Clostridioides difficile whole-genome sequencing (WGS) data enables fine scaled investigation of transmission and is increasingly becoming part of routine surveillance. However, these analyses are constrained by the computational requirements of the large volumes of data involved. By decomposing WGS reads or assemblies into k-mers and using the dimensionality reduction technique MinHash, it is possible to rapidly approximate genomic distances without alignment. Here we assessed the performance of MinHash, as implemented by sourmash, in predicting single nucleotide differences between genomes (SNPs) and C. difficile ribotypes (RTs). For a set of 1905 diverse C. difficile genomes (differing by 0–168 519 SNPs), using sourmash to screen for closely related genomes, at a sensitivity of 100 % for pairs ≤10 SNPs, sourmash reduced the number of pairs from 1 813 560 overall to 161 934, i.e. by 91 %, with a positive predictive value of 32 % to correctly identify pairs ≤10 SNPs (maximum SNP distance 4144). At a sensitivity of 95 %, pairs were reduced by 94 % to 108 266 and PPV increased to 45 % (maximum SNP distance 1009). Increasing the MinHash sketch size above 2000 produced minimal performance improvement. We also explored a MinHash similarity-based ribotype prediction method. Genomes with known ribotypes (n=3937) were split into a training set (2937) and test set (1000) randomly. The training set was used to construct a sourmash index against which genomes from the test set were compared. If the closest five genomes in the index had the same ribotype this was taken to predict the searched genome’s ribotype. Using our MinHash ribotype index, predicted ribotypes were correct in 780/1000 (78 %) genomes, incorrect in 20 (2 %), and indeterminant in 200 (20 %). Relaxing the classifier to 4/5 closest matches with the same RT improved the correct predictions to 87 %. Using MinHash it is possible to subsample C. difficile genome k-mer hashes and use them to approximate small genomic differences within minutes, significantly reducing the search space for further analysis.Comparative analysis of  Clostridioides difficile, an important healthcare-associated pathogen. It can also be used to approximate traditional schemes used to classify C. difficile into smaller subgroups in transmission analyses, such as ribotyping.The genetic code, or DNA, of bacteria is increasingly used to track how infection spreads and to guide infection control interventions, as similar or identical DNA sequences are expected in samples from pairs of individuals related by transmission. While obtaining the DNA sequence for bacteria is increasingly straightforward, comparing thousands or even millions of sequences requires substantial computing power and time using current approaches. Here we describe how a method for summarising sequencing data, MinHash, can be used to rapidly reduce the number of possible close sequence matches in The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.Clostridioides (formerly Clostridium) difficile, previously believed to be transmitted predominantly from other hospitalised cases, has been shown to be mostly acquired from other sources [Whole genome sequencing (WGS) has transformed our understanding of the epidemiology of many bacterial pathogens. For example,  sources . WGS als sources  and high sources , and can sources  and surv sources . HoweverC. difficile on seven housekeeping genes [C. difficile core genes [C. difficile that are not readily inter-operable with open-source software [C. difficile, removing the need for a centralised, curated database [C. difficile, PCR ribotyping [in silico ribotyping method has been developed to detect ribotype (RT) 027, but not ribotypes more generally [One potential approach to this challenge is to ‘divide and conquer’, such that a rapid screen is used to define which subgroup of a species a genome of interest is from, reducing the number of genomes that need to be considered in a more detailed analysis. For example, multi-locus sequencing typing (MLST), based in ng genes , can be ng genes . MLST care genes  for far re genes . Howeversoftware  due to ddatabase . Other tbotyping  remains enerally . There ienerally .We therefore investigated the performance of alternative approaches based on k-mers for screening isolates to identify subsets with closely related genome sequences. K-mers are fragments of sequence data of length k. Therefore, these approaches are potentially species agnostic, and could be deployed widely, without the need for species-specific schemes such as MLST, cgMLST or ribotyping. They also have the advantage that they do not necessarily require prior genome assembly or alignment as required for cgMLST or SNP based analyses respectively. Bacterial genome sequences contain millions of k-mers and so dimensionality reduction is used to improve performance. The MinHash dimensionality reduction method is a form of local sensitivity hashing  that wasC. difficile and assess whether these algorithms can be used to approximate the most common typing scheme in use, ribotyping, and identify sequence pairs within SNP thresholds in order to provide backwards compatibility with existing surveillance systems and to identify groups of closely related isolates for in depth phylogenetic characterisation.Here we test the performance of k-mer based algorithms in n=973), Leeds (n=1876), C. difficile UK ribotyping network (n=684), EUCLID and CLOSER studies (n=678), Leeds and ECDC collections (n=369), RT078 genomes from the NCBI short read archive (n=68) and RT244 from Australia (n=16) with available ribotypes were considered for inclusion (n=4655). The large majority of ribotypes in the study were assigned by the national C. difficile reference laboratory in Leeds, UK using capillary gel electrophoresis as previously described [Previously sequenced genomes from studies of clinical isolates in the CD-link study  (https://github.com/tseemann/VelvetOptimiser), without prior read quality trimming or filtering. Contigs <1000 bp in length were filtered out of assemblies to avoid fragmented assemblies. Assembly quality was assessed using QUAST-5.0.2 [n=163 and n=53 respectively). Assemblies with >1000 contigs were also excluded to avoid excessively fragmented assemblies (n=109). Multi-locus sequence types (STs) were determined in silico from assemblies using mlst [et al. [C. difficile MLST were excluded (n=297). We screened the dataset for potential sample mislabelling by looking for incongruent ST-ribotype pairs with a novel ST-RT network approach. The Python package NetworkX [n=96), as re-running the sequencing and ribotyping for this current study was not practical. A random sample of 1905 genomes were taken to assess k-mer vs SNP distances. For analyses without prior genome assembly reads were quality trimmed using TrimGalore! [K-mer based comparisons were undertaken with and without prior genome assembly. Use of assemblies potentially allows most sequencing errors to be removed first, but is more computationally intensive. Sequencing adapters were removed using BBDuk . Reads wST-5.0.2 . Assembling mlst  and the lies n=10. Multi-lcluded n=7. We scrmGalore!  with minmGalore!  requirinWe assessed the performance of several k-mer based approaches. Sourmash  was usedC. difficile simulated genome read datasets were simulated using the reference genome 630 [To explore explanations for discrepancies between k-mer based metrics and SNPs, in addition to the actual sequencing data described above, we simulated perfect Illumina short sequence reads and reads with sequencing errors using wgsim . Three snome 630  (length=kmer), we evaluated the number of true positive pairs (TP), i.e. ≤10 SNPs and distance ≥dkmer  and false positive pairs (FP), i.e. >10 SNPs and distance ≥dkmer. Sensitivity, specificity and positive predictive value (PPV) were calculated. The PPV value represents the proportion of genome pairs ≥dkmer that are true positives. The reduction in search space was calculated as the proportion of overall genome pairs <dkmer. NetworkX [We analysed the capacity of the k-mer based algorithms to identify sequence pairs within SNP thresholds such as ≤10 SNPs, i.e. samples sufficiently closely related to be related by transmission within the last 5 years [NetworkX  was usedNetworkX  khash foUnique k-mers in a genome can arise from SNPs and indels, but also from other genetic material . We evaluated the size of the association between greater than expected k-mer distances versus SNPs and the accessory genes present. Accessory genes were predicted from assemblies using roary  with then=3937) were randomly divided into training  and test datasets (n=1000). The training dataset was used to generate a sourmash index. We then compared each sequence in the test dataset to all in the training dataset (sourmash index). The RT of the five most closely related genomes (using sourmash search) in the training dataset were identified, considering the accuracy of sourmash search determined ribotypes based on requiring all five closest matches to be the same ribotype and requiring only 4/5 matches to be the same.We assessed if k-mer based distances could be used to predict ribotypes directly from sequence reads. Genome sketch signatures were generated with sourmash  compute Using 500 simulated genomes without sequencing errors or indels  we evaluated the ability of k-mer distances to identify the 24 (0.02 %) of pairs with ≤10 SNPs. Comparing sourmash signatures from assemblies, at the largest mash distance (0.999) with 100 % sensitivity to identify all 24 ≤10 SNP pairs, the search space was reduced by 98.5 % (to 1841 from 124 750 genome pairs), with a PPV of 1.3 % (24/1841) . As expected (assuming no SNPs are within k base pairs of one another), the full jaccard distance from all k-mers (dashing khash) predicted ≤10 SNP pairs with 100 % sensitivity and a PPV of 100 %. In simulated genomes with SNPs and indels, and mash distance threshold 0.999, the search space reduction was 98.4 % (1970/124750), with a PPV of 1.9 % (37/1970) . Using full k-mer jaccard distance for this simulation at 100 % sensitivity predicted ≤10 SNP pairs with a PPV of 56.1 % (37/66). Performance for the SNPs only and SNPs and indels simulations were very similar when sourmash signatures were generated directly from sequencing reads . A third set introducing high sequencing error  markedly reduced performance from reads by comparison . The simulation had only SNPs and a high level of sequencing errors, and genome pairs differed by three to 1046 SNPs (24 pairs ≤10 SNPs) . Performance at 100 % sensitivity (mash distance threshold 0.105), produced only a 10 % reduction in search space (111502/124 750 genome pairs) with a PPV of 0.02 % (24/111 502).C. difficile genomes (n=1905) was selected to assess the precision of mash distances at predicting small SNP cut-offs in real data. The genomes ranged from 0 to 168 519 SNPs different from one another; of 1 813 560 pairs, 52 020 (2.9 %) were within ≤10 SNPs, in 172 clusters, each containing a median (range) 3 (1–39 716) pairs.A random subsample of Using sourmash signatures from assembled genomes, the largest mash distance (0.884) with 100 % sensitivity for identifying all≤10 SNP pairs, reduced the search space by 93.9 % (to 109 914/1 813 560 genome pairs) with a PPV of 32.1 % (52 020/161 934)  and S3. To assess whether k-mer based approaches could improve on existing typing schemes we examined the within-lineage performance of sourmash distance thresholds at 100 % sensitivity for predicting ≤10 SNPs between assembled genomes. This evaluates performance in sets of more similar genome pairs with within-lineage population structure . For comn=11), four of the 11 MLST clusters contained larger SNP distances than the largest within sourmash clusters; the largest in ST3 (13 874) and ST7 (10 296). The remaining seven ST clusters contained smaller SNP distances than the largest SNP distance within sourmash clusters.One possibility for k-mer based approaches would be to rapidly exclude genome pairs with large SNP distances resulting in clusters of pairs with comparatively smaller distances for further, fine scaled investigations. This process is comparable to clustering by MLST . We compared the SNP distance ranges in sourmash clusters to MLST clusters. The largest SNP distance for instance in sourmash clusters from assembled genomes using a distance with 100 % sensitivity to identify pairs with ≤10 SNPs was 4144 . ClusterP<0.001) than pairwise gene number difference at 95 % blast identity . In pairs within ≤10 SNPs, there was a weaker correlation between sourmash distance dissimilarity and increasing SNPs as expected  but the correlation between sourmash distance dissimilarity and pairwise gene number difference was higher . Trends were concordant at 75 and 50% blast identity .We assessed the number of gene differences between genome pairs using roary to assess whether core genome heterogeneity or accessory gene differences were linked to the performance of the sourmash distance approximation. Correlation between sourmash distance dissimilarity was higher with number of SNPs  compared with k-mers generated from assemblies.We therefore assessed the performance of k-mer distances using extracted core genome alone. Core genes (gene clusters present in ≥99 % isolates) were extracted, signatures were generated based on multi-fasta core gene files and the performance predicting core SNPs assessed. At the largest mash distance (0.978) with 100 % sensitivity to identify all ≤10 SNP pairs, the search space was reduced by 91.3 % (157 114/1 813 560 genome pairs) with a PPV of 33.1 % (52 020/157 114), providing comparable performance with whole genome assemblies overall (PPV 32.1 % at 100 % sensitivity) (n=259 comparisons). At 95 % blast percent identity defining gene presence/absence, the median (range) gene differences between replicate sequences was 95 (28-831), showing the extent to which this metric may be affected by factors other than biological variation. Sourmash distance ranged from 0.914 to 1 with a median of 0.994. Number of genes differing between replicates was highly correlated with sourmash distance .We also investigated the number of gene differences reported by roary between replicate sequences. Following quality filtering, 355 assembled genomes were identified from isolates sequenced more than once (median (range) 2 (2–27) times). One genome was selected at random for each isolate and compared to all others from the same isolate (n=197), RT078 (n=90), RT014 (n=87), RT002 (n=65) and RT015 (n=65). There were 51 test ribotypes with <5 signatures in the database and 46 with <4; both including 18 with none.As the majority of historical surveillance data has been based on ribotypes it is natural to ask whether sourmash signatures could be used to predict ribotypes from WGS to enable continuity. We investigated whether sourmash search can be used to query a sourmash index (database) of genome signatures with known ribotypes to rapidly assign ribotypes to the search genome signature. We predicted ribotypes for 1000 randomly selected test genomes from 95 ribotypes by considering the ribotypes of the five closest matching genomes in a database (sourmash index) of 2937 genomes from 154 ribotypes. Two rules were tested, requiring 5/5 or 4/5 ribotype concordance among the signatures predicted to be most similar to the search genome signature. The five most common test ribotypes searched were RT027  ribotypes matched, with estimated sourmash percent identity ranging from 77.7–100.0 % for correct ribotype matches and 52.3–100.0 % for incorrect ribotype matches. Of the 95 test ribotypes, 25 never had a correct individual match, resulting in incorrect ribotype predictions. Of these 25 ribotypes (23 of which were represented only once in the test dataset and two twice), 18 were only present in the test dataset and not in the search database.Requiring all five database matches to be the same ribotype resulted in 780 (78 %) correctly predicted ribotypes, 200 (20 %) undeterminable  and 20 (2 %) incorrect predictions  . SensitiFor comparison, we also determined ribotype predictions using core SNP distances to evaluate the potential of a distance-based approach without the noise introduced by the sourmash approach. Requiring 4/5 matching resulted in 863 (86 %) correct ribotype predictions, 106 unknown (11 %) and 31 incorrect (3 %) . The numSalmonella can be differentiated with mash with high accuracy [C. difficile genomes could be identified with k-mer hash set based approaches (full Jaccard distance) and k-mer hash subsampling (MinHash). In real genome data both full k-mer sets and k-mer subsamples comparisons were partially successful. We investigated with simulated genomes, replicate sequences and by correlation with real genome features the possible main performance factors.The use of genomic epidemiology for pathogen surveillance depends on the ability to compare potentially large numbers of genomes that may also be diverse. Hashing k-mers and randomly subsampling is fast and scalable making it possible to compare large numbers of genomes. It has previously been shown that serovars of accuracy . BuildinC. difficile genomes, we demonstrate k-mer based approaches can be used to reduce the search space for subsequent comparisons by identifying potentially closely related genomes. The performance observed for highly similar genomes required using genome assembles rather than raw reads, filtering assemblies to avoid contamination, removal of excessively fragmented assemblies and removal of small contigs (<1000 bp). Removing small contigs prevented some outlying results where genomes appeared more distantly related than they were by sourmash distance. Many genome pairs with small SNP distances also reported many whole gene differences that were correlated with increased sourmash distance. Similarly, we observed up to several hundred reported gene differences in sequences generated from the same isolate or same pool of extracted DNA, raising questions as to how many reported gene differences are relevant to transmission analysis versus artefactual from the sequencing process. Simulations of genome pairs without accessory genes suggested that sequencing errors could be a major driver of poor concordance between SNP and sourmash distance. The largest available k-mer size with sourmash was selected (k=51) in order maximise discriminatory power. Large k-mers avoid the likelihood of false positive matches from too small k-mers [Using a diverse set of l k-mers . The sizl k-mers .in silico, seven gene MLST is, including from raw sequencing reads. MLST carries the disadvantage of incomplete schemes and un-typeable genomes but the benefit of known inconclusive results. A MinHash threshold for clustering could be suggested from our results for C. difficile diverse lineage exclusions, but the possibility of false negatives (even greater than observed in this study) cannot be excluded. However, MinHash clustering of similar genomes and further alignment could be conducted, rapidly correctly identify outbreaks while alignment-based investigation of the full sample of genomes is ongoing.In a diverse dataset, clustering genomes by MinHash could rapidly exclude the majority of dissimilar genome pairs from further alignment and fine-scaled analysis. The genomes clustered by MinHash were comprised of more similar pairs, comparable with genomes clustered by fractional typing schemes such as ribotyping. While ribotyping is not readily possible in silico Salmonella spp. serotype prediction with SISTR (94.6%) [in silico prediction of all known ribotypes however (as with MLST) remains an unsolved challenge, that may become more tractable as read lengths increase sufficiently to allow more complete genome reconstruction. Where other traits are associated with overall population structure it is also possible to use k-mer based approaches for predicting these, e.g. of antimicrobial resistance [We also explored the possibility of a rapid distance-based ribotype prediction method. Subsampling k-mers, using sourmash search and a match consensus rule from a database (sourmash index) of genomes with known ribotypes successfully identified the correct ribotype for most searched genomes. The performance of this method was comparable to using core genome SNP distances, such that the speedup from MinHash did not introduce substantial error. With cross validation of SNP distance-based k-NN prediction, the random selection of train and test data was shown to be appropriate and robust to training input data variation. Mean accuracy was 90.6 % which approaches  (94.6%) . With grsistance .C. difficile assembled genomes at scale offered modest performance. In a genomic surveillance context where hundreds or thousands of genomes for comparison are becoming routine, it does provide the opportunity to computationally inexpensively and rapidly subset genomes for alignment and outbreak detection while full, fine-scaled investigations are ongoing.In conclusion, k-mer based approaches to comparing Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


C4H, HCT, HQT, C3H, F3H, ANS, MYB1), which might relate to the phytochemical content of those cultivars. The carbohydrates’ content differs among and within cultivars, while the rest of the phytochemicals differ only among cultivars. The cultivars ‘EMI’ and ’Lagkada’ scored higher than the rest in phenolics, anthocyanins, ascorbic acid, caffeoylquinic acid, and antioxidant capacity. Moreover, significant correlations were observed between various ingredients and the antioxidant capacity (FRAP and DPPH). Sequence analysis revealed several SNPs in C4H, HQT, F3H, ANS, and MYB1 among the cultivars studied. According to chi-square and logistic regression analyses, the missense mutation C4H4-108 correlates significantly with flavonoids, anthocyanins, and proteins; the synonymous mutation HQT-105 correlates with anthocyanins and ascorbic acid; the missense mutation HQT-438 correlates with flavonoids and chlorogenic acid, while the missense mutation ANS1-65 correlates with anthocyanins and sugars. These polymorphisms can be potentially utilized as molecular markers in eggplant breeding, while our data also contribute to the study of eggplant’s secondary metabolism and antioxidant properties.Eggplant is a widely consumed vegetable, with significant nutritional value and high antioxidant content, mainly due to its phenolic constituents. Our goal was to determine the levels of carbohydrates, proteins, total phenolics, anthocyanins, flavonoids, chlorogenic acid, and the antioxidant capacity in thirteen eggplant cultivars cultivated in Greece and to identify sequence polymorphisms in key regulating genes of the phenylpropanoid pathway ( Solanum melongena L.) is the fifth most popular vegetable worldwide, with around 56.6 million tons (Mt) of global production. China (36.6 Mt) is the top producer, followed by India (12.7 Mt) and Egypt (1.3 Mt) . The eggplant was introduced in Greece, around the 13th century as an ornamental plant, but now is an important part of the Greek gastronomy and the Mediterranean diet, with many local landraces possessing considerable morphological and genetic diversity  × 100.The antioxidant activity was determined with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method ,42. For 2]3+ to [Fe(TPTZ)2]2+ and the results were expressed as μmol Fe2+ per 100 g FW. In brief, 80 μL of FRAP reagent  were mixed with 55 μL acetate buffer  and 60 μL of samples (20.0 mg of FW of extract mL−1). Absorbance was measured at 595 nm. Ferrous sulfate heptahydrate  was used for the construction of a standard curve .In addition, we used the ferric reducing antioxidant power (FRAP) method , which mhttp://eggplant.kazusa.or.jp/, accessed on 17 October 2020) [https://www.ncbi.nlm.nih.gov/tools/primer-blast/, accessed on 22 January 2019) and the Oligo Analysis Tool . The exons selected, as well as the primer pair sequences, can be found as Total genomic DNA was extracted by homogenizing 100 mg of young leaf tissue from each accession with liquid nitrogen and following the CTAB protocol , with moer 2020) . Primers2, and approximately 1 ng of DNA template. PCR was performed in a C1000 Touch™ Thermal Cycler (Bio-Rad), Hercules, CA, USA, using different annealing temperatures, depending on the gene amplified , 1.5 mM MgClmplified .HCT3, OP295901-OP295950 for HQT, OP295951-296000 for C4H2, OP296001-OP296050 for C4H4, OP296051-OP296100 for C3H, OP296101-OP296150 for F3H, OP296151-OP296200 for MYB1, OP296201-OP296250 for ANS1.For PCR products’ verification, 5 μL of each PCR product were electrophoresed in 1% agarose gels, along with a 100 bp NEB DNA ladder . Amplified products were purified using the Nucleospin Gel and PCR clean-up kit ; concentration and quality of PCR products were determined using Quawell UV-Vis Q5000 spectrophotometer  and prepared for sequencing. The gene sequences obtained were deposited in GenBank under accession numbers OP295851-OP296250: OP295851-OP295900 for https://web.expasy.org/translate/, accessed on 23 August 2022) and standard genetic code.Exon sequences were manually edited using Finch TV version 1.4.0.  and SeqMan II software . For the enzymes that the whole coding sequence was selected, exon sequences were combined and analyzed as one data set. Multiple sequence alignments were performed with Sequencer Demo 4.8  and Clustal Omega multiple sequence alignment package ,46, usin𝛼 = 0.05). Spearman’s correlation was performed for all variable pairs at a 5% level of significance (𝛼 = 0.05) and estimated values of r > 0.90, r > 0.70, and r > 0.50 are interpreted as very high, high, and moderate, respectively. Heatmapping of the average values of the quality traits for the selected genotypes was performed with the online web tool Heatmapper [Means, standard deviation, and standard error for instrumental measurements were calculated using three replicates from each eggplant variety measurement. Fruits from each variety were subjected to mean comparison by one-way analysis of variance (ANOVA) using Tukey’s test at a 5% level of significance (atmapper .2) was performed to reveal the statistically important correlations. The SNPs significantly correlated were used as independent variables for the logistic regression performed afterward. This way, we tried to examine if the chemical content (dependent variables) correlates with specific mutations (SNPs). Statistical analysis was performed using IBM SPSS Statistics v.27 software.Furthermore, we proceeded to find out if any significant correlation exists between the phytochemicals with molecular data, and we tested if certain SNPs (missense and synonymous) are linked with the concentration levels of total phenolics, flavonoids, anthocyanins, chlorogenic acid, ascorbic acid, proteins, and sugars. For this purpose, the concentrations of each phytochemical were divided into three groups  by calculating their range and appointing two cut points. Cut point 1 ((range/3) + minimum value) indicates the point that divides low from moderate values and cut point 2 ((2*range/3) + minimum value) indicates the point separating moderate from high values. Then a chi-square test (XSolanum melongena L. is one of the main edible species worldwide with high nutritional value due to its plethora of chemical constituents, that are beneficial for human health. Despite its importance, it lacks genetic and genomic tools, compared to other members of the Solanaceae family such as tomato, but recently serious efforts have been made to bridge this gap [this gap ,23. ThisANS1-65 shows potential to be used as a molecular marker since homozygotes tend to have a high content of anthocyanins and sugars, but further study needs to be done. Other SNPs with potential interest are C4H4-108, HQT-105, and HQT-438. Breeding programs increasingly consider the concentration and content of bioactive compounds as a major breeding objective [Differences were observed within and among cultivars both at chemical and molecular levels. Cultivars ‘EMI’ and ‘Lagkada’ scored higher than the rest in phenolics, anthocyanins, ascorbic acid, caffeoylquinic acid, and antioxidant capacity. At the molecular level, out of the sixteen SNPs detected, only SNP bjective . Therefo


Efficient partitioning of above and below-ground biomass in response to nitrogen (N) is critical to the productivity of plants under sub-optimal conditions. It is particularly essential in vegetable crops like spinach with shallow root systems, a short growth cycle, and poor nitrogen use efficiency. In this study, we conducted a genome-wide association study (GWAS) to explore N-induced changes using spinach accessions with diverse genetic backgrounds. We evaluated phenotypic variations as percent changes in the shoot and root biomass in response to N using 201 spinach accessions grown in randomized complete blocks design in a soilless media under a controlled environment. A GWAS was performed for the percent changes in the shoot and root biomass in response to N in the 201 spinach accessions using 60,940 whole-genome resequencing generated SNPs. Three SNP markers, chr4_28292655, chr6_1531056, and chr6_37966006 on chromosomes 4 and 6, were significantly associated with %change in root weight, and two SNP markers, chr2_18480277 and chr4_47598760 on chromosomes 2 and 4, were significantly associated with % change shoot weight. The outcome of this study established a foundation for genetic studies needed to improve the partitioning of total biomass and provided a resource to identify molecular markers to enhance N uptake via marker-assisted selection or genomic selection in spinach breeding programs. However, excessive N inputs through fertilizers have led to N losses to the environment via leaching or gaseous emissions, resulting in adverse impacts on the climate, environment, and human health. Enhancing the NUE of crops has become an apparent strategy to promote yield and sustainability2. It is projected that a 1% increase in the NUE of crops could save over a billion dollars annually, which could be a considerable economic benefit3.N is critical to a plant's growth and development as an essential macronutrient. To meet the food demand of the growing human population, the utilization of N fertilizers has become inevitable6 due to its shallow root system7 and short production cycle. N plays a critical role in root and shoots communications to maximize plant productivity and agronomic applications8. Enhancing the NUE in spinach by exploiting genetic diversity in the leaf traits has been proposed10. We have previously shown that using a uniform soil-less matrix allowed us to use the supervised machine learning algorithm to predict the importance of root traits in spinach11. Biomass allocation among above (leaves) and below-ground (roots) organs is influenced by N uptake and distribution, which have crucial impacts on plant growth and development. N-induced changes in the shoot and root biomass have practical value as selection tools in vegetable breeding programs.Despite the advances in high throughput phenotyping and genetic resources in major crops  to evaluate traits associated with N utilization and productivity, such knowledge of N-induced changes in the biomass in leafy vegetables is still emerging. Commercial spinach producers use high levels of N fertilizers to produce higher biomass and better quality like many leafy greens. Spinach has poor NUEGenome-wide association studies (GWAS) have proven to be a powerful tool for characterizing the genetic constitution and identifying potential candidate genes associated with traits of interest. With the advent of genome editing techniques, it is now possible to manipulate the genetic makeup and facilitate speed breeding to enhance NUE by employing the genes identified by GWAS, selective sweeps, or other computational methods.In this study, we used GWAS to analyze percent changes in the shoot and root biomass in spinach at two N regimes to (1) depict the phenotypic variation in the biomass of shoot and root under low and high N levels, (2) categorize SNP markers associated with these changes, and (3) identify candidate genes influenced by N perturbation. Identifying QTLs for N-induced responses to N uptake offers a promising way in leafy greens to optimize N fertilizer requirements while maintaining a balance between assimilated N and vegetative growth needed for yield stability.S. oleracea) accessions obtained from the USDA-National Plant Germplasm System (NPGS) (https://npgsweb.ars-grin.gov/) at the USDA-ARS North Central Regional Plant Introduction Station, Iowa State University, Ames, Iowa, USA were used for the study. The plants were grown in a growth chamber at the Texas A&M AgriLife Research and Extension Center, Uvalde, Texas, in 2020, under controlled conditions of 12/12 h (light/dark), 22 °C, and 75% relative humidity. The seeds of each accession were sown in triplicate in turface  in small pots (10.2 cm × 10.2 cm and 8.9 cm deep) following a randomized complete block design. Additional details of the experimental set-up are detailed previously11. Plants were fertilized with two concentrations of N, low N , and high N  using Peters® professional ready mix  after seedling emergence. The plants were harvested after 41 days for the above-ground shoot and root biomass by carefully separating roots from the Turface. The fresh shoot and root weights were measured and expressed as % change (100 × LN/HN) for each accession. The accessions used for the study were distributed across 28 countries  was used as a reference for mapping the Illumina short reads of 201 spinach accessions using Burrows-Wheeler aligner software 15. SAMtools  was used for sorting the indexed binary alignment map files. The program Picard ) was used to remove duplicate reads and merge the bam files from the same sample. Genome Analysis Toolkit software 16 was used to detect and filter SNPs and InDels, while ANNOVAR17 was used to annotate all sequence variants. Around 2.357 million SNPs were detected in the 201 spinach genotypes. After filtering and keeping the SNPs with minor allele frequency (MAF) > 2% and missing allele < 15%, heterogeneous rate < 35%, 60,940 SNPs distributed on six chromosomes (Chr) were used in this study. The SNPs distribution across chromosomes was as follows; 6,788 SNPs on Chr 1; 6,134 SNPs on Chr2; 17,931 SNPs on Chr 3; 12,563 SNPs on Chr 4; 11,830 SNPs on Chr 5; and 5,694 SNPs on Chr 6  method built-in GAPIT 3 was used to draw phylogenetic trees.In this study, the 60,940 SNPs  MLM (PCA + K). PCA matrix was estimated by the PCA tool in TASSEL 5, where covariance  and the number of components = 2 were set. Kinship (K) was estimated by the tool Kinship with Scald_IBS method in TASSEL 5. The GWAS was also performed by BLINK (bayesian-information and linkage-disequilibrium iteratively nested keyway) model in GAPIT 3 (Genomic Association and Prediction Integrated Tool version 3)  22 by setting PCA = 2 using the 60,940 SNPs. Significant threshold of associations was determined using Bonferroni correction of P-value at an α = 0.05 (0.05/SNP number) . In this study, for the panel of all 201 accessions, the significant –log threshold value was 6.09 based on the 60,940 SNPs. Candidate genes were searched within 100 kb on either side of significant SNPs. Candidate genes were retrieved from the spinach Sp75 genome reference annotation at the SpinachBase site (http://www.spinachbase.org/).We used the 60,940 SNPs to perform association analysis in TASSEL 5  dividing in the GWAS panel of the 201 accessions were identified based on PCA using the 60,940 SNPs distributed 6 chromosomes and phylogenetic analysis by neighbor-joining (NJ) method drawn by GAPIT 3 Fig. . Based oWe performed BLINK analyses in GAPIT 3 and MLM (PCA + K) analyses in TASSEL 5, by setting PCA = 2 for the panel of 201 spinach accessions using 60,940 distributed across six chromosomes Fig.  filteredBased on BLINK MLM model, the distributions of the QQ plots between the observed vs. expected LOD [−log10(p)] showed a moderate divergence from the expected distribution for both % change in root and shoot weights Fig. , suggestSignificant SNPs were aligned against the spinach Sp75 reference genome, and regions within 100 kb were searched for candidate genes. A total of 13 and five unique genes were identified for % root and shoot weight, respectively, within these regions Table , most of3− losses to ground and surface waters. Spinach is often regarded as one of the highest NO3− accumulator vegetables due to its relatively poor nitrate reduction efficiency24. Although spinach is a rich source of specific health-promoting nutrients, it accumulates high amounts of anti-nutrient oxalates25. Studies have shown increased oxalate accumulation28, and reduced quantities of beneficial nutrients like ascorbic acid and flavonoids24 with increasing N amounts in spinach leaves. Spinach breeders thus have a significant challenge to mitigate these adverse effects by developing varieties that can utilize lower N input without compromising productivity. About 60% of N in spinach production is lost through leaching primarily due to its shallow root system7, inability to access NO3- below the root zone29, and short production cycle. Although several studies have confirmed the genotypic differences in N accumulation in spinach30, developing varieties responding to increased N by producing higher shoot biomass and expansive root systems would benefit high-density commercial production. Although higher yield with high N is a significant incentive for growers, given the high production cost and environmental damage, such varieties that can efficiently partition the N in leaf and root biomass will be rewarding. As the success of a breeding program for improving specific traits depends on existing genetic variability in the base population, it is necessary to define the genetic variation in the available germplasm for NUE traits for introgression into varieties. This study identified several accessions that showed the highest percent increase in shoot and root biomass which can be used as inbreds in breeding programs to enhance the NUE of cultivated varieties.Applying N-rich fertilizers for more food production is continually rising with the growing demand. Vegetables like spinach are often over-fertilized for higher production, enhancing appearance traits such as leaf greenness, brightness, or luster. However, vegetable growers are under increasing regulatory pressure to improve nutrient management and reduce NO2 value  with a peak at 3.8 cM, explaining 15.1 % phenotypic variation for low N and 13.9 % for high N; two QTLs at P01 and P05 LGs for shoot fresh-weight under low N, 29.8 % of the variation; and a QTL for dry matter percentage on P05 under low N. Whether the QTLs identified in this study are the same as those reported earlier would need experimental validation.In this study, GWAS was performed using the MLM model in both GAPIT3 and Tassel 5 programs to identify SNP markers associated with the percent changes in the shoot and root biomass in response to N. Three SNP markers, chr4_28292655, chr6_1531056, and chr6_37966006 on chr4 and chr6 were significantly associated with % change in root weight, and two SNP markers, chr2_18480277 and chr4_47598760 on chr2 and chr4 were significantly associated with % change shoot weight (Table SPY) is one of the enzymes involved in O-glycosylation in higher plants that play a role in plant growth and development31. A SPY-dependent protein O-fucosylation of DELLAs, transcription regulators of gibberellin (GA) signaling, is critical in regulating plant development32. A molecular approach that evaluated the role of GA metabolism and signaling in nitrate-regulated growth processes in Arabidopsis and wheat showed that nitrate increased bioactive GA levels and the degradation of DELLAs, thereby activating cell proliferation and root and shoot growth33. The role of O-fucosylation in establishing root hair cell patterning has been recently validated in Arabidopsis by showing a defective root hair patterning in O-fucosyltransferase SPINDLY (SPY) loss-of-function mutant34. Root hairs play an essential role in water and nutrient uptake by impacting root surface area. The distribution pattern of root hairs is regulated by hormones and external stimuli like nutrient availability. Functional validation of the putative gene Spo07469 identified by the SNP marker chr6_37966006 in enhancing root biomass would help understand its significance in N acquisition and serving as a serve marker for introgression breeding in spinach. The basic helix-loop-helix (bHLH) transcription factors (TF) represents one of the most prominent TF families in plants involved in a broad range of biological functions, including the root and shoot cell fate determination35. Many bHLH TF family members develop root hair cells critical to nutrient and water absorption and interact with soil microbiomes37. Specifically, bHLH14, which was identified in our study (Spo07458), acted as a transcription repressor to negatively regulate JA responses in Arabidopsis quadruple mutant with defective bHLH genes showing severe severity sensitivity to JA-inhibited root growth38. Mechanosensitive channels of small conductance (MscS) are pore-forming transmembrane proteins involved in the movement of ions across the plasma membrane across the electrochemical gradient. Characterization of MscS-Like (MSL) channels in Arabidopsis suggested their mechanosensitive activities in the plasma membrane of root cells39. Upon exposure to various stimuli, these proteins have been proposed to increase cytosolic Ca2+ concentration or activate other Ca2+ channels40. It is plausible that the gene Spo07459 identified in this study may play a similar role in root cells to regulate ion exchange in response to N availability.This study identified the O-fucosyltransferase family protein as one candidate gene enhancing root weight. The O-fucosyltransferase SPINDLY  gene for % shoot weight increase in response to N, showing similarity (73%) to Arabidopsis (At2g40730) N-terminal protein kinase like-domain protein involved in cytoplasmic tRNA export44.Another candidate gene, Spo07468 encoding DUF538 family protein, was identified for % root weight. These proteins have been characterized in stress-challenged plants, including those grown under nutrient deficiency and drought stressesWe found 18 candidate genes close to significant markers in the spinach panel within a 100-kb region window in either direction of a significant SNP. The most significant SNP marker, chr6_37966006 was located on O-fucosyltransferase (Spo07469) gene. This gene is likely to play a critical role in regulating root growth, and development in spinach in response to N. Additional studies would facilitate validation of identified genes and their integration into the spinach breeding programs to enhance NUE. The application of nitrogen-rich fertilizers for improved crop production is continually rising. Developing spinach cultivars to increase production proportionate to the applied nitrogen would allow efficient nitrogen management. We observed a large phenotypic variation as percent changes in the shoot and root biomass in response to N among a spinach panel of 201 accessions and identified significant associations between SNPs markers and increases in root and shoot biomass in the present study. These markers will help improve the efficiency of spinach breeding programs and represent necessary steps toward the selections for NUE parental lines for developing Kompetitive Allele-Specific PCR (KASP) markers to be used in marker-assisted selection (MAS) and future introgression breeding.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.


Good feed efficiency (FE) is an important trait to ensure the economic output of the livestock and poultry industries. Herein, a genome-wide association study was conducted to identify potential variants and genes associated with seven FE measures in ducks. Genomic DNA samples of 308 ducks were collected and sequenced. All animals were evaluated concerning body weight gain (BWG), feed intake (FI), residual feed intake (RFI), feed conversion ratio (FCR), and weight at 21 (BW21) and 42 days of age (BW42). Overall, 4 (FCR), 3 (FI), 36 (RFI), 6 (BWG), 8 (BW21), and 10 (BW42) single nucleotide polymorphisms (SNPs) were significantly associated with these FE traits, respectively. Moreover, candidate genes close to the identified variants were found to be mainly involved in key pathways and terms related to metabolism. In summary, these findings improve our understanding of poultry genetics and provide new foundations for breeding programs aimed at maximizing the economic potential of duck breeding and farming.Feed efficiency (FE) is the most important economic trait in the poultry and livestock industry. Thus, genetic improvement of FE may result in a considerable reduction of the cost and energy burdens. As genome-wide association studies (GWASs) can help identify candidate variants influencing FE, the present study aimed to analyze the phenotypic correlation and identify candidate variants of the seven FE traits in ducks. All traits were found to have significant positive correlations with varying degrees. In particular, residual feed intake presented correlation coefficients of 0.61, 0.54, and 0.13 with feed conversion ratio, and feed intake, respectively. Furthermore, data from seven FE-related GWAS revealed 4 (FCR), 3 (FI), 36 (RFI), 6 (BWG), 8 (BW21), and 10 (BW42) SNPs were significantly associated with body weight gain, feed intake, residual feed intake, feed conversion ratio, and weight at 21 and 42 days, respectively. Candidate SNPs of seven FE trait-related genes were involved in galactose metabolism, starch, propanoate metabolism, sucrose metabolism and etc. Taken together, these findings provide insight into the genetic mechanisms and genes involved in FE-related traits in ducks. However, further investigations are warranted to further validate these findings. Feed efficiency (FE) is an important trait that is often related to measures of animal productivity ,2,3. In Advances in diet formulations have significantly improved the FE for poultry production . HoweverAll experiments with ducks were performed in accordance with the Regulations on the Administration of Experimental Animals issued by the Ministry of Science and Technology  in 1988 (last modified in 2001). Experimental protocols were approved by the Animal Care and Use Committee of Yangzhou University (YZUDWSY2017-11-07). All efforts were made to minimize animal discomfort and suffering.2 segregating populations were used for the GWAS. To construct the F2 segregating population, the F1 generation was produced from orthogonal crosses between 86 Chinese Crested and 13 Cherry Valley ducks that were randomly selected and divided into seven families. The number of offspring in the F1 generation exceeded 500 individuals, none of which had crest traits, and the male:female ratio was consistent. The F2 generation was produced from the natural mating of F1 hybrids and mating was internally limited to orthogonal experiments. The families were established while considering the following principles: (1) a male:female ratio of 1:3, (2) males and females in the same family were not from the same nest, and (3) female ducks within a family were not half-siblings. To avoid half siblings, we designed a mating system. The F2 generation was composed of almost 2000 ducks that displayed segregation of various genetic characteristics, including meat quality-related traits. When the ducklings hatched, they were weighed each week afterward. Three weeks after hatching, all members of the F2 generation were moved from the duckling house to a designed individual shed and raised to the age of 6 weeks. A slaughter experiment was performed with more than 800 ducks and several traits were measured, including meat quality. In all families, the color (white and black) trait followed the recessive inheritance of Mendel’s law of separation.A total of 308 FBWG is the weight gain from 21 to 42 days of age, a is the intercept, and BWG, respectively.To obtain the RFI data, the BW of each duck at 42 and 21 days of age was measured. The FCR was calculated as the ratio of the FI using the grDevies R package. Briefly, after removing outliers , the random remaining 308 ducks were used to calculate RFI as the residuals from a regression model of FI on metabolic BW. Finally, RFI was calculated using the previously described formula   was used to perform correlation analysis between all traits evaluated. The phenotypes that did not follow a normal distribution were transformed using the Johnson method implemented using the Johnson R package [To help identify candidate variants, a normal distribution test was performed on all phenotypic data using the stats R package. The quickcor function in the ggcor R package  and agarose gel electrophoresis, respectively. The final concentration was adjusted to 50 ng/μL, and samples with an A260/280 ratio of 1.8–2.0 were finally submitted for sequencing. Two paired-end sequencing libraries with insert sizes of 350 bp were constructed according to the Illumina protocol . All libraries were sequenced using the Illumina NovaSeq platform, with an average clean read sequence coverage of 11.60.To isolate high-quality genomic DNA, blood samples from Fhttps://gatk.broadinstitute.org/hc/en-us, access on 10 February 2022) with the UnifiedGenotyper method. SNPs were filtered using the Perl script [A total of 308 sequences were aligned to the mallard genome using the Burrows-Wheeler Aligner (BWA) software (settings: mem−t 4 −k 32−M−R) . The saml script . After fhttps://zzz.bwh.harvard.edu/plink/, access on 20 February 2022). The window size for LD calculation was set based on the number of SNPs located in each genome. Pairwise LD was determined using squared allele frequency correlations, and assessed by calculating 2r for pairs of SNP loci. Principal component analysis (PCA) was conducted using PLINK with the 12,201,978 variants of the 308 ducks to estimate the population structure. The ggplot2 R package [Linkage disequilibrium (LD) among the markers was calculated for all SNPs using PLINK   the SNPs were sorted by their p-values determined for one characteristic; (ii) the SNP with the lowest p-value was selected as the sample for each chromosomal bin. To create kinship, the most important bins were selected. To optimize the constrained maximum probability for a trait, the size and number of bins used were considered as parameters. For the subsequent association test, the selected SNPs (each representing a bin) were used as the basis for the SNP pool. As a result, SNPs that were in LD (2r > 0.8) with the tested SNP were not included in the SNP pool. The random-effects model was set as follows (Equation (3)):The multilocus linear mixed model of fixed and random model circulating probability unification (FarmCPU) method was used to conduct the association analysis between the SNPs and FE traits . The Far2u), indicating that N , and the rate was maintained at that level.The Bonferroni correction method was used to set the significance threshold for selecting important SNPs. To maintain the type I errors at 5%, the genome-wide significance threshold was set at 4.097696 × 10Based on the LD attenuation distance calculated by PopLDdecay , annotatDescriptive characterization of the 308 ducks concerning BW at 42 and 21 days of age, FCR, FI, BWG, and RFI is provided in 2r = 0.1 at distances of 80,977 , 3 (FI), 36 (RFI), 6 (BWG), 8 (BW21), and 10 (BW42) candidate SNPs are associated with the BWG, FI, RFI, FCR, and weight at 21- and 42-days traits, respectively. These findings improve our understanding of poultry genetics and provide a basis for breeding programs aimed at maximizing the economic potential of duck breeding and farming.


A total of 227, 921 filtered single nucleotide polymorphisms (SNPs) evenly distributed across the entire genome were detected to perform GWAS. A total of 64 SNPs were identified for six meat quality traits using the mixed linear model (MLM), of which 24 SNPs were located in previously reported QTL regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43% to 16.32%. The genomic heritability estimates based on SNP for six meat-quality traits were low to moderate (0.07–0.47) being the lowest for CL and the highest for DL. A total of 30 genes located within 10 kb upstream or downstream of these significant SNPs were found. Furthermore, several candidate genes for MQTs were detected, including pH45 (GRM8), MC (ANKRD6), MA (MACROD2 and ABCG1), WL (TMEM50A), CL (PIP4K2A) and DL . This study provided substantial new evidence for several candidate genes to participate in different pork quality traits. The identification of these SNPs and candidate genes provided a basis for molecular marker-assisted breeding and improvement of pork quality traits.Meat quality traits (MQTs) have gained more attention from breeders due to their increasing economic value in the commercial pig industry. In this genome-wide association study (GWAS), 223 four-way intercross pigs were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) and phenotyped for PH at 45 min  RYR1, PRKAG3, PHKG1, and IGF2 . Among these QTLs, 805, 765, 136, 30, 91, and 1,092 are found to be associated with PH and meat color, marbling score, water holding capacity, cooking loss, and drip loss, respectively. However, most of these QTLs detected by linkage mapping cover large regions of the genome containing hundreds of genes. Furthermore, only a few genes have been successfully applied to improve the MQTs of pigs at present. Consequently, identifying accurate QTL locations and novel candidate genes remains a major challenge.Pork quality is a comprehensive indicator, including meat color, pH, marbling, water-holding capacity, intramuscular fat (IMF), tenderness, etc. , which iand IGF2 . To dateCXXC5, RYR3, BNIP3, and MYCT1) for meat traits were identified. For Sus Scrofa with larger genomes, GWAS based on whole-genome sequencing (WGS) is prohibitively expensive. Considering these limitations, specific-locus amplified fragment sequencing (SLAF-seq), a technology based on high-throughput sequencing was developed, which is a cost-effective method for large-scale genotyping (Genome-wide association study (GWAS) has been increasingly used to identify genomic regions and markers related to quantitative traits more precisely. In recent years, GWAS based on SNP array for MQTs has identified a large number of QTLs and candidate genes . Gao et notyping . SLAF-senotyping , chickennotyping , ducks  × [Yorkshire × (Landrace × Saba)] hybrid segregation population was established. As we know, Duroc, Landrace, and Yorkshire pigs are typical lean-type Western commercial breeds widely distributed all over the world and used for commercial production. The shared disadvantage of Western commercial pigs is poor meat quality. However, Chinese native pigs are quite different from Western commercial pigs in meat quality traits. As an invaluable Chinese genetic resource, the fat-type Saba pigs are widely distributed in Yunnan Province, China , which epost mortem (pH45), meat color score (MC), marbling score (MA), water loss rate (WL), cooking loss (CL), and drip loss (DL). Subsequently, GWAS based on SLAF-seq was performed, and identified potential loci influencing these traits. The findings served as the foundation for molecular marker-assisted breeding and the improvement for meat quality traits in pigs.Here, we examined 223 four-way crossbred pigs raised under the same environmental conditions for six meat quality traits, including pH at 45 min All of the animals utilized in this study were handled and used in accordance with the standards established by China’s Ministry of Agriculture and Rural Affairs for the care and use of experimental animals. The entire study was given the nod by the Yunnan Agricultural University’s  ethics committee.A four-way crossbred pig population was established as described previously . In shorSix meat quality traits were noted after slaughter, including PH45, MC, MA, WL, DL, and CL. The measured muscle samples were from the left side of the carcass. PH45, MC, MA, WL, and DL were measured on the longissimus muscle between the 10th rib and the first lumbar vertebra, and CL was measured on the psoas major muscle. PH45 values were measured at 45 min after slaughter using an automatic pH-STAR. MC , and MA  were subjectively evaluated according to National Pork Producer Council (NPPC) guidelines. The WL was determined using the filter paper press method as described by Three measurements of PH45, WL, CL, and DL were taken for each sample. Further analyses were conducted using the averages.The SAS  MEANS procedure was used to create descriptive statistics for meat quality traits under investigation. Using the R package “ggpubr”, the sample distribution was represented as a frequency distribution histogram. The R function “PerformanceAnalytics” carried out the phenotypic correlation analysis. The genetic correlations and genome heritability for six meat quality traits were estimated using the GCTA software .ftp:/ftp.ensembl.org/pub/release-102/) as the reference genome. RsaI and HaeIII restriction enzyme combinations were selected to digest eligible genomic DNA according to the selection principle of the enzyme digestion scheme  to the identical sequencing procedure. Briefly, SLAF library construction and sequencing for each individual was carried out as previously described (SLAF library construction and sequencing were performed as described previously . In shorn scheme . The enzn scheme  sequenciescribed  with a f10p) were measured to assess the sequencing accuracy. And then, raw paired-end reads were aligned to the pig reference genome (Sscrofa 11.1_102) using BWA software  > 5 (control threshold) were regarded to be significant SNPs for meat quality traits  and 4.39 × 10−7 , respectively, according to the number of filtered SNPs . A marker was deemed to be significantly related to the target trait if it passed the threshold score or above the threshold −log10p given the complexity of the target traits. Finally, the manhattan and Quantile-quantile (Q-Q) plots of GWAS were drawn using the R package “qqman”  and six meat quality phenotypic data, an association analysis was carried out. We used mixed linear model (MLM) of GEMMA software  to detecsoftware , and Z wsoftware . α was tsoftware  was usedy traits . The thr “qqman” .www.ensembl.org), the relevant information of genes within 10 kb upstream or downstream of each significant SNP was obtained. Using Gene Ontology Consortium (http://geneontology.org), GO annotation results of candidate genes were then obtained. GO and KEGG enrichment analyses were performed based on genes located 10 kb upstream and downstream of significant SNPs using the database for annotation, visualization, and integrated discovery . GO terms and KEGG pathways with the threshold p-value ≤ 0.05 were regarded to be significantly enriched.Based on the reference , the genr2) value between SNP pairs>0.7 was defined as a LD block.Haplotype block analysis was performed with LDBlockShow software . LD . WL had the strongest negatively correlated with PH45 . The six meat quality traits showed low to medium phenotypic correlation (0.01<|r |<0.38), indicating that there was no strong phenotypic correlation between the six meat quality traits. The genetic correlations among six meat quality traits are shown in The phenotypic correlation coefficients for PH45, MC, MA, WL, CL, and DL are showed in Oryza sativa indica was used as a control during sequencing. The results showed that the enzyme digestion normally efficiency and paired-end comparison efficiency of control data were 90.77% and 95.4%, respectively, indicating that the construction of SLAF libraries was normal.A total of 223 individuals were genotyped and descriptive statistics of the sequence data were presented in our previous study . In shorSus Scrofa autosome and are shown in Sus Scrofa chromosomes was one SNP every 10.28 kb on average, demonstrating the data was reliable.After genomic mapping and SNP calling, a total of 16,997 polymorphic SLAFs were detected across the accessions. Furthermore, 10,784,484 SNPs in all were identified for all individuals. Based on the selection criteria , a series of quality control filtering of SNPs was carried out to identify 227,921 SNPs used in the subsequent study. p < 1.0 × 10−5) for the traits studied using MLM (λ) at each trait ranged from 1.03 to 1.07.To lessen the impact of population structure and boost the accuracy of GWAS results, the MLM was used to perform GWAS for six meat quality traits. GWAS could be impacted by population stratification, hence quantile-quantile (Q−Q) plots of six meat quality traits were drawn. The Q−Q plot of each trait was shown following the Manhattan plot of the corresponding traits , 2. A tosing MLM . The genSus scrofa chromosome), and SSC18. For MC, SNPs were distributed in SSC1, SSC6 and SSC17. For MA, SNPs were distributed in SSC1, SSC3, SSC5, and SSC13. For WL, SNPs were distributed in SSC6, SSC14, and SSC15. For CL, SNPs were distributed in SSC1 and SSC10. For DL, SNPs were distributed in 14 chromosomes except for SSC11, SSC16, SSC17, and SSC18. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43% to 16.32%. Furthermore, 30 genes were thought to be potential candidate genes that were located within 10 kb up- or down-stream of these significant SNPs  was not located in any genes. The significant SNP (rs321002713) on SSC18 explained 11.32% phenotypic variance, which was located within ANKRD6 and ENSSSCG00000032113, respectively. Among, the rs327814455 explained 10.75% phenotypic variance.A total of three SNPs were significantly associated with MC. The two significant SNPs, rs327814455 on SSC1 and rs690751971 on SSC6, were located within ENSSSCG00000004081. The significant SNP (rs341748571) on SSC17 explained 10.47% phenotypic variance, which was located in the MACROD2 gene. The SNP rs325690789 on SSC5 was located within FGD4, and rs342013877 on SSC13 was located 5 kb upstream of the ABCG1 gene.For MA, the most significant SNP (rs696643958) on SSC1 was located within TMEM50A gene and 7.9 kb upstream of the RHCE gene. The significant SNP (rs693644154) on SSC15 was located 2.7 kb upstream of the RRM2 gene.A total of three SNPs  were significantly associated with WL. The most significant SNP (rs1113389876) on SSC6 was located within the MED27 gene. Furthermore, two nearby significant SNPs (rs331296609 and rs344980768) on SSC10 were located in the PIP4K2A gene. These two SNPs were mapped to one haplotype block spanning 16 bp affecting CL on SSC10  was located within the on SSC10 , which eCDYL2 gene. Eight SNPs  exceeded the 10% genome-wide significance level. Among these significant SNPs, two nearby SNPs on SSC13 (rs323693055 and rs703586532) were located in the CHL1 gene. The rs703586532 and rs323693055 explained 11.58% and 13.46% phenotypic variance, respectively. The SNP rs320599347 was located within the ABCA4 gene.A total of 47 significant SNPs were identified for DL. Among these SNPs, two SNPs (rs321165533 on SSC6 and rs323693055 on SSC13) exceeded the 1% genome-wide significance level. The SNP rs321165533 explained 14.55% phenotypic variance, which was located within the FA2H. On SSC3, four nearby significant SNPs were located in a region from 7863132 to 7863391 bp , which were located within the ZAG gene. Two adjacent significant SNPs (SSC2:25635102 and SSC2:25635114) were located within SLC1A2. The two significant SNPs were mapped to one haplotype block spanning 12 bp affecting DL on SSC2  were located within  on SSC2 , which eFurthermore, several significant SNPs explained more than 13.35% phenotypic variance, which were not located any known genes, including rs345860122 on SSC4 (13.48% PVE for DL), rs324617714 and rs325613231 on SSC7 (13.35% PVE for DL).https://www.animalgenome.org/cgi-bin/QTLdb/SS/index, 25 Apr 2022) was searched based on SNP and QTL locations to evaluate if QTLs linked to meat quality traits in this study repeat any previously reported QTLs. A total of 64 SNPs significantly associated with meat quality traits in four-way crossbred pigs were identified using the MLM, of which 24 SNPs were located in previously reported QTL regions that were associated with the meat quality traits of pigs (The Pig Quantitative Trait Locus (QTL) Database   and one KEGG pathway (ABC transporters) were significantly enriched ( ≤ 0.05) .r = 0.38; p < 0.001).In this study, we used SLAF-seq technology to obtain 227,921 highly consistent SNPs. Previous studies have proven the advantage of the SLAF-seq method in the GWAS, genetic diversities analysis, and construction of genetic map for animals and plants . SLAF-seλ) at each trait ranged from 1.03 to 1.07 (The standard deviation (SD) of phenotypic values for PH45, MC, and MA were 0.31, 0.48, and 0.56, respectively, which were similar to the results of  to 1.07 , and nonMAF by the significant SNPs was from 0.05 to 0.49 . Some siIn previous studies, most of the SNPs and candidate genes for important economic traits of pigs identified based on GWAS mainly used F2 generation populations, which were generated by crossbreeding local pig breeds from different countries with Western lean pig breeds  and pureIn the present study, 64 SNPs in all were detected using MLM as significant for the meat quality traits studied, of which 24 SNPs were located in previously reported QTL regions for meat quality traits in pigs. Three genomic regions, including 9.35-Mb  on SSC1, 5.29-Mb  on SSC6, and 0.09-Mb region  on SSC9 for DL were located in previously reported QTL regions on SSC1, 6 and 9 for DL . Besidesr = 0.14; p < 0.05) and low genetic correlation (0.33 ± 0.29)  and High genetic correlation (1 ± 0.42)  on SSC1 was identified as being significantly associated with CL and DL, containing SSC1:271857436 for CL, and rs710333950 and rs326037487 for DL . A 9.08- ± 0.29)  between  ± 0.42)  between GRM8). The GRM8 gene encodes a G protein-coupled metabotropic glutamate receptor involved in glutamatergic neurotransmission in the central nervous system  or DFD  meat. We identified three significant SNPs as being significantly associated with pH45. Among which, the significant SNP (rs321002713) on SSC18 was located within glutamate metabotropic receptor 8 (s system . Group Is system . A study of pigs . GRM8 wais trait . PerhapsANKRD6). ANKRD6 belongs to the ankyrins gene family. Ankyrins are a family of structural proteins that include binding sites for cytoskeleton proteins and a variety of integral membranes  promoter region have been linked to intramuscular fat levels and tenderness of beef  (ANKS1B) gene was found to be a significantly upregulated expression (ANKRD1) in pig muscle correlates with the ultimate pH  . In pig pression . Additioimate pH . ConsequENSSSCG00000004081. GO annotation result showed that the gene participated in muscle cell differentiation and actin filament binding (MACROD2). The MACROD2 gene encodes the mono-ADP-ribosyltransferase two catalyzing ADP-ribosylation . A low marbling score will affect the pork quality and flavor. The most significant SNP (rs696643958) on SSC1 was located within  binding . The dep binding . Thus, tsylation . ADP-ribsylation . Some stf cattle  and may f cattle . BesidesABCG1). In the study, GO annotation results showed that the ABCG1 gene was involved in negative regulation of lipid storage, response to lipid and phospholipid homeostasis. The ABCG1 gene has been known to be associated with controlling cellular lipid levels (ABCG1 can promote lipid accumulation by regulating the lipoprotein lipase (LPL) bioavailability and fat mass growth in a triglyceride (TG)-rich environment  on SSC13 was located 5 kb away from ATP binding cassette subfamily G member 1 . A study shows that the related gene TMEM217 is associated with meat color . The result of GO annotation showed that RRM2 was involved in deoxyribonucleotide biosynthetic process and oxidation-reduction process . Previous studies have shown that the two SNPs (ASGA0048292 and ASGA0048295) of PIP4K2A were associated with meat quality of pigs  on SSC6 for DL was located within chromodomain Y-like 2 (CDYL2). GO annotation results showed that CDYL2 was involved in catalytic activity and metabolic processes. A study finds that CDYL2 is related to porcine teat number . The study finds that CHL1 can regulate the cell cycle via the p53 pathway and inhibit cell proliferation through the ERK pathway, and was associated with insulin secretion and glucose metabolism (CHL1 can be considered a strong candidate gene for DL. The SNP rs320599347 on SSC4 was located within ATP binding cassette subfamily A member 4 (ABCA4). ABCA4 is a member of the ABCA subfamily of ATP-binding cassette transporters participating in the transport of phosphatidyle thanolamine , which was participated in fatty acid biosynthetic process, lipid modification, and regulation of cell proliferation  on SSC13 were located in cell adhesion molecule L1 like , which is a glycoprotein included in the class I family of the major histocompatibility complex (MHC). Several studies show that ZAG is related to lipid loss (SLC1A2). Researchers report that the related genes SLC15A4 c.658AA genotype has better water-holding capacity , which was located in the SIL1 nucleotide exchange factor (SIL1) gene. SIL1 related to stress protection, and moderately increased SIL1 also ameliorates cellular fitness under stress conditions , of which 24 SNPs were located in previously reported QTL regions. Three QTLs were identified to be related to DL: 0.08-Mb region on SSC5 (72.91–72.99Mb), 3.6-Mb region on SSC13 (53.28–56.88Mb), and 0.09-Mb region on SSC9 (63.38–63.47Mb). Some novel candidate genes for meat quality traits were identified, including pH45 (GRM8), MC (ANKRD6), MA (MACROD2 and ABCG1), WL (TMEM50A), CL (PIP4K2A), and DL . Overall, the study presented substantial new evidence for the involvement of several candidate genes in different pork quality traits. These SNPs and candidate genes identified in the study provided a basis for molecular marker-assisted breeding and improvement for meat quality traits in pigs.We conducted a GWAS based on SLAF-seq for six meat-quality traits in 223 four-way crossbred pigs. A total of 64 SNPs distributed on 16 chromosomes were identified using MLM (


Bubalus bubalis), belonging to the Bovidae family, is an economically important animal as it is the major source of milk, meat, and drought in numerous countries. It is mainly distributed in tropical and subtropical regions with a global population of approximately 202 million. The advent of low cost and rapid sequencing technologies has opened a new vista for global buffalo researchers. In this study, we utilized the genomic data of five commercially important buffalo breeds, distributed globally, namely, Mediterranean, Egyptian, Bangladesh, Jaffrarabadi, and Murrah. Since there is no whole-genome sequence analysis of these five distinct buffalo breeds, which represent a highly diverse ecosystem, we made an attempt for the same. We report the first comprehensive, holistic, and user-friendly web genomic resource of buffalo (BuffGR) accessible at http://backlin.cabgrid.res.in/buffgr/, that catalogues 6028881 SNPs and 613403 InDels extracted from a set of 31 buffalo tissues. We found a total of 7727122 SNPs and 634124 InDels distributed in four breeds of buffalo  with reference to the Mediterranean breed. It also houses 4504691 SSR markers from all the breeds along with 1458 unique circRNAs, 37712 lncRNAs, and 938 miRNAs. This comprehensive web resource can be widely used by buffalo researchers across the globe for use of markers in marker trait association, genetic diversity among the different breeds of buffalo, use of ncRNAs as regulatory molecules, post-transcriptional regulations, and role in various diseases/stresses. These SNPs and InDelscan also be used as biomarkers to address adulteration and traceability. This resource can also be useful in buffalo improvement programs and disease/breed management.Water buffalo ( Bubalus bubalis, is the major source of milk, meat, and drought in various countries, making it an economically important animal. This livestock species belonging to the Bovidae family is mainly distributed in tropical and subtropical regions. Based on morphology and behavior, the two categories of domestic Asian water buffalo are river buffalo (2n = 50) and swamp buffalo (2n = 48)  . The glo2n = 48)  with ∼822n = 48) . Buffalo2n = 48) .https://www.ncbi.nlm.nih.gov/assembly/?term=Bubalus) (Last accessed: July 2021): GCA_003121395.1 of the Mediterranean breed from University of Adelaide, Australia; GCA_019923935.1 of the Murrah breed from National Dairy Development Board, India; GCA_004794615.1 of the Bangladesh breed from BGI-Shenzhen, China; GCA_002993835.1 of the Egyptian buffalo breed from Agriculture Genetic Engineering Research Institute and Nile University, Egypt, and GCA_000180995.3 of the Jafarabadi breed from Anand Agricultural University, India. GCA_003121395.1 and GCA_019923935.1 are chromosome level assemblies with 25 chromosomes, however, RefSeq annotation has not yet been provided for GCA_019923935.1.The fast decline in DNA sequencing costs has paved the way for researchers across the globe to revolutionize genome analysis. Assembly and decoding of the genome of water buffalo is in continuous progress. The following five genome assemblies of water buffalo exist on the NCBI database  are the information source for genetic diversity among different breeds/varieties of the same species . There aLong non-coding RNAs are a group of RNAs which are greater than 200 nt and lack open reading frames or have <100 amino acids in length. lncRNAs regulate gene expression through methylation and demethylation  and thromiRNAs are 18–25 nucleotide-long regulatory sequences, which play an important role in response reactions during anorganism’s exposure to biotic or abiotic conditions . They reOther regulatory non-coding RNAs, known as circular RNAs (circRNAs), spawn through back-splicing of RNAs. They are more stable than RNAs . The funBos taurus reference genome. The available whole-genome assemblies of five buffalo breeds represent a highly diverse ecosystem. Their utilization in whole-genome sequence analyses and in extraction of rapid polymorphic markers at lower costs for the breeders is warranted. In 2018, a buffalo reference genome with 24 chromosomes along with X and MT chromosomes was released by the Italian Buffalo Genome Consortium (https://www.ncbi.nlm.nih.gov/assembly/GCA_003121395.1). For the current study, the different omics studies in buffalo were performed using the GCA_003121395.1 buffalo reference genome to extract non-coding RNAs such as miRNAs, lncRNAs, and circRNAs in the 31 buffalo tissues, which had not been attempted earlier. Also, the various genetic markers such as SSRs, SNPs, and InDels from five breeds of buffalo  were mined. After extraction of the mentioned molecular markers and non-coding RNAs, a web-based genomic resource, BuffGR was developed to facilitate the buffalo research community with user-friendly, single-window retrieval of buffalo omics data to be utilized for further scientific research and studies. This buffalo web resource is state-of-the-art, holistic, and currently the largest collection related to buffalo including the most important breed of India, i.e., Murrah from the latest 2021 assembly as well as the world, i.e., Mediterranean from the latest 2018 assembly.Before release of the buffalo reference genome, most of the studies related to buffalo involving omics analyses were based on the namely, Mediterranean, Egyptian, Bangladesh, Jaffrabadi, and Murrah, their genome assemblies were retrieved from NCBI  were mapped to the water buffalo reference genome of the Mediterranean breed . These mapped reads of RNA-seq data were first sorted and indexed using Samtools .For extraction of variants, Samtools  along wibcftools  call. FiMIcroSAtellite (MISA)  was usedBos tauras from miRBase  using BLASTn and sequences with 0 gap and ≤3 mismatches were taken along with 500 nt up and downstream stretches, making these >1000 nt length sequences , j , o (lncRNAs with exonic overlap with a known transcript), u (intergenic lncRNAs), and x (exonic overlap on the opposite stand) as classified by For the identification of lncRNAs, from RNA-seq data of 31 buffalo tissues, first, mapping was performed using HISAT2 , followeBuffGR is a ‘three tier architecture’ relational database developed using client, server, and database tiers. The analyzed datasets were catalogued in BuffGR on a Linux server. The following steps were involved in the development of BuffGR  ExtrcircRNAs ; 3) deveA total of 6028881 SNPs and 613403 InDels were extracted from the set of 31 buffalo tissues. The highest number of SNPs and InDels was extracted from milk tissue (1625901 SNPs/174256 InDels) followed by testis (448640 SNPs/46172 InDels) and large intestine (152608 SNPs/17552 InDels) . HoweverA total of 12 genes  with abundance of milk tissue SNPs from the present study were found to be common out of 19 candidate genes reported to be associated with milk production trait by Maximum number of SSRs were observed in Jaffrabadi (1028180), followed by Bangladesh (9463410) and Mediterranean (908402), while the least was found in Egyptian (726405) . The numWe identified a total of 938 miRNAs from the genome assembly of the Mediterranean breed. The pre-miRNA sequences, secondary structure, target information, and location of origin were extracted for each miRNA along with mature miRNA sequence and anti-miRNA star sequence. It was observed that chromosome 11 had the maximum frequency of miRNAs (132 miRNAs) followed by chromosomes 23 (81 miRNAs) and 13 (80 miRNAs) . A targeOut of the total 1702 circRNAs extracted from the 31 buffalo tissues, 1458 were unique circRNAs. A total of 44221 lncRNAs were identified in the 31 buffalo tissues. Abundance of lncRNAs was observed in milk tissue 17387) followed by testis (5048) and pooled embryo 4419) . GenomicBuffGR is a comprehensive, first-of-its-kind web resource, with a holistic collection of buffalo molecular markers and variants of five buffalo breeds . It is a user-friendly web resource, which catalogues SNPs, InDels, and SSRs along with ncRNAs such as mircoRNAs, lncRNAs, and circRNAs from the five buffalo breeds and 31 tissues. It has a left vertical section which provides access to varying sections of the web page including Home, Statistics, Data, and Team. The Home page includes the brief introduction of the buffalo web genomic resource along with a description about RNA/transcripts and molecular markers of buffalo. The Statistics section provides the statistics of extracted buffalo genomic data represented in the form of various graphs and pie charts.Data section includes hyperlinked images of each data point included in the web resource, and by clicking on the image, the user navigates to the next page of the respective data which provides the user varying options including type of tissue or chromosome number or breed, etc. , Italy (Mediterranean), Bangladesh (Bangladesh), and Egypt (Egyptian). The catalogued SNP/InDel markers from different breeds could be used to study genetic diversity among different breeds of buffalo  including genomic findings of five commercially important buffalo breeds, namely Mediterranean, Egyptian, Bangladesh, Jaffrarabadi, and Murrah. BuffGR catalogues a total of 6028881 SNPs and 613403 InDeLs extracted from the set of 31 buffalo tissues. Collectively, a total of 7727122 SNPs and 634124 InDels were distributed in the four breeds of buffalo  with reference to the Mediterranean breed. The web resource has 4504691 SSR markers from all the breeds, 1458 unique circRNAs and 37712 lncRNAs from 31 buffalo tissues, and 938 miRNAs from the genome assembly of the Mediterranean breed. This information can be widely used by the buffalo researchers across the globe for studying the genetic diversity among the different breeds of buffalo, studies involving post-transcriptional regulation, and their role in various buffalo diseases. The provided markers can be used as biomarkers in the meat industry to trace adulteration, trafficking, and breed traceability. These can be used not only for knowledge discovery research but also for marker trait association, which will be helpful in the improvement and management of buffalo breeds.Through this study, we report the first comprehensive and user-friendly web genomic resource for buffalo (


By lowering the a posteriori probability threshold for no-calls, we obtained a higher call rate than GenomeStudio. Using a higher a posteriori probability threshold, we achieved a higher concordance with the WGS data than GenomeStudio. Our method had SNP call and concordance rates with WGS data of approximately 99%.We introduce a within-sample SNP calling method, called the “butterfly method”, that improves the quality of SNP calling with the Illumina Infinium Omni5-4 SNP Kit. This was done by improving how no-calls are determined from allele signal intensities. High confidence of SNP allele calling is extremely important in forensic genetics and clinical diagnostics. This paper is accompanied by two open-source R packages, omni54manifest and snpbeadchip that make SNP calling easy by helping with bookkeeping and giving easy access to meta-information about the SNPs typed with the Illumina Infinium Omni5-4 Kit . We compared the results from our method with those obtained with the Illumina GenomeStudio software , and with whole-genome sequencing (WGS). Given the signal intensities, the SNP calling quality was optimised using a threshold for the  In forensic genetics and clinical diagnostics, high certainty of SNP allele calling is essential. Particularly in criminal cases with small amounts of DNA from crime scenes, the interpretation of DNA results is of utmost importance. The presently used and published SNP allele calling methods are reasonably reliable for optimal amounts of DNA. However, the existing SNP calling methods can be improved and adapted to handle results from suboptimal DNA amounts.1. The Omni5-4 is based on the Illumina BeadChip technology that has two types of probes, type I and type II. The nucleotides are detected by two colour channels: red (detects A/T nucleotides) and green (detects C/G nucleotides). Probe type I has two bead addresses such that one nucleotide is interrogated by the beads with one address and the other nucleotide by the beads with the other address. The light emitted by the fluorophores attached to the probes are measured by the same colour channel on both bead addresses by the design of the probe sequences, i.e., either red or green. The other colour channel is not used for this probe type but can potentially be used for estimating, e.g., the background noise. The probe type II has only one bead address such that the signal from one nucleotide is measured by the red colour channel and the other nucleotide is measured by the green colour channel. The output from the array is the mean signal intensity from each colour channel together with the standard deviation and number of beads investigated.We introduce a new method for SNP calling, exemplified with data from the Illumina Infinium Omni5-4 Kit3. Simplified, the A/B system tells whether the SNP position has homozygous reference alleles, homozygous alternative alleles, or heterozygous alleles. The A/B alleles must be converted to the standard DNA bases A, T, G, and C using a manifest file4 created by Illumina.The Illumina method calls one or two alleles that—to some confusion—are called A/B alleles5 package called snpbeadchip6 for selecting the correct colour channels and converting the A/B alleles to plus/minus alleles and an accompanying R package called omni54manifest7 that provides easy access to the information about the probes such as the manifest4 and mapping information8. snpbeadchip uses illuminaio9 to read idat files.Bookkeeping of the selection of the right colour channel for the right probe and conversion of the A/B allele system to the standard DNA bases is a tedious task with many different rules that must be applied. We have developed open-source software that can help do these tasks. The software is published as an ROnce the signal intensities of the reference and alternative alleles are obtained, the SNP can be called. We propose a method referred to as the “butterfly method” cf. Fig. .10.We tested the butterfly method against high-quality PCR-free (shotgun) whole-genome sequencing (WGS) data, which are considered the gold-standard for “concordance”. Furthermore, we compared the butterfly method with SNP calls from the Illumina GenomeStudio software11 is not publicly available12. It uses the data of the other samples to analyse the sample in question, which can be problematic because the SNP calling of a sample should for this kind of analysis, in principle, not be influenced by other samples. One such problematic situation may arise if the samples analysed together are of varying quality, e.g., the combination of samples with high quality and partly degraded DNA, as may be the case in a forensic setting. Also, the GenTrain does not provide a posteriori genotype probabilities for each SNP of a sample. Hence, there is no obvious way to adjust the no-call algorithm. The GenTrain Score is given for each SNP and is a combined score for all samples.The aims of this paper and the purpose of the method are to provide an open description of a SNP calling method with transparent handling of no-calls that others can use, replicate, and improve. The description of the method used by GenomeStudio called GenTrain 3.05 version 4.1.2 and tidyverse13.All statistical analyses were made using RThe study was approved by the Committees on Health Research Ethics in the Capital Region of Denmark (H-2-2012-017). The biobank where the samples are held is registered at the University of Copenhagen’s joint records of processing of personal data in research projects and biobanks (514-0725/22-3000) and complies with the rules of the General Data Protection Regulation (Regulation (EU) 2016/679). According to The Danish National Committee on Health Research Ethics, informed consent is not necessary for the samples used in this study (H-2-2012-017). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments and comparable ethical standards.Peripheral blood samples from three individuals were collected and stored at − 20 °C until DNA extraction. DNA extraction was carried out using the DNeasy Blood & Tissue Kit (Qiagen), following the manufacturer’s recommendations for purification of total DNA from whole blood.™ system (Illumina) following the manufacturer’s recommendations.All samples were analysed using the Illumina Infinium Omni5-4 Kit following the manufacturer’s recommendations with varying DNA amounts. The DNA concentration was measured using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Two-fold serial dilutions of DNA from the three samples were performed using nuclease-free water to obtain samples with the following DNA amounts: 400 ng, 200 ng, 100 ng, 50 ng, and 25 ng. Briefly, the DNA was hybridised to the probes attached to the BeadChips. Hereafter, the attached probes were subject to single-base extension and stained. The BeadChips were scanned using the iScan14. AdapterRemoval version 2.1.315 identified and removed adapter sequences from the reads using the collapsed option. Consecutive stretches of low-quality bases . GATK version 4.0.0.0 with HaplotypeCaller17 with standard settings was used for variant calling. The reads were approximately normally distributed with a mean coverage of 37, and we only used biallelic SNPs with at least 25 reads.Samples were sequenced with the NextSeq500 platform  using paired-end sequencing  the three-component mixture model: 1) each sample was its own reference using all SNPs simultaneously, 2) like 1, except using separate models for the two probe types (I/II), and 3) an ensemble model using all samples to estimate a single model.10 and the Genotyping Project Wizard to import sample data into Genome Studio based on sample intensities (not based on existing cluster files) and the “Cluster All SNPs” function under the “Analysis” tab in GenomeStudio to generate SNP clusters.We used the recommended settings in GenomeStudioa posteriori threshold, except for situations with no-calls (NC). We chose always to make a NC if the mean signal intensities for both alleles A and B were 0.For the butterfly method, we called the SNP genotype with the maximal a posteriori probability was below a certain threshold, we made a NC. This was done for a range of thresholds (from 0.5 to 0.999). Secondly, we chose to consider the number of beads with which the SNPs had been investigated, and on which the mean signal intensities were based. If the number of beads was below five, we made a NC. We also used a threshold of zero beads.We investigated two ways of making a NC. Firstly, if the maximal For probe type II, the same beads capture both alleles, so there is only one number of beads for each investigated position. For probe type I, different beads capture each allele, so there is a certain number of beads for allele A and another one for allele B. For probe type I, both numbers of beads must be above the threshold.Imposing such NC thresholds results in calling fewer alleles but with higher confidence in the alleles called.19 is a within-sample method that uses a four-component mixture of t-distributions , where the fourth cluster is a “null class” for capturing outliers. The calls are made by identifying the cluster with the maximal a posteriori probability. Hence, the no-calls are selected when the null class has the highest a posteriori probability. The classification of the outliers in a single “null class” is problematic as the outliers do not behave in the same way. Outliers are not expected to be distributed according to a t-distribution and grouped in the same cluster in the The GenoSNP method introduced in20 is similar to that in19, except that a four-component mixture of normal distributions is used and the focus is on calling rare variants. In20, the a posteriori probability is mentioned, but only in connection with calculating the average a posteriori probability for each SNP.The M3 method introduced in11; b) demonstrating how the a posteriori probabilities and the numbers of beads can be used to categorise NC  and analyse how they impact the concordance with WGS calls; c) showing how a non-sample specific, a general model, and a sample and probe type specific model perform. Our method is available as the R software packages snpbeadchip6, omni54manifest7, and the existing R software package mclust18 for estimating the mixture model with the function mclust and calculating a posteriori probabilities with the function predict.To summarise, our paper contributes the following novel work: a) analysis of data obtained with the Illumina Infinium Omni5-4 Kit by comparing SNP calls made by GenomeStudio and GenTrain 3.0We chose not to include all the above methods because the main focus of this paper was to explore the possibility of adjusting the NC rate and offering open-source software for this purpose.7 has 4,327,108 SNPs. We removed 271,680 SNPs ) took about 3 minutes to run per sample  on a AMD EPYC 7351 16-Core Processor. The x object contained the The mclustThe densities of the numbers of beads for both probe types for the three individuals are given in Fig. The mean signal intensities with an illustration of the models are shown in Fig. We only used biallic SNPs and alleles that were called with a read depth of at least 25 with WGS. This resulted in 3,139,554 SNPs called  for Sample 1; 3,875,844 SNPs called  for Sample 2; and 3,972,024 SNPs called  for Sample 3. We considered these SNP calls reliable.Focusing on only SNPs reliably called with WGS, we calculated the concordance rates as described below.a posteriori probability threshold for NC of 0.8.The SNP calling is illustrated in Fig. Of the SNPs called by WGS, the number of SNPs called by the other methods are given in Fig. For the SNPs with WGS-based SNP calls  and SNP calls with other methods , the concordances between the WGS call and the methods were calculated Fig. . The figa posteriori probability threshold and the number of beads threshold, the calls will be more reliable  for 400 ng DNA is shown in Fig. Based on Figs. An overview of the discordant calls  for 400 ng DNA is shown in Fig. Figure a posteriori probability and number of beads were used . The adjustment is easily done using the R packages mclust18 and snpbeadchip6.We began with 4,055,428 SNPs Table . Extrapoa posteriori probability threshold can be seen in Fig. a posteriori threshold should generally be increased for smaller DNA amounts.The importance of the DNA amount and the choice of the We did not inspect the SNP calls from GenomeStudio and the butterfly method individually, except for excluding some SNP data at the preliminary filtering stage Table . HoweverImproving the SNP calling is a topic of future research. There are many ways to improve the SNP calling method proposed here. Using the WGS calls as a reference, a natural next step of modelling is discriminant analysis based on Gaussianity in a supervised learning setting. Including more explanatory variables would also enable more advanced statistical learning methods. In this study, we did not include signal variance information, which may improve SNP calling. Another option is to use probe information like base composition, colour channel, neighbour bases, etc., as explanatory variables/features. This may enable statistical learning methods like multinomial logistic regression, random forests, and deep learning.1 without using Illumina’s GenomeStudio software10. The method is a within-sample method and does not use other samples or population frequencies to call the SNP alleles. The butterfly method is based on a three-component mixture of normal distributions, in which parameters are easily estimated using the open-source statistical software R. The method is transparent, it is straight-forward to change the parameters according to the user’s needs, and easy to analyse the data within R after SNP calling. We have published two open-source R packages, omni54manifest7 and snpbeadchip6, that make SNP calling easy by helping with bookkeeping and giving easy access to meta-information about the SNPs typed with the Illumina Infinium Omni5-4 Kit .We introduced the “butterfly method” for SNP allele calling with the Illumina Infinium Omni5-4 Kita posteriori probability of belonging to a SNP cluster and by using the number of beads to adjust the stringency of the no-call mechanism.We tested our method on > 4 mio. SNPs and compared the results with those obtained with the GenTrain method used by Illumina GenomeStudio as well as SNPs obtained by PCR-free (shotgun) WGS. We demonstrated two variants of our method: one where we take into account potential probe type bias by estimating a separate model for each probe type (type I and type II) and another model that uses a general model such that the model’s parameter estimates do not depend on the sample that is being analysed. We focused on varying the no-call rate and showed how it changed the concordance with that of WGS. This was done by using a threshold on the a posteriori probability threshold for no-calls, we obtained a higher call rate than GenomeStudio, and by increasing the a posteriori probability threshold, we achieved a higher concordance with the WGS data than GenomeStudio.With the butterfly method, we achieved a SNP call rate of around 99% and a SNP concordance with the WGS data of around 99%. By lowering the