Ascaris lumbricoides. We report a 296 megabase (Mb) reference-quality genome comprised of 17,902 protein-coding genes derived from a single, representative Ascaris worm. An additional 68 worms were collected from 60 human hosts in Kenyan villages where pig husbandry is rare. Notably, the majority of these worms (63/68) possessed mitochondrial genomes that clustered closer to the pig parasite Ascaris suum than to A. lumbricoides. Comparative phylogenomic analyses identified over 11 million nuclear-encoded SNPs but just two distinct genetic types that had recombined across the genomes analyzed. The nuclear genomes had extensive heterozygosity, and all samples existed as genetic mosaics with either A. suum-like or A. lumbricoides-like inheritance patterns supporting a highly interbred Ascaris species genetic complex. As no barriers appear to exist for anthroponotic transmission of these ‘hybrid’ worms, a one-health approach to control the spread of human ascariasis will be necessary.Human ascariasis is a major neglected tropical disease caused by the nematode  Ascaris lumbricoides in 2017, resulting in an estimated 3206 deaths and a loss of over 860,000 Disability-Adjusted Life Years . Many infections go undiagnosed, but like other soil-transmitted helminths (STH), Ascaris spp. infections contribute significantly to global DALYs, perpetuating the cycle of poverty in areas of endemic infection  and A. suum-type (pig-associated) clades .Population genetic studies of frequent . Some stfrequent . This is hybrids . Furtherotically . Studieshe world  are the ) clades . Other w) clades . AscarisAscaris genome (ALV5) based on sequences from a single female worm collected from a person in Kenya. This person was presumed to be infected with A. lumbricoides as there is a lack of local pig husbandry. Draft A. suum genomes have previously been constructed from worms obtained from pigs in Australia (Ascaris genome ALV5 was found to be highly similar (99% identity) to the A. suum genome from worms collected from pigs in the United States . This approach led to a reference-quality human Ascaris genome assembly with many fewer gaps (only 0.98 Mb of sequence) and no unplaced contigs. The Ascaris genome assembled into 415 scaffolds with a combined size of 296 Mb. An additional 15.4 Mb of sequence was present in 4072 unscaffolded short contigs. The assembly N50 value was 4.63 Mb, with the largest scaffold measuring 13.2 Mb. The largest 50 scaffolds combined to represent 78% of the genome. The assembly was further polished using additional Illumina reads from the same worm to more accurately reflect single base differences, indels, and any potential local mis-assembled regions.Mapping of the human e genome  revealedAscaris Illumina reads back to the reference-based Ascaris genome assembly and found that >99% of the Illumina reads could be mapped, indicating that the reference-based assembly excluded very few Ascaris reads. We then mapped and transferred the extensive set of A. suum transcripts . The other two assemblies are available online.To evaluate the quality of the assembled genome, we mapped the nscripts  to the hng genes . As thisng genes , we suggA. suum embryos, A. lumbricoides embryos undergo programmed DNA elimination during the differentiation of the somatic cells from the germline in early development  was identical to that described for the pig A. suum sample  (Earlier annotations of protein coding genes for s (ALV5) . We perfs (ALV5) .A. suum PRJNA62057 and PRJNA80881 in A. lumbricoides  is likely due to the differences in protein coding gene annotations and split genes seen in previous assemblies.Phylogenetic trees derived from orthologue analyses of the predicted proteomes of ALV5 with the predicted proteomes of other nematodes across all clades indicated the similarity among the published genomes of ricoides  with ALVs branch . The varAscaris spp. mitochondrial genomes from individual worms , indicating an excess of low frequency polymorphisms within the global data set suggesting population size expansion. Despite the Fu’s F not being significant it was positive  potentially indicating a deficiency in diversity as would be expected in populations that have recently undergone a bottleneck event. The same pattern was also seen in the Kenyan sequences but neither the Tajima’s D nor the Fu’s were significant . AlthougAscaris worms isolated from infected Kenyans, the nuclear genomes of the 68 individual worms were analyzed to assess intraspecific population genetic diversity, heterozygosity, and ploidy. Single-nucleotide polymorphisms (SNPs) and insertion/deletions (indels) across the nuclear genomes were assessed for the first 50 largest scaffolds, which comprised 78% of the genome (see methods). Each Ascaris worm was sequenced to a mean coverage depth of ~27 fold. A total of 11.15 million SNP positions were identified in the first 50 scaffolds among the Ascaris nuclear genomes. Approximately 25% of these variants were intergenic  and allelic diversity were determined. Worms were disomic, with little to no evidence of aneuploidy  and the most parsimonious explanation for these results is that recombination of a limited number of distinct alleles had occurred in the regions of increased FST  from 69 Ascaris worm specimens, including the que SNPs . Notablyombinant . This geased FST .Ascaris genomes sequenced, we calculated the Dunn index, which supported 3–6 ancestral populations (To estimate the number of supported ancestries (K) that could be resolved in the ulations . A graduulations . We nextAscaris specimens at chromosome resolution, a color hue representing a local genetic ‘type’ present was assigned and integrated to construct haplotype blocks across each chromosome for the ancestries present. Chromosome painting based on shared ancestry revealed a striking mosaic of large haplotype blocks of different admixed color hues, consistent with limited genetic recombination between a low number of parentage haplotypes. These admixture patterns were readily visualized by shared color blocks between different specimens across entire scaffolds including AlgV5R019X  versus between groups (such as between villages). We found significant genetic separation between worms in different villages , althougreatment .To expand on our observations that genetically similar worms are found around the world, but that similar worms cluster within a village, based on our nuclear SNPs data, we plotted genetic distances against geographic distances. Surprisingly, we found no significant correlations between genetic and geographic distance, neither across all five studied villages nor within the two most heavily parasitized villages .A. lumbricoides. Our comparative phylogenomic analyses of this new Ascaris spp. genome against existing draft genomes of A. lumbricoides and A. suum suggest that A. suum and A. lumbricoides form a genetic complex that is capable of interbreeding, which has apparently undergone a recent worldwide, multi-species Ascaris population expansion.In this study, we generated a high-quality reference genome from a single worm presumed to be human A. suum-like) worms circulating in this human population in Kenya were acquired, perhaps generations ago, by humans who lived in closer proximity to pigs. It is also possible that these worms were acquired from non-human primates  suggests that the worms collected in Kenya mirror the separation into clade A (worms from pigs in non-endemic regions and humans in endemic regions) and clade B (worms from humans and pigs from endemic and non-endemic regions) described elsewhere . It is lprimates , or someAscaris genome provide significantly greater power in understanding Ascaris speciation. Importantly, our nuclear genome SNP analysis suggests that the 68 Kenyan Ascaris are distributed across multiple clades in a phylogeny based on the nuclear genomes. Overall, data from our study and other studies are consistent with a pattern where hybrid genotypes in Ascaris populations were observed  were readily resolved indicating that the genetic mosaicism observed could not be solely attributed to genome mis-assembly. Ultimately, future studies using ultralong PacBio  on dry ice.A modified DNA extraction method was developed based on Phenol/Chloroform and Qiagen methods (available on request) and used on 75 samples . For theA. lumbricoides DNA samples were sequenced using Illumina HiSeq 2500 (www.illumina.com) short-read paired-end sequencing. DNA was quantified by UV Spec and Picogreen. A 100 ng of DNA based on picogreen quantification was used as template for NGS library preparation using the TruSeq Nano DNA Sample library prep kit without modification. Primer-dimers in the libraries were removed by additional AMPure beads purification. Sequencing was performed to obtain a minimum genomic depth of 20X coverage for each sample.Paired-End Genome Libraries – Sixty-eight Mate-Pair Genome Libraries – Two samples were selected for mate-pair sequencing, based on the quality of the DNA preparation. Three independent DNA isolations (corresponding to what region of the worm or what is the sample for DNA isolation) from specimen ‘119_2.3’ were combined to obtain one μg DNA input. The mate-pair libraries were generated using the Nextera Mate Pair Library Prep Kit, following the gel-free method with the only modification that M-270 Streptavidin binding beads were used instead of M-280 beads. The libraries were amplified for 15 cycles given the low DNA input going into the circularization phase. The mate-pair fragment size averaged 6 kb with a range of 2–10 kb fragments.A. lumbricoides germline genome assembly was constructed using the A. suum genome as a reference. Briefly, sequencing reads from a single A. lumbricoides worm  were mapped to the A. suum germline genome assembly . Remaining reads were then ref-mapped to the A. lumbricoides genome ALV5 reference genome (described in this paper) using either Bowtie2 v2.2.9  by dividing the chromosomes into 10 kb sliding windows and averaging the coverage within each window. The windows with zero coverage were not included in any further analyses due to sequencing noise or repeat regions  and VariantAnnotation (http://bioconductor.org/packages/release/bioc/html/VariantAnnotation.html) R packages . The proportions of heterozygous and homozygous SNPs were estimated in 10 kb sliding windows using custom Java scripts to generate histogram plots in Circos  based on the genome-wide phased haplotype data. For fineSTRUCTURE (version 0.02) (The SNP data (VCF file) was first phased accurately to estimate the haplotypes using SHAPEIT  after keon 0.02) , both thPopulation genetic structure was constructed using POPSICLE  by compaIn order to determine the phylogenetic relationship between samples, we selected 19005 base positions where variants were detected in a representative sample vs the reference (ALV5), and where each sample had at least 20x coverage for each locus. Using this list, the base calls for each sample were pooled together to generate a single multi-sequence fasta file.http://tree.bio.ed.ac.uk/software/figtree/).Next, both maximum likelihood (ML) trees and bootstrap (BS) trees were generated with a final ‘best’ tree generated from the best scoring ML and BS trees using RAxML v8.2.10 . The treSimilarity within and between worms from different villages, households, people and time-points was analyzed based on the distance matrix of the patristic distances from the phylogenetic tree described above, using permutational multivariate analysis of variance (Adonis Vegan in R). The distance matrix underlying the phylogenetic tree was analyzed in order to measure the significance and contribution of different factors to variance between samples. Each factor  was analyzed both separately and sequentially. The sequence chosen was ordered based on significance of each factor when tested individually. Since multiple groupings were considered using the same dataset, multiple comparison corrections were applied. Sample sizes and descriptions of each group are shown in Ascaris genomes. For each individual, the Ascaris mitochondrial reads in the total DNA sequencing were identified by mapping the Ascaris reads to the A. suum reference mitochondrial genome (GenBank accession: NC_001327). Adaptor sequences were trimmed prior to de novo assembly. To reduce the complexity of the de novo assembly, we randomly sampled 1000x reads from each individual (the use of higher read coverage often resulted in fragmented scaffolds) and assembled these reads using the SPAdes assembler  as described above for the whole genome SNP alignment. Subsequent tree files were formatted in FigTree and MEGA v7. The variation in nucleotide diversity across the mitochondrial genome was measured using sliding window analyses, with a window of 300 bp and a step of 50 bp, using DNAsp v6 . In ordeAscaris, several analyses were performed to identify the effect of any population level processes that may be affecting the diversity of the parasites within Kenya. Initially, diversity indices were calculated for each of the genes within the mitochondrial genome across the entire Kenyan data set as well as considering the mitochondrial genome as a whole. In order to account for the diversity within the genic regions, we removed non-coding and tRNA sequences for these analyses. To provide a genealogical perspective of population structure of the Kenya Ascaris worm specimens, we constructed the most parsimonious haplotype network based on the protein coding sequences using the TCS algorithm as implemented in PopArt .Thank you for submitting your article "Extensive hybridization between pig and human The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Ascaris/human Ascaris phenomenon constitutes an Ascaris species continuum.This manuscript addresses a question that has debated for decades and is both timely and interesting. It deals with human ascariasis and confirms a long held hypothesis that the pig Ascaris worms collected from ~60 infected people in five villages of rural Kenya. The investigators present findings from genomic, proteomic and mt comparisons and phylogenetic trees, and compared the new sequences with available sequences from around the globe for the human large roundworm, Ascarislumbricoides and the pig parasite Ascarissuum. They now report that most of these Kenyan worms exhibited mt genomes more similar to those described elsewhere to A. suum than to A. lumbricoides, and that the nuclear genome included mixed A. suum-like and A. lumbricoides like regions, such that the genomes could be described as chimeric of the pig and human Ascaris species. The investigators consider that their data indicate that these worms of the chimeric genotypes were predominantly inbreeding within each of the study villages.It provides a substantial new body of nucleotides sequences from the nuclear – both somatic and germline genome and the mitochondrial genome of >60 individual Ascaris parasites circulating in the study region. The phylogenetic trees from mt, from nuclear genome, and from protein encoding genes, and other details, together provided cogent evidence of the chimeric (lumbricoides-suum) nature of the Ascaris parasitizing the participants of the study. This study looks to have been carried out in a professional manner without obvious scientific or technical flaws. The voluminous amount of novel new nucleotide sequences represent a marked advance in genetic information for this major neglected human pathogen.The paper provides a novel characterization of the Essential revisions:1) Provide an adequate description of the study site. The readers are directed for this to Easton et al., 2016, Easton et al., 2017. Yet, descriptions there are themselves cursory, and refer to a report from a decade earlier . Provide – even in supporting materials – an informative, illustrative description of the study site  characteristics, and so forth.Ascarislumbricoides and A. suum jointly represent a species complex – is novel, given the earlier papers that have stated similar conclusions, e.g. Leles et al., 2012; Monteiro et al., 2019; Sadaow et al., 2018.2) In the Discussion, state clearly why you consider that these new findings – Ascaris proteome' section of the Results, consider revising to more clearly indicate what the findings revealed and what they mean: What did the predicted proteome details indicate about the chimeric (lumbricoides-suum) nature of the Ascaris parasitizing the participants of the study? Or were the findings employed only for the phylogenetic tree presented as Figure 1C.3) With respect specifically to the 'Gene content and Ascarislumbricoides-suum' chimera – species complex, comment on the host range of this Ascaris species beyond humans and pigs. Provide supporting citations.4) Given your use  of the term anthroponotic, which indicates that Homo sapiens is the reservoir of the 'Ascaris based on your brief comments advising the need for a One Health approach. Provide supporting references to buttress the recommendation.5) Elaborate of what you recommend for the control and treatment of parasitism by 6) Please revise the title, a reader might read the title and understand that experimental hybridization might have been undertaken. A suggested modification: "Molecular evidence of.…. indicates…."7) The narrative of the manuscript is very nice. However, there are some issues that could be improved. First, the text contains typographical and grammatical errors that should be eliminated – also the errors in the reference list and the citations need to be corrected. Please meticulously edit the manuscript. Second, "isolates" and "strains" are confusing terms, if one is referring to individual worms (which maybe the case). As it is written, DNA samples were isolated from muscle or intestine from single worms, thus using the terms "worm" or individual" or "individual worm or specimen" is recommended8) Genomic DNA was not consistently isolated from muscle, but rather from muscle or intestine; might this have an impact on the nuclear genomic sequences of individuals? In other words, can one be certain that there is no genomic variation within individuals (excluding germline)? Essential revisions:1) Provide an adequate description of the study site. The readers are directed for this to Easton et al., 2016, Easton et al., 2017. Yet, descriptions there are themselves cursory, and refer to a report from a decade earlier . Provide – even in supporting materials – an informative, illustrative description of the study site  characteristics, and so forth.We have revised the manuscript to include a more detailed description of the study site, and a new supplementary figure with a map. The new description that appears in the Materials and methods is as follows:“Worm collection (and study site)Worms were expelled as part of a larger study in rural western Kenya described previously . Worms collected from study participants in five villages  following treatment with 400 mg albendazole were isolated, washed, labelled and stored frozen (-15 C). […] All samples were stored in Kisumu from which they were subsequently transported to the KEMRI-CDC offices until they were shipped to the NIH  on dry ice.”2) In the Discussion, state clearly why you consider that these new findings – Ascaris lumbricoides and A. suum jointly represent a species complex – is novel, given the earlier papers that have stated similar conclusions, e.g. Leles et al., 2012; Monteiro et al., 2019; Sadaow et al., 2018.While the conclusions have been previously suggested, the current manuscript and data provide comprehensive and more definitive support for a species continuum than previous work. We tried to put our findings in the context of these and similar works in the Introduction (third paragraph) and Discussion. Leles is already quoted in the Introduction. We feel that the papers we quoted in the Discussion  were field-defining. We appreciate the suggestion of the papers by Monteiro and Sadaow, but elected to include them in the broader list of manuscripts quoted in the Introduction, rather than adding them into the Discussion. However, we thank the reviewers for bringing these two papers to our attention, especially because they had access to excellent samples from both human and pig samples . Thus we have added the references in the verbiage that is appended below.“Some studies have shown that cross-species transmission occurs between pigs and humans living in close proximity ”.3) With respect specifically to the 'Gene content and Ascaris proteome' section of the Results, consider revising to more clearly indicate what the findings revealed and what they mean: What did the predicted proteome details indicate about the chimeric (lumbricoides-suum) nature of the Ascaris parasitizing the participants of the study? Or were the findings employed only for the phylogenetic tree presented as Figure 1C.To make our goals more clear  we have added the following sentence to the Results:Ascaris spp., to provide potential targets for future diagnostics to differentiate between nematodes and even between pig and human Ascaris, and to detail the potential functions of hypothetical or unknown proteins in the Ascaris genome.”“Our aims were to highlight the phylogenetic relationship with other helminths and between Ascaris lumbricoides-suum' chimera – species complex, comment on the host range of this Ascaris species beyond humans and pigs. Provide supporting citations.4) Given your use  of the term anthroponotic, which indicates that Homo sapiens is the reservoir of the 'We have added the following text:Ascaris sp. infections also occur naturally in monkeys and apes, and Ascaris sp. eggs are sometimes found in the feces of dogs but this is likely a result of coprophagy by the dogs, rather than due to infection (https://www.cdc.gov/parasites/ascariasis/biology.html).”“5) Elaborate of what you recommend for the control and treatment of parasitism by Ascaris based on your brief comments advising the need for a One Health approach. Provide supporting references to buttress the recommendation.Ascaris in humans will never be sufficient if animals remain a reservoir. We have added two key references to bolster this point as follows:In short, we believe that the fact that a pig-origin worm can spread human-to-human is direct evidence that controlling Ascaris. The COVID-19 pandemic has highlighted the importance of one health approaches to zoonotic diseases ; we must use a one health approach to ensure that pigs do not serve as a reservoir and potential breeding ground for drug resistance in a parasite that can sustain community transmission in humans ”.“The ability of pig-associated worms to become endemic in human populations indicates that a one-health approach may be necessary for the control of 6) Please revise the title, a reader might read the title and understand that experimental hybridization might have been undertaken. A suggested modification: "Molecular evidence of.…. indicates…."The title has been revised to:Ascaris indicates a highly interbred species complex infecting humans”“Molecular evidence of extensive hybridization between pig and human 7) The narrative of the manuscript is very nice. However, there are some issues that could be improved. First, the text contains typographical and grammatical errors that should be eliminated – also the errors in the reference list and the citations need to be corrected. Please meticulously edit the manuscript.We have hand checked this final version and done our best to be consistent throughout and following the appropriate formatting for eLife. We have examined the format of all the references individually and tried to ensure that they are all formatted correctly.Second, "isolates" and "strains" are confusing terms, if one is referring to individual worms (which maybe the case). As it is written, DNA samples were isolated from muscle or intestine from single worms, thus using the terms "worm" or individual" or "individual worm or specimen" is recommendedWhere “isolate” and “strain” occur, we have changed this to worm/specimen/sample and in some cases deleted the word as extraneous. One instance of strain remains, where other words would be incorrect.8) Genomic DNA was not consistently isolated from muscle, but rather from muscle or intestine; might this have an impact on the nuclear genomic sequences of individuals? In other words, can one be certain that there is no genomic variation within individuals (excluding germline)?We were not able to compare muscle and intestinal DNA as part of this study. However, we believe that the genomic vs. somatic distinction was the most meaningful. We have added the following:“Our previous work did not reveal any differences between a variety of somatic samples including the intestine and muscle , thus we do not expect any significant variations in the muscle and intestine genomic DNA used in this study.”


Carcharhinus falciformis, previously surveyed with the mtDNA control region in the Atlantic and Indian Oceans. Pool-seq methods were able to recover the entire mitochondrial genome as well as thousands of nuclear markers. This volume of sequence data enabled the detection of population structure between regions of the Atlantic Ocean populations, undetected in the previous study . Our results reinforce the conclusion that sampling the mitochondrial control region alone may fail to detect fine-scale population structure, and additional sampling across the genome may increase resolution for some species. Additionally, this study shows that the costs of analyzing 4,988 loci using pool-seq methods are equivalent to the standard Sanger-sequenced markers and become less expensive when large numbers of individuals (>300) are analyzed.Conservation genetic approaches for elasmobranchs have focused on regions of the mitochondrial genome or a handful of nuclear microsatellites. High-throughput sequencing offers a powerful alternative for examining population structure using many loci distributed across the nuclear and mitochondrial genomes. These single nucleotide polymorphisms are expected to provide finer scale and more accurate population level data; however, there have been few genomic studies applied to elasmobranch species. The desire to apply next-generation sequencing approaches is often tempered by the costs, which can be offset by pooling specimens prior to sequencing (pool-seq). In this study, we assess the utility of pool-seq by applying this method to the same individual silky sharks,  Many elasmobranchs around the globe have experienced devastating population declines due to overfishing in both target and non-target fisheries . These sGenetically distinct populations are isolated management units known as stocks; however, stocks can be defined on a smaller scale than genetic populations through other criteria, such as an exclusive economic zone boundry . ReducedFor the past two decades the standard for examining population structure in elasmobranchs has been a section of the mitochondrial genome, usually the control region (mtCR) . Though High-throughput sequencing is a powerful tool for revealing fine-scale population structure that may be missed by single locus studies . HoweverCarcharhinus falciformis ) is the second most commonly harvested shark on Earth , the North Atlantic , Brazil , and the Red Sea . These sample sizes are slightly lower than A total of 143 silky shark fin clips or muscle sections were sampled from commercial or artisanal fisheries across four geographic regions and are the same samples examined in DNA was extracted using Qiagen DNeasy Blood & Tissue kit , following manufacturer protocols. Extracted DNA quality was assessed visually by gel electrophoresis and imaged using Gel Doc E-Z System . Only DNA aliquots with strong genomic DNA bands were further processed, while degraded or overly digested DNA was discarded. Aliquots of high-quality DNA were quantified using an AccuClear Ultra high sensitivity dsDNA quantitation kit  and a SpectroMax M2 . Libraries were pooled with an equal amount of DNA (ng/µl) contributed per individual to minimize individual contribution bias, totaling 2,000 ng of DNA per library. Numbers of individuals per pool are displayed in Carcharhinus falciformis was used as a reference (GeneBank accession number KF801102). Raw paired-end reads were trimmed with TRIMMOMATIC, mapped to the mitochondrial genome reference BWA , and variants called using the dDocent bioinformatics pipeline, modified for pool-seq .MultiQC was used to assess sequence quality scores, sequence length distributions, duplication levels, and overrepresented sequences . To analhttp://ddocent.com/assembly/). Before assembly reads were trimmed using default settings and then an overlap (OL) assembly was performed, followed by clustering with CD-HIT with a–c parameter of 90% similarity. For mapping using BWA  all match, mismatch, and gap open penalty score parameters were also default settings. Different parameters were tested during optimization but did not improve mapping. Within-pool (K1) and between-pool (K2) minimum locus depth values selected for the de novo assembly did impact the results. dDocent provides graphical outputs to help select these values; however, testing a few different values of each is recommended to fully explore the potential of the data by balancing number of contigs by coverage depth (see ddocent.com/UserGuide for details). Selected values for K1 and K2 were 3 and 3 respectively. Once assembled, sequences were mapped, SNPs were called within the dDocent pipeline using FreeBayes, modified for SNP calling in pools (https://github.com/ekg/freebayes). Any contigs that aligned to the mitochondrial genome were removed from this nuclear dataset. The contigs that aligned specifically to the mitochondrial control region were saved for SNP validation to directly compare the results from this pool-seq approach to those previously reported by The bioinformatics pipeline included dDocent followed by AssessPool. Given that no reference genome was available, a reference was constructed using the dDocent de novo assembly and optimized utilizing the reference optimization steps provided on the dDocent assembly tutorial , and SNPs from these data were compared directly to SNPs called within the control region of the mitochondrial pool-seq data generated here. Concordance of this validation set of SNPs was determined by Mantel test in R . INDELs and multiallelic SNPs remain a challenge for quantification software, so we restricted our analysis to biallelic loci . AssessPFst values were all significant  than any inter-Atlantic comparison (ranging from 0.029 to 0.135). However, all comparisons within the Atlantic still showed significant STF values, the highest being between the North Atlantic and Brazil, and the lowest between Brazil and Gulf of Mexico  revealed 804 variable sites: 681 biallelic and 17 multiallelic SNPs. Because coverage in this dataset was fairly low on average, most of these SNPs did not meet the filter threshold. After further filtering for the highest quality markers, 30 SNPs were selected to calculate allele frequencies. Pairwise nificant . The Redf Mexico .STF values between locations than the mitochondrial data, yet all comparisons were still significant  was observed between Gulf of Mexico and the Red Sea, whereas the lowest (FST = 0.014) was between the North Atlantic and Brazil, which had the highest STF value within the Atlantic for the mitochondrial data.Our nuclear data showed 4,988 variants, of which 3,422 were biallelic SNPs and 151 were multialleleic SNPs. A total of 346 SNPs remained after the same filtering process for the highest quality SNPs was applied as for the mitochondrial genome. Nuclear markers showed lower nificant . The RedST value . The higFST values and should not bias interpretations of population structure , and comparisons between the Red Sea and all three Atlantic populations showed the same relative magnitude between both methods.SNPs called in the mitochondrial control region using the pool-seq protocol were compared with those reported in tructure . Three Structure . Despitehttps://usegalaxy.org/) or CyVerse (https://cyverse.org/). It is also important to note that the choice of pool-seq methodology has many caveats, which are discussed in greater detail in the ‘considerations on pool-seq’ section of the discussion below.The findings for cost analysis indicate that pool-seq reaches a threshold at approximately 300 individuals, after which this approach offers cheaper results than individual Sanger sequences. Furthermore, the cost is only twice as expensive at just over 100 individuals . The pooElasmobranchs are being harvested at unsustainable levels in several commercial fishing industries around the world. A fundamental step in successful management of any species is resolving population boundaries so they can be managed on a genetic stock-by-stock basis. As genetic sequencing technologies advance, there is greater opportunity to detect even small-scale genetic differences between populations. When these differences amount to statically significant allele frequencies at the population level, this indicates limited exchange among distinct stocks.Here, we validate the utility of pool-seq using the same individuals as a previous study  and showOne advantage of this pool-seq approach is that we recover SNPs throughout the entire mitochondrial genome along with thousands of additional nuclear loci that together provide greater statistical power to detect finer scale population structure . The pooIn this case, pool-seq lived up to the promise of increased power to detect fine-scale structure, but does it live up to the promise  of beingAs with any sequencing technique, there are still several factors to consider before deciding if pool-seq is appropriate for a particular study. Multiple reviews have been published on high-throughput and pool-seq approaches demonstrating pros, cons, and considerations with these methods, which are beyond the scope of this study. Interested readers should consult STF from pooled data remains a subject of some debate, and new approaches and bias corrections are being actively developed . Therefore, only those Fst values calculated by PoPoolation2 are reported in the main text for ease of presentation.Pooling assumes individuals are from the interbreeding individuals within a single population of the same species. Therefore, care needs to be taken to avoid cryptic species, combining multiple populations (Wahlund effect), or other unintentional bias when selecting individuals to pool . For wideveloped . To accoeveloped  package eveloped  that explues see , howeverThough pool-seq has been shown to be an affordable and reliable tool for population genomics , projectCetorhinus maximus, Prionace glauca, Acanthocybium solandri, The finding of population structure on the scale of North Atlantic/Gulf of Mexico/Brazil is nearly unprecedented for a pelagic shark. Population structure in globally distributed sharks is typically detected on a scale of ocean basins  are listed separately for the Clarke study because MAC < 3 were filtered out for pool-seq data.Click here for additional data file.10.7717/peerj.10186/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10186/supp-3Supplemental Information 3STF matrices produced among the Popoolation approach and Poolfstat approach shows strong correlation . This indicates, at least for this study, the difference in the analytical approaches does not make an overall difference in this study’s conclusions. Additionally poolfstat is not yet capable of calculating p-values for confidence, so for the sake of this study only FST values calculated from Popoolation are used. This may need to be re-evaluated for studies with more pooled regions examined or larger sample sizes. Regional Abbreviations: GM = Gulf of Mexico, NA = North Atlantic, BR = Brazil, RS = Red Sea.Comparison of Click here for additional data file.10.7717/peerj.10186/supp-4Supplemental Information 4Click here for additional data file.


One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV).The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP.These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.The online version contains supplementary material available at 10.1186/s12864-021-07404-9. Aquaculture has great potential to enhance food security and meet the increasing consumer demand for seafood , 2. HoweFillet yield has been ranked among the top traits impacting the industry returns . Also, qStatistical models based on phenotypes and pedigree information have been widely used in traditional genetic improvement programs to estimate EBV and identify the best selection candidates in animal populations . HoweverSelection based on GEBV can be performed at an early age with high accuracy, once a DNA sample for selection candidates is obtained . The useAlthough the use of genomic information generated from high-density SNP arrays has been demonstrated to expedite the rate of genetic gain in breeding programs, the genotyping cost is still high and alternative strategies are needed to reduce the cost of identifying elite breeding candidates . Cost-efRecently, the accuracy of genomic prediction was assessed for fillet yield in rainbow trout using a 57 K genomic SNP panel ; howeverThe total numbers of phenotyped fish for fillet yield and shear force were 775 and 772, respectively, and varied per year-class (YC) with 471 fish in YC 2012, and 304 fish in YC 2010. Descriptive statistics of the data are provided in Table A total of 1572 genotyped fish passed the quality check (QC) and were available for this study (four samples were removed due to fish ID conflicts). The total number of genotyped individuals from YC 2010 and 2012 were 400 and 558, respectively  was used , awas used . Incorpowas used .The regression coefficients (b1), representing inflation for fillet yield and firmness, are shown in Table R2) values of 0.56 and 0.01 were observed, respectively .Predictive ability = Further, inflation was assessed as the regression coefficient (b1) of adjusted phenotypes (y*) on (G) EBV as follows:∗ = b0 + b1 × (G) EBV + e.ywhere b1 < 1 indicates inflation and b1 > 1 indicates deflation of (G)EBV .PA, Pa, PB, andPb represent the allele frequencies; D = PAB − PAPB where PAB is the frequency of the genotype AB. The mean LD was estimated for each chromosome as the average estimate of r2 from all pairwise SNPs [The LD was calculated using PREGSF90  accordinise SNPs .dij is the distance between the SNP-marker pair i and j; Net is the effective population size for chromosome t; Ne was calculated using the equation proposed by Saura et al. [For each chromosome, LD decay with the distance between SNP markers was calculated by fitting Sved’s equation  as folloa et al. :dt is the average length of chromosome t in Morgan, r2 is the average LD of chromosome t, (n)−1 represents the adjustment term for the sample size, and α is a fixed parameter that equals to 1 in the absence of mutations or to 2 in the presence of mutations. In this study, α = 2 was used.r2). In brief, LD was calculated between each pair of SNPs within a genomic window of 50 SNPs. A pair of SNPs was removed if the LD between the two pairs of SNPs exceeded the LD value, e.g., 0.7. The window was shifted 5 SNPs forward, and the procedure was repeated. The command line was re-executed using the four other r2 values. Each one of the five resulting pruned SNP subsets with 20,042, 11,433, 1153, 497, and 128 SNPs were used in genomic predictions.PLINK 1.9  was usedThe second approach used to reduce the density of the SNP panel was based on the percentage of additive genetic variance explained by SNPs in each trait. The SNP variances were previously computed using wssGBLUP for fillet yield and fillet firmness , 19. In Additional file 1: Figure S1: A heat map for Pedigree matrix (A22) and Genomic matrix (G) where the color density reflects the relationship between individuals used in this study. Table S1: Genetic parameters of fillet yield and fillet firmness using ssGBLUP.


Gossypium barbadense) has markedly superior high quality fibers, which plays an important role in the textile industry and acts as a donor for upland cotton (G. hirsutum) fiber quality improvement. The genetic characteristics analysis and the identification of key genes will be helpful to understand the mechanism of fiber development and breeding utilization in sea island cotton.Sea island cotton (GB_A03G0335 encoding an E3 ubiquitin-protein ligase in QTL TM6004 had SNP variation (A/C) in gene region, was significantly correlated with FL, FS, FU, and FE, implying a crucial role in fiber quality.In this study, 279 sea island cotton accessions were collected from different origins for genotyping and phenotyping fiber quality traits. A set of 6303 high quality single nucleotide polymorphisms (SNPs) were obtained by high-density CottonSNP80K array. The population characteristics showed that the sea island cotton accessions had wide genetic diversity and were clustered into three groups, with Group1 closely related to Menoufi, an original sea island cotton landrace, and Group2 and Group3 related to widely introduced accessions from Egypt, USA and Former Soviet Union. Further, we used 249 accessions and evaluated five fiber quality traits under normal and salt environments over 2 years. Except for fiber uniformity (FU), fiber length (FL) and fiber elongation (FE) were significantly decreased in salt conditions, while fiber strength (FS) and fiber micronaire (MIC) were increased. Based on 6303 SNPs and genome-wide association study (GWAS) analysis, a total of 34 stable quantitative trait loci (QTLs) were identified for the five fiber quality traits with 25 detected simultaneously under normal and salt environments. Gene Ontology (GO) analysis indicated that candidate genes in the 25 overlapped QTLs were enriched mostly in “cellular and biological process”. In addition, “xylem development” and “response to hormone” pathways were also found. Haplotype analyses found that The present study provides a foundation for genetic diversity of sea island cotton accessions and will contribute to fiber quality improvement in breeding practice. Gossypium spp.) is one of the most important crops producing natural fibers for the textile industry. Upland cotton (G. hirsutum) and sea island cotton (G. barbadense) are the two main cultivated tetraploid species accounting for more than 97% of the total cotton production [G. barbadense for improving cotton fiber quality in breeding.Cotton  of the target traits and marker assisted selection (MAS) greatly accelerated the breeding of cotton varieties. With the development of molecular marker, SNP array and resequencing technology, a batch of QTLs for fiber yield, quality and salt tolerance of upland cotton have been explored. Using SSR markers, Shen et al. 2005) identified 39 QTLs associated with fiber quality traits of upland cotton  identifi. Via speG. hirsutum, less studies for genetic diversity and QTL mining related to target traits were reported using natural population in G. barbadense. In addition, Xinjiang is the main production area of sea island cotton in China, however, most of the cultivated land in Xinjiang is saline alkali land [+ levels in soil can decrease fiber length, strength, and micronaire [Compared with ali land . Salinitali land . In uplacronaire . HoweverThe 279 sea-island cotton accessions  indicated that the Fst value between Group1 and Group2 or between Group1 and Group3 was higher than that between Group2 and Group3 , and the 118 genes specifically identified in normal conditions were mainly involved in “cellular process”. We found the 785 candidate genes simultaneously identified under normal and salt conditions were enriched mostly in “cellular process” and “biological process”. Also, “xylem development”, “response to hormone” and “regulation of post-embryonic development” were also detected  and sea island cotton (G. barbadense) are two fiber-producing tetraploid cultivated cotton species, with markedly superior fiber quality in sea island cotton. However, there are few studies on the genetic characteristics of sea island cotton in natural population. In this study, we used high quality 6303 SNPs to investigate the genetic diversity and population characteristics of 279 sea island cotton accessions. Compared with the previous studies based on other molecular markers, such as ISSR [Upland cotton  using the method described by Singh et al. (2017) . The aveFor SNP genotyping, young leaves were collected from the 279 cotton accessions  and genomic DNA was extracted using CTAB method described by Paterson et al. (1993) . A Cottohttp://tree.bio.ed.ac.uk/software/figtree/). Principal component analysis (PCA) was employed using Tassel 5.0 software and plot was drawn by R package “ggplot2”. Admixture 1.3 software was applied to population structure analysis with K = 3 [st) between pairwise groups of sea island cotton accessions were calculated using Vcftools software [The SNP data format was adjusted by self-written shell script and used to conduct similarity analysis of 288 cotton accessions by PLINK V1.90 software . For theth K = 3 . Nucleotsoftware .P-value in rMLM: 0.001; Search radius of candidate gene (Kb): 100; and Critical LOD score in mrMLM: 3. Meanwhile, the Q + K model was also set with a population structure (Q) matrix calculated by admixture 1.3 and a kinship (K) matrix calculated by R package “mrMLM”. The Manhattan plot of associated QTN was performed by R software using the BLUP values of five traits in two environments.The GWAS analysis was conducted using a multi-locus random-SNP-effect mixed linear model (mrMLM)  in R pacWe define the flank 200 Kb regions of QTN as the same QTL, and merge the overlapped QTLs to confirm the number of QTL. Putative candidate genes were identified in the QTL regions from the reference genome by self-written shell scripts. To determine the function of the candidate genes, Gene ontology (GO) analysis was implemented using AgriGO V2.0 with the SEA method . The traAdditional file 1 Table S1. Information on 279 sea island cotton accessions used in this study.Additional file 2 Figure S1. Call frequency distribution of total SNPs genotyped by CottonSNP80K array.Additional file 3 Figure S2. Distribution of SNP number on each chromosome.Additional file 4 Table S2. Information on high quality SNPs genotyped by CottonSNP80K array.Additional file 5 Figure S3. Density distributions of five fiber quality traits in sea island cotton natural population.Additional file 6 Figure S4. Correlation analysis of five fiber quality traits under different conditions. The number in these boxes indicated correlation coefficient . *, **, and *** indicated P value at the 0.05, 0.01 and 0.001 levels, respectively.Additional file 7 Table S3. Stable QTLs of five fiber quality traits identified by multi-loci mixed linear model.Additional file 8 Figure S5. Distribution on detected times for 81 associated QTLs of five traits, respectively. The x-axis represents the detected times; y-axis represents the number of QTLs.Additional file 9 Table S4. Information on the associated times and corresponding traits for stable QTLs.Additional file 10 Table S5. Candidate genes associated with fiber quality traits within QTL regions.Additional file 11 Table S6. GO analysis of genes specifically and simultaneously associated under normal and salt environments, respectively.Additional file 12 Table S7. Expression profile of genes located in QTL regions related to fiber qualities.Additional file 13 Figure S6. Box plots for the phenotypic values of QTN TM68443.


Oryza sativa L.), has been problematic in rice-production area worldwide. Although we started to know about the origin of some weedy traits for some rice-growing regions, an overall assessment of weedy trait-related loci was not yet available. On the other hand, the advances in sequencing technologies, together with community efforts, have made publicly available a large amount of genomic data. Given the availability of public data and the need of “weedy” allele mining for a better management of weedy rice, the objective of the present study was to explore the genetic architecture of weedy traits based on publicly available data, mainly from the 3000 Rice Genome Project (3K-RGP).Weedy rice, a conspecific weedy counterpart of the cultivated rice  without admixed genomic composition for genome-wide association analysis (GWAS). Five traits were investigated: awn color, seed shattering, seed threshability, seed coat color, and seedling height. GWAS was conducted for each sub-population × trait combination and we have identified 66 population-specific trait-associated SNPs. Eleven significant SNPs fell into an annotated gene and four other SNPs were close to a putative candidate gene (± 25 kb). SNPs located in or close to Rc were particularly predictive of the occurrence of seed coat color and our results showed that different sub-populations required different SNPs for a better seed coat color prediction. We compared the data of 3K-RGP to a publicly available weedy rice dataset. The profile of allele frequency, phenotype-genotype segregation of target SNP, as well as GWAS results for the presence and absence of awns diverged between the two sets of data.Based on the results of population structure analysis, we have selected 1378 individuals from four sub-populations (Rc, can be developed to diagnostic SNPs to trace the origin of weedy trait occurred in the field. The difference of results from the two publicly available datasets used in this study emphasized the importance of laboratory experiments to confirm the allele mining results based on publicly available data.The genotype of trait-associated SNPs identified in this study, especially those located in or close to  Oryza sativa L.), is probably the most intractable problem to deal with in rice-growing countries. Herbicide inapplicability, as well as the morphological resemblance, have made WR management an uphill struggle for rice farmers around the world , a conspecific weedy counterpart of the cultivated rice  and the straw awn-less (SH) populations  is a major outcome of the 3000 rice genome project (3K-RGP). SNP-Seek harbors genotypic and phenotypic data for more than 3000 accessions of cultivated rice  technologies has brought plant genetics into a new era . The full set of phenotypes consisted of 47 agronomic traits recorded in ordinal scale between 1994 and 2010, maintained in the International Rice Genebank Collection Information System (IRGCIS). For our purpose, we have selected five traits which are often viewed as notable features distinguishing WR from cultivated rice  were downloaded from the Rice SNP-Seek Database  via Burrows-Wheeler Alignment tool (BWA) version 0.7.17-r1188 (Li and Durbin http://broadinstitute.github.io/picard/) and IndelRealigner implemented in The Genome Analysis Toolkit (GATK) version 3.8-0-ge9d806836 . We used BEAGLE  and the model-based maximum likelihood approach implemented in software ADMIXTURE v1.23 , indica (696), temperate japonica (152), and tropical japonica (375).PCA was performed in GCTA  within each sub-population using PLINK. Pairwise r2 values were computed between all SNPs within 300 kb along the same chromosome according to the study of Wang et al.   . The amount of genetic variation explained by PC1 to PC4 was 11.5%, 4.76%, 3.3%, and 2.72%, respectively Fig. , the fivr2 dropped to half of its maximum value. The sub-population of temperate japonica exhibited the shortest LD decay (91 kb), followed by aus (146 kb) and tropical japonica (157 kb). The mean r2 value observed in indica tended to be much lower than other populations, implying almost no LD between markers within the indica sub-population. Nevertheless, the genome-wide marker density with such low LD was already high  and markers were well distributed along the genome  described here is an agronomic trait distinct from shattering. While shattering is defined as the proportion of grains detached at reproductive stage, threshability is defined as the proportion of grains removed when firmly grasped by hand  seedling height at 5-leaf stage.Across the full panel of 1378 accessions, target traits usually had a missing rate smaller than 0.1, except for shattering degree (PSH) which had a missing rate of 0.35 , AWCO_REV, PTH, and SDHT_CODE in indica (1 PC each), AWCO_REV in temperate japonica (2 PC) and PSH, PTH, and SDHT_CODE in tropical japonica .GWAS were conducted separately in each sub-population given that the existence of population structure may result in spurious associations and that sub-population specific variants may be ignored in a combined population , RAE2 (23.4 kb upstream of S8_24022229 associated to awn), WOX10 (2.4 kb upstream of S8_25508381 associated to seedling height), and GRF8 (7.3 kb downstream of S11_20514671 associate to seedling height).Overall, 66 trait-associated SNPs were identified over five target traits Table : 23 for ca Table . Twelve ca Table . Based oaus, the frequency of genotype AA at S6_7624195 was 0.74. Among all the individuals carrying AA genotype of S6_7624195, 73.8% were awn-less individuals. The homozygous G at S7_13096799 in tropical japonica indicated the tendency of difficult threshing, while the homozygous A, although of lower frequency in the population (0.23), indicated the tendency of easy threshing. Seed coat color could be predicted with an average of 88.7% accuracy by the genotype at target loci: two SNPs identified for aus, one SNP for indica, and one SNP for tropical japonica, located in or in the vicinity of Rc, a bHLH protein known to involved in the pigment synthesis at seed coat  and temperate japonica (S1_31283533).As our objective was not only to identify trait-associated loci, but also to assess the probability a weedy trait would appear when an individual carries a certain genotype at loci of interests, we inspected the segregation ration between target traits and associated markers for each sub-population × trait combination. We have identified nine SNPs whose allele could indicate with at least 60% of accuracy the target phenotype Table . Here, aRc on chromosome 7 . The two alleles T and G of this SNP had no functional evidence yet. S7_6067855 was identified within indica and the two alleles were synonymous mutation  in Fig. aus: the frequency of individuals carrying the 14-bp insertion showing effective red seed coat in aus was 0.66, while 96% of individuals carrying the “colored” allele of S7_6068017 possessed colored seed coat in aus. No seed coat color-associated SNP was identified for the sub-population of temperate japonica which should be a result of too few individuals exhibiting colored seed coat.The high prediction accuracy of SNP genotypes for seed coat color drove us to investigate in detail these seed coat color-associated SNPs located within or in the vicinity of r = − 0.48, p-value < 0.001; Fig. In addition to the analysis using the 3K-RGP data, we attempted to compare our results with publicly available WR data. For this purpose, we have obtained sequence reads of 205 WR accessions from two studies  in Fig. aus whose genotype could explained close to the total occurrence of red seed coat. The A allele of S7_6068017 was previous identified as rc-s allele  and in aromatic  but not at all in indica, temperate or tropical japonica -related Homeobox (WOX) gene family which have been shown to coordinate gene expression both in shoot and root meristem in Arabidopsis  family, a plant specific transcription factor. GRF are involved in various plant developmental processes and take part in the coordination of growth under adverse environmental conditions (Omidbakhshfard et al. We used GWAS to detect trait-associated SNPs within each sub-population. Relatively few trait-associated SNPs were identified compared to the number of SNPs used for GWAS. One possible reason was that the available phenotypic data were categorical data which reduced the variation and information of quantitative traits. The other possible reason was that the phenotypic distributions were skewed towards cultivated types, such as absence of awn, low degree of shattering, color-less seed coat, and short stature Fig. . A more ale Fig. . There aSince our work using the data from 3K-RGP was to pave the way for our understanding of the WR occurrence, we have also obtained WR data from public resources to get a first glance of the usefulness of our results from cultivated pool. Although our most promising diagnostic markers were for seed coat color, the common trait from WR panel was the presence/absence of awn, which restricted our analysis. A substantial number of markers were in common between the 3K-RGP panel and the WR panel, but when we investigated quality-filtered markers, allele frequency behaved differently between the two panels, as well as the genotype vs. phenotype segregation and, the genomic position of awn-associated markers Fig. . Indeed,Rc on chromosome 7. We have shown that rc-s was particularly abundant in aus and that Rc alleles could therefore serve as diagnostic markers to assess the origin of weedy rice in the field, especially to understand the occurrence of colored seed coat. Further, we have compared the data from 3K-RGP to another publicly available data of WR. The constitution of the two panels was so different that their results did not converge toward the same direction. This work showed the potential of publicly available data but also remind the indispensable validation by lab experiments.We have analyzed publicly available data to explore the genetic architecture of weediness-related traits on cultivated pool from 3K-RGP. After assessing population structure and LD, we have performed GWAS within each sub-population and have identified trait-associated markers. Candidate genes were identified for weediness-related traits and the most significant were SNPs from Additional file 1: Table S1. Number of PC included in the final GWAS model. Table S2. Relationship between genotype and phenotype for seed coat/pericarp “color-less” allele of SNPs inside or close to the known candidate gene Rc. Figure S1. cross-validation error for K = 1 to 15. Figure S2. LD decay based on 4.8 M SNPs. Figure S3. Genome-wide marker distribution for each sup-population. Figure S4. Manhattan plots and Q-Q plots for final GWAS model for different sub-population × trait combination.


The quality and yield of wool determine the economic value of the fine-wool sheep. Therefore, discovering markers or genes relevant to wool traits is the cornerstone for the breeding of fine-wool sheep. In this study, we used the Illumina HiSeq X Ten platform to re-sequence 460 sheep belonging to four different fine-wool sheep breeds, namely, Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Aohan fine-wool sheep (AHS) and Qinghai fine-wool sheep (QHS). Eight wool traits, including fiber diameter (FD), fiber diameter coefficient of variance (FDCV), fiber diameter standard deviation (FDSD), staple length (SL), greasy fleece weight (GFW), clean wool rate (CWR), staple strength (SS) and staple elongation (SE) were examined. A genome-wide association study (GWAS) was performed to detect the candidate genes for the eight wool traits.UBE2E3 and RHPN2 associated with fiber diameter, were found to play an important role in keratinocyte differentiation and cell proliferation. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results, revealed that multitude significant pathways are related to keratin and cell proliferation and differentiation, such as positive regulation of canonical Wnt signaling pathway (GO:0090263).A total of 8.222 Tb of raw data was generated, with an average of approximately 8.59X sequencing depth. After quality control, 12,561,225 SNPs were available for analysis. And a total of 57 genome-wide significant SNPs and 30 candidate genes were detected for the desired wool traits. Among them, 7 SNPs and 6 genes are related to wool fineness indicators , 10 SNPs and 7 genes are related to staple length, 13 SNPs and 7 genes are related to wool production indicators (GFW and CWR), 27 SNPs and 10 genes associated with staple elongation. Among these candidate genes, This is the first GWAS on the wool traits by using re-sequencing data in Chinese fine-wool sheep. The newly detected significant SNPs in this study can be used in genome-selective breeding for the fine-wool sheep. And the new candidate genes would provide a good theoretical basis for the fine-wool sheep breeding.The online version contains supplementary material available at 10.1186/s12864-021-07399-3. The wool industry produces approximately 1160 million kilograms of clean wool every year from a global herd of over a billion sheep. The economic value of wool depends on various parameters such as the fiber diameter, fleece weight, clean fleece rate and staple strength. In general, wool traits are affected by diverse genetic and environmental factors simultaneously, with moderate to low heritability , 3, woolWith the development of sequencing technology and commercial SNP array genotyping technologies, researchers can now identify quantitative trait loci (QTL) by performing genome-wide association studies (GWAS) between genetic markers and phenotypic records . GWAS ofHowever, genotype data of the above mentioned studies were obtained based on the SNP array. The currently available commercial SNP array such as the Illumina Ovine SNP50K BeadChip cannot cover all the SNPs involved in the fine-wool sheep genome. Given to the limited number of SNPs, the power of GWAS is also limited, indicating that some genes affecting traits may not be detected. Eventually this may cause difficulty in understanding the molecular mechanisms of wool trait formation. Whole genome re-sequence data containing the majority of SNPs were optimized to enhance the accuracy and power of GWAS. With reference to the genetic background of Chinese fine-wool sheep breeds, the previous GWAS was mainly based on one breed, which inevitably affected the applicability of QTL for wool traits. In this study, we utilized the re-sequencing data and wool phenotypic data of 460 sheep belonging to four different genetic backgrounds of fine-wool sheep breeds in China including Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Aohan fine-wool sheep (AHS) and Qinghai fine-wool sheep (QHS) to conduct GWAS aiming to explore the candidate genes and the common potential causal genetic variants involved in the development of wool traits in different breeds. We thus expect that the potential genetic markers identified in this study will be applicable to genome-selective breeding of fine-wool sheep across the world. Moreover, we believe that the detected candidate genes will facilitate the comprehension of the development mechanisms of wool traits in the future.The descriptive statistics of eight phenotypic wool traits and the numbers of sheep are presented in Table The population stratification revealed different genetic backgrounds contributed by factors such as different varieties, strains, and family. The GCTA software was used to conduct PCA on the AMS, CMS, AHS, and QHS population in order to understand their genetic background. The AMS was more dispersed than the CMS group, based on the first two principal component. With regard to the composition and the second principal component, the AHS group was more dispersed than the CMS group. However, the QHS group was not separated from the other groups. In fact, the genetic background of the CMS, AHS, and AMS showed some differences, albeit they were not completely separated. The scatterplots of the first 1.30%), second (0.84%), and third (0.75%) principal components are displayed in Fig. .30%, sec2 values were equal to 0.056 (AMS), 0.077 (AHS), 0.073 (CMS), 0.066 (QHS), and 0.045 . Further details about the LD analysis using r2 are included in Supplementary Table S4. Our results indicated that the LD decay tends to be stable when the distance is 100 kb. Therefore, we considered the genes located within ±50 kb near the significant SNP sites as the candidate genes.The linkage disequilibrium (LD) decay is illustrated in Supplementary Fig. S2. With increasing distance in the AMS population, the LD dropped more quickly than in the other three breeds. For SNPs up to 50 kb apart, the average rGenetic variance, residual variance, and the heritability of wool traits were estimated by the AI-REML using the genomic BLUP (gBLUP) for the data of four breeds. The estimated genetic parameters of wool traits are shown in Table LOC101117971 was related to FDSD. For the FD trait, 2 significant SNPs were detected on OAR2 (OAR: Ovis aries chromosome) and OAR14. The most significant SNP annotated RHPN2 was located on OAR14  related to the above wool traits by GO and KEGG. Among them, FD, FDCV and FDSD were put together for enrichment analysis. The biological processes in GO (Table p = 6.72E-05), positive regulation of monocyte chemotaxis (p = 0.02651675), and presynaptic membrane (p = 0.030568552). The p-value of the GO term associated with SL and GFW was approximately 0.05. Among them, WDPCP and KANK1 were enriched in the regulation of cell polarity establishment. PID1 was enriched in the negative regulation of insulin receptor signaling pathway (GO:0046627). In terms of the wool stretch performance, the most significant pathway showed a positive regulation of canonical Wnt signaling (p = 0.01). There are two pathways related to the bone, osteoclast development (p = 0.01), and bone resorption (p = 0.04), namely, the extracellular-glutamate-gated ion channel activity (p = 0.039331749) and the excitatory postsynaptic potential (p = 0.051115614). The KEGG pathway analysis revealed that the candidate genes were significantly enriched in the four pathways  = 7.1), in consistence with previous reports. For instance, EXT1 located on OAR9 include the SNPs  was associated with FD near a known QTL (75.9 Mb) [PAX9 located on OAR18, which include the SNPs , was identified to be related to hair defects [KRT17 located on OAR11, which is near the SNPs , is required for the correct growth of hair follicles, especially for continuous growth [p = 2E-06 value as the threshold to search for more genes for the enrichment as analysis in order to better understand the functions of candidate genes.As the Bonferroni method is extremely restrictive, some useful sites may be screened . Based o75.9 Mb) . PAX9 lo defects ; this fi defects . KRT17 ls growth . TherefoUBE2E3, TNFSF4, and RHPN2), participated in cell differentiation or proliferation. A past study verified that UBE2E3 is required for cell proliferation [TNFSF4 can regulate apoptosis, proliferation, and differentiation of cells [TNFSF4 expression is significant for the outer root sheath (ORS) of postnatal mouse skin [TNFSF4 plays a role in the feather follicle initiation as well as in the development phase of feather follicle morphogenesis [TNFSF4 were located next to the genomic regions by selection signature detection in Spanish Merino [RHPN2 is involved in the Rho pathway [RHPN2 was upregulated in the production of hair follicle stem cells in mice [In this study, seven SNPs were found to be associated with FD-related traits , which were then used to evaluate the characteristics of fiber diameter. More interestingly, three SNPs lying in the genomic regions, including three candidate genes  were related to cell differentiation or proliferation. From the perspective of affecting cell differentiation, NLGN1 is a cell surface protein involved in cell-cell interactions that is enriched into significant cell adhesion molecules (oas04514), which play an essential function of keratinocyte differentiation control of cell adhesion [NLGN1, promoted morphogenesis in the early hair follicle placodes in mice [GEM belongs to a new family within the Ras superfamily that is involved in the origination of mouse keratinocyte stem cells from the bulge of hair follicles [GEM may be a candidate gene for the large colonies-keratinocyte stem cell locus 2 (KsC2), which is produced in cellular responses to growth stimulation [NF2 (Merlin), is a regulator of the Hippo signaling pathway that plays a key role in tumor inhibition by limiting proliferation and promoting cell apoptosis [For another important wool trait SL, we obtained 10 significantly associated SNPs. Although these sites were not located in the known QTL, three candidate genes  via gene expression profiling [VAV3 transcripts induced cytoskeleton defects, increased the cell area and enhanced the fiber stress [SRC is an important component of adhesion reaction, and the cell-cell contact between neighboring keratinocytes is mainly mediated by connecting the junctions and desmosomes [SRC was enriched into the Canonical Wnt signaling (GO:0090263), which is essential for the induction of hair follicle development [SRC was also enriched into the osteoclast development (GO:0036035), and the interaction between keratinocytes and fibroblasts induces osteoclasto genesis, which in turn can lead to bone resorption [For the SE trait, the function of the candidate gene rofiling . In addir stress . Anothersmosomes . A prevismosomes . In our elopment . The Canelopment , and is elopment . SRC wassorption . This prBased on extensive search and literature review, we believe that this is the first GWAS research for wool traits in multiple Chinese fine-wool sheep breeds via re-sequencing data. Despite our results being different from those of previous studies for wool traits, the functions of candidate genes detected in our study were directly or indirectly related to keratinocytes or wool follicles development. The disagreement in our results relative to that in previous studies can be possibly attributed to the difference in the breed and the density and quantity of SNPs used in the analysis. These newly detected candidate genes may be involved in the formation of wool traits, warranting further research of the biological function of these genes.UBE2E3 and RHPN2 are known to play important roles during keratinocyte differentiation and cell proliferation. Most of the significant GO and KEGG pathways were found to be related to keratin and cell proliferation as well as to differentiation. The newly detected significant SNPs in this study can be applied to genome-selective breeding for fine-wool sheep. The new candidate genes are expected to provide a good theoretical basis for optimizing fine-wool sheep breeding.In this study, we performed genome-wide resequencing of 460 sheep belonging to four fine-wool sheep breeds . A total of 8.222 Tb of raw data was generated, with an average of approximately 8.59X sequencing depth. After filtration and screening, 12,561,225 SNPs yielded. GWAS was conducted for eight wool traits by using the EMMAX model. A total of 57 genome-wide significant SNPs were detected in associated with the selected wool traits. Moreover, 30 candidate genes were detected within the 100-kb area near the genome-wide significant SNPs. Among them, Wool traits were sampled from 460 sheep (adults aged > 550 days) belonging to 4 fine-wool sheep breeds in China. Briefly, 220 AMS  were sampled from Gansu Provincial Sheep Breeding Technology Extension Station ; 120 CMS  were sampled from Gongnaisi Breeding Sheep Farm ; 60 AHS  were sampled from Aohan Banner Breeding Sheep Farm  and 60 QHS  were sampled from Sanjiaocheng Sheep Farm . All sheep in this study were randomly selected without considering any pedigree information.The wool samples were collected from the posterior edge of the sheep’s scapula, slightly above the midline of the sheep’s body. We released the sheep after sampling in our study. The samples were dispatched to the National Animal and Rural Ministry of Animal and Fur Quality Supervision and Inspection Center , where the following seven wool traits were measured and recorded. Fiber diameter (GB/T 10685–2007), FD coefficient of variance (GB/T 10685–2007), FD standard deviation (GB/T 10685–2007), staple length (GB/T 6976–2007), clean fleece rate (GB/T 6978–2007), staple strength (GB/T 13835.5–2009), and staple elongation (GB/T 13835.5–2009). The operating procedures were in accordance with the corresponding national standards. The greasy fleece weight trait was measured on-site at the sampling farm.2EDTA [Reads containing the linker sequence were filtered out;The N content in single-ended access reads exceeding 10% of the read length was set as the standard for deleting paired reads;When the number of low-quality (≤5) bases contained in the single-ended sequencing read exceeded 50% of the length of the read length, the paired reads were removed.Genomic DNA was extracted by the phenol-chloroform method using sheep whole blood (5 mL) collected through the jugular vein into vacutainers containing anticoagulant K2EDTA . We releHigh-quality sequencing data were aligned to the reference sheep genome assembly Oar_v4.0 using the Burrows-Wheeler Aligner (BWA) software   is the dk average value, and the individual sample covariance n × n matrix is calculated by X = MMT/S. We used the GCTA [Where, E (dthe GCTA  for PCA r2) between pairwise SNPs was calculated using the Haploview software [r2 value was calculated for pairwise markers in a 500-kb window across the entire genome.We compared the pattern of LD using high-quality SNPs. To estimate the LD decay, the degree of LD coefficient (software . For thihttps://github.com/xiaolei-lab/hiblup) package of R software. The BLUP model is depicted as follows:In this study, the variance components and heritability were estimated using the Average Information Restricted Maximum likelihood algorithm (AI-REML)  in the “e2), I is an identity matrix, and σe2 is a residual variance. u ~ N, in which σu2 is additive genetic variance, and G is derived from genomic information and constructed by Van Raden method [Where, y is the vector of phenotypic values; b and u represent the fixed effects and breeding values, respectively; X and Z were design matrices for b and u, respectively; e is the residual error vector with a normal distribution of e ~ N. In this study, the wool traits were processed in the EMMA eXpedited (EMMAX) software. This software offers the advantage of empirical relatedness matrix containing a wide range of sample structure, including population stratification and hidden relatedness [p-values for suggestive genome-wide significance was set to -log10  = 7.1, p-value = 1/N . Moreover, the potential candidate SNPs were screened out by the associated significance  as follows:For GWAS, the main factors resulting in false-positive associations were population structure and individual relationships . EMMAX satedness . Using tg2 K, where K is the t × t kinship matrix inferred from genotypes, and e is the vector that an n × n matrix of residual effect Var(e) = σe2 I [Where, y is an n × 1 vector of an observed phenotypic value and X is an n × q matrix of fixed effects. β is a q × 1 vector representing coefficients of the fixed effects. Z is the incidence matrix relating observations to SNP effects. u is the random polygenic effect Var(u) = σ = σe2 I .https://www.ncbi.nlm.nih.gov/assembly/GCF_000298735.2). Then, these genes were submitted to the DAVID database for Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (http://david.abcc.ncifcrf.gov/).Based on the results of LD attenuation distance analysis, the biochemical function of the related genes within 50-K base pairs upstream and downstream of the physical position of the significant SNP locus were examined. The candidate genes significantly associated with the SNP loci were annotated in comparison with the sheep reference genome Ovs_aries_v4.0 (Additional file 1: Table S1. Summary of the statistics of 8 wool traits of 4 fine-wool sheep breeds.Additional file 2: Table S2. The sequencing data and quality assessment of each sample.Additional file 3: Table S3. 460 samples mapping and coverage statistics.Additional file 4: Table S4. The linkage disequilibrium decay in populations of AMS, CMS, QHS, and AHS.Additional file 5: Table S5. Summary information of significant SNPs with p-value<2E-06.Additional file 6: Table S6. 460 phenotype data of wool traits.Additional file 7: Fig. S1. SNP density across the genome for data of the four fine-wool sheep breeds.Additional file 8: Fig. S2. The LD decay of the four fine-wool sheep breeds.


Clustered regularly interspaced short palindromic repeats (CRISPR) is a promising novel technology that holds the potential of treating genetic diseases. Safety and specificity of the treatment are to be further studied and developed prior to implementation of the technology into the clinic. The guide-RNA (gRNA) allows precise position-specific DNA targeting, although it may tolerate small changes such as point mutations. The permissive nature of the CRISPR-Cas system makes allele-specific targeting a challenging goal. Hence, an allele-specific targeting approach is in need for future treatments of heterozygous patients suffering from diseases caused by dominant negative mutations. The single-nucleotide polymorphism (SNP)-derived protospacer adjacent motif (PAM) approach allows highly allele-specific DNA cleavage due to the existence of a novel PAM sequence only at the target allele. Here, we present CrisPam, a computational tool that detects PAMs within the variant allele for allele-specific targeting by CRISPR-Cas systems. The algorithm scans the sequences and attempts to identify the generation of multiple PAMs for a given reference sequence and its variations. A successful result is such that at least a single PAM is generated by the variation nucleotide. Since the PAM is present within the variant allele only, the Cas enzyme will bind the variant allele exclusively. Analyzing a dataset of human pathogenic point mutations revealed that 90% of the analyzed mutations generated at least a single PAM. Thus, the SNP-derived PAM approach is ideal for targeting most of the point mutations in an allele-specific manner. CrisPam simplifies the gRNAs design process to specifically target the allele of interest and scans a wide range of 26 unique PAMs derived from 23 Cas enzymes.https://www.danioffenlab.com/crispam.CrisPam is freely available at  The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system enables precise genome editing mediated by the guide RNA (gRNA), an RNA molecule that directs the CRISPR associated (Cas) protein to the target DNA within the genome. The Cas enzyme, the catalytic unit of the CRISPR-Cas system, generates a DNA double-strand break in the presence of a DNA:gRNA complementary and a protospacer-adjacent motif (PAM) in immediate proximity to the target DNA . The divvia SNP-derived PAM. CrisPam does not refer to a reference genome and supports multiple input methods to facilitate simple user experience. Experimental researchers attempting to specifically target a SNV, either for development of CRISPR-based therapeutics or disease modeling, will benefit the utility of CrisPam for successful allele-specific gRNA designs.The CRISPR-Cas system can tolerate mismatches between the gRNA and the target DNA. The bases at positions 8–13 at the PAM-proximal end of the spacer (regarding type II Cas proteins) are termed the seed sequence along with the first base at the 5' end . Mismatchttps://github.com/RoyRabinowitz/CrisPam.CrisPam is a pythonic code that performs sequence analysis of SNVs. The input of the code is the variant position (reference and variation nucleotides) and its flanking sequences (upstream and downstream). The code is available at The dataset of human pathogenic SNPs was generated from dbSNP (build 153), incorporating data from UCSC Genome Browser and ClinVar, as previously described CrisPam is webserver-based tool. Thus, no software installation effort is required. The CrisPam DB is a.xlsx file and can be opened by Excel. The CrisPam script is written in Python 3.6 and uses standard libraries.The presence of a PAM solely within the desired target allele is the guiding principle of the SNP-derived PAM methodology. The following workflow occurs to detect unique PAMs generated by a SNP: the code obtains the reference and variation bases and the flanking DNA sequences upstream and downstream to the SNV. The complementary strand is generated and analyzed to detect unique PAMs on the complementary strand as well. Some SNVs have more than a single variation nucleotide; each variation is handled individually. CrisPam attempts to detect PAMs on both the reference and the variant alleles and suggests suitable Cas variants, in case their PAM is present exclusively within the variant allele. For a given SNV, more than one PAM may be generated; therefore, CrisPam reports all the matches for the query SNV . The sugTo assess the potential of the SNP-derived PAM approach to treat human pathogenic point mutations, we used CrisPam to analyze a total of 43,673 SNPs (pathogenic and likely-pathogenic SNPs) from dbSNP . Successvia CRISTA , and a batch file that allows analysis of multiple SNVs. User-defined PAM is supported to enable researchers to identify suitable Cas proteins that are yet to be reported. The CrisPam web tool also supports single-nucleotide insertion and deletion variations. For NGG-based Cas variants, off-targets assessment is available a CRISTA .https://www.danioffenlab.com/crispam.CrisPam is available at The SNP-derived PAM targeting approach for enhancing allele specificity is a promising method for future CRISPR-based novel genome-editing therapies. As many patients suffering from genetic conditions are heterozygous, carrying one copy of a pathogenic allele, it is essential to develop SNP customized treatments for increasing treatment’s safety. Currently, CrisPam supports off-targets assessment only for NGG-based Cas enzymes. For non-NGG enzymes, it is suggested to use complementary tools to assess potential off-targets . We recoBy utilizing CrisPam, we demonstrate the high compatibility of the SNP-derived PAM approach to preform allele-specific targeting using varied CRISPR-Cas systems. As demonstrated on human pathogenic SNPs data, 90% of the SNPs were found to have a SNP-derived PAM. Our findings underline the relevance of this allele-specific targeting approach in developing genome-editing therapeutic strategies. While CRISPR applications have been widely expanded, the SNP-derived PAM approach may be utilized for gene silencing (using inactive Cas), genetic screening, and more applications other than allele-specific DNA cleavage. This study emphasizes the emerging importance of broadening PAM compatibility of Cas proteins to enable allele-specific targeting and overcome the PAM limitation. Furthermore, CrisPam offers a simple interface to design an allele-specific targeting experiment using the CRISPR-Cas system.https://www.danioffenlab.com/crispam. Programming language: Python 3.6. The script is available at https://github.com/RoyRabinowitz/CrisPam. The SNPs dataset that was used to generate the CrisPam DB for PAM-generating pathogenic \ likely-pathogenic SNPs was previously described in “Prediction of Synonymous Corrections by the BE-FF Computational Tool Expands the Targeting Scope of Base Editing” (https://github.com/shiranab/parse_dbSNP. The obtained dataset of SNP-derived PAM targetable SNPs is available at the supplementary files (Project home page: Editing” . The scrConceived and designed the study, analyzed the data, and wrote the manuscript: RR. Programing: SA, RD, and RR. Webserver and user interface integration: SA. Principle investigator: DO. All authors contributed to the article and approved the submitted version.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.