FZD6) was further genotyped using the Sequenom MassARRAY iPLEX platform. The association of FZD6 gene polymorphisms with 73 egg production and weight traits in 329 female ducks were estimated. A total of 65,535 single nucleotide polymorphisms (SNPs) and 4,702 indels were identified across the genome. Fourteen GO terms and 14 KEGG pathways were determined for the genes of common differential variants, including MAPK signaling, Wnt signaling, melanogenesis and calcium signaling pathways, which are key functional pathways for poultry egg production reported in previous reports. Further analysis showed that 27 SNPs of FZD6 were associated with three early egg production of duck and egg weight traits, including egg production at 17 weeks (EP17), 18 weeks (EP18) and 19 weeks (EP19) and egg weight at 59 weeks (EW59). The FZD6 should be considered a novel candidate gene for egg production traits in laying ducks.Egg production traits are economically important in laying ducks. Genetic molecular mechanisms and candidate genes underlying these traits remain unclear. In this study, whole genome variants were identified through whole-genome resequencing using three high-egg producing (HEN) and three low-egg producing (LEN) laying ducks. The gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for the genes of common differential variants between HEN and LEN ducks were determined. Frizzled class receptor 6 ( In chickens, egg production traits are quantitative traits whose genetic architecture has been uncovered using a quantitative trait locus (QTL) analysis in a previous genome-wide association study (GWAS)4. Over 659 QTLs for egg-production traits have been found5 and many promising genes and mutants for egg-production traits have been identified6. In contrast the genetic basis underlying the egg production traits in ducks is still not fully understood, because the heritability of egg production traits is low to intermediate, ranging from 0.13 to 0.437. Some candidate genes for egg production traits have been investigated, mainly focused on the genes associated with reproductive physiology, such as the follicle-stimulating hormone receptor gene8, melatonin receptor gene9 and the prolactin gene10. However, candidate gene method has limited ability to identify genetic basis of egg production traits in duck.In the modern poultry industry, egg production traits, including egg number (EN), egg weight (EW), and age at first egg (AFE), are important reproductive and economic factors11. In chickens, whole-genome resequencing from eight chickens with slow- and fast-feathering rates showed 54,984 SNPs and mutated genes were involved in the response to stimuli, growth and reproduction processes and two SNPs involved in feather development were identified in the exonic regions of the Wnt signaling gene12. In ducks, selection signatures were uncovered by genome-wide comparisons among Mallards, indigenous-breeds and Pekin ducks and two selective sweeps for two important economic traits of the Pekin duck were identified through further fine-mapping based on resequencing of more than 1,000 ducks from an F2 segregating population generated by wild crossed with domestic birds13. In geese, the KIT gene underlying white or gray plumage color in Chinese domestic geese was identified by resequencing the whole genome of 18 geese from six populations, including white and gray goose breeds14.With the development of proteomic technologies, it is possible to investigate the genetic basis for key traits of livestock and poultry by whole-genome resequencing. In pigs, 13,955,609 SNPs and 2,666,366 indels have been found and the common differential SNPs and indels between five high- and five low-prolificacy Yorkshire sows using whole-genome resequencing technology and several differential variants within candidate genes for reproduction traits were confirmed by comparing selective regions and published quantitative trait locus (QTL) dataAnas Platyrhynchos) raised every year and as one of the main Chinese indigenous egg laying duck types, makes up about half laying duck population raised in China. The breed originated from Longmen town, in Longyan city and in January 2017, the Ministry of Agriculture and Rural Affairs of China officially approved the registration and protection of agricultural products based on the Longyan Shan-ma duck from this region.There are 250–300 million Shan-ma ducks (In this study whole-genome resequencing was performed on three full-sib high- and three full-sib low-egg producing Shan-ma ducks. The study aimed to identify the mutations across the genome and the genetic basis underlying egg production traits in egg-laying ducks.P = 0.007) and 27.38% (P = 0.011), respectively, compared with the LEN group. There was no significant difference in EW between the two groups.Statistics results of egg number (EN) and egg weight (EW) traits between HEN and LEN duck number ducks at 71 weeks are shown in Table A total of 82.69 Gb raw base reads were obtained from three HEN and LEN ducks, shown in Table 16, because the trial birds were full sibling individuals of one breed. In this study, the number of SNPs in intergenic, intronic and exonic regions including exonic stop gain, exonic stop loss, exonic synonymous, and exonic non-synonymous were 3,531,432 (52.21%), 3,232,152 (47.79%) and 114,824 (1.70%), respectively.A total of 7,243,250 SNPs and 864,777 indels were identified in the Shan-ma duck genome in Table In the exonic region, 79,997 (69.67%) of the SNPs were synonymous mutations, 34,357 (29.92%) were non-synonymous mutations, 421 (0.37%) were stop gain and 49 (0.04%) were stop loss. The transition to transversion ratio (Ts/Tv) was 2.429, which indicated the SNP quality was comparable to human and animal Ts/Tv ranges of 2.0 to 2.4 . For the indels, 365,133 (42.22%) were insertion and 499,644 (57.78%) were deletion variants. In the exonic region, indels consisted of 2135 frameshift, 959 non-frameshift, 54 stop gain and nine stop-loss variants.https://david.ncifcrf.gov/)17. Fourteen significantly enriched GO terms with FDR corrected P value < 0.05 were identified, including two molecular function GO terms, including nucleic acid binding transcription factor activity and transcription factor activity sequence-specific DNA binding, two cell components GO terms, including plasma membrane and cell periphery and ten biological process GO terms, including homophilic cell adhesion via plasma membrane adhesion molecules, signaling, cell communication, regulation of signaling, single organism signaling, regulation of cell communication, regulation of signal transduction, signal transduction, cellular response to organic substance and cell–cell adhesion via plasma-membrane adhesion molecules in Table A total of 65,535 common differential SNPs and 4,702 common differential indels were identified between the HEN and LEN groups. The detailed annotated information is shown in Tables P value < 0.05) as shown in Table 20. Because nine SNPs including exon 3: c.C855T:p.L285L, c.T852C:p.I284I, c.C741T:p.N247N, c.G645C:p.P215P, c.A435G:p.R145R, c.A405G:p.T135T, c.T372C:p.N124N, c.A329G:p.N110S and exon 4: c.A1437G:p.K479K located at two exons of FZD6 were found using whole-genome resequencing and enriched in both Wnt signaling and melanogenesis signaling pathways, it was selected for further study.Fourteen KEGG pathways were significantly enriched  and three low-egg producing (LEN) female ducks from one full-sib family were used in this study. Blood was sampled from the brachial vein using citrate-based anticoagulant syringes before the ducks were euthanized by electrical stunning and exsanguination. The blood samples were snap-frozen in liquid nitrogen then held at − 80 °C until used.The genomic DNA (gDNA) of blood samples was extracted using the phenol–chloroform method. The integrity of the DNA was estimated using electrophoresis in a 1% agarose gel and the purity of DNA was assessed using a NanoDrop 2000 spectrophotometer . The concentration of DNA was measured using an Invitrogen Qubit 2.0 fluorimeter . The OD260/OD280 ratios were between 1.8 and 2.0 and the concentration above 1.5 μg of each DNA sample were used to construct the DNA libraries. The DNA samples were randomly interrupted using a Covaris crusher  to create fragments of 350 bp in length. The DNA Libraries were constructed using a TruSeq Library Construction Kit  following the manufacturer's instructions. The DNA library was sequenced on the HiSeq 2500 high-throughput sequencing platform .Raw data reads were obtained by sequencing and clean data reads were acquired following the quality control (QC) procedure to remove unusable reads. Usable reads contained the Illumina library construction adapters, more than 10% unknown N bases and one end of the read had to contain more than 50% of low-quality bases, with a sequencing quality value less than or equal to five.25. The SNPs and indels were detected using the SAMtools (version 0.1.19)26 with the parameters as “-q 1 -C 50 -m 2 -F 0.002 -d 1000” and the filtering criteria “the mapping quality > 20 and the depth of the variate position > 4”. The functional annotation of these variants was carried out using ANNOVAR (version 2013Aug23)27 and known genes and region annotations were determined using the UCSC genome browser database28.All clean reads were aligned to the reference duck genome (BGI_duck_1.0) using Burrows–Wheeler aligner (BWA) software (version 0.7.8-r455) with default parameters29 were determined between the two groups. Genes located within 500 kb with these common differential variants were annotated.To identify differential variants across genomes between high- and low-egg producing ducks, the SNPs with the same genotype were distinguished in each group first, then differential variants and then the above-mentioned differential or “common differential variants”https://david.ncifcrf.gov/home.jsp)17. The KEGG pathway function enrichment analyses for the genes of the common differential variants were performed in web-based software Kobas 3.0 (http://kobas.cbi.pku.edu.cn/)31. The copyright permission (No. 221304) to publish the corresponding KEGG pathways was officially granted by Kanehisa Laboratories34. False discovery rates (FDR) with a corrected P value < 0.05 were considered significant for GO terms and pathways.The GO enrichment analyses for the genes of the common differential variants were conducted using the DAVID v6.8 online server , according to the manufacturer’s instructions. Quality control of SNP genotyping data were estimated by Haploview 4.1 software35 and any with a minimum allele frequency (MAF) of less than 0.05 and a Hardy–Weinberg equilibrium (HWE) test P value < 1.0 × 10–4 were excluded. Associations between SNPs and egg production traits were analyzed with the general linear model (GLM) in PLINK v1.9036 using the following model:Y is the trait value, µ is the overall mean, F is the family effect, BW is the body weight effect, G is the fixed effect of genotype and e is the random error.Based on the reproductive physiological function of genes in the pathways, Statistical analysis of egg production and weight traits between high- and low-egg production birds was performed with the independent-sample T test using SPSS 19.0  and the results are presented as the mean ± standard deviation (SD). For the traits used in the association study, abnormal values of these traits were excluded before analysis using Grubbs’ method and the descriptive statistics were analyzed by Minitab 17.0 Statistical Software .https://arriveguidelines.org).This study protocol was approved by the Longyan University Ethics Committee. All animal studies were conducted in accordance with the Guidelines for Experimental Animals established by the Ministry of Science and Technology . This study is also reported in accordance with the ARRIVE guidelines (Supplementary Information.


The receptor tyrosine kinase (RTK) EphA2 is expressed in epithelial and endothelial cells and controls the assembly of cell–cell junctions. EphA2 has also been implicated in many diseases, including cancer. Unlike most RTKs, which signal predominantly as dimers, EphA2 readily forms high-order oligomers upon ligand binding. Here, we investigated if a correlation exists between EphA2 signaling properties and the size of the EphA2 oligomers induced by multiple ligands, including the widely used ephrinA1-Fc ligand, the soluble monomeric m-ephrinA1, and novel engineered peptide ligands. We used fluorescence intensity fluctuation (FIF) spectrometry to characterize the EphA2 oligomer populations induced by the different ligands. Interestingly, we found that different monomeric and dimeric ligands induce EphA2 oligomers with widely different size distributions. Our comparison of FIF brightness distribution parameters and EphA2 signaling parameters reveals that the efficacy of EphA2 phosphorylation on tyrosine 588, an autophosphorylation response contributing to EphA2 activation, correlates with EphA2 mean oligomer size. However, we found that other characteristics, such as the efficacy of AKT inhibition and ligand bias coefficients, appear to be independent of EphA2 oligomer size. Taken together, this work highlights the utility of FIF in RTK signaling research and demonstrates a quantitative correlation between the architecture of EphA2 signaling complexes and signaling features. SPIDA wThe EphA2 receptor is highly expressed in epithelial and endothelial cells, where it triggers diverse downstream signaling pathways that control the assembly of cell–cell junctions. This receptor has been implicated in many physiological and disease processes, such as cancer , 20, 21,EphA2 belongs to the receptor tyrosine kinase (RTK) family. It is a single-pass transmembrane receptor with an extracellular region that binds the activating ligands (ephrins) and an intracellular region that contains the tyrosine kinase domain. The kinase domain is activated by autophosphorylation of tyrosine residues occurring upon close contact of neighboring EphA2 molecules. Therefore, lateral interactions of EphA2 molecules are the first required step in EphA2 signal transduction in the plasma membrane.While most of the 58 RTKs signal mainly as dimers, EphA2, in addition, can form high-order oligomers , 40, 41.This work was empowered by the recent discovery of a series of small engineered peptides that bind specifically to EphA2 and activate it . The pepThe different dimeric peptide ligands used in this study have different potencies and different efficacies, depending on their sequence and configuration . These dThe dimeric peptide ligands have been engineered from monomeric precursors through N-terminal, C-terminal, or N–C-terminal linkages . Based ovia a 15 amino acid (GGS)5 flexible linker has been previously shown to not affect EphA2 autophosphorylation (A) or treated with different ligands (B), the plasma membrane in contact with the substrate was imaged by confocal microscopy as previously described (A). However, upon ligand addition, heterogeneities appear within a minute or two (B). The appearance of such “puncta” of EphA2 fluorescence in response to ligand binding has been reported in the literature (B).We sought to assess the oligomerization state of EphA2, labeled with enhanced YFP (eYFP), using FIF spectrometry . Attachmrylation . Followirylation A or trea ligands B, the plescribed . We obseescribed A. Howevee or two B. The apterature , 55 and terature . Interesterature B.Figure A, P1) is divided into smaller segments with a preset size . Next, the distribution of the 225 pixel-level intensity values in each segment is fit with a Gaussian function, yielding for each segment the mean (<Ιsegment>) and the width (σsegment) of the fitted Gaussian. The variance (σsegment)2 and <Ιsegment> are then used to calculate the molecular brightness in each segment of the plasma membrane (εsegment) according to Equation Fluorescence micrographs including ∼200 to 300 cells were analyzed with the FIF spectrometry software . In the Fig. 1A, along with the measured brightness distribution of the monomeric control LAT (linker for activation of T cells)  , 17 and T cells) . The disT cells) , 41.Figu50) for EphA2 Y588 phosphorylation in cells, so that most EphA2 molecules are ligand bound (B). All the brightness distributions were scaled so that the area under the curve is the same, allowing direct comparisons. We observed that most of the ligands shifted the distributions of brightness to higher values, indicative of the induction of high-order oligomers that are larger than dimers. In contrast, the EphA2 brightness distribution in the presence of one of the ligands, the YSA peptide, is very similar to that of TrkA + NGF , indicating that YSA induces the formation of EphA2 dimers rather than high-order oligomers. There are also differences among the other ligands, suggesting differences in the size of the oligomers induced. For example, the brightness distribution in response to treatment with monomer 10 is the least shifted to higher brightness values, and the distribution for dimer 8 is the most shifted.Next, we performed FIF experiments in the presence of different ligands. The ligands were used at concentrations that greatly exceed their measured dissociation constants and/or their potency , which describe the ability of the different ligands to inhibit AKT serine 473 phosphorylation as compared with increasing EphA2 Y588 phosphorylation; (B) ligand-specific efficacy of EphA2 phosphorylation on Y588 (Etop pY588); (C) ligand-specific efficacy of inhibition of AKT phosphorylation (Etop pAKTinh), a well-known EphA2 downstream signaling response; and (D) ligand-specific ratios of Y588 phosphorylation to AKT inhibition potencies (EC50 pY588/EC50 pAKTinh).To determine if EphA2 signaling properties correlate with the parameters of the brightness distributions, we plotted previously determined parameters that describe EphA2 signaling  as a funlig) for each ligand, which reports on the existence and magnitude of ligand bias . Furthermore, ephrinA1-Fc exhibits an intermediate brightness value. Thus, there appears to be no correlation between bias coefficients and mean brightness. This was confirmed by fitting a linear function to the peptide and m-ephrinA1 βlig values (A), and determining whether a significant correlation is present by comparing the slope to the null hypothesis of 0 slope (corresponding to no correlation) using a one-sample t test. The p value obtained confirms that there is no correlation (B). To determine if a correlation exists in this case, we fit a linear function to the data points for m-ephrinA1 and the peptide ligands, again excluding ephrinA1-Fc. Comparing the slope to the null hypothesis of 0 slope yielded a p value <0.01, which is indicative of a significant correlation (C) and the ratios of the potencies of Y588 phosphorylation to AKT inhibition (D) show no correlation  might reflect the presence of larger EphA2 oligomers . Comparison of the EphA2 concentrations obtained from the whole membrane and puncta analyses shows average concentrations approximately three times higher in the puncta  and the right y-axis (referring to the puncta). To directly compare the puncta brightness distributions to the whole membrane brightness distributions, we plotted them side by side while again using two different y-axis scales .We found that ligands that promote different arrangements of the EphA2 extracellular region can induce puncta with distinct appearance and different receptor oligomerization states, which might be responsible for distinct signaling properties . The YSAFig. 3The fitting of the FIF distributions with log-normal functions revealed a large difference in the mode  of the distributions, when comparing whole membranes to puncta compare  and S2. lig, revealed that all the peptide ligands and m-ephrin-A1 are significantly biased toward AKT inhibition when compared with ephrin-A1 Fc. In addition, we found that the factors used to calculate βlig, including the efficacy and potency of the responses, differ among the ligands  with brus EphA2 , 62, 63.us EphA2 , and we Although bias coefficients are not different for the peptides and m-ephrinA1, there are quantitative differences in the features of EphA2 signaling used to calculate bias. For instance, the efficacies of the responses induced by the different ligands are significantly different from each other . The effvia a flexible 15 amino acid (GGS)5 linker.The EphA2-eYFP complementary DNA was cloned into the pcDNA3.1 (+) mammalian expression vector . The eYFd-glucose, and 1.5 g/l sodium bicarbonate. Cells were seeded in 35-mm glass-bottom collagen-coated dishes (MatTek's Corporation) at a density of 2.0 × 104 and kept in an incubator at 37 °C with 5% carbon dioxide.HEK293T cells were cultured in Dulbecco's modified Eagle's medium, supplemented with 10% fetal bovine serum (Thermo Fisher Scientific), 1.8 g/l Cells were transfected with varying amounts of DNA using Lipofectamine 3000 (Invitrogen) according to the manufacturer's recommended protocol. About 12 h after transfection, the cells were rinsed and starved for 12 h in phenol red–free and serum-free medium containing 0.1% w/v bovine serum albumin.The membranes of cells transfected with EphA2-eYFP were imaged on a Leica SP8 confocal microscope using a photon-counting detector. eYFP was excited using a 488 nm diode laser at 0.1% to avoid photobleaching, at a scanning speed of 20 Hz. Cells were subjected to osmotic stress with a hypo-osmotic medium of 25% starvation medium and 75% water. The swelling induced by the hypo-osmotic medium minimizes the effect of ruffles, folds, invaginations, and other irregularities in the plasma membrane, while also preventing EphA2 endocytosis induced by ligands .A), which was divided into segments of 15 × 15 (225 pixels) as described  Gaussian distribution and σ is the width of the distribution. These two parameters were used to calculate the mean, median, and mode of the log-normal distributions according to:The brightness distributions were fitted using OriginLab (OriginLab Corp) with a log-normal function given by:https://www.originlab.com/), and the calculated areas were used to normalize distributions such that they have the same area.Note that the mean, median, and mode are the same for a normal distribution but are different for a log-normal distribution. The errors of composite values were determined using error propagation algorithms . To compt test. p = 0.05 was the cutoff for the significance of the correlations.GraphPad Prism 8.3  was used to assess the correlations between (i) previously reported EphA2 ligand bias coefficients, efficacies, and potency ratios  and (ii)reported  times threported  and S3 wMost fluorescence images of cells treated with EphA2 ligands exhibited an abundance of puncta (or “spots”), that is, small groups of pixels with average intensities significantly higher than the surrounding membrane regions. The puncta were identified and separated for further analysis as discussed . BrieflyAll data are included in the article.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Sus scrofa) breeds is a key factor to (i) sustain the pork chain  including the production of high-quality branded products, (ii) enrich the animal biobanking and (iii) progress conservation policies. Single nucleotide polymorphism (SNP) chips offer the opportunity for whole-genome comparisons among individuals and breeds. Animals from twenty European local pigs breeds, reared in nine countries , and three commercial breeds  were sampled and genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. A dataset of 51 Wild Boars from nine countries was also added, summing up to 1186 pigs (~ 49 pigs/breed). The aim was to: (i) investigate individual admixture ancestries and (ii) assess breed traceability via discriminant analysis on principal components (DAPC). Albeit the mosaic of shared ancestries found for Nero Siciliano, Sarda and Moravka, admixture analysis indicated independent evolvement for the rest of the breeds. High prediction accuracy of DAPC mark SNP data as a reliable solution for the traceability of breed-specific pig products.Preserving diversity of indigenous pig ( The domestication aspects have often been investigated by the study of mitochondrial DNA, while genetic diversity was initially studied using simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) in intensively selected breeds6 but also in indigenous populations of limited diffusion10. The development of single nucleotide polymorphisms (SNPs) panels with SNPs distributed across the entire genome provided new opportunities to investigate and decipher the complex relationship between indigenous pig breeds12. This is a topic that has to be taken on in order to enhance the safeguard of local pig populations. Considering the region of Europe and Caucasus, the Food and Agriculture Organization (FAO) identified 48 already extinct pig breeds, representing ~ 20% of the global pig breeds. Among the existing breeds in the region, 14 breeds are classified at critical risk of extinction, 5 are in a critical-maintained status, 24 are endangered, 11 are defined as endangered-maintained and 6 in a vulnerable situation . This means that more than 25% of the local European pig population is in a worrisome demographic status. The improvement of breeding and conservation programs for these indigenous breeds is becoming extremely important for multiple reasons. Firstly, it is well known that indigenous breeds are well-adapted to their local environment and are a unique genetic pool that might be essential, not only as pig biobank13, but also for the sustainability of the global pork chain. In addition, local pig farming is strongly related to niche products of high quality, which contribute to the local economy development and sustainability14. No less important is the increasing demand for organic and high welfare animal-based food products15, which has led consumers to prefer local breed products that are considered more nutritious, tasty, healthy and safe16 and because animals are usually reared freely and outdoors17.The process of domestication of pigs and the spread of the species around the world has been the subject of some studies in the recent pasthttps://ec.europa.eu/eurostat/databrowser/view/tag00042/), heavily based on the use of cosmopolitan pig breeds. Moreover, Germany, Spain, France, Poland, and the Netherlands are the largest consumers in Europe. In this context, a powerful system to ensure pig breed traceability is required, that will enable products from pure local breeds to be clearly differentiated from their cosmopolitan counterparts and controlling fraud. Currently, the administrative traceability is not infallible, and the possibility of errors and frauds exists. The use of genetic markers could overcome these limits18. Microsatellites and SNP have been mainly used for traceability purposes, with the latter nowadays prevailing over the former, presenting many advantages such as easier laboratory handling, low mutation rate, and better suitability for standardization19. Several SNP based studies, often using a runs of homozygosity approach, aimed to detect candidate genes which allowed the identification of a specific breed20 and/or focused on genomic regions which discriminated populations from each other21. A pairwise fixation index (FST) distances method was used to differentiate indigenous from commercial pig populations23, and to determine breeds belonging to different production systems24. Moreover, SNP detection from genome wide sequencing was used to develop a SNP chip for discriminating between purebred or crossbred Iberian origin of live pigs, meat and dry-cured pig products25. Other methods applied to distinguish breeds from each other are the investigation of the proportion of ancestry shared among the breeds27 and the clustering of genetically related individuals by discriminant analysis28. This latter method has been applied to trace sheep, using sets of SNPs able to separate breeds belonging to different geographic areas29 and for assigning animals to their true population30. A similar approach has been applied in cattle, where Dimauro et al.31 argued that the canonical discriminant analysis was able to efficiently distinguish the three breeds studied . Moreover, various other methods exist in human32 and animal studies35 to identify a small set of ancestry informative SNPs, derived from genotyping or sequence data, that are helpful for population identification and breed traceability.It is important to note that the European pork production amounts on 21—22 thousand tonnes of meat per year , admixture and discriminant analysis of principal components (DAPC) to evaluate pig breeds  and wild boars traceability via the whole set of SNPs revealed by the GGP Porcine HD Array. The last two methods allow to predict the breed of origin (DAPC), and the proportions of ancestry per pig ., Swallow-Bellied Mangalitsa, Majorcan Black and Basque , the reason for this could be the sharing some parts of the genome linked to phenotypic characteristics and origin of Lithuanian White pigs; however, the amount of variability explained by the first PCs is largely limited with respect to the overall genetic variability possessed by populations in the entire dataset. Moreover, although in PCA based on the scatterplot of the first two PCs Fig. a Duroc a43. Previous analysis on the same local breeds reported a close relationship, based on neighbour-joining tree constructed with Nei’s distances, between the Wild Boar and Alentejana and Iberian breeds12. In our analysis, introgression of Wild Boar was also found, besides the two aforementioned breeds, for the Italian breeds Nero Siciliano and Sarda. Common features between the PCA, admixture and the un-supervised DAPC were also observed, as explained below.Admixture analysis revealed common ancestries shared between some indigenous and the commercial breeds. More precisely, Duroc shared ancestries mainly with Cinta Senese, Iberian and Sarda; Landrace with Bísara, Moravka, Nero Siciliano and Sarda; and Large White with Lithuanian White Old Type, Nero Siciliano, Sarda, and Lithuanian Indigenous Wattle. Regarding Wild Boars, our dataset consisted of a set of 51 samples from seven European countries, Tunisia, and Russia, to capture as much variability and to avoid country-specific bias. Indeed, a recent study investigating the history of the domesticated European pigs indicated an interbreeding between the local pig breeds and Wild BoarsThe un-supervised DAPC method could represent a real lab scenario for testing the “blind” or external to TRN set samples. In the un-supervised DAPC, many of the breeds, except Alentejana, Iberian, Black Slavonian, Cinta Senese, Lithuanian Wild Old Type and Turopolje, were mainly assigned as Sarda. This is not surprising, given the high admixture level of the Sarda breed. Black Slavonian was assigned to Cinta Senese in 76% of the cases, while Cinta Senese was predicted as Black Slavonian with 96% rate. Similarly, in the admixture analysis ~ 7.5% of the Black Slavonian was shared with Cinta Senese, while Turopolje was classified as Black Slavonian (100%). Interestingly, in the admixture analysis, Turopolje was assigned to two major ancestral groups sharing common ancestries mainly with Black Slavonian Table . Regardi41, where there were many breeds with percentages of correct reassignment < 80%. Moreover, the actual differences might be even higher, since in that analysis an external validation was not considered and the whole data were analysed simultaneously. The correct reassignment was further improved for the Moravka, Nero Siciliano and Sarda breeds that had the lowest values in the DAPC analysis by Muñoz et al.41. However, in that study only a limited number of 39 SNPs in candidate genes was used.A second objective was to study traceability of pigs based on genome-wide SNP data. To resemble a practical application, the efficiency of the DAPC method was evaluated using an external validation. Furthermore, to assess the effect of sample size, the analyses were repeated several times with subsets of the dataset ranging from 30 to 90%. Although the correct assignment of the breeds was > 90% in all subsets, the variation of the correct assignment decreased with increased sample size, indicating a more robust model Fig. . This leUsing the complete dataset, the majority of the breeds were correctly assigned to its breed of origin, with the exceptions of Black Slavonian, Cinta Senese, Krškopolje, Lithuanian White Old Type, Moravka and Turopolje, with the lowest value (86%) being observed for Black Slavonian Fig. . In the 12 and (ii) a whole-genome analysis was conducted compared to the candidate gene approach and the 39 SNP of Muñoz et al.41.It should be noted that discrepancies between our results and previous genomic analyses on the same set of breeds were to some extent expected. There are two main reasons for this: (i) we considered three cosmopolitan breeds and a more diverse Wild Boar panel compared to Muñoz et al.We report a whole genome SNP analysis on admixed ancestries and classification of 20 European indigenous pig breeds, together with three commercial breeds and Wild Boars. Our results confirm previous analysis on the genomic diversity of the local breeds. Classification results using the 70 K HD porcine SNP chip were reliable and robust, hence DAPC could be considered as a potential tool for local pig breed traceability in the future. Our results indicate that robustness of the model could further benefit with bigger sample sizes. Nevertheless, cost of genotyping might be a limiting factor for a wide scale application. To overcome this limitation, a search for the minimum set of SNPs, that could achieve similar results obtained with the medium density SNP chip, could be proposed. Indeed, it would be useful to genotype a high proportion of the individuals belonging to the breeds with the highest risk of extinction or in any case with a greater risk of introgression from other populations. The cost of the set of SNPs is therefore fundamental given that for many of the breeds considered in this study there is a limited budget for genotyping. Our results suggest that integration of statistical methodologies to investigate genomic variability within and between breeds should be considered. We hope our findings to contribute and enhance the indigenous pig farming.https://treasure.kis.si/). Blood samples were collected from each institution by specialized professionals, following standard guidelines. No interventions with animals were applied that would require ethical protocols (according to Directive 2010/63/EU-2010) , (ii) three commercial breeds including Duroc (n = 53), Landrace (n = 52) and Large White (n = 52) and (iii) a sample of Wild Boars (n = 51) from Finland, Hungary, Italy, Spain, Poland, Russia, The Netherlands, Tunisia, and Greece was carefully selected from the Dryad Digital Repository: DOI: 10.5061/dryad.30tk6 (10.5061/dryad.30tk6)44. Further details on the selection of the Wild Boars are provided in the 12. All pigs from the indigenous and the three commercial breeds were genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip containing 68,516 SNPs. The Wild Boars were genotyped with the Illumina 60 K SNP data45. The merged data contained 42,464 autosomal SNP. Samples with more than 10% and SNPs with more than 5% of missing values were excluded. The final data consisted of 1,186 pigs and 40,364 SNP  were obtained from three sources: (i) 20 European indigenous breeds (n = 987) reared in 9 countries , and retrieved from the European funded project TREASURE  to be retained in admixture (K = 2–24) was evaluated via a fivefold cross-validation (CV) and the model with minimum CV error was selected for further analysis. Results were also summarized per breed for an easier representation.Admixture and PCA were used to investigate the data structure in terms of distinct populations. The two approaches, are complementary to each other. More precisely, PCA produces orthogonal projections of the original data, variance driven (from the highest to the lowest), focusing on how different populations are structured (between and within). In contrast, an admixture analysis provides the proportions from each of the source populations in each sample, i.e., how the individual samples are related to the source populations (ancestries). The PCA was performed in R software48 was applied to assess breed traceability, as implemented in the R package adegenet50. DAPC replaces the original SNP data with a small set of principal components (PCs) and then applies a linear discriminant analysis on the selected PCs. In this way, DAPC maximizes the differences among groups while overlooking at the variability within groups. The number of PCs to be used in the discriminant analysis is determined via CV and the targeting function can be either the lowest root mean squared error or the highest mean success. To select the best option both methods were evaluated: In brief, data were randomly sampled in sets starting from 30% and augmenting by 10% up to the complete dataset, one repetition each, having all the breeds represented (stratified sampling), and the overall model assignment accuracy was recorded . Data were randomly (without replacement) split at 80–20% for the TRN-VAL set, and the split was repeated 10 times. Random sampling was conditioned such that all the breeds were present in both TRN and VAL sets (stratified sampling).Scenario 2 (un-supervised learning). Each breed was analysed separately and consisted of the VAL set. In this scenario, no pigs of the VAL set were present in the TRN set, hence pigs had to be classified in one of the other 23 breeds. The TRN set consisted of pigs from the rest of the 23 remaining breeds, randomly selected (without replacement). This procedure was repeated 10 times. Scenario 2 can be seen as a method to assess similarity among breeds.The objective of DAPC was to represent real case scenarios, i.e., to identify an external individual membership to a group . In such a case, the discriminant function is developed in a training set (TRN) and then applied on genotypes of an external validation set (VAL). The function In both scenarios, the design of the DAPC analysis included: (i) tenfold CV for the selection of the optimum number of the PCs, (ii) the maximum number of PCs tested was set to 300 and (iii) minimum prediction error as the target function for model selection. Results were summarized over the 10 repetitions. Moreover, to assess the effect of the sample size and the robustness of the model, the complete dataset was split in sets of 10% increase (from 30 up to 100%). The terms (semi/un)-supervised should not be confused with the terminology in machine learning. These terms were used to distinguish between the two scenarios of DAPC, and although they are analogous to same terms used in the statistical field of machine learning they are not identical.Supplementary Information 1.Supplementary Information 2.


Atlantic salmon is a diadromous species that has the particularity that it returns to reproduce to its river of birth. This means that, in most cases, each river can be considered a population and that these populations can be adapted to the characteristics of the river. The Atlantic Salmon in the rivers of the Iberian Peninsula has been in decline since the 80’s of the last century. At that time, to alleviate the decline in the number of individuals, the rivers were stocked with salmon from Scotland and other northern European rivers, but the result was not positive. In this work, we studied eight salmon populations using a 220,000 high-density SNP array (Affymetrix) specific to Atlantic salmon. Our results revealed potential evidence of local adaptation for North Spanish and Scottish populations, which may be the cause of the failure of restocking programs. In addition, by performing a review of previous studies, we demonstrated that, as suggested, NADP-dependent malic enzyme-2 is a candidate for local adaptation in salmon from the Iberian Peninsula.Salmo salar) in the Iberian Peninsula. The restocking effort carried out in the 1980s, with salmon from northern latitudes with the aim of mitigating the decline of native populations, failed, probably due to the deficiency in adaptation of foreign salmon from northern Europe to the warm waters of the Iberian Peninsula. This result would imply that the Iberian populations of Atlantic salmon have experienced local adaptation in their past evolutionary history, as has been described for other populations of this species and other salmonids. Local adaptation can occur by divergent selections between environments, favoring the fixation of alleles that increase the fitness of a population in the environment it inhabits relative to other alleles favored in another population. In this work, we compared the genomes of different populations from the Iberian Peninsula (Atlantic and Cantabric basins) and Scotland in order to provide tentative evidence of candidate SNPs responsible for the adaptive differences between populations, which may explain the failures of restocking carried out during the 1980s. For this purpose, the samples were genotyped with a 220,000 high-density SNP array (Affymetrix) specific to Atlantic salmon. Our results revealed potential evidence of local adaptation for North Spanish and Scottish populations. As expected, most differences concerned the comparison of the Iberian Peninsula with Scotland, although there were also differences between Atlantic and Cantabric populations. A high proportion of the genes identified are related to development and cellular metabolism, DNA transcription and anatomical structure. A particular SNP was identified within the NADP-dependent malic enzyme-2 (mMEP-2*), previously reported by independent studies as a candidate for local adaptation in salmon from the Iberian Peninsula. Interestingly, the corresponding SNP within the mMEP-2* region was consistent with a genomic pattern of divergent selection.Pollution and other anthropogenic effects have driven a decrease in Atlantic salmon ( Salmo salar) from the Iberian Peninsula suffered a drastic decrease in the last three decades of the 20th century, mainly due to anthropogenic actions such as pollution [Wild populations of Atlantic salmon  and River Shin (Scotland), the former being one of the rivers used in the restocking . The resThe fact that salmon populations located in different areas are genetically different is related to an innate preference to return to their river of origin, a behavior known as homing . In linemMEP-2*) has been identified as a candidate for local adaptation, particularly for populations in the Iberian Peninsula, as its allelic frequencies vary at macro- and microgeographic scales in relation to freshwater temperatures and the at-sea age of returning adults [For example, in Atlantic salmon, the NADP-dependent malic enzyme malic ST outliers) associated with particular markers/loci.Detection of atypically high values of differentiation (F(2)Detection of unusual haplotypic patterns associated with a selective carryover effect, such as increased homozygosity in certain regions.The combined effect of natural selection and interpopulation isolation can produce a fingerprint in genomes. This effect of selection on the genomes of different populations of the same species can be detected by following at least two different methodologies ,29, nameRegardless of the method, genome-wide fingerprinting simply provides indirect evidence of the possible selective effect, so it is necessary to annotate candidate genomic regions and address the consequent study of candidate genes potentially involved in the selective process. Identification of these selective signatures may reveal genomic regions of biological and commercial interest. In addition, understanding local adaptation could be important for interpreting how salmonid populations respond to habitat alterations, climate change and fisheries.Therefore, the aim of this work is to compare the genomes of different populations from the Iberian Peninsula and Scotland to identify SNPs and candidate regions associated with high values of interpopulation differentiation and unusual haplotype patterns. The processes in which these genes are involved may explain the adaptive differences between populations and the failures of the restocking carried out during the 1980s.A total of 282 returning adult individuals  sampled between 2008 and 2009 were considered. To cover the entire distribution range of salmon in the Iberian Peninsula, 250 individuals from six Spanish rivers  were analyzed. This included the Atlantic coast  and the Cantabric coast . The vast majority of the salmon analyzed came from recreational fishing. A few salmon came from sampling conducted by Atlantic salmon program management. For both the salmon caught by the rod (and dead) and those caught at the capture stations, the adipose fin is cut off and stored in ethanol by the management staff responsible for the salmon program. Additionally, 32 individuals from Scotland  coming from two Scottish tributaries (Baddoch and Girnnock) of the Dee River see  were avaTo purify the genomic DNA from ethanol-preserved adipose fins, an NZY Tissue gDNA Isolation Kit (NZYtech) was used. Quantification and purity were assessed using a Nanodrop-1000 spectrophotometer. DNA samples were adjusted to a final concentration of 100 ng/μL and frozen until use. Morphological sex was confirmed by the presence/absence of the SDY intron gene (~200 bp) successfully amplified in males and absent in females, by using the primers SDY E1S1 and SDY E2AS4 ,32. SampAfter applying these filters, a total of 165,774 SNPs remained allocated across the genome and were proportionally distributed according to the size of each chromosome .To evaluate the occurrence of selection, we applied two methods based on the identification of FST outliers and three haplotype-based methods.ST values [10 = 1.5–2.0), as these indicate a very strong or decisive signal of divergent selection [ST outliers [The first family of methods is one of the most common ways to detect local adaptation in non-model organisms. These methods rely on allelic frequencies, assuming that loci under selection will provide unusually high FT values ,35,36. TT values  and the T values . BayeScaelection . On the outliers . We perfoutliers .Nevertheless, outliers’ strategy has some drawbacks and challenges that require the methods to be used with caution; for example, because the markers are considered independent, there is a loss of power when their number is large. In addition, the assumed null model can be wrong, leading to false positives if the population suffered demographic or historical events ,41,42,43ST test [ST variance as implemented in SmileFinder [To overcome these drawbacks, we also applied haplotype-based methods for detecting diversifying selection and performed three different methods: (1) a cross-population extended haplotype homozygosity model (XP-EHH) , as implST test , and (3)leFinder . HaplotyleFinder .Although all three methods require haplotypes, the methodology for searching for selective patterns is different.(1)XP-EHH. The cross-population extended haplotype homozygosity model is based on the inspection of patterns of linkage disequilibrium decay around selected loci and detects selection based on an excess of specific haplotypes in one of the populations. This method requires a linkage map. Since no map was available for our 220 K array, we used the physical distance.(2)ST. The nvdFST statistic combines two measures: a normalized variance difference (nvd) and an FST index. The nvd measure divides the haplotypes into two sets for each candidate SNP: one set carrying the major allele for the SNP and the other set carrying the minor allele. Only SNPs shared by both populations were considered. A variance of mutational distances is computed within each set and a normalized difference between variances defines the statistic that will increase under selection. The FST measure takes advantage of the fact that if selection acts on a SNP pointed by a high nvd value, then the FST at that site will be higher when compared to the overall FST assuming equilibrium in the presence of migration. A resampling method is used to compute the p-value under the hypothesis of panmixia and the final candidate SNPs are those with highest nvd values that additionally reject panmixia [ST test under any window size.nvdFpanmixia . We used(3)ST variance using sliding windows of haplotypes of increasing size. In this case, the highest values of variance indicate the presence of selection.SmileFinder. This method uses a resampling-based strategy to infer the significance of multiloci FDanio rerio, Xenopus laevis, Mus musculus and Homo sapiens.The gene content within genomic regions identified by candidate SNPs was explored using SalmoBase  and geneGenotyped samples were classified into three groups after clustering with PCA according to samples from Scotland (Baddoch and Girnnock) and both slopes of the Iberian Peninsula: rivers from the Atlantic coast (Miño and Ulla) and from the Cantabric coast  . From noSsa22 in a region of around 20 Mb. These same two SNPs were also detected by EOS, which found 142 significant SNPs in the comparison Cantabric–Scotland and 412 in Atlantic–Scotland. There were no significant SNPs in the comparison Atlantic versus Cantabric  as evidence for local adaptation, but only the comparison between Atlantic and Scotland showed evidence of positive selection and only for two significant unannotated SNPs (ctg718000187612_7079_SGT and ctg7180001695888_3098_SCT) in chromosome antabric .Ssa04, for the comparison of Atlantic versus Scotland , where BayeScan and EOS found two significant (non-annotated) SNPs  SNPs .Notably, 64 SNPs were significantly detected under all three methods for the Atlantic–Scotland comparison and 19 of these were for the Cantabric–Scotland one.The complete list of significant SNPs in one or two population comparisons and for two or three of the haplotype-based methods can be found in the Ssa03  categories of the genes containing those SNPs correspond mainly to developmental and cellular processes. Among the annotated genes , we founsistance ; the glysistance ; MAM domelopment , and haselopment ; WD repetabolism  and trantabolism  and Zinc tilapia .Ssa03, the gene tpr is related to response to temperature changes in mice [pgk, a locus related to growth in the turbot in Ssa04 [agrn and pomt1 in Ssa15  and Ssa20  [e2f4 in Ssa10 and fra10ac1 in Ssa28 and are related to growth and late maturation in salmon [When the SNPs detected only by two haplotype-based methods were also considered, most of the genes identified were related to metabolic or cellular developmental processes, DNA transcription and anatomical structure. In  in mice  affectedin Ssa04 . Other g,286 bp) . In addiSsa15 1,1,514 bp amMEP-2*) has been reported to be a candidate for local adaptation in salmon from the Iberian Peninsula [Ss25 [Ssa25  at the left and +0.64 Mb (SNP id 3604) at the right. These distances are minimal because, contrary to the SNPs presented in In a previous study using the same microarray and populations (from Spain), genomic linkage disequilibrium highly decreased for SNPs separated 1 Mb apart .Therefore, we expect that if the malic SNP reflects a selective pattern affecting the variance of haplotype allelic classes, it could be detected under a window size of less than 50 SNPs or even 125 SNPs, since linkage disequilibrium is still appreciable up to 1 Mb away.ST test and look for a more direct way of estimating statistical significance by comparing haplotype variances.Moreover, considering a candidate SNP that has been previously identified, we can perform the test only for this SNP so that multiple testing corrections are not required. Unfortunately, because the distribution of the nvd statistic is not known, we still face the problem that the statistical significance of a SNP that we assigned to the 1% of candidates with the highest nvd is meaningless if we have only one candidate. Fortunately, we can still do a FmMEP-2*. For each population, we compared the homogeneity of variances between the partitions having one or other malic allele, i.e., C or T at ctg7180001794010_7928_SCT SNP. These are the same variances compared by nvd, and we can use a robust test of variances  to assess whether the variance for the major allele partition is significantly lower, which would indicate the presence of a positive selection effect in the population. We considered divergent selection when the comparison of variances was significant in at least one population and when both populations had a significantly high FST value  of the new method using simulated data. We note that, at least with the tested window sizes (from 25 to 400 SNPs), the method appears conservative, with an FPR below 5% see , supportIn this work, we performed a genome-wide scan using a high-density SNP array with the aim of investigating different patterns of selective variation in the Atlantic salmon genome to identify candidate loci that may be involved in the local adaptation of Atlantic salmon in the northern Iberian Peninsula, which is the southern limit of the distribution of this species.ST outliers) and haplotype-based selection detection methods and observed little overlap in the regions detected by the two types of strategies. The lack of overlap between frequency-based and haplotype-based methods has already been reported in other studies [Ssa24 (29 genes), Ssa09 (13 genes), Ssa11 (9 genes), Ssa01 and Ssa17 (8 genes) and Ssa10 and Ssa03 . Genes potentially more affected by divergent selection were those resulting from the comparison between the peninsular slopes and Scotland, as expected, since the intensity of local adaptation appears to be correlated with the geographical distance between salmonid populations [We used different frequency-based  gene and the SIX homeobox (six6), the latter related to circadian timing processes [ST also detected SNPs within the region located at 77–78 Mb on Ssa13, corresponding to the SLC25A14 gene, which encodes a brain mitochondrial transporter protein and has also been found to diverge between European and North American populations [In addition to the coincident SNPs for at least two haplotype-based methods, we found a well-known ulations . This rerocesses . For theulations . Similarulations .Ss25 was specifically explored for candidate SNPs, since early electrophoretic studies on genetic variation in Atlantic salmon referred specifically to this gene [mMEP-2* alleles (isozymes 100 and 125) presents strong correlations with environmental temperature, both within and among rivers, and associations with phenotypic performance [mMEP-2* is higher in northern European Atlantic salmon populations than in southern European populations. Moreover, in the Atlantic populations of the Iberian Peninsula, the frequency of the *125 allele is almost zero [The gene encoding the malic enzyme deserves special mention. As previously explained, this gene located at his gene . Variatiformance . Indeed,ost zero . In factost zero . mMEP-2*. However, this result was expected. First, the allele frequencies of the malic SNP were 0.02229 in the Atlantic sample, 0.134 in the Cantabric sample and 0.4219 in the Scotland sample, which implies that the frequencies of the three samples are in the same half of the frequency range. Second, since we are analyzing several thousand SNPs, multitest correction implies a loss of statistical power. Thus, it is expected that other more powerful signals will be more easily detected. Fortunately, after modification of the HacDivSel method to consider some candidates a priori, the malic mMEP-2* SNP, was clearly detected as a candidate for local adaptation.However, with the genomic selection detection methods initially assayed, we could not detect any significant SNP within the region coding for Although the maladaptation of northern European salmon demonstrated by the garden experiment described by García de Leániz et al.  is not emMEP-2*), previously reported by independent studies as a candidate for local adaptation in salmon from the Iberian Peninsula. Interestingly, the corresponding SNP within the mMEP-2* region was consistent with a genomic pattern of divergent selection.In this study, we provided additional evidence of local adaptation for north Spanish and Scottish populations of Atlantic salmon using genome-wide information. Defining the spatiotemporal scales over which adaptation operates is important from the point of view of management and conservation. Ignoring the existence of locally adapted populations may pose a greater risk to the conservation management of threatened populations . We modiIn summary, our results suggest that Spanish and Scottish populations may differ at the functional gene level due to local adaptation, which would explain the failure to restock Spanish rivers with Norway and Scottish individuals.


Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data , and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage. PRDM9, changes the distribution of crossover hotspots in human and mouse , for complementary tasks. These packages have been implemented using appropriate programming languages (Nim and R) and released in multiple release formats to suit the expected use-case scenarios Figure .https://nim-lang.org/). The software imports the hts-nim library (distributions package that is openly accessible (see Data Availability). In crossover-calling scenarios with known phased haplotypes such as F1 hybrid samples generated by crossing known reference strains of species, and cases in which the haplotypes of donors are provided in an input VCF file, sgcocaller xo—the crossover calling module—can be applied directly. It requires three input files to call crossovers: the mapped BAM file with cell-barcoded DNA reads from all gametes, the VCF file of phased heterozygous SNP (hetSNP) markers and the list of cell barcodes  implemented using the programming language Nim  for each chromosome in gametes. It is implemented as an open-source Bioconductor/R package, which offers easy integration with other packages in Bioconductor for analysis of biological data and statistical testing. comapr includes functions to directly parse output files generated by sgcocaller xo or sgcocaller sxo with tunable parameters to systematically filter out potential false positive crossover calls and create structured data objects to represent the crossover information across all cells  and uses the cell’s genotype sequence along the chromosome as the template haplotype for the chromosome  for a sphase and swphase) can be applied in one step by calling autophase which runs phase and swphase subsequently. In application scenarios where ideal template cells are expected, users can choose to run phase only and save computational time. sgcocaller phase also generates auxiliary files for conveniently plotting diagnostic plots that help to inspect the quality of the inferred haplotypes and check for switch errors  and adopts binomial distributions for modelling the emission probabilities of the observed allele read counts  in the gametes’ genomes, the transition from one state to another in the hidden state sequence between two SNP markers corresponds to a crossover detected. As currently designed, sgcocaller is intended to work on data of gametes collected from diploid individuals. The sgcocaller sxo module supplements the crossover calling module sgcocaller xo. It runs the same core function as sgcocaller xo but uses the generated outputs  from sgcocaller phase instead of the original BAM and VCF files, thus eliminating unnecessary double handling of large DNA sequencing data.With this HMM, the commonly used dynamic programming method, the Viterbi algorithm , is implIn addition to applying the HMM to the allele counts and using the Viterbi algorithm for inferring the hidden state sequences, we also included calculation of a crossover confidence score, which is the log-likelihood ratio of the Viterbi state segment. The log-likelihood ratio of a segment is derived by finding the difference between the log-likelihood of the segment given the inferred state and the log-likelihood of the segment given the altered state  and provided examples of applying sgcocaller on such datasets ,28  is used as the main data structure in comapr for storing the SNP interval regions and the number of called crossovers per cell  or bootstrap confidence intervals for the estimate of the group differences (see Supplementary Methods).To test for differences in the number of crossovers between any two groups of cells, we have implemented re-sampling methods in comapr, specifically permutation and bootstrapping tests . The twofastp-v0.20.1 (minimap2-2.7_x64-linux (GATK MarkDuplicates and GATK AddOrReplaceReadGroup from the GATK-v4.2 pipeline (samtools-v1.10 (appendCB (see AVAILABILITY OF DATA AND MATERIALS) was applied to add barcode sequences to each sperm’s DNA reads with tag CB using its SRR sequence. The DNA reads in each sperm sample were sub-sampled to retain half of the reads and merged into one barcode-tagged large BAM file that was analysed in this study as the mouse sperm dataset. The merging and indexing of BAM files were achieved using samtools-v1.10  using GATK-HaplotypeCaller. Only the hetSNPs with mapping quality score larger than 50, and depth of coverage within the range of 10–80 were kept (MQ > 50and DP > 10 and DP < 80). Since the mouse donor was an F1 hybrid (C57BL/6J X CAST/EiJ), the list of reference hetSNPs was downloaded (CAST_EiJ.mgp.v5.snps.dbSNP142.vcf.gz and C57BL_6NJ.mgp.v5.snps.dbSNP142.vcf.gz) from the Mouse Genome Project (GT==1/1) in CAST_EiJ.mgp.v5.snps.dbSNP142.vcf.gz and not overlapping with variants in C57BL_6NJ.mgp.v5.snps.dbSNP142.vcf.gz. Scripts are publicly available (see Data Availability).We followed the main steps described in the original study for finding heterozygous SNPs in the mouse donor’s genome . We firs Project . The calsamtools-v1.10 , the DNA reads from the merged mouse sperm BAM file (as described before in section Mouse sperm dataset) were sub-sampled using ls-v1.10  to a frasamtools-v1.10 /(FORMAT/DP) > 0.38 & (FORMAT/AD[0:1])/(FORMAT/DP) < 0.62). These filtering settings mean we only kept the SNPs with quality score larger than 200, total depth of allele coverage smaller than 180, genotypes as heterozygous, and the alternative allele frequency is in the range of 0.38-0.62. Gametes from the two experiments were merged in the analysis and gametes with barcode collisions were removed. Barcodes from 367 gametes were kept. Scripts are publicly available (see Data Availability).Two experiments were conducted to sequence the apricot gametes in the original published study . The prels-v1.10 . To keep64-linux . The idenal study (QUAL > fastp-v0.20.1 (minimap2-2.7_x64-linux (MarkDuplicates and AddOrReplaceReadGroup from the GATK-v4.2 pipeline (appendCB (see Data Availability).Raw fastq files of 11 sperm cells from a human individual were downloaded from the NCBI Sequence Read Archive (SRA) with SRR accessions provided in the original study . Like th-v0.20.1  on the r64-linux  was run pipeline  were appTo test the phasing performance of sgcocaller, the 367 gametes from the apricot dataset  were usesgcocaller phase before calling crossovers using sgcocaller xo. For the apricot dataset, crossover results when using the phased hetSNPs obtained from the published study .We applied sgcocaller and comapr on public datasets to demonstrate the application of the software tools . The application examples cover different use-case scenarios: (i) a mouse sperm dataset  for whiced study  were alssgcocaller xo and sgcocaller sxo (see Supplementary Methods) on different datasets using functions from comapr including cell filtering, false positive crossover filtering and genetic distances calculation and visualisation  and sgcocaller swphase (for identifying and correcting switch errors in the inferred haplotypes) on an apricot pollen dataset generated  with the 10X scCNV protocol (a droplet-based single-cell DNA sequencing protocol) . The phasing results of sgcocaller phase demonstrate very high concordance with the haplotypes from the published assembly from the same study  and the low depth of read coverage at each SNP.We applied rotocol) . Althougy Figure , B. To ee Figure . We defie Figure  supportee Figure , C. Thessgcocaller|comapr is publicly available (see Data Availability). We demonstrate and provide reproducible code for running the functions to generate plots of the number of crossovers per sperm cell  . The rawl Figure , geneticl Figure , and thel Figure  in the pcountCOs and genetic distances are derived by the calGeneticDist function that applies a user-selected mapping function such as the Kosambi mapping function  were highly consistent with the crossovers called from the original paper. There were 7 cells (4%) that were called with a different number of crossovers compared to the original paper  was consistent with the published number of crossovers for these gametes by using both the published haplotype (sg.xo KnownHaps) and the sgcocaller phase| swphase generated haplotype (sg.sxo SgHaps)  Figure . Few chr) Figure . Out of ) Figure .It is worth noting that not all cells available in the published study were analysed and included in the application results of sgcocaller due to the cell filtering step included in comapr which filtered out cells with excessive numbers of crossovers called and cells with poor SNP coverage. Chromosomes with excessive numbers of crossovers called are likely due to library preparation artefacts or abnormal chromosome segregation that results in implausibly many heterozygous SNPs in single gametes . comapr To test the performance of calling crossovers from gametes with increased crossovers comparing to the apricot and mouse sperm gametes, we simulated 100 cells each with 6 manually inserted crossovers spanning a 5M genomic region (see Supplementary Methods). We phased the hetSNPs in the 5M region and called crossovers for the 100 cells using sgcocaller. Only 95 hetSNPs (out of 21k hetSNPs) were phased incorrectly and all six crossovers were discovered for the 100 cells .sgcocaller phase , mouse sperm dataset (n = 174 or 175 sperm cells), and the low coverage mouse sperm dataset (n = 174 or 175 sperm cells). We ran Hapi and sgcocaller phase|swphase on these constructed datasets. The phasing accuracy, measured by calculating the fraction of SNPs agreeing with the ground-truth haplotype sequence, and the number of phased SNPs by each approach were compared , the advantage of e Figure , B. We a|swphase . In contsgcocaller phase|swphase achieved equivalent or greater haplotype completeness in terms of the number of phased SNPs compared to Hapi , whereas Hapi repeatedly generated unusable phasing results (accuracy <0.6) for chromosome 12, 14, 17, 18 for the mouse sperm datasets , i Figure , D. sgcos Figure . Hapi di. Figure . Hapi alWe compared the differences among the four datasets, which showed that the human sperm sequencing dataset and the mouse sperm sequencing data have much higher SNP coverage per cell and cell coverage per SNP compared to the apricot dataset Figure . The lowsgcocaller phase|swphase outperforms Hapi on both high coverage data and low coverage data with sgcocaller phase|swphase more dominant in the low coverage datasets.In summary, sgcocaller phase plus sgcocaller swphase was only 11–38% of the total time taken by Hapi (comparing the median running time of each chromosome). Considering sgcocaller phase timing results includes an extra step of parsing the DNA read file (BAM file) and variant file (VCF file), sgcocaller phase is even more advantageous in terms of speed than these results indicate. Examining diagnostic plots generated from sgcocaller phase can help with identifying switch errors so that sgcocaller swphase can be run selectively. Nevertheless, swphase was applied to all chromosomes and the total running times were still many-fold shorter than Hapi. In terms of memory usage, since sgcocaller phase and sgcocaller swphase were run consecutively, the process that used larger memory was regarded as the memory used by sgcocaller. From comparing the reported max_rss (the maximum amount of memory occupied by a process at any time that is held in main memory (RAM)) by Snakemake  on the large mouse sperm dataset and measured the running time as well as memory usage on each of the ten dataset repeats. The running time required by nakemake , sgcocalnakemake , G.sgcocaller sxo. We observed substantial improvement of processing speed of sgcocaller sxo over sgcocaller xo but sgcocaller sxo also required much more memory for larger datasets. Therefore, we would recommend using sgcocaller xo with the generated phased VCF from sgcocaller instead of running sgcocaller sxo when processing large datasets  and memory is not sufficient.We tested the scalability of different modules in sgcocaller with input files of varying sizes including different numbers of cells, numbers of DNA reads per cell, and numbers of hetSNPs available. We ran each module and recorded the computational usages by each module on the simulated datasets See . Across Snakemake is also beneficial when running sgcocaller phase and crossover calling.Our gamete-based haplotype phasing model has limitations. The model uses the hetSNP linkage information in the gametes and assumes the nearby hetSNPs are close and crossovers rarely break the linkages among them. For cases where the density of hetSNPs is low, but evenly distributed along the chromosome region, the phasing model can still produce correctly phased haplotypes with reduced completeness , sgcocaller fills the gap of a highly accurate and efficient tool with simple installation for constructing personalised haplotypes and calling crossovers in single gametes from single-cell and bulk DNA sequenced gametes. The availability of the companion Bioconductor/R package comapr enables the downstream statistical analysis of crossovers in the cells and among individuals, integrating well with existing R packages for various visualisation and exploratory analysis tasks. In concert, these packages represent a comprehensive, user-friendly toolkit for the construction and analysis of personalised crossover landscapes from single-gamete sequencing data.sgcocaller|comapr are available. The source code and analysis repositories included in this study are listed below:Source code of the latest version of sgcocaller is publicly available at a GitLab repository and comapr at a GitHub repository under a MIT license. comapr is also available as a Bioconductor package. The datasets used in this study are public datasets with raw sequencing data downloaded from GEO with accession GSE125326 and ENA with project ID PRJEB37669. The analysis steps including pre-processing and scripts for generating the figures are openly accessible at GitLab repositories and workflowr  web pagehttps://gitlab.svi.edu.au/biocellgen-public/sgcocallersgcocaller: https://github.com/ruqianl/comapr and https://bioconductor.org/packages/comaprcomapr: https://gitlab.svi.edu.au/biocellgen-public/hinch-single-sperm-DNA-seq-processingmouse sperm data analysis: https://gitlab.svi.edu.au/biocellgen-public/calling-crossover-from-single-gamete-sequencing-of-apricotapricot gamete analysis: https://github.com/ruqianl/appendCBappendCB: https://github.com/ruqianl/distributionsdistributions: https://biocellgen-public.svi.edu.au/hinch-single-sperm-DNA-seq-processing/Crossover-identification-with-sscocaller-and-comapr.htmlweb page report of analyses on msperm dataset: gkac764_Supplemental_FileClick here for additional data file.


In breeding research, the investigation of regulatory SNPs (rSNPs) is becoming increasingly important due to their potential causal role for specific functional traits. Especially for crop species, there is still a lack of systematic analyses to detect rSNPs and their predicted effects on the binding of transcription factors. In this study, we present agReg-SNPdb-Plants, a database storing genome-wide collections of regulatory SNPs for agricultural plant species which can be queried via a web interface.Brassica napus, Helianthus annuus, Hordeum vulgare, Oryza glaberrima, Oryza glumipatula, Oryza sativa Indica, Oryza sativa Japonica, Solanum lycopersicum, Sorghum bicolor, Triticum aestivum, Triticum turgidum, Vitis vinifera, and Zea mays). agReg-SNPdb-Plants can be queried via a web interface that allows users to search for SNP IDs, chromosomal regions, or genes. For a comprehensive interpretation of GWAS results or larger SNP-sets, it is possible to download the whole list of SNPs and their impact on transcription factor binding sites (TFBSs) from the website chromosome-wise.Single nucleotide polymorphisms (SNPs) that are located in the promoter regions of genes and affect the binding of transcription factors (TFs) are called regulatory SNPs (rSNPs). Their identification can be highly valuable for the interpretation of genome-wide association studies (GWAS), since rSNPs can reveal the biologically causative variant and decipher the regulatory mechanisms behind a phenotype. In our previous work, we presented agReg-SNPdb, a database of regulatory SNPs for agriculturally important animal species. To complement this previous work, in this study we present the extension agReg-SNPdb-Plants storing rSNPs and their predicted effects on TF-binding for 13 agriculturally important plant species and subspecies ( Climate change and its anticipated consequences pose severe challenges to mankind. For agriculture, global warming means that pathogens previously restricted to warmer climates will threaten local animal and plant species as well as expose plants to drought stress due to the increasing water shortage. A rapid and effective adaptation to the new environmental conditions is of paramount importance and can only be achieved through supportive plant breeding programs ,2. WhileHordeum vulgare, Oryza sativa Indica, Oryza sativa Japonica, Solanum lycopersicum, Sorghum bicolor, Triticum turgidum, Vitis vinifera, and Zea mays). The R packages MotifbreakR [Oryza sativa and Vitis vinifera. In both, the user has to provide the SNPs as well as TFBSs  and experience in R programming is imperative.To the best of our knowledge, there exist currently three tools, which generally allow the analysis of plant rSNPs. As a web-based tool, the RSAT variation-tool  allows tifbreakR  and atSNifbreakR  principaifbreakR , which ihttps://azifi.tz.agrar.uni-goettingen.de/agreg-snpdb-plants/ (accessed on 24 March 2022).In our previous studies, we addressed this limited knowledge and created a pipeline for the systematic detection of rSNPs, which we applied to different agriculturally important species such as rapeseed , faba beBrassica napus reference genome (version 4.1) and gene annotations were obtained from [https://www.genoscope.cns.fr/brassicanapus/data/ (accessed on 3 March 2022).In our previous work, we have established a pipeline for the detection of rSNPs , which rned from  and are In brief, the pipeline can be described in the following five steps. For a more detailed description, we refer to .Selection of SNPs in the promoter and surrounding region: For each gene, we considered a promoter region of 7.5 kb upstream to 2.5 kb downstream from the transcription start site (TSS) and selected all SNPs located within that region. On the website, the user has the possibility to insert a user-defined promoter region with the default being −1 kb to +100 bp.Extraction of the SNP-flanking region: Using the reference genomes under study, we extracted 25 bp on each side of a SNP to obtain 51 bp long sequences with the SNP in the central position. During this step, we discarded sequences with a total length of less than 51 bp, sequences containing N’s, and sequences in which the nucleotide at position 26 differed from the reference allele of the SNP  gene_info stores general information about the genes, (iii) snp_region connects the tables snp_info and gene_info for all SNPs located in the promoter region of at least one gene, and (iv) TFBS_results stores the rSNPs and their consequences with respect to TF-binding. agReg-SNPdb-Plants is centered around four tables: (i) https://azifi.tz.agrar.uni-goettingen.de/agreg-snpdb-plants/, accessed on 24 March 2022). The basic functionality was inherited from agReg-SNPdb, e.g., the ability to query the database by searching for (i) SNP identifiers, (ii) SNP position, (iii) chromosomal region, or (iv) gene. Additionally, we enabled the search for several SNP IDs at a time, by pasting white-space separated SNP IDs in the search field.Following the concept of Ensembl and Ensembl Plants, we created an extra web interface for agReg-SNPdb-Plants . If a SNP occurs in the promoter of two different genes, one on the plus and one on the minus strand, there will be two different tables showing the TFBSs for the plus and minus strands separately. The column ‘PWM’ (position weight matrix) represents the TFBS. The names of the PWMs are defined by TRANSFAC [factorname_version, where the P$ indicates that the PWM originated from a plant TF and factorname specifies the name of the represented TF. The core and matrix similarity scores are the MATCH™ [Furthermore, we simplified the visualization of the _results  shows thTRANSFAC  as P$face MATCH™  output sSimilar to our previous studies ,6, we fiThe distributions of SNPs and genes along the chromosomes are exemplary shown for maize . As expeThe average number of rSNPs (SNPs that cause a loss or gain of TFBS or a score-change for at least one TFBS) per gene differs strongly across the species. For example, in sunflower we only detected an average of 0.0015 rSNPs per promoter region (−1 kb to +100 bp) while we observed 28.48 rSNPs per promoter in tomato  and Indica in (B).To obtain further insights into the data, we investigated the distribution of rSNPs relative to the TSS . SimilarqSH1). This rSNP is causative for the loss of seed shattering and thus paved the way for rice domestication [eIF4E) and control its expression level [TaGW2-6A gene, a candidate gene for grain weight [Transcription factors bind to the promoter region to fine-tune the level of gene expression in all higher organisms. A regulatory SNP within a TFBS can influence this transcriptional gene regulation to a great extent and hence could have a causative effect on the phenotype. In plants, several studies investigated (single) rSNPs with respect to a specific trait or phenotype ,33,34,35n weight . To thishttps://azifi.tz.agrar.uni-goettingen.de/agreg-snpdb-plants/, accessed on 24 March 2022). This pipeline could be highly valuable for scientists to interpret their results from e.g., a GWAS or next generation sequencing (NGS) experiments.Due to the increasing interest in finding causative rSNPs yet limited availability of resources to detect rSNPs in crop species, we used our rSNP detection pipeline to systematically analyze 13 crop plants and provide a database of genome-wide rSNPs which can be queried via a web interface . Such issues indicate that there is still a need for further investigation or updates to improve the genome sequences as well as SNP annotations. In our pipeline, we excluded such SNPs from further analysis to ensure the highest possible reliability of our results.However, for some species the available information, e.g., the reference genome, might not be of the same quality compared to other, well-investigated species. Furthermore, due to the amount of repetitive sequences in some plant species such as bread wheat or maize, both the reference genome annotation as well as locating genomic variants can be challenging ,44. The In breeding research, the knowledge about rSNPs can help to unravel the regulatory mechanisms underlying specific phenotypes and could hence lead to the identification of causal SNPs, which are of great importance for the establishment of robust markers. To the best of our knowledge, until now there exists no database storing genome-wide rSNPs and their consequences on TF binding in plant sciences which can be queried in various ways. In order to address this lack of information, and thus complementing our previous work, we created agReg-SNPdb-Plants, a database of rSNPs for 13 agricultural plant species and subspecies with currently available SNP annotations. Its web interface is a helpful resource for scientists who are conducting association analyses such as GWAS, gene expression experiments, expression QTL (eQTL) studies, or population studies. Consequently, they can automatically investigate the candidate SNPs or specific genes to rate them by their importance or causality. In this regard, our user interface provides different search functions and delivers information on the consequences of rSNPs on TF binding such as (i) gain of TFBS, (ii) loss of TFBS, (iii) change of binding affinity, or (iv) no change. Due to regular updates of genomes, gene- and SNP-annotations, our database will be regularly updated to add new plant species when available and to update existing ones.