Chst11  in mice . The mRNpromoter . Furthern cancer , endometn cancer , and porn cancer . Howeversyndrome . SDCCAG8 in mice ,37 and t in mice . Interesogenesis ,40, indiP and FP . Taken tSERPINA14, RSAD2, and PPIG) at FP and five  at LP  and 1665 DEGs (826 up- and 839 down-regulated) were identified in HG at LP and FP, respectively . For she5) at LP . Of thesd low LS . Additioecundity . IGF1 isnd sheep ,44,45, sLIPG as a candidate gene for piglet uniformity measured as the coefficient of variation of birth weights within one litter [RSAD2 is a marker for dendritic cell maturation [RSAD2 was up-regulated in early atresia and progressive atresia groups in pigs [GPRIN3, CHST15, and PPIG in reproduction are unclear. Collectively, it is likely that these DEGs influence female reproduction by multiple processes, such as follicular development, immunoregulation, and pregnancy maintenance.Previous studies reported the differential expression of LIPG mRNA or protein in the theca interna during the transition from small to large antral bovine follicles , in the e litter . These fe litter ,51,52. Rturation  and earlturation . Moreove in pigs . The funAt the single gene level, it is possible to infer convergent signals from overlapping DEGs or PSGs between goats and sheep. At the gene set level, GO and KEGG pathway analyses for PSGs and DEGs were implemented to identify the overlapping biological pathways that include a series of functionally connected genes. For goat PSGs, 192 GO and 30 ko terms were revealed, as expected, six acrosome reaction-related GO terms were detected: binding of sperm to zona pellucida (GO:0007339), regulation of acrosome reaction (GO:0060046), single fertilization (GO:0007338), regulation of fertilization (GO:0080154), sperm–egg recognition (GO:0035036), and fertilization (GO:0009566) . AdditioADCY3, which is necessary for male fertility [TGFB2, which participates in ovine fecundity [IGFBP5, which is associated with LS in pigs [For goat DEGs, as expected, the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway (ko04151), oocyte meiosis (ko04114), progesterone-mediated oocyte maturation (ko04914), and estrogen signaling pathway (ko04915) were in the top 10 ko terms , which rertility ; TGFB2, ecundity ; and IGF in pigs . TherefoFrom a genomic and transcriptomic standpoint, we attempted to provide a genome-wide landscape to reveal the genetic convergence of prolificacy between goats and sheep. The results may indicate the physiological convergence of goats and sheep; however, more research is needed to characterize reproductive hormones and biological factors. However, these results may differ among other populations/breeds because of the sample size and/or genetic backgrounds in this study. Therefore, it would be useful to integrate an abundance of multi-omics data, such as genomics, transcriptomics, proteomics, and metabolomics, for additional populations or reproductive organs in a large population.CHST11 and SDCCAG8), (2) DEGs , and (3) biological pathways . These results indicated potential physiological convergence and enhanced our understanding of the overlapping genetic makeup underlying LS between goats and sheep.By combining genomic and transcriptomic data, this study identified the genetic convergence of prolificacy between goats and sheep for the first time on the basis of (1) PSGs (


Valgus-varus deformity (VVD) is a lateral or middle deviation of the tibiotarsus or tarsometatarsus, which is associated with compromised growth, worse bone quality and abnormal changes in serum indicators in broilers. To investigate the genetic basis of VVD, a genome wide association study (GWAS) was performed to identify candidate genes and pathways that are responsible for VVD leg disease, serum indicators and growth performance in broilers.BARX2 (BARX homeobox 2) and Panx3 (Pannexin 3) related to skeleton diseases and bone quality were obtained according to functional analysis. According to the integration of GWAS with transcriptome analysis, HYLS1 (HYLS1 centriolar and ciliogenesis associated) was an important susceptibility gene.In total, VVD phenotype, seven serum indicators and three growth traits were measured for 126 VVD broilers (case group) and 122 sound broilers (control group) based on a high throughput genome wide genotyping-by-sequencing (GBS) method. After quality control 233 samples (113 sound broilers and 120 VVD birds) and 256,599 single nucleotide polymorphisms (SNPs) markers were used for further analysis. As a result, a total of 5 SNPs were detected suggestively significantly associated with VVD and 70 candidate genes were identified that included or adjacent to these significant SNPs. In addition, 43 SNPs located on Chr24 (0.22 Mb - 1.79 Mb) were genome-wide significantly associated with serum alkaline phosphatase (ALP) and 38 candidate genes were identified. Functional enrichment analysis showed that these genes are involved in two Gene Ontology (GO) terms related to bone development (cartilage development and cartilage condensation) and two pathways related to skeletal development . The results provide some reference for understanding the relationship between metabolic mechanism of ALP and pathogenesis of VVD, which will provide a theoretical basis for disease-resistant breeding of chicken leg soundness.The online version contains supplementary material available at 10.1186/s12864-021-08236-3. Valgus-varus deformity (VVD), a common leg disorder in broilers and turkeys, is characterized by a lateral (outward) or middle (inward) angulation of the tibiotarsal bone that results in deviation of the tarsometatarsus . It has In humans and animals, the blood components are considered to reflect immune activity and nutrient metabolism. Serum Ca, P and serum ALP are important and evaluable indicators for bone metabolism and mineral status . ALP is Up to now, the precise etiology and pathogenesis of VVD still remain uncertain . The incGenome-wide association studies (GWAS), a mature analysis way, have been extensively used to identify single nucleotide polymorphisms (SNPs) related to complex diseases or traits during the last decade , 14. To The phenotype information for growth performance, serum indicators and leg disorder were listed in Table SP = 8.49 × 10 − 6) at Chr 8. A total of 70 putative candidate genes were obtained by annotating each significant locus . The estimated genomic inflation factor (λ) for ALP was 1.0, which indicated the low false positives to analysis results. For H-HDL, a significant SNP  was identified on Chr 2 at position 61.93 Mb   was focused for TG on Chr 2  on Chr 3 at position 92.15 Mb  was detected which was located on Chr 2 at 132.19 Mb  was located on Chr 2 at 61.3 Mb. The genomic control inflation factor (λ) calculated for serum indicators ranged from 0.99 to 1.01, indicating acceptably low false positives, and revealing relatively good consensus between the observed distributions of the P-value and expected.A total of 43 genome-wide significant SNPs were identified for serum ALP, which were located region at 0.22 Mb to 1.79 Mb on Chr 24  was identified on Chr 2 at position 63.02 Mb (Table P = 4.02 × 10− 6) located on Chr 15 at position 5.79 Mb. A top SNP site  for shank girth was detected on Chr 9 at 14.63 Mb  was enriched to cartilage development (GO: 0051216) and cartilage condensation (GO: 0001502) GO term. Pathway enriched analysis results showed that Toll−like receptor signaling pathway (gga04620) and p53 signaling pathway (gga04115) were important to bone development and bone disease. By integrating analysis of GWAS and previous transcriptome results, we identified HYLS1 (centriolar and ciliogenesis associated) as an important susceptibility gene.Putative candidate genes of each significant locus were annotated. A total of 38 protein coding genes were obtained for ALP Table S. GO and LRP2 knockout mice, bone disorder and Vitamin D deficiency were found. In human, LRP2 is closely related to bone mineral density and bone quality. These data revealed that LRP2 gene merits closer attention in our future study. A total of 70 candidate genes were performed functional enrichment analysis. Our previous study demonstrated that VVD mainly reduces skeletal quality and development to broilers [A total of 5 SNPs were detected for VVD trait at the suggestive significant level. Among these SNPs, the rs18584148 was located on Chr7. Notably, a study indicated that the regions related to chicken leg disease score on chromosome 7 by the ssGWAS methods . Thus, tThe etiology and pathogenesis of VVD were complex and multiple factors, including genetics, exercise, nutrient distribution, growth rate and lighting program . The herThe sample size is insufficient which causes the result was limited in this study. However, seven significant SNPs and four genes associated with Piglet splay leg (PSL) syndrome were obtained for the limited sample size  using the linear mixed model in GWAS . In a hoIn addition, as one of the Reduced-Representation Genome Sequencing (RRGS) methods, ddGBS was double-digest genotyping by sequencing method with high genotyping accuracy and high-density SNP markers to chickens and pigs . HoweverST3GAL4 . ST3GAL4 is associated with the pathogenesis of osteoarthritis (OA) and LDL-C level [ST3GAL4 genetic variation loci and serum ALP in chicken. We determined 9 SNPs within ST3GAL4 and ST3GAL4 were closely related to ALP in VVD broilers (unpublished data). The findings indicated that ST3GAL4 plays an important role in abnormal serum ALP in VVD birds (unpublished data). Furthermore, among 38 putative candidate genes, Panx3 (Pannexin 3) was important at the early stages of bone development [Panx3 plays a specific catabolic role in articular cartilage [Panx3 merits closer attention in our follow-up study. To gain insight into the function of 38 candidate genes, GO and KEGG enrichment analyses were performed. The cartilage development, cartilage condensation GO term and candidate genes in two GO terms merit our attention in further study. According to GO enrichment analysis results, BARX2 was focused. BARX2, a new homeobox gene of the Bar class, is expressed in the articular cartilage of the developing limb, suggesting that it is involved in regulation of chondrogenesis, which acts downstream of BMP signaling and in concert with Sox proteins to regulate chondrogenesis [Hematological parameters are a valuable tool for assessing the individual’s state of health in human diagnostics. Certain economic traits are indirectly measured using serum components in animal breeding. Serum ALP, a by-product in osteoblast activity or bone formation mainly derived from liver and bone, is essential in bone mineralization and vascular calcification , 20. Our-C level , 22. In elopment . As a poartilage  and can ogenesis . Of courogenesis . In miceogenesis . This stShank length is an important indicator for leg health in chicken. Identification of SNPs and genes affecting shank length traits were valuable for leg health in birds. For shank length, a SNP was obtained mapped on Chr 2 at position 63.02 Mb in our study. A study found that shank length was mapped to Chr 2, indicating the common effect of the chromosome on shank length and leg disease . Lipid mIdentification of SNP sites and candidate genes involved in VVD, growth performance and serum indicators may provide insight into disease mechanisms. For VVD, growth performance and serum indicators, several association signals and some putative genes were detected in the current study. Future study is need to confirm this finding. Our current research has provided some in-depth understanding of the interaction relationship between VVD and growth performance or serum indicators.In summary, we firstly exploited GWAS method to explore VVD trait, growth performance and serum indication in Hubbard broiler. This study excavated that some SNPs and functional candidate genes may play significant roles in the interaction relationship between ALP and VVD, which provides some reference for understanding of the pathogenesis of VVD and improving leg healthy in poultry industry.n = 56,000) were reared in Zhonghong Sanrong Group Co., Ltd.; Liaoning, China. A total of 15 individuals with leg weakness were anesthetized by an intravenous wing injection of sodium pentobarbital  in the wing vein. Subsequently, these individuals were euthanized by intravenous KCl (1–2 mg/kg). Subsequently, these individuals were anatomical. According to clinical examination, anatomical results and the bent degree of abnormal leg, Hubbard broilers with leg weakness were diagnosed as VVD at 38 -day- old [Hubbard broilers  and diluted to a normal 50 ng/μL. The DNA quality satisfied the requirements for library construction of double-digest genotyping-by-sequencing (ddGBS) . The quaRemoving these reads of that were polluted by adapter sequence and the low-quality reads from raw reads. The clean reads were aligned to the chicken reference genome  using Bowtie2 and TASSEL GBS analysis pipeline was used to call SNP , 27. Theα is the fixed effect vector including gender , chicken house , β is the effect size of the genotype, g is a n-vector of random individual genetic effects, e is a vector of residuals; W, X are incidence matrix corresponding to the fixed effects in α and the genotype effect of β. Association analysis results were adjusted using Bonferroni correction (P < 0.05/ N). The significance threshold of GWAS analysis was 0.05/33030 = 1.51e-6, where 33,030 is the number of independent markers. The significance threshold of GWAS analysis was 1/33030 = 3.02e-5 at the suggestive levels. The manhattan and Q-Q plots were obtained from the GWAS results with the CMplot package within the R software.Principal component analysis (PCA) was conducted using GCTA software to determine whether stratification exists in our study population. All SNPs were pruned to ensure their independence using the option of “indep-pairwise 25 5 0.2” in PLINK software to avoid the highly correlated SNP may distort the resulting PCs. A total of 33,030 independent effective tests were therefore obtained. GWAS was accomplished with the single-locus mixed linear model procedure implemented in GEMMA software using effective SNPs.Gallus gallus reference genome (GRCg6a) using the Ensembl Genome Browser to find genes that are located in the vicinity of the significant SNPs [P-values < 0.05 [To identify the positional candidate genes that are potentially associated with VVD trait, serum indicator and growth trait, the genomic regions that were close to the significant SNPs with a distance less than 300 Kb were selected. These regions were then referenced against the ant SNPs . The cans < 0.05 . In ordes < 0.05  were oveAdditional file 1: Table S1. The phenotypic information of VVD and sound broile. Table S2. Annotated candidate genes to VVD trait. Table S3. Annotated candidate genes to ALP trait. Table S4. GO enrichment analysis of the candidate genes for VVD trait in Valgus-varus Deformity Broilers. Table S5. KEGG enrichment analysis of the candidate genes for VVD trait. Table S6. GO enrichment analysis of the candidate genes for ALP trait in Valgus-varus Deformity Broilers. Table S7. KEGG enrichment analysis of the candidate genes for ALP trait in Valgus-varus Deformity Broilers.Additional file 2: Figure S1. Distribution of SNPs on each chromosome. Figure S2. Distribution of SNPs on chromosome position. Figure S3. Population structure evaluated by the first two principal components. Figure S4. Manhattan and quantile-quantile (QQ) plot on Serum TG, T-CHO, LDL-C and P. Figure S5. Manhattan and quantile-quantile (QQ) plot on body weight and shank girth.


We identified several loci exposed to adaptive selection in HEB-25. In total, 48.7%  of initially heterozygous SNP calls in HEB-25 were fixed in BC1S3:8 generation, either toward the wild allele (19.9%) or the cultivated allele (28.8%). Most fixed SNP loci turned out to represent gene loci involved in domestication and flowering time as well as plant height, for example, btr1/btr2, thresh-1, Ppd-H1, and sdw1. Interestingly, also unknown loci were found where the exotic allele was fixed, hinting at potentially useful exotic alleles for plant breeding.Nowadays, genetic diversity more than ever represents a key driver of adaptation to climate challenges like drought, heat, and salinity. Therefore, there is a need to replenish the limited elite gene pools with favorable exotic alleles from the wild progenitors of our crops. Nested association mapping (NAM) populations represent one step toward exotic allele evaluation and enrichment of the elite gene pool. We investigated an adaptive selection strategy in the wild barley NAM population HEB-25 based on temporal genomic data by studying the fate of 214,979 SNP loci initially heterozygous in individual BC Around 10,000years ago, domestication of crops enabled mankind to settle and to commence agriculture. Year after year, early farmers and breeders selected the best performing plants for the next season. Domestication and selection were accompanied by a progressive depletion of genetic diversity, known as bottleneck effect . To cope1S3 lines resulting from crosses of 25 highly divergent wild barley accessions (Hordeum vulgare ssp. spontaneum and ssp. agriocrithon) with the elite barley cultivar Barke . Subsequent backcross steps with the elite cultivar may serve as a first step to integrate exotic alleles in adapted breeding material and increase the reliability of estimated wild allele effects. The wild barley NAM population HEB-25 is backcross-based and comprises 1,420 BCar Barke . In thisar Barke  and sevear Barke , yield far Barke , grain nar Barke , as wellar Barke  and biotar Barke  stressesar Barke , complicIn this context, outsourcing the job of selection to mother nature may help to define what is beneficial in a certain environment. Over the long term, this enables the prevalence of certain ideotypes with a complex of optimally coordinated properties rather than focusing on single traits in classical selection. This principle of natural selection is key of the evolutionary plant breeding concept .1S3) after 5years of selfing and field propagation without conscious selection as a by-product of population conservation. A clear fixation of exotic alleles could hint at potentially useful exotic alleles that were more successful in contributing to the next field generation.In the present study, we investigated an adaptive selection strategy in HEB-25 by temporal screening of initially heterozygous loci  with the German elite barley cultivar Barke . F1 plants of the initial crosses were backcrossed with Barke and after three subsequent rounds of selfing the population comprised 1,420 BC1S3 plants  population HEB-25 resulted from parallel crosses of 25 highly divergent wild barley accessions  in small plots (double rows of 1.50m length) in the field  and 20 randomly chosen ears of each line, displaying a representative subset of the whole plot, were harvested at maturity. After threshing and manual seed processing, 60 seeds thereof were randomly selected for sowing in 2012 . The same process of harvesting, processing, and sowing was repeated with 60 BC1S3:6 seeds in 2013 and 100 BC1S3:7 seeds in 2014. Then, 20 BC1S3:8 seeds (harvested from the BC1S3:7 plants in 2014) were grown and leaf material from 12 randomly chosen seedlings per line (50–100mg) was harvested to form pooled samples for DNA extraction. This way, initial heterozygosity could be reconstructed through heterogeneity within the 12 plants  were removed from the dataset. Furthermore, 256 SNPs were removed as they revealed exact segregation among all HEB lines, indicating that they were in complete linkage disequilibrium (LD). Only one of these duplicates was kept. Altogether, 4,717 SNPs, genotyped in both SNP arrays, met the quality criteria. In the present study, 57 of the initial 1,420 lines were eliminated due to clearly inconsistent genotypes between BC1S3 and BC1S3:8.DNA was extracted according to the manufacturer’s protocol, using the BioSprint 96 DNA Plant Kit and a BioSprint96 work station , and finally dissolved in distilled water at approximately 50ng/μl. The original 1,420 BCNP array , consistNP array  at TraitNP array . At Trai1S3 generation were screened for their allele state in BC1S3:8. The allele fixation rate (AFR) for each SNP was determined asOnly heterozygous loci in BC1S3:8 line, “-1” represents SNPs that were fixed for the homozygous Hv allele, and “+1” represents SNPs that were fixed for the homozygous Hsp allele. The RFD for each SNP was then determined asTo determine the relative fixation direction (RFD) in which an originally heterozygous SNP allele moved, a reconstructed genotype matrix was created  containi1S3  to avoid strong bias.Both AFR and RFD were only calculated for SNPs containing at least 10 heterozygous HEB lines in generation BCHv to homozygous Hsp genotypes in BC1S3:8. For this purpose, a sliding window approach summarizing allele counts of 10 consecutive SNPs was applied. A significant deviation was accepted at a Bonferroni-corrected value of p < 0.01.To test for significance of RFD, chi-square goodness-of-fit tests were conducted to measure deviations from a 1:1 ratio of homozygous Hsp allele SNP effects estimated in the whole HEB-25 population were calculated. For this purpose, SNP effects were obtained from a simple linear model regressing published data on plant height and flowering time  of the previously harvested generation. The probabilities of accidental sampling of 12 or≥9 plants of the same homozygous allele class, leading to homozygous genotype calls in Illumina genotyping, were then estimated for each preset allele segregation.To estimate the impact of accidentally biased sampling on genotype calling in BC1S3 of population HEB-25  of each single SNP  cases, the SNP was fixed toward the Hv allele  and showed a clear tendency of fixation for the Hv allele (negative RFD). This was also true for the Vrn-H2 and mlo loci conferring vernalization independency and resistance to powdery mildew, respectively. However, at denso/sdw1, the Hsp allele was clearly favored (positive RFD).In total, 214,979  heterozygous SNP calls were obtained from 3,573,466 polymorphic SNP assays in generation BCn HEB-25 , which ingle SNP , indicatv allele . Mappingectively , enablinelection . AFR was1S3 plants with a pooled DNA sample of the resulting progeny in BC1S3:8, we observed that the original heterozygosity present in the HEB lines was halved as indicated by the reconstructed heterozygosity in BC1S3:8. In other words, half of the lines segregating for a specific SNP locus were fixed toward a homozygous allele during population conservation. To interpret this number, one has to consider that the obtained genotype score is the product of leaf sampling for DNA extraction and subsequent microarray-based SNP genotyping and allele calling. Since the initial heterozygosity declines after five selfing generations, we collected leaf material of 12 plants to reconstruct heterozygosity. Noteworthy, the selection of 12 plants itself harbors the potential to bias the true heterozygosity score if by chance only plants with the same homozygous genotype are collected. However, the probability that either only Hv or only Hsp genotypes are collected in this way is <0.2% for each homozygous allele class  in BC1S3. Therefore, a clear statement for this locus is not possible. However, in general, we observed that loci affecting the grain number per ear in HEB-25  seems to be phenologically optimally adapted to the investigated environment Halle. The most extreme example in this context is Ppd-H1, the main determinant of photoperiod response in barley (1S3 74 (95%) were fixed in BC1S3:8 biotic stress tolerance, for instance against drought, which frequently occurs in the studied environment Halle. In 2011, the first year of field propagation, the lowest total precipitation sum until maturity was observed  and via the European Research Area Network for Coordinating Action in Plant Sciences . Open access publishing was funded by the publication fund of Martin Luther University Halle-Wittenberg.This work was supported by the German Research Foundation (DFG) 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.The handling editor declared a shared consortium with one of the authors KP at time of review.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.


Maize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines.A total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic.These high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.The online version contains supplementary material available at 10.1186/s12870-021-02932-8. Zea mays ssp. mays L.) is one of the three most important cereal crops in the world, largely used as animal feed and source of industrial raw material. Maize has also been used as a classic model system in genetic research for over a century. In the face of rapidly growing populations and deteriorating environmental conditions, there is a pressing need to rapidly increase maize productivity through breeding. To date, the genetic cloning of key agronomic traits and marker-assisted selection (MAS) have been explored to enhance the efficiency of breeding programs. Molecular marker technology is a revolutionary technological advance in biological research and has become a critical tool for MAS. Over the past three decades, various types of molecular markers including AFLP, RFLP, RAPD, SSR, and InDel markers have been developed and successfully used in a wide range of applications in maize, [Maize  data, RNA-Seq data could provide a rich resource for the development of potential functional markers. For instance, a total of 5344 InDel markers were developed from the transcriptome data of sudangrass S722 and sorghum Tx623B , and 127Furthermore, apart from the lack of high-density KASP SNP markers, another limitation to the application of KASP technology in map-based cloning of genes and MAS breeding programs is the success rate of PCR amplification, which can affect the performance of KASP SNP genotyping reactions. Therefore, there is an urgent need to close the knowledge gap between the large amounts of sequencing data and effective KASP SNP markers. In some cases, breeders and experimental researchers have struggled to handle the large amount of data generated through sequencing due to a lack of familiarity with the bioinformatic tools used to extract interesting information from the data. Additionally, the different types of molecular markers and methods used for genotyping have different properties. Therefore, the methods used to develop specific molecular markers must meet specific standards. After in silico analysis of the factors affecting the efficiency of SNP genotyping, the success rates and conversion rates will be improved, thus yielding accurate genotypes.2 segregating population derived from LM11 × CML25 [crtRB1 gene affecting the content of provitamin A (PVA) effectively distinguished between favorable and unfavorable alleles in the 70 maize inbred lines [GRMZM2G315401 and GRMZM2G430362 were determined to be closely related to fertility through genotyping 50 sterile and 50 partially rescued plants by KASP assays [In recent years, KASP assays have been widely applied in maize studies on genetic diversity, genetic purity, quantitative locus mapping, QTL/genes fine mapping, marker-assisted selection, and marker-assisted breeding , 20. For × CML25 . Moreoveed lines . Two SNPIn this study, to meet the requirements of genotyping fewer loci in a large number of samples, KASP SNP markers were developed based on the RNA-Seq data of developing kernels of 368 maize inbred lines. A set of 71,311 user-friendly KASP SNP markers with high-density and high polymorphism were identified, of which 50 KASP SNP markers were selected to experimentally validate the accuracy and polymorphism. The application of these high polymorphic markers will be helpful in enhancing the efficiency of map-based cloning and marker-assisted selection in maize.To mine SNPs in the RNA-Seq data sets of developing kernels of 368 maize inbred lines, resulted in a total of 2,948,985 SNP loci were identified. The number of SNP on individual chromosome ranged from 196,912 to 460,512, and the average density of SNPs in the maize genome was 1.4 SNPs/kb. Of these, 2,545,815 (86.33%) SNPs were mapped to the genic regions of 26,820 genes, with an average density of 94.92 SNPs/gene. Subsequently, the polymorphism information content (PIC) value of each SNP was calculated based on the genotypes of 368 maize inbred lines, and the PIC values ranged from 0.01 to 0.75, with an average of 0.13 , the presence of bi-allelic polymorphisms, unique primer sequences unfiltered by mapping the primer sequence to the reference genome with 1 mismatch, and highly conserved primer sequences among maize individuals . As a result, 71,311 KASP SNP markers were identified in 2,948,985 SNPs, which accounted for 2.42%. These KASP SNP markers were distributed on 10 chromosomes, ranging from 4678 to 11,756 on each chromosome. The highest density of KASP SNP occurred on chromosome 5 (39.26 SNP/Mb) and the lowest on chromosome 4 (25.77 SNP/Mb), and the average density was 33.40 SNP/Mb. In addition, the density of KASP SNP located at the end of the chromosomes was high, whereas near centromere it was low  Fig. . OverallTo validate the accuracy and polymorphism of the KASP SNP markers, 50 KASP SNP markers with three to seven KASP SNP markers on each chromosome were randomly selected for PCR amplification detection. The PIC values of these 50 KASP SNP markers were 0.14–0.50 in the 368 RNA-Seq data sets; they all exhibited polymorphism between the maize inbred lines 1212 and B73 in in silico analysis  and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) , 27. FirThe PIC value has been proven to be the most important indicator of the usefulness of molecular marker; the higher the PIC value is, the greater the probability of polymorphism between two arbitrary individuals is. Markers with the PIC values≥0.5 are usually considered highly informative , 35. DurDue to the sharply declining costs of NGS technology, millions of SNPs have been generated and published or deposited in public databases such as Panzea and MaizeGDB. To date, very little work has been done to comprehensively convert SNP markers to KASP markers in maize. Undoubtedly, not all SNPs meet the requirements of KASP SNP marker design . The selCompared to molecular markers randomly distributed on chromosomes, the genic KASP SNP markers developed in this study have several significant advantages in fine-mapping of QTL/genes and MAS . As a geFacing a high demand for genotyping several loci in a large number of samples in basic and applied research, our study provides a panel of 71,311 KASP SNP markers with an average of one KASP SNP marker per 29.94 kb that were identified with stringent selection criteria by combining RNA-Seq data of 368 maize inbred lines and bioinformatic methods. The accuracy ratio of the KASP SNP markers validated in the maize inbred lines 1212 and B73 reached 92.00%, and the PIC values among the 20 inbred lines were agreed with those of in silico analysis. Taken together, these KASP SNP markers will be beneficial for map-based gene cloning and MAS in maize research and the bioinformatics pipelines of marker development and screening parameters could be applied in other species.http://www.maizegdb.org/assembly/) [http://www.ncbi.nlm.nih.gov/sra/?term=SRP026161) and were utilized to mine SNPs  and working gene set with annotated exon-intron structure were both downloaded from maizeGDB (sembly/) , 45. TheThe waxy maize inbred lines 1212 and B73 were selected for RNA-Seq analysis. The top of the immature kernels (20 DAP) from the 1212 and B73 were cut and snap frozen immediately in liquid nitrogen. Total RNA was isolated from each of the two samples using a Bioteke RNA extraction kit  according to the manufacturer’s protocol, and RNA-Seq libraries were constructed according to the manufacturer’s recommendations. Both libraries were sequenced using a HiSeq2500 platform and each library produced more than 18 Gb of clean data. The RNA-Seq data of 1212 and B73 were used for in silico analysis.Twenty maize inbred lines  selected from tropic and sub-tropic germplasms were used to evaluate the accuracy and polymorphism of KASP SNP markers.i = 1- ij was the frequency of the jth pattern for the ith marker [After filtering the low quality reads with < Q20 in the 368 RNA-Seq data sets using the NGSQC toolkit v2.3.3 , the cleThe unique and conserved primer sequences were identified by the pipeline. First, 20 bp DNA sequences located upstream of the corresponding SNP locus were extracted using python scripts. Then, the 20 bp DNA sequences were aligned to the B73 reference genome using the Bowtie software with up to 1 mismatch . Lastly,http://bioinfo.cau.edu.cn/agriGO/index.php) [P-value with the default parameters.Genes harboring KASP SNP markers were chosen for GO analysis. GO annotation and GO enrichment analyses were performed using the online agriGO GO Analysis Toolkit with the singular enrichment analysis (SEA) (dex.php) , and theA set of 50 bi-allelic SNPs was selected to validate the polymorphism level and accuracy of the KASP SNP markers. The genomic DNA was extracted from the 3-week-old seedlings of 22 maize inbred lines following the CTAB (cetyltrimethylammonium bromide) DNA extraction protocol. The PCR amplification for the SNP genotyping analysis was performed in a BioRad CFX-96 RT-PCR thermal cycler in a reaction mixture of 10 μL containing 1× KASP reaction mix, 0.17 μM KASP assay mix  and 30–40 ng of genomic DNA. The reactions were performed with the following cycling conditions: an initial denaturation at 95 °C for 5 min, 35 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 90 s, extension at 72 °C for 90 s, and a final extension at 72 °C for 10 min. Fluorescent SNP genotyping was analyzed using the CFX96 manager software. The PIC value for each SNP marker was calculated using the formula described above.The authors declare that all methods complied with relevant institutional, national, and international guidelines and legislation.Additional file 1. The detailed information of 71,311 KASP SNP markers.Additional file 2. The detailed information of the top 8,275KASP SNP markers.Additional file 3. The GO annotations of the genes harboring KASP SNP markers.Additional file 4. The results of validation of the accuracy and polymorphism of the 50 KASP SNP markers.Additional file 5. The information of SNP mining in the 368 RNA-Seq data sets.


This SNP is in the second exon of the MYBPH gene, and its allele frequency deviates from Hardy–Weinberg equilibrium. In conclusion, our study provides evidence that the c.482C > T SNP in the MYBPH gene is a putative causal mutation for fat deposition in meat-type chickens.Chicken is an important source of protein for human nutrition and a model system for growth and developmental biology. Although the genetic architecture of quantitative traits in meat-type chickens has been the subject of ongoing investigation, the identification of mutations associated with carcass traits of economic interest remains challenging. Therefore, our aim was to identify predicted deleterious mutation, which potentially affects protein function, and test if they were associated with carcass traits in chickens. For that, we performed a genome-wide association analysis (GWAS) for breast, thigh and drumstick traits in meat-type chickens and detected 19 unique quantitative trait loci (QTL). We then used: (1) the identified windows; (2) QTL for abdominal fat detected in a previous study with the same population and (3) previously obtained whole genome sequence data, to identify 18 predicted deleterious single nucleotide polymorphisms (SNPs) in those QTL for further association with breast, thigh, drumstick and abdominal fat traits. Using the additive model, a predicted deleterious SNP c.482C > T (SIFT score of 0.4) was associated ( Gallus gallus gallus), considered the ancestor of the domestic chicken (G. g. domesticus)  , and comesticus)  has alloesticus)  and thesesticus) . The mosesticus) .High-density SNP panels were previously used in genome wide association studies (GWAS) to identify QTL for body weight , severalSingle nucleotide polymorphisms can have a direct impact on coding or indirect impact on regulation of gene expression and thus affect traits of economic interest in animal models and livestock species . SNPs thPrevious whole-genome resequencing studies performed in parental individuals from the meat-type TT chicken reference population studied herein identified several predicted deleterious SNPs . HoweverThe TT Reference population used in this study was generated from the Brazilian TT broiler line, developed by the Embrapa Swine and Poultry National Research Center. This line has been under multiple-trait index selection since 1992, representing many generations, with the goals of increasing body weight and carcass yield, improving viability, fertility, hatchability, feed conversion, and reducing abdominal fat . The TT Body weight at 42 days of age (BW42) was measured six hours after fasting, then chickens were euthanized by cervical dislocation followed by bleeding. Blood samples were collected for DNA extraction during bleeding. Feathers were mechanically removed following a hot water bath (60°C for 45 s) and carcass cuts representing breast weight (BTW), thigh weight (THW), drumstick weight (DRW) and abdominal fat weight (ABFW) were individually measured in grams. Drumstick yield (DR%), abdominal fat yield (ABF%), thigh yield (TH%) and breast weight yield (BT%) were estimated as a percentage of live body weight at 42 days of age. More details about the slaughter and phenotype measurements have been previously described .® Genomic DNA kit  and quantified using Qubit® 2.0 Fluorometer . For genotyping analyses, we used the 600 K Affymetrix AxiomTM Genotyping Array . Sample filtering parameters were DishQC ≥ 0.82 performed with AxiomTM Analysis Suite (Affymetrix®) software and sample call rate ≥ 90% via PLINK v.1.9 software , those located in the sex chromosomes, and those not annotated in the chicken assembly . More details about the genotyping and filtering steps are in Genomic DNA was extracted from 1,430 blood samples with the PureLinksoftware . For locWe used GenSel software for GWAS based on a Bayesian methodology approach for genomic prediction. Following y represents the vector of phenotypic values ; X is the incidence matrix for fixed effects; b is the vector of fixed effects; k is the number of SNP loci; ja is the column vector representing the SNP at locus j as a covariate, coded with the number of B alleles; βj is the random substitution effect for locus j assumed to be normally distributed N  when δj = 1 but 0 when δj = 0, with δj being a random variable 0/1 indicating the absence (with probability π) or presence (with probability 1-π) of locus j in the model, and e is the residual associated with the analysis. Sex and hatch were included as fixed effects in the model and BW42 (slaughter age) as a fixed covariate for THW, BTW, ABFW and DRW.As described by th iteration of the MCMC chain based on the marker effects sampled in that iteration as performed by We assumed π = 0.9970 in the BayesB models and obtained 41,000 Markov chain Monte Carlo (MCMC) samples with the first 1,000 samples being discarded. A map file was used to position the consecutive markers into 947 non-overlapping 1 Mb windows. So, for each window, the proportion of the genetic variance explained by the QTL was computed among individuals for every 1001. Further details about library preparation, sequencing and filtering are already published  sequencer and that produced a dataset of good quality SNPs we deposited in the EVA-EMBL databaseublished . Functioublished ] and delublished . To inve2 > 0.7, just one potential causative SNP was chosen to be genotyped by sequencing in the offspring generation.This study investigated only predicted deleterious SNPs located within significant QTL regions associated with the carcass traits and abdominal fat traits , to narr® Genomic DNA kit  and then quantified using a Qubit® 2.0 Fluorometer . DNA integrity was evaluated in 1% agarose gel. Library preparation was performed according to Truseq® Custom Amplicon Low Input Kit Reference Guide (Illumina Technology). Libraries were quantified with quantitative real time PCR, using KAPA® Library Quantification kit (KAPA Biosystem) and fragment size was estimated using either Bioanalyzer® (Agilent Technologies) or a Fragment Analyzer . Paired-end sequencing with a read length of 150 bp was performed on a MiniSeqTM (Illumina Technology).Genomic DNA of 237 offspring was extracted using the PureLinkGallus_gallus5.0 (NCBI) with BWA-MEM (v.0.7.15). For SNP calling, we used SAMtools v.1.3.1 . As adopted by For association analysis, we tested 18 predicted deleterious SNPs located in QTL regions using the package lmerTest 3.1-0  in R sofy is the vector of observations for the measured phenotype; X is the incidence matrix relating the fixed effects for sex and hatch to y; β is the vector of sex and hatch fixed effects; W is the genotype matrix for the predicted deleterious SNPs located in the associated QTL regions; and a is the vector of fixed effects for the SNPs. The matrix Z is an incidence matrix relating u to y; u is the vector of length 41 representing the random effect of dam family; and e is the vector of residual effects. Associations were considered significant at comparison-wise p-value < 0.05.Where Descriptive statistics such as number of animals, averages and standard errors from traits and animals utilized in GWAS are presented in We conducted a GWAS to identify genomic regions associated with traits of interest. Nineteen unique QTLs for THW, TH%, DRW, DR%, BTW, and BT%, were detected on GGA 1-4, 6, 12, 14, 17, 20, 22, 25, 26 and 28 . The QTLWindows that explained a high proportion of genetic variance were selected for each carcass trait to further search for predicted deleterious SNPs. The genomic windows selected were: GGA 1 (166 Mb) for drumstick traits; GGA 22 (4 Mb) for thigh traits; GGA 26 (1 Mb) for abdominal fat traits [9]; GGA 25 (2 Mb) and GGA 26 (3 Mb) for breast traits.We searched for predicted deleterious SNPs located within the QTLs to identify potential causative mutations. We scrutinized SNP data from whole-genome resequencing of 14 founding animals (TT line), approximately 11 million SNPs , concordGallus_gallus5.0 (NCBI) genome assembly was 99.74%. After variant calling, quality control and functional annotation, the 18 predicted deleterious SNPs were genotyped by sequencing with an average coverage of 7,000X.Libraries sequenced using MiniSeq produced on average 298,791 raw reads per amplicon. The average overall mapping rate of the raw reads against the rs IDs, and four SNPs were annotated in novel genes. Seven SNPs did not have any animal genotyped as homozygous for the alternative allele, and five SNPs exhibited significant deviation from HWE.Detailed information of the 18 predicted deleterious SNPs  is in p-values and effects) between the SNPs and the eight traits are presented in We used both additive and non-additive models to verify if any of the deleterious SNP identified in each of the candidate genes were associated with the phenotype detected in the GWAS analysis. Detailed results for association tests (MYBPH) gene and within the QTL region for ABFW and ABF% traits in GGA26. This polymorphism is a C > T change, resulting in the amino acid change of threonine to methionine at amino acid position 161 (T161M). In the additive model, the effects of the tested predicted deleterious SNP c.482C > T were −0.17% and −3.43g for ABF% and ABFW traits, respectively. Functional annotation analysis performed in Amigo 2 GO of this gene reported gene ontology (GO) terms related to protein binding (GO:0005515), regulation of striated muscle contraction (GO: 0006942), cell adhesion (GO:0007155), structural constituent of muscle (GO:0008307) and myosin filament (GO:0032982).The SNP c.482C > T is in the Myosin Binding Protein H . Deviations from HWE may suggest that selection has affected the allele frequency.Deviation from HWE is an indication that allele frequency is not consistent with genotype frequency. Several evolutionary forces can affect HWE and a deleterious SNP could drive a deviation from HWE. Four predicted deleterious polymorphisms exhibited significant departures from HWE  that presents gene ontology terms related to oxidoreductase activity. Out of the 237 animals genotyped, the expectation based on the observed allele frequency was that 4.45 animals would be homozygous for the alternative allele. The lack of homozygous animals for the alternative allele provides suggestive evidence of a lethal polymorphism when in homozygosity. However, further genotyping studies should be performed to confirm these findings based on matings between heterozygotes. Previous studies in cattle and pigs have identified lethal mutation by searching for lack of homozygous alleles .MYBPH gene is a novel SNP that caused a threonine-to-methionine substitution at amino acid position 161 (T161M). Threonine amino acid is polar (uncharged), while methionine is a non-polar amino acid. In Myosin-binding protein H, the substitution is located in the Fibronectin type-II domain (UniProt accession number Q05623 and PROSITE accession number PS50853) and this domain is the most common of the fibronectin subdomains  in a group of cattle exhibiting low intramuscular fat content  with two other polymorphisms in the MYBPH gene, rs313948592  and rs312491203 . We are aware that polymorphism in intron or upstream regions can have regulatory impact and we cannot rule predicted deleterious SNP as causative mutations  – EMBL-EBI (accession PRJEB25004) for further studies of causal mutations in these regions.It is important to mention that the polymorphism c.482C > T herein might not be the causal mutation because it was in strong linkage disequilibrium  are available from the corresponding author on reasonable request.All data generated or analyzed during this study are public and included in this published article. The SNP report (identified by sequencing) was submitted to European Variation Archive (EVA) – EMBL-EBI, accession number In this study, all experimental protocols that used animals were performed in agreement with resolution number 010/2010 approved by the Embrapa Swine and Poultry Ethics Committee on Animal Utilization (CEUA) in Concordia, Santa Catarina State – South of Brazil, in agreement with the rules of National Council of Animal Experimentation Control (CONCEA) to ensure compliance with international guidelines for animal welfare.PT, GCM, CB, ML, GBM, and LC conceived the idea of this research and participated in the experimental design. ML and LC provided the experimental environment, phenotype, and data analysis support. PT, GCM, CB, JP, AC, GRM, and DG performed data analysis. PT drafted the manuscript. PT, GCM, CB, AC, JP, GRM, ML, GBM, DG, and LC collaborated with interpretation, discussion, and writing of the manuscript. All authors have read and approved the final manuscript.FAPESP, CAPES, CNPq, and the Brazilian Government through Embrapa provided financial support to generate the data; however, they did not participate in the design of the study, sample collection, analysis, data interpretation, and writing of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as apotential 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.