However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at  Structural variations (SVs) represent genomic rearrangements such as deletions, insertions, inversions, duplications, and translocations whose sizes are larger than 50 bp . As the Efforts have been made to develop short-read-based SV calling approaches , 13. MosWith the rapid development of long-read sequencing technologies, such as Pacific Bioscience (PacBio)  and OxfoDe novo assembly-based approaches –29 aim ahttps://github.com/PacificBiosciences/pbsv), and SVIM [https://github.com/PacificBiosciences/pbmm2), were usually employed for read alignment.Read alignment-based approaches directly align reads against the reference and detect SVs by analyzing the alignment results. Such approaches are more cost-effective to computational resources without lack of sensitivity and have been widely used in long read-based SV calling. Several read-alignment-based SV callers have been proposed, such as PB-Honey , SMRT-SVand SVIM . They usand SVIM . Moreoveand SVIM , NGMLR [and SVIM , Minimapand SVIM , and PBMHowever, read alignment-based SV calling is still non-trivial. Under the circumstance of high sequencing errors and complicated SVs, the alignment of the reads around SV breakpoints are chimeric and heterogeneous, usually less sensitive and accurate. Therefore, the SV signatures implied by read alignments are highly complicated, and it is difficult to collect and analyze them to implement sensitive detection for various kinds of SVs. Mainly, state-of-the-art tools have the following technical issues to be addressed: (1) overall, the sensitivity is still not satisfying ; (2) some approaches  cuteSV has better SV detection yields than those of state-of-the-art SV callers. Especially, it has higher sensitivity for low coverage datasets without lack of accuracy. (2) cuteSV supports the datasets produced by mainstream long-read sequencing platforms with various error rates and can discover various types of SVs . (3) cuteSV has faster or comparable speed to state-of-the-art approaches with lower RAM usage. More importantly, it has outstanding scalability, i.e., enables to achieve almost linear speedup with the number of CPU threads. With these features, cuteSV is suited to large-scale data analysis tasks and has potentials to the cutting-edge genomics studies.Step 1: cuteSV uses multiple signature extraction methods to comprehensively collect the signatures of various types of SVs. Furthermore, the insertions and deletions are heuristically combined to recover the evidence of real SVs from fragile alignments.Step 2: cuteSV uses a specifically designed clustering-and-refinement approach to cluster the chimerically aligned reads in local regions and further refines the clusters to precisely distinguish the SV signatures from heterozygous SVs.Step 3: cuteSV uses several tailored rules to implement SV calling and genotyping based on the refined clusters of SV signatures.Using sorted BAM file(s) as input, cuteSV extracts large insertions/deletions and split alignments in aligned reads as SV signatures and clusters and analyzes them to call SVs. The approach has three major steps as follows.A schematic illustration is in Fig. The major contribution of cuteSV approach is that it uses tailored heuristics to address three difficult technical issues in the alignment-based SV calling.Firstly, due to the scoring systems of read aligners, some large SV events could be divided into several smaller insertions/deletions in a local region. Such cases are usually treated as multiple smaller SVs mistakenly. However, in step 1 of cuteSV, a heuristic method is used to combine insertions/deletions in nearby genomic regions to unbroken signatures of larger SVs. This method not only reduces the errors caused by the fragile read alignments, but also enables to produce more homogenous SV signatures from various reads, which is beneficial to the processing of later steps.Secondly, reads spanning the same SV usually have heterogeneous breakpoints in their alignments, which also cause false-positive SV calls. In step 2 of cuteSV, the specifically designed clustering-and-refinement approach enables to adaptively cluster alignment breakpoints mapped to relatively large local regions but potentially belonging to identical SVs, so that heterogeneous breakpoints can be merged more effectively and more false positives can be prevented.Thirdly, there are occasionally complex heterozygous SVs which multiple SV alleles are in same loci. Such SVs are difficult to detect and genotype. In step 2 of cuteSV, they are handled by a novel heuristic refinement on the SV signature clusters. That is, cuteSV investigates each of the clusters to check if there are heterogeneous signatures. If so, it implements a precise analysis to distinguish the reads potentially from various SV alleles and re-cluster them into more homogeneous sub-clusters. Taking the advantage of this approach, the complex SVs can be detected and genotyped correctly.In addition, cuteSV uses a block division-based approach to process input data in a parallel way with multiple CPU threads. This implementation greatly helps it to achieve outstanding scaling performance, which has been demonstrated on several datasets in various sizes (see below for details).We used simulated datasets in various sequencing coverages to assess the “baseline” sensitivity and accuracy of cuteSV. More precisely, we collected 20,202 various types of known non-overlapping SVs, i.e., 6167, 9899, and 44 deletions, insertions, and inversions from CHM1 sample callsets   was used to assess the precision, recall, and F1 score of the callsets produced by various callers. Their yields are shown in Fig. We further benchmarked cuteSV, Sniffles, PBSV, and SVIM with several real sequencing datasets. The SV callers were implemented on a 69× HG002 PacBio CLR dataset   more comprehensively, we further employed GIAB Ashkenazi Trio PacBio CLR datasets  to assess the recall rates and Mendelian-Discordance-Rates (MDRs). cuteSV and SVIM obtained > 95% mean recall rate, i.e., more than 95% homozygous parental SVs has been confirmed in the offspring . The results  of the callsets with various aligners are shown in Additional file The false positives (FPs) of the callsets with PBMM2 and NGMLR are shown in Additional file We further employed PBMM2 and NGMLR to separately align the reads of the 69× HG002 PacBio CLR dataset to produce various callsets. Mainly, two issues were observed.We assessed the yields of cuteSV with various configurations of two critical parameters, i.e., --min_support and --min_size , on various coverages  of HG002 CLR datasets.The --min_support parameter indicates the minimal number of supporting reads to make an SV call. As the results displayed in Additional file The --min_size parameter indicates the minimal size of SV signature considered in clustering. Keeping the --min_support parameter fixed, we investigated the results of cuteSV with two different settings for --min_size   was emplThe precisions, recalls, and F1 scores of the benchmarked SV callers are shown in Additional file Long-read sequencing technologies are promising to discover the SVs of sequenced sample comprehensively. However, it is still non-trivial to exert the advantages of long reads due to the high sequencing errors and the complexity of SVs. Herein, we propose cuteSV, a novel read alignment-based SVs calling approach, to show how to achieve higher yields, speed, and scaling performance in SV calling with tailored signature extraction, clustering, and refinement methods.The heuristic SV signature extraction methods enable to well-handle the fragile read alignments around SV breakpoints, which helps to build high-quality SV evidence. This is one of the major reasons that cuteSV outperforms state-of-the-art tools. An example is in Additional file The clustering-and-refinement method enables to distinguish the reads from the multiple SV alleles in the same loci and generate correct signature clusters. Thus, complex heterozygous SVs can be detected and genotyped sensitively and accurately, which also outperforms state-of-the-art tools. Two examples are shown in Additional file With its novel heuristic methods, cuteSV improves the sensitivity of SV calling especially for lower coverage datasets. The benchmark results suggest that cuteSV can discover most of the SVs in 20× coverage datasets for human samples without loss of accuracy; meanwhile, the performance of genotyping is consistent with its yields as well. This is helpful to make more flexible and cost-effective sequencing plans in large-scale genomics studies.With its block division-based implementation, cuteSV has outstanding scaling performance, which enables to achieve a nearly linear multiple-thread speedup during data processing. This is very suited to modern HPC resources and helpful for large-scale genomics studies.Mainly, the proposed approach has four advantages as follows.There are still some SVs that cannot be successfully detected by cuteSV. We investigated the intermediate results of cuteSV and found that most of the false-negative calls were due to the read alignments being inaccurate or not informative enough. Two typical examples are shown in Additional file The current version of cuteSV is tailored for human genomes or diploid genomes. For multi-ploidy genomes such as many plant genomes, cuteSV can also extract the signatures to detect SVs; however, genotyping is still implemented with the diploid model. Such tasks are still non-trivial to cuteSV , since there could be more heterozygous SVs and less SV supporting reads , and the genotypes of SVs are also more complicated as well. With the current implementation, cuteSV could miss some SV alleles in multi-ploidy genomes due to low numbers of supporting reads; meanwhile, it may make mistakes in genotyping. This is an important work for us to develop more sensitive SV signature extraction methods as well as specifically designed genotyping model to the SV detection of multi-ploidy genomes.Although it has higher scaling performance, the speed of cuteSV in single thread is still slower than some of state-of-the-art approaches. This is mainly due to that the SV signature extraction of cuteSV is more complicated. An optimization on the data structures and operations of cuteSV could be feasible to further reduce the time cost.However, from the view of alignment-based SV calling, there are still a few shortcomings for cuteSV as follows, which are also important future works to further improve the approach.Moreover, from a higher-level view, de novo assembly-based approaches have their own advantages to cuteSV as well as other alignment-based SV callers. Firstly, although the mappability of long reads is much higher than that of short reads, their alignments are still heterogeneous and have potential errors due to many factors, such as sequencing errors, SV complexity, and repetitive sequences. However, de novo assembly-based approaches are free of such alignment artifacts so that they could have less systematic errors. Secondly, de novo assembly-based approaches are able to well-handle large novel insertions by assembling them as distinctive contigs; however, this type of SV is still non-trivial to alignment-based approaches. Thirdly, haplotype-resolved assembly inherently helps to unravel the haplotype configuration of SVs, which is useful to many downstream analyses.However, de novo assembly-based approaches also have some non-neglectable bottlenecks. Firstly, the assembly could be not complete, i.e., some of the haplotype sequences  are missing, which may affect the sensitivity of SV calling. Secondly, assembly mistakes still exist, which may cause false positives. Thirdly, de novo assembly is still computational-intensive, which is not cost-effective for large-scale data analysis tasks. Fourthly, it usually needs high-coverage sequencing or the combination of multiple types of data  to implement haplotype-resolved assembly ; howeverIn this article, we propose a novel long-read-based SV detection approach, cuteSV. It enables the thorough analysis of the complex signatures of SVs implied by read alignments. Benchmark results demonstrate that cuteSV achieves good yields and performance simultaneously. Especially, it has good sensitivity to detect SVs, even with low coverage sequencing data, and it also has outstanding scaling performance which is suited to handle many large datasets. We believe that cuteSV has the potentials to cutting-edge genomics studies.Some details on the implementations of cuteSV approach and the benchmark are as follows. Please also refer to Additional file cuteSV uses sorted BAM files as input, and it is supported by employing state-of-the-art long-read aligners to compose SV detection pipelines. Aligners with a good ability to handle large insertions/deletions in reads and/or produce accurate split alignments are preferred, since cuteSV extracts important SV signatures from such alignments. In the “s, SVL, ReadID), where Refs indicates the starting coordinate on the reference genome, SVL indicates the size of SV, and ReadID indicates the unique read ID). cuteSV clusters and analyzes Sigs of various reads to call SVs.Given a set of aligned reads, cuteSV separately analyzes the detailed alignment of each read. Mainly, it extracts two categories of SV signatures, i.e., long insertions/deletions in CIGARs and split alignments. The signatures of SVs are represented as 3-tuples . This method enables to recover the SV signatures of long insertions/deletions which are initially partitioned as multiple trivial indels by read aligners. Further, all the remaining signatures after merging are used as informative signatures.cuteSV uses a heuristic method to extract and merge the long insertions/deletions in CIGARs during SV signature extraction. In details, cuteSV extracts insertions/deletions > 30 bp in size as described by the CIGARs of the reads and composes them into Sigs with their positions, lengths, and read IDs. For two signatures, s, Reade, Refs, Refe, Chr, Strand) , where Reads, Reade, Refs, Refe respectively indicate the starting and end coordinates on the read and reference genome, and Chr and Strand respectively indicate its chromosome and orientation. cuteSV uses the following heuristic rules to recover SV signatures from Segs.1 and Seg2, are adjacent on the read and aligned to the same chromosome with identical orientations, cuteSV computes Diffdis = (Ref2s − Ref1e) − (Read2s − Read1e) and Diffolp = Ref1e − Ref2s. If Diffolp < 30 bp and Diffdis ≥ 30 bp, cuteSV considers that the two segments indicate a deletion event and composes a deletion signature:Extraction of deletion/insertion signatures. If two segments, SegFor a read having split alignments , cuteSV records each split by a 6-tuple Seg = olp ≥ 30 bp) and in identical orientations, cuteSV composes a duplication signature:Extraction of duplication signatures. If two adjacent segments are mapped to similar positions Extraction of inversion signatures. If two adjacent segments are mapped to the same chromosome but to different strands, cuteSV composes an inversion signature:hh and INVtt indicate head-to-head inversion signal and tail-to-tail inversion signal, respectively.(4)Extraction of translocation signatures. If two adjacent segments are mapped to different chromosomes, and the two segments are < 100 bp distant on the reads, cuteSV composes a translocation signature:(5)cuteSV uses a specifically designed method to extract the signatures of a complex kind of SV, when there is a mobile insertion in between two duplicated sequences. An example is shown in Additional file cuteSV uses a two-step approach to cluster SV signatures into bins, where each bin has a set of SIGs belonging to a specific SV allele. In the first step, it clusters the signatures by their genomic positions and types in order to bin the signatures in various local regions. In the second step, it refines the clusters of signatures by their length in order to distinguish the signatures of the various alleles of complex heterozygous SVs .i, cuteSV adds it into the cluster if there is at least one signature SIGj in the cluster which meets the following condition:type is a threshold of the distances among the clustered signatures, and different values are used for various types of SVs . If Sigi cannot be added into the cluster, cuteSV creates a new cluster only having Sigi and goes to the next SV signature.In the first step, cuteSV sorts all the SV signatures by their genomic coordinates and types . For each category, cuteSV initially creates a new cluster and scans all the signatures from upstream to downstream and adds them into the cluster using an iterative approach. More precisely, for a newly scanned SV signature SIGL, with the following equation:Refinement of deletion/insertion clusters. Given a deletion or insertion cluster, cuteSV sorts all its signatures by their sizes and computes a parameter, BiasIn the second step, cuteSV discards the clusters with too few signatures, and the remaining clusters are refined by various methods according to their SV type.α is a weighting parameter, |GroupSig| is the number of the signatures in the cluster, and Lenk is the length of the k-th longest signature in GroupSig. The configuration of α depends on SV types, i.e., the default value of α is 0.2 for an insertion cluster and high error rate reads (PacBio CLR and ONT reads), α = 0.65 for low error rate reads , and α = 0.3 for a deletion cluster (regardless of error rate).L, cuteSV divides the cluster into sub-clusters of which each is a potential SV allele in a local genomic region. It initially adds the signatures with the largest size into a new sub-cluster and then iteratively scans the signatures by size . A newly scanned signature is added into the sub-cluster if the difference between its size and that of the last signature added to the sub-cluster is smaller than BiasL. Otherwise, a new sub-cluster is created.Using BiasSR and SRmin are the number of its supporting reads and the threshold of the minimum number of supporting reads, respectively. μ is a weighting parameter. For an insertion cluster, its default value is 0.6 (for PacBio CLR or ONT reads) or 0.65 (for PacBio CCS reads). For a deletion cluster, the default values are 0.7 and 0.35 for the data types mentioned above, respectively. If a major allele sub-cluster exists, cuteSV recognizes the remaining sub-clusters having more than SRmin signatures as “minor allele” sub-clusters.cuteSV recognizes the generated sub-clusters with the highest number of signatures as a “major allele” sub-cluster if it meets the following conditions:first and SRsecond as major allele and minor allele sub-clusters if they meet the following conditions:There is occasionally no major allele sub-cluster due to lack of enough supporting reads. cuteSV uses another heuristic rule in which it recognizes the two largest sub-clusters SR(2)μ = 1/3.Refinement of duplication/inversion clusters. Given a duplication or inversion cluster, cuteSV initially creates one or more sub-clusters such that the breakpoints of all the signatures in the same sub-cluster are within 500 bp. cuteSV recognizes the major allele and minor allele sub-clusters with a heuristic rule similar to Eq.(3)μ = 0.6 and SRmin as half of the value used for deletion/insertion clusters. This setting helps to fully consider the diverse combinations of chromosomes and orientations of translocation events to achieve higher sensitivity.Refinement of translocation clusters. Given a translocation cluster, cuteSV initially creates one or more sub-clusters such that the breakpoints of all the signatures in the same sub-cluster are within 50 bp. Furthermore, cuteSV recognizes the major allele and minor allele sub-clusters with a heuristic rule similar to Eq.This rule indicates that almost all of the SV signatures support the two alleles; meanwhile, both of them occupy > 40% of the supporting signatures in the cluster.For each cluster of signatures, cuteSV computes the weighted average of the positions and sizes to predict the breakpoint(s) and size of the corresponding SV and removes the predicted SVs of < 30 bp in size.ε is the probability that a read is being mapped to a given zygosity erroneously , assuming it is constant and independent between all observations, SRRef and SRALT are the numbers of supporting reads for reference and SV allele, respectively. After the calculation of the original likelihoods, cuteSV normalizes these values by computing the log sum of exponentials. The final genotype is determined by the zygosity with the maximum genotype likelihood. After genotyping, cuteSV produces the phred-scaled genotype likelihoods, conditional genotype quality, and phred-scaled quality score of SV in order to further quality control and higher accuracy callsets’ generation.cuteSV employs a bi-allelic assumption to perform SV genotyping. If an SV site has more than one alternative alleles, each alternative allele is treated respectively. All possible bi-allelic genotypes are kept. It uses a local genomic region for a predicted SV to analyze the likelihood of various zygosity of SVs by their supporting reads, which is defined as:The SVs were transformed into BED format according to the requirement of VISOR HACk. Deletions, insertions, or inversions were directly spiked into the reference to generate haplotype in silico donor genomes correspondingly. Employed translocations and duplications were preprocessed as reciprocal translocations and tandem duplications with CN = 2 before input to VISOR, respectively. It is also worth noting that translocations in same strand orientations  have 2 kinds of breakpoint combinations and 4 kinds of breakend combinations, and translocations in different strand orientations  have 4 kinds of breakpoint combinations and 4 kinds of breakend combinations. A schematic illustration is in Additional file For a given donor genome, its in silico chromosomes were randomly selected as “homozygous” and “heterozygous” chromosomes to mimic homozygous and heterozygous SVs. For a homozygous chromosome, all the reads were simulated from the in silico chromosome with SVs. For a heterozygous chromosome, 50% of the reads were simulated from the in silico chromosome and 50% from the corresponding reference chromosome. With this rule, four PacBio-like datasets in various coverages  were produced by VISOR LASeR (using PBSIM and minimap2 with default setting). Thus, there are 20 datasets produced for the five donor genomes and the specific sets of SVs and their genotypes were used as ground truth.In total, 6167, 9899, and 44 deletions, insertions, and inversions were extracted from CHM1 sample callsets (nstd137 in dbVAR database). The extracted SVs are non-overlapping and meet the condition that experiment IDs are 1 or 2 and all of ACs, ANs, and AFs are higher than 0. Similarly, 3712 and 380 non-overlapping duplications and translocations were extracted from KWS1 sample callsets (nstd106 in dbVAR database), respectively. Then, the five types of SVs were respectively integrated into human reference genome (hs37d5) to build five in silico donor genomes to generate simulated datasets with VISOR simulator. For a specific type of SVs, the simulation is done by the following two steps.The used command lines for data simulation are in the Additional file PBMM2 (version 1.0.0), NGMLR (version 0.2.3), and Minimap2 (version 2.17) were employed to implement the read alignment of the benchmarking datasets. The parameter “--preset” of long-read aligners was tuned for various types of sequencing data. Samtools (version 1.9) was employed for read extraction, sorted BAM generation, and sequencing data down-sampling.For Sniffles (version 1.0.11), the configuration “-l 30 -s 1/2/3/4 --genotype” was used for simulated datasets, “-l 30 -s 2/3/4/4/5/10 --genotype” was used for PacBio CLR datasets, “-l 30 -s 1/2/3 --genotype” for was used for PacBio CCS reads, and “-l 30 -s 2/3/4/10 --genotype” was used for ONT PromethION reads.For PBSV (version 2.2.0), default settings were used for simulated, PacBio CLR and ONT PromethION data, and “--preset CCS” was used for PacBio CCS data.For SVIM (version 0.4.3), the configuration “--min_sv_size 30” was employed for all datasets in this study.For cuteSV (version 1.0.6), the configuration “-l 30 -s 1/2/3/4 --genotype” was used for simulations, “-l 30 --genotype” was used for PacBio CLR (“-s 2/3/4/4/5/10”) and ONT PromethION reads (“-s 2/3/4/10”), and “-l 30 -s 1/2/3 --genotype --max_cluster_bias_INS 200 --diff_ratio_merging_INS 0.65 --diff_ratio_filtering_INS 0.65 --diff_ratio_filtering_DEL0.35” was used for PacBio CCS reads.The used command lines for the tools are in the Additional file s, compe, compL, and compt indicate start coordinate, stop coordinate, size, and SV class of a prediction, and bases, basee, baseL, and baset are starting coordinate, end coordinate, size, and SV class of a SV recorded in the ground truth, respectively. In terms of translocations, a prediction is determined as a TP call at breakpoint level if it meets the following conditions:cnt and basecnt are the connectivity of breakends on the prediction and base, respectively. A prediction is determined as a false positive (FP) if it does not satisfy Eqs.The SV calls from simulated data were assessed based on the ground truths in the following approach. For deletions, insertions, duplications, and inversions, a prediction is determined as a true-positive (TP) when meeting the following conditions:Similarly, recall  is defined as:F1 score is defined asMoreover, when considering the zygosity, if the prediction satisfies Eqs.https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/NIST_SVs_Integration_v0.6/HG002_SVs_Tier1_v0.6.vcf.gz) and high confidence regions  published by GAIB consortium were used as ground truth. Before evaluation, we preprocessed the SV calls of each tool. For Sniffles, we discarded inversions and translocations, and transform duplications to insertions. For SVIM, we transformed duplications to insertions as well, deleted SV calls with a quality score of less than 5, and kept the supporting reads consistent with Sniffles and cuteSV in each corresponding dataset. For PBSV and cuteSV, we only selected insertions and deletions for evaluation. Then, BGZIP and TABIX were employed to compress and index the processed VCF files. In addition, only SV calls between 50 bp and 10 kbp being within the GIAB high confidence regions were considered for evaluation.The evaluation of the HG002 human sample was done by Truvari (version 1.2), and the high confidence insertion and deletion callsets  calls in cuteSV and SVIM. We used Eq.For the evaluation of the Ashkenazi trio, we used the homozygous SV calls in parents  to measure the recall rate via Eq.We also used all parental SV calls to assess MDR, i.e., the percentage of SV calls of the son (HG002) that cannot be detected in its parents:To evaluate the computational performance of the SV callers with various numbers of CPU threads w/o genotyping, runtime and memory footprint were assessed by using the “/usr/bin/time -v” command of the Linux Operating System. In the output results of the command, “Elapsed  time” and “Maximum resident set size” indicate the elapsed runtime and memory consumption, respectively. It is worth noting that we used the sum of the wall clock time of both steps as the final elapsed runtime, because SV calling performed by PBSV involves two steps . Meanwhile, the memory footprint depends on the maximum memory usage of the two runs.Refer to Additional file Additional file 1: Figs. S1-S11. Supplemental Figures. Tables S1-S20. Supplemental Tables. Supplementary Notes. Command lines for evaluation.Additional file 2. Review history.


Foot warts (FW), a leading cause of foot problems in dairy cattle, is a welfare concern and causes financial losses due to treatment and reduced milk production. Foot warts, or the technically correct term of digital dermatitis, result from a bacterial infection followed by delayed healing due to both genetic and environmental factors. Dairy farmers are already combatting FW through environmental control, but they do not have genetic selection tools because the genetics influencing FW susceptibility are largely unknown. We sought to identify the genetics associated with FW which can be incorporated into genetic selection tools. Farmers can then use these genetic selection tools to breed cows that are less susceptible to FW. We identified promising genes that play a role in the immune response and wound healing—immune functions that, if impaired, could increase a cow’s susceptibility to FW. Though these genes were promising, their associated genetic markers had very little influence on FW susceptibility when compared to environmental management. Thus, the findings imply that the best approach for reducing FW prevalence is likely through combining a genetics approach with environmental management.n = 129 vs. FW n = 85). Linear mixed model (LMM) and random forest (RF) approaches identified the top SNPs, which were used as predictors in Bayesian regression models to assess the SNP predictive value. The LMM and RF analyses identified QTL regions containing candidate genes on Bos taurus autosome (BTA) 2 for the binary and recurrent phenotypes and BTA7 and 20 for the quantitative phenotype that related to epidermal integrity, immune function, and wound healing. Although larger sample sizes are necessary to reaffirm these small effect loci amidst a strong environmental effect, the sample cohort used in this study was sufficient for estimating SNP effects with a high predictive value.Digital dermatitis (DD) causes lameness in dairy cattle. To detect the quantitative trait loci (QTL) associated with DD, genome-wide association studies (GWAS) were performed using high-density single nucleotide polymorphism (SNP) genotypes and binary case/control, quantitative (average number of FW per hoof trimming record) and recurrent (cases with ≥2 DD episodes vs. controls) phenotypes from cows across four dairies (controls  Lameness, or abnormal gait, affects 16% of dairy cows in the United States, making lameness the second most prevalent disease in dairy cattle after mastitis ,3. FurthTreponema are consistently found in DD lesions [Heritability estimates for DD range from 0.01 to 0.4 ,7,8, ind lesions . Accordi lesions ,11, thou lesions . Additio lesions  and carc lesions , respect lesions . Bos taurus autosomes (BTAs) 1, 3, 5, 6, 8, 9, 10, 14, and 26 [Currently, no selection index exists specifically for DD susceptibility. Targeted genetic selection against DD and associated lameness requires the identification of genomic regions influencing DD susceptibility. To find the contributing quantitative trait loci (QTL), genome-wide association studies (GWAS) have been undertaken, although the results to date have been discordant. Previous studies found significant and suggestive single nucleotide polymorphisms (SNPs) on , and 26 ,17,18,19, and 26 . In an effort to improve upon and refine past studies, the present study used strict phenotyping, dairies with similar management practices, and a high-density SNP genotyping array to identify the associations between DD and genomic regions. We hypothesized that certain genetic markers would be associated with DD susceptibility, and that those markers would have small effects. Our results revealed multiple small-effect SNPs were associated with DD and defined QTL that contained candidate genes related to immune function and wound healing, supporting our hypothesis.Four commercial dairies in the Central Valley of California, two of which had participated in our previous heritability study , provideHoof trimming records were used to generate binary and quantitative phenotypes. Claw lesions and lameness issues were diagnosed by a single hoof trimmer servicing three of the dairies , and a different hoof trimmer servicing the fourth dairy (dairy D). The hoof trimmer servicing three dairies was trained by Dr. Steven Berry, a veterinarian specializing in claw lesions who offered hoof trimming training workshops to the industry and was a coauthor of our earlier paper , and theBoth hoof trimmers utilized similar criteria for defining instances of DD in the cattle to reduce the phenotypic classification variability; specifically, DD was recorded for cows with raised, inflamed lesions on the skin above the heel of the foot or above the interdigital space on the front of the foot. Routine hoof trimming varied by dairy: cows were trimmed at the beginning and middle of lactation, during dry off, and when exhibiting altered gait (dairy A); during dry off and when exhibiting altered gait (dairy B and C); and only when exhibiting altered gait (dairy D). Cases were defined as cows who had exhibited at least one DD lesion, whereas controls had no DD or other lameness records and were 6.5 years of age or older to avoid misphenotyping younger cows who had insufficient time to develop lameness events. Cows may have multiple bouts of DD over their lifetime and, in some cases, the first instance of DD may have occurred before the cows were in milk . Because DD lesions typically last for four to six months ,22, we dGenomic DNA was extracted from whole blood samples using the QIAGEN QIAamp DNA Blood Mini Kit  and quantified using the NanoDrop (ND-2000 v3.2.1) spectrophotometer . DNA samples were genotyped on the BovineHD BeadChip  by GeneSeek . Raw and processed microarray data were submitted to the NCBI Gene Expression Omnibus database (GEO series record GSE159157). Illumina’s GenCall algorithm was used to call genotypes.https://www.animalgenome.org/repository/cattle/UMC_bovine_coordinates/), version last modified 11 September 2018). The quality filtering of SNP genotypes was performed using PLINK 1.9 [p < 1 × 10−6) in controls were removed to exclude systematic genotyping errors.GWAS were performed using the binary, quantitative, and recurrent phenotypes using the SNP coordinates from the ARS-UCD1.2 map  after linkage disequilibrium (LD) pruning was determined using the Genetic Type I error calculator (GEC) and used as the denominator for Bonferroni-corrected thresholds [p < 0.05/Me, whereas suggestive SNPs were defined as having p < 1/Me [GC), chi-squared test statistics were first generated from association p-values, and the median of the resulting chi-squared distribution was divided by the median of the expected chi-squared distribution. Quantile-quantile plots (qqplots) and Manhattan plots were plotted in R [Family structure is extremely prevalent in the dairy population from breeding elite bulls to hundreds to tens of thousands of cows. To visualize the genetic similarity among cows at this initial dairy, a multi-dimensional scaling (MDS) analysis was performed and the first two dimensions were plotted. The GWA analyses were performed using the genetic analysis program Genome-wide Complex Trait Analysis (GCTA)  to fit aresholds . Signifip < 1/Me . To calcted in R  using thted in R .Random forest (RF) analysis was performed as an additional method for identifying SNPs that appeared to importantly contribute to disease phenotypes. Random forests do not make any assumptions about the inheritance model  and are able to test multiple SNPs jointly for association with phenotype. Additionally, the RF approach is unaffected by an uneven farm distribution of cases and controls because RF builds decision trees and estimates the importance of each feature by the frequency it appears in the decision trees, rather than estimating parameters for a model. Consequently, RFs avoid estimating parameters for which there are no data. These properties make RFs well equipped to identify structure within complex genetic architectures like DD susceptibility. Specifically, RF can accommodate data despite uneven sampling across farms, in which contributing SNPs may have different modes of inheritance and where epistasis is likely prevalent. 2 ≥ 0.90 to avoid diluting the importance of SNPs in strong LD during the RF analysis [mtry, the number of predictors considered at each node of the tree. The value of mtry that yielded the most accurate model was used as the final model. The most important explainer was assigned an importance variable of 100, and the other explainers were assigned importance variables relative to the most important explainer . To assess the accuracy of the final model, the remaining third of cows was used as the test population, using the explainers and their relative importance to predict phenotype.After converting quality-filtered binary PLINK files into VCF files split by chromosome in PLINK 1.9 ,32, all analysis  and recoanalysis ,35. For After evaluating the model accuracy using the test population, a threshold of importance was determined by ranking and plotting the SNPs the RF identified as important for each chromosome in a scree plot and finding the rank of the second-order point of inflection using the d2uik option in the inflection package in R ,37. SNPsTo assess the collective predictive ability of the top SNPs identified in the LMM and RF analyses, the top SNPs from each analysis  were tested for association with phenotype using Bayesian regression. Bayesian regression was selected because of its ability to fit multiple SNPs simultaneously while also recognizing that the majority of SNPs have small effects on DD susceptibility ,38, thatSuggestive and significant SNPs from the LMM GWAS and important SNPs from the RF analysis were used as predictors along with farm in each Bayesian regression model. Similar to the RF analyses, SNP genotypes were coded as 0/1/2 minor alleles. A Bayesian regression model was fitted for each combination of GWAS method (LMM and RF) and phenotype (binary and quantitative), such that four models were fitted: LMM-binary and RF-binary were fitted using a Bayesian logistic regression model, and LMM-quantitative and RF-quantitative were fitted using a Bayesian generalized linear model for continuous data. Susceptibility to DD appears to be complex and the majority of SNP effects are likely to be small ,38. To rk diagnostic values outputted from the LOO analysis. Posterior predictive checking (PPC) from the bayesplot package [Unlike frequentist regression, which would output a point estimate of each SNP effect, Bayesian regression outputted a distribution of where the true value of each SNP effects fell, defined by the Bayesian uncertainty interval (UI). SNPs with 95% UIs that did not include zero were considered significantly associated with DD susceptibility. For each significant SNP, the probability of disease given a genotype at the significant SNP  and a 0 genotype at all other SNPs was calculated using the median of SNP effect estimates as point estimates in the inverse logit equation using the R package arm . Diagnos package  was used2 ≥ 0.5) were considered as belonging to the same QTL. The SNPs furthest upstream and downstream that were in LD with the target suggestive or significant SNP defined the boundaries of the QTL. Overlapping QTL were combined into one QTL. QTL from the LMM and RF analyses were compared to discern whether the two analyses found the same QTL. QTL regions that were identified in both LMM and RF analyses were explored for candidate genes. Additionally, QTL defined by SNPs that were significant in the Bayesian regression analyses were also explored for candidate genes. Candidate genes were defined as genes falling in QTL regions identified in both LMM and RF analyses or in QTL defined by SNPs that were significant in Bayesian regression and were functionally relevant to DD etiology.For the significant and suggestive SNPs identified in the LMM analyses and the important SNPs identified in the RF analyses, the QTL boundaries and regions were defined and annotated. Because SNPs are more likely to be in LD with causal variants than be causal themselves, the linkage disequilibrium in the regions flanking these top SNPs was used to define the boundaries of QTL, per the methods used in previous GWAS studies ,45. SpecB. taurus were used as the background population. To identify the functions of individual genes, protein coding genes in QTL defined by SNPs that were significant in two analyses  were searched in the Mouse Genome Informatics batch query database (http://www.informatics.jax.org/batch) using the mammalian phenotype option [To annotate the QTL regions, the genomic regions search in FAANGMine v1.1  using the option .Hoof trimming records for 1382 DD-affected cows at dairies A, B, and D from 2002 to 2019 were used to calculate the age of onset statistics. Dairy C did not have hoof trimming records from the beginning of the cows’ lives and was thus excluded from calculating the age of onset statistics. The average age of onset for the first episode of DD observed was 3.7 (SD = 1.6) years old and the median was 3.5 years old, indicating a minimum age of 6.5 years old for controls was sufficiently stringent to avoid misphenotyping younger cows. The cases and controls were sampled from 2013 to 2020. Cases were sampled from all four dairies, whereas only dairies A and D had control cows that met our stringent age and soundness requirements . In tota2 > 0.90). The MDS analysis indicated no obvious population stratification (−7 or 6.5 on the −log10(p) scale and a suggestive cutoff at 6.3 × 10−6 or 5.2 on the −log10(p) scale. Manhattan plots for the LMM binary and quantitative analyses are shown in After quality control filtering, 560,277 SNPs remained for the LMM analysis, and 222,060 SNPs (40%) for the RF analyses remained after LD pruning (rfication . The effSLC9A9 and two suggestive intergenic SNPs on BTA2 (SLC9A9. The BTA2:63365256 (BTA-47853-no-rs) SNP on BTA 2 identified a 2.4 Mb QTL region at BTA2:60971364–63389576 containing 25 genes, whereas the other SNP identified on BTA 2, BTA2:65836042 (BovineHD0200019142), was not in LD with neighboring SNPs (r2 < 0.5). Because the number of genes discovered from the LMM QTL was limited, no gene ontologies or pathways were overrepresented.The binary LMM GWAS detected 22 suggestive SNPs on BTA1 that fell in the last three introns of  on BTA2 . When usWhen suggestive SNPs from the LMM-binary GWAS were used as predictors in the Bayesian regression models, MCMC sampling was able to efficiently explore the posterior. Though the effects of SNPs on BTA1 were not significantly different from zero at 50% UI, the effects of the two SNPs on BTA2 (BTA-47853-no-rs and BovineHD0200019142) were significantly different from zero at 95% UI , Table 2Using the LMM-binary suggestive SNPs as predictors in the LOO analysis, Pareto k diagnostic values were acceptable (k ≤ 0.7) for all cows, indicating that the estimated SNP effects were collectively predictive of phenotype within the original population. The LOO analysis indicated that the effective number of predictors in the model was 6.6, considerably lower than the 27 predictors that were actually in the model due to correlated predictors: the SNPs on BTA1 were in LD, and this correlation among predictors reduced the effective number of predictors. The PPC indicated that the observed and simulated data were similar to each other , supportmtry that were tested , mtry = 666 yielded the most accurate model and was selected for further analyses. The accuracy of the selected model (0.69 with 95% CI 0.57–0.80) was not significantly different from the baseline no information rate , indicating that the model was unable to call case and control phenotypes more accurately than simply calling the more common phenotype. Random forest analyses found 26 important SNPs from the RF-binary, and using LD to determine the QTL boundaries defined 23 QTL for the RF-binary dataset . Random forest analysis revealed that farm was ranked as the most important explainer, and consequently the importance of SNPs was expressed as the percentage of importance relative to farm. Of the three values of  dataset , one of When important SNPs from the RF-binary analyses were used as predictors in the Bayesian logistic regression model, four SNPs had estimated effects that were significantly different from zero, including the SNP defining the QTL at BTA1:125550933–125822143 . The imp2 > 0.5) and defined a 2 Mb QTL region at BTA2:77930065–79925981 . Furthermore, the SNPs that defined the QTL regions containing promising candidate genes were significant, suggestive, or important in the LMM and RF analyses, some of which also had nonzero effect sizes estimated from Bayesian regression despite the small sample size. For the quantitative phenotypes, a larger sample size might have more normally distributed phenotypes that the model expects, thereby improving the efficiency of MCMC sampling and more accurate SNP effect estimates. Although the sample size of this study was limited due to our intentionally reducing phenotypic variation, which may have prevented the detection of additional small SNP effects, the sample size was sufficient to very accurately predict the phenotype within the original population. Future replication studies are necessary to determine how well the SNP effects estimated in this study population can be extrapolated to larger populations in different geographical regions and other dairies.In addition to minimizing phenotyping variation, our GWAS used high-density SNP genotyping to increase the resolution of QTL detection. Previous studies ,20,38,54The lack of congruence in the genomic regions associated with DD across published studies and the small effect sizes of those QTLs identified further supports that, in addition to many low-impact loci, non-genetic factors strongly influence DD susceptibility. The ranking of farm as the most important predictor in the RF analyses supports the concept that farm management  plays a significant role in reducing DD prevalence. Employing genetic selection in combination with environmental management will likely further reduce DD prevalence.CXCR4, MGAT5, CACNA1A, TERT, SLC9A3, and AHRR. Bayesian estimation of SNP effects was used to additionally distinguish between informative and noninformative SNPs and indicated that the top SNPs from LMM-binary and RF-quantitative were collectively predictive of binary and quantitative phenotypes. Despite our identifying significant QTL, the absence of the congruency of associated SNPs in this study compared to other studies and the consistent ranking of the farm as the most important predictor in the RF analyses support the notion that DD susceptibility is heavily influenced by management, and the remaining genetic component is heterogeneous and highly complex. Thus, although farm management may be the most effective short-term method for reducing DD prevalence, combining genetic selection with management will likely be the most effective and sustainable long-term solution.GWAS using LMM and RF approaches identified loci containing six genes on BTA1, 7, and 20 that regulate skin integrity, immune function, and wound repair:


For the production of healthier fruit snacks, vacuum frying is a promising alternative for atmospheric frying, to reduce the oil content, while maintaining a high nutritional quality. This paper evaluates the effect of ripening stages, frying temperature, and time on the quality of vacuum-fried mango. Unripe mango was dehydrated faster than ripe mango and had a higher hardness after frying at 110 and 120°C. Fat content in fried ripe mango was higher. Total ascorbic acid and β-carotene in both ripening stages were not different, but after frying total ascorbic acid in unripe mango remains higher. A novel image analysis was applied to quantify the color distribution of fried mango. Color changes in unripe mango were more susceptible to temperature and time. Considering all quality parameters, vacuum frying of unripe mango at the optimal condition of 100°C for 20 min is preferred for producing high-quality healthier fruit snacks. Consumers have a strong desire for fried food products because of their unique flavor–texture combination. However, the increased awareness of consumers toward the relationship between food, nutrition, and health stimulates the food industry to use alternative processing methods complying the demand for healthier snacks. In this paper, we study an alternative frying process to meet these demands by reducing the oil uptake and maintaining a high nutritional value.Technically deep-frying is heating and dehydrating foods with associated oil uptake by immersing them in an edible fat at 165–190°C  and ripe  mango (Mangifera indica L. cv. Kent) from Brazil were supplied by Bakker Barendrecht B.V. (The Netherlands) and stored at 11°C and used within 5 days after arrival. Just prior to the frying experiment, the mangoes were selected based on ripeness indicators, including total soluble solids (TSS), to indicate the sugar content and firmness  and was expressed in °Brix. After selection, mangoes were peeled, and the seed was removed and halved. The halved fruits were cut into 4-mm-thick slices with a mandolin  to ensure the fast heat penetration to the center of the chips but not collapse during the processing.Firmness was measured using fruit penetrometer FT327, equipped with an 8-mm tip . The firmness measurements were done on each peeled mango cheek with three repetitions and were expressed in kg/mMango slices were vacuum fried in 2-kg batches using a pilot scale industrial vacuum fryer  containing 250 L fresh high oleic sunflower oil. A high oil-to-fruit ratio was needed to diminish temperature drop after the fruit was submerged into the oil. The vacuum fryer was equipped by an automatic basket rotator, two heat exchangers to cool and heat the oil, and an atmospheric spinner to remove surface oil. Some pilot experiments have been carried out to determine the optimal conditions for thickness of mango slices and vacuum frying pressure. Based on these results, 4-mm mango slices were fried at 10 kPa at times and temperatures as listed in Two samples of mango slices (1 kg of each of the two ripening stages) were loaded into the vacuum chamber. After 60 s, the desired vacuum pressure was reached, and the basket was submersed into the oil to initiate the frying time. During frying, the basket was rotated back and forth at 17 rpm for 60 s to ensure that the heat and oil were evenly distributed. To stabilize the temperature, a heat exchanger to heat and cool the oil was used. Once the frying was finished, the basket was lifted from the oil and shaken for 20 s inside the vacuum chamber to remove excess of oil. The vacuum chamber was then pressurized, and the vacuum-fried mango was centrifuged to remove surface oil for 60 s at 100 g . Then, mango chips were packed in sealed plastic bags and stored at −20°C until further use. All frying experiments were performed in duplicate.Moisture content of the samples was determined in triplicate per frying experiment with a forced convection oven at 100°C until constant weight and described in % fresh weight. Fat content of the fried mango chips was determined in duplicate per frying experiment with the Soxhlet method using 200 ml petroleum ether 40–60°C after drying overnight and then described in % dry basis (db) .Texture of the mango chips was measured with a texture analyzer  using a three-point bending test according to Da Silva and Moreira  with somR2 = 0.997. HPLC analysis was done using thermo separation products Spectra Series HPLC with a binary gradient pump and UV detector at 245 nm and Polaris 5 C18 A 150 × 4.6 mm 5 μm column. The twenty-μl sample was injected using orthophosphoric acid 0.2% in Milli Q water as mobile phase, with a 5.5-min run time at a 1.0-ml/min flow.Since ascorbic acid (AA) easily oxidized into L-dehydroascorbic acid (DHA), vitamin C was calculated as total ascorbic acid (TAA) which sums of AA and DHA. AA was reduced into DHA using TCEP (tris-2-carboxyethyl phosphine) and then calculated together into TAA. The extraction and HPLC analysis was conducted according to the methods of Hernández, Lobo , and WecR2 = 0.999. HPLC analysis was done using HPLC with a Dionex Ultimate 3000 RS equipped with Phenomenex Onyx monolithic C18 column . Twenty μl of sample was injected with 60% acetonitrile (ACN), 30% MeOH, 10% ethyl acetate (ETAC), and 0.1% trimethylamine (TEA) mobile phase, with a run time of 10 min at flow rate of 1.0 ml/min. Compounds were detected at a wavelength of 445 nm.The extraction and HPLC analysis of β-carotene was conducted according the methods of Salur-Can, Türkyilmaz , with moThe color distribution of mango chips was described by a new approach developed specially for this study . TherefoImages of mango chips were taken using a color digital camera (Canon 1000D with Canon EFS 18-55 mm F3.5-5.6 IS lens) mounted on Kaiser RT1 base 25 cm from the product and were placed inside a closed picture chamber. The light used was produced by a 4 × 36 watt 5,400 k 40 Hz fluorescent light mounted at 22° from the sample axis. Color calibration was done using X-Rite ColorChecker Passport and Adobe Lightroom.L*a*b* values using the method developed by . The L* value describes the lightness with 0 for darkest black and 100 for brightest white and was divided into 3 levels, 0 ≤ dark ≤ 60, 60 < medium-light ≤ 80, and 80 < light ≤ 100. The a* value describes the transition from green to red, ranging from −128 for the purest green and +128 for the purest red, and were divided into 3 levels, green ≤ 0, 0 < medium-red ≤ 10, and red >10. The number of pixel in each lightness and redness level is then divided by total pixel of the mango chips and multiplied by 100% to produce % of pixels of each lightness and redness level.An uncompressed picture file (CR2) with image size 3,888 × 2,592 pixel was produced and later converted to another uncompressed file (Exif-tiff 8 bit) at the same resolution by image processing software  prior to further analysis. Image background was removed using a quick selection tool from an image processing software (Adobe Photoshop CC 2015). The tiff images were then analyzed using Color Inspector 3D v 2.3(21) within Fiji , an Imagloped by . Only thT-Test for TSS and firmness to test the difference between ripe and unripe mango. On the other hand, ANOVA with Tukey post hoc analysis was performed for moisture, fat, texture, TAA, β-carotene, and color. Numbers and graphs were made based on mean and standard error. Standard error of the mean (SEM) was used to describes the uncertainty of how the sample mean represents the population mean, which is possible at a large number of measurements  and 110°C for 25 min (p = 0.0034), but at 120°C, there was no significant difference for all frying times. The difference could be caused by the higher pectin content in unripe mango; BeMiller  values also increased. However, when the temperature was increased to 138°C, these maximum force and work values decreased because of the brittleness of the chips. A phenomenon was also observed in our data for unripe mango fried at 90 and 100°C and ripe mango fried at 110°C.The hardening process accelerates at higher temperatures. Unripe mango chips fried for 15 min at 90°C had a hardness value of 3.9 ± 0.6 N; this value was at the beginning of the slow phase which increased at a longer frying time. On the other hand, unripe mango chips fried at 120°C for 15 min had a hardness value of 10.6 ± 0.9 N; this value was at the end of the slow phase. Nunes and Moreira  did a sip = 0.000) is observed after frying at 110°C for 10 min, with a hardness of 6.8 ± 1.0 N for unripe mango and 0.3 ± 0.1 N for ripe mango. When the temperature increased to 120°C, the difference (p = 0.000) was observed at a shorter frying time (7.5 min): 7.1 ± 0.9 N for unripe mango, and 1.2 ± 0.4 N for ripe mango. Again, the difference in matrix could play a role here. The crust formed by unripe fried mango is harder because the pectin polymer is still there, and when the water evaporates, it is able to form a stronger network and becomes hard  is an important nutritional parameter for fried food products. The raw material used in this study is characterized by a high total ascorbic acid content; raw unripe mango had a higher TAA content of 156.2 ± 6.4 mg /100 g compared to ripe mango (126.6 ± 3.0 mg/100 g). The TAA decrease could be a result of ascorbate peroxidase (APX) activity which uses ascorbates as electron donor to remove hydroxyl radical from the cell that produced during fruit ripening .As expected, thermal degradation of total ascorbic acid (TAA) increased with increasing frying temperature; a similar pattern was observed in both ripening stages . AA lossHowever, the TAA values remain higher in the unripe mango for all temperatures, although only significantly at 100°C. Frying for 35 min at 100°C retained 65.8 and 59.8% of TAA in unripe and ripe mango. Even at this severe heating, 100 g unripe and ripe vacuum-fried mango is able to provide 114.3 and 84.1% of the recommended daily allowance of vitamin C for adults (90 mg), respectively. This difference shows the importance of the fruit matrix as the container of the ascorbic acid. Davey et al.  mentioneRaw ripe mango had a higher β-carotene content compared to unripe mango, even though not significant . This result was expected since carotenoid content increases during mango ripening .There was no significant difference in β-carotene content for each temperature/time combination between the two ripening stages. While in both ripening stages the initial low β-carotene value increases after frying at 90 and 100°C, there is a clear decline in β-carotene content for both ripening stages when temperature is increased .The possible explanations for the increased ß-carotene concentrations after frying at 90 and 100°C, which were also found in vacuum-fried apricot at 70–90°C , could bHowever, after vacuum frying at 110 and 120°C, the ß-carotene concentrations decreased, which shows the thermal sensitivity of ß-carotene in vacuum frying. Da Silva and Moreira  and green to red (a*)  , 4.The area in levels of lightness decreased gradually upon increasing frying time and temperature. However, a faster trend in lightness reduction was observed for the unripe mango chips, which was most distinct at 110°C and 120°C. The reduction in levels of the light area clearly led to an increase in levels of medium-light and dark areas, also differentiating between the two maturities. Similar results were found by Maity and Bawa , which s* value by 168% and by 303% when extended to 20 min of frying. Furthermore, when fried at 100°C, the a* value was increased to 367% after 20 min, whereby a faster and stronger increase in redness areas was observed for unripe compared with ripe mango chips. At all temperatures, the green area decreased in unripe mango, while a fluctuation was seen in ripe mango chips. This trend was most pronounced at the highest temperatures. Additionally, as water evaporated, the boiling point of the moisture in the fruit will be increased and so will the fruit temperature , but at higher temperatures (110–120°C), unripe mango had a higher hardness value compared to ripe mango. Vacuum-fried ripe mango had a higher fat content compared to unripe mango. No differences between the ripening stages were found on the degradation of ascorbic acid and β-carotene during frying. Unripe mango is more susceptible to temperature and time toward lightness and redness changes compared to ripe mango. Considering all quality parameters, unripe mango is preferred over ripe mango for vacuum-frying processing. Furthermore, vacuum frying at 100°C for 20 min was sufficient to decrease the moisture content and produce high-hardness chips without adsorbing too much oil, maintain the color without losing too much ascorbic acid, and preserve the β-carotene content.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.FA did the study conception and design, acquisition of data, analysis and interpretation of data, and drafting of the manuscript. EG and JV did the study, acquisition of data, analysis and interpretation of data, and drafting of the manuscript. MD did the analysis and interpretation of data, study conception and design, drafting of the manuscript, and critical revision. VF did the drafting of manuscript and critical revision. RV did the study conception and design, drafting of the manuscript, and critical revision. All authors contributed to manuscript revision and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~ 6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R2 = 0.09).A previously developed 50 K gene-transcribed SNP chip, containing ~ 21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs. Aquaculture is a growing agribusiness that enhances food security and increases economic opportunities worldwide . A key cSelective breeding improves heritable traits, taking advantage of existing genetic variation between individuals/families. Previous studies showed that selective breeding programs can improve animals’ bodyweights, thereby contributing to increased aquaculture production , 7. SeleQTL mapping has been extensively applied in plants and farmed animals to determine the genetic architecture of the complex traits. Several QTL mapping studies were performed to assess the genetic basis of growth in Atlantic salmon, Coho salmon, and rainbow trout . For insGenomic resources have been developed for rainbow trout, including the release of the first genome assembly draft  and a neIn this study, we used a 50 K transcribed gene SNP chip, recently developed in our laboratory, to perform GWA analyses . The chiGrowth performance defines fish production, and therefore, it affects aquaculture industry profitability. Progress in growth-related traits could lead to reductions in time and cost to market size . Traditin = 50) in windows explaining additive genetic variance for the studied trait (Table n = 100) were located within the 3’UTR of their genes suggesting a role of these SNPs in microRNA, post-transcriptional regulation of gene expression. All QTLs associated with bodyweight gain are listed in Table  of the additive genetic variance for bodyweight gain Table . The genit Table , Fig. 2.in Table . To gainin Table .Fig. 2MCoordinated hypertrophy and hyperplasia are essential for growing organisms . Five chA total of 21 SNPs were identified on chromosomes 4, 9, and 13. Chromosome 4 had 9 SNPs in 3 genes coding for transcription factor AP-1 (AP-1), protein PRRC2C (PRRC2C), and myocilin (MYOC). Transcription factor AP-1 transduces growth signals to the nucleus, mediated by upregulation of positive cell cycle regulators , which eChromosome 14 had 11 SNPs in seven genes coding for prominin-1-A (PROM1A), fibroblast growth factor-binding protein 1 (FGFBP1), cyclin A2 (CCNA2), re-initiation and release factor (MCTS1), septin-6 (SEPT6), tenomodulin (TNMD), and 60S ribosomal protein L36a (RPL36A). PROM1A has a role in cell proliferation and differentiation . FGFBP1 Fatty acid synthesis is essential to meet the demand for phospholipids required for membrane expansion in growing cells . We haveR2) value of 0.50 with WBW [n = 31) involved in lipid metabolic processes, including AMPK, were DE in fish families (YC 2010), showing contrasting WBW [In total, 14 SNPs were identified on chromosomes 8, 13, 14, and 18. Chromosome 8 had three SNPs in 2 genes encoding acetyl-coenzyme A synthetase (ACSS2) and peroxisomal trans-2-enoyl-CoA reductase (PECR). ACSS2 activates acetate that can be used for lipid synthesis . In addiwith WBW . Many geting WBW . These rA total of 19 SNPs involved in proteolytic activities were identified in 12 genes Table . Out of n = 19) in this category. These SNPs were located in genes coding for methyltransferase-like protein 2-A (METTL2A), telethonin (TCAP), synaptonemal complex protein SC65 (SC65), peptidyl-prolyl cis-trans isomerase FKBP10 (FKBP10), 2′,3′-cyclic-nucleotide 3′-phosphodiesterase (CNP), and histone acetyltransferase KAT2A (KAT2A). METTL2A has GO terms belonging to methyltransferase activity [Forty-five SNPs were identified in 21 genes involved in development and chromatin remodeling Table . Chromosactivity . Four SNactivity . SC65 haactivity . Loss ofactivity . CNP hadactivity . KAT2A eactivity . KAT2A iactivity . Six SNPactivity . ELF1 haactivity . In addiactivity . Sixty-tactivity . In agreactivity , our resp < 0.001) yielding 29,451 filtered SNPs. In order to identify single SNP markers associated with bodyweight gain, filtered SNPs were subjected to a general linear regression analysis which allows accounting for multiple fixed effects but does not account for familial correlation. Next, residuals of the regression model were regressed on the genetic factors using QFAM, available in PLINK [p-value < 0.001) following 20,000 permutations. However, a two-stage analysis that calculates residual-outcome from the regression of the outcome on multiple covariates then uses the adjusted-outcome for downstream analysis, showed bias and loss of power in genetic association studies [Genotyped SNPs were filtered out at a minor allele frequency (MAF) < 0.05 and Hardy–Weinberg equilibrium (HWE) , elongation factor 2 (eEF2), and basic leucine zipper and W2 domain-containing protein 1-A Table . Each SNp-value based peaks, the highest peaks based on the variance explained depends on allele frequency which means a high-effect SNP of low frequency reduces the variance explained [Single SNP GWA analysis provided an additional set of SNPs, potentially regulating variation in bodyweight gain. In the current study, dividing the genome into chromosomal segments/windows, defined by 50 adjacent markers, outperformed the single-marker analysis in identifying a larger number of SNPs  describing the genetic architecture of the studied trait. On chromosome 13, there was a single common significant SNP detected by the two GWA approaches in a gene coding for synaptic vesicle membrane protein VAT-1 homolog (VAT-1). This protein interacts with Talin-1; the key driver of cell migration . Similarxplained . The Wssxplained . The twoxplained .Consistent with our data, a previous GWA study in rainbow trout identified small-effect QTL on chromosome 9 that affected additive genetic variance for bodyweight . HoweverThe current GWA study identified growth-related QTL and novel genes associated with the growth rate in rainbow trout. Compared to previous GWA studies in Atlantic salmon and rainbow trout, this work revealed relatively large-effect QTL associated with growth, which appears to be a polygenic trait in nature controlled by many genes on multiple chromosomes. Chromosomes 4 and 14 had the most significant peaks that explained a reasonable proportion of the additive genetic variance for bodyweight gain. The majority of SNP were within genes involved in developmental processes. Intriguingly, the gene harboring the most significant nonsynonymous SNP was previously reported to encode a protein vital to embryonic development. These findings provide a genetic basis that will enhance our understanding of the molecular mechanisms regulating growth in teleost fish as well as provide putative markers that could be prioritized when estimating genomic breeding values for growth rate.Fish population was previously described , 25. Brih2 (h2ped) for growth was estimated according to Zaitlen et al., [A total of 789 fish representing 98 families from YC 2010 and 99 families from YC 2012 were phenotyped. For fish sampling of each generation, a single fish from each family was randomly assigned to one of five collection groups (~ 100 fish each) over five consecutive weeks (one group/week). The YC 2010 fish were collected between 410- and 437-days post-hatch with a mean bodyweight of 985 g (SD = 239 g). Fish from the YC 2012 were collected between 446- and 481-days post-hatch with a mean bodyweight of 1803 g (SD = 305 g). The bodyweight gain was calculated as the fish body weight in grams divided by the fish age in days. The pedigree-based heritability  et al., .The 50 K transcribed gene SNP-chip used in this study was recently developed and used to identify QTL associated with muscle yield , fillet As described before, a total of 1728 fish from the NCCCWA growth- and Bacterial Cold Water Disease (BCWD)-selection lines  were useThe Weighted single-step GBLUP (WssGBLUP) has been used to perform GWA analysis, as we previously described , 25. Wssa, w, and e are the vectors of direct additive genetic , random family, and residual effects, respectively. The matrices X, Z1, and Z2 are incidence matrices for the effects contained in b, a, and w, respectively.where y is the vector of the phenotypes, b is the vector of fixed effects including fish data-collection group and hatch-year, H− 1):This model combines all the relationship information based on pedigree and genotypes into a single matrix , A−1 is the inverse of the relationship matrix based on pedigree information, G−1 is the inverse of the genomic relationship matrix.where A modified REMLF90 (AIREMLF90)  was usedSimilar to our previous WssGBLUP analyses , 25, twoP-values were adjusted by Bonferroni correction to account for multiple testing. The qqman package was used to generate a Manhattan plot showing −log10  obtained from the GWA analysis.Two different algorithms were used to perform family-based association analysis of the SNP genotypes with bodyweight gain, and detect signals robust for population stratification. First, QFAM in PLINK version 1.07  was usedSNPs bed file and the rainbow trout genome gff file were provided to Bedtools to annotate the SNPs as previously described , 114. ToAdditional file 1: Table S1. All QTLs associated with bodyweight gain. Table S2. Enriched terms included lysosomal proteins/enzymes and fatty acid biosynthesis (highlighted). Table S3. The remaining SNPs associated with the variation in bodyweight gain.