Taro has a long history of being consumed and remains orphan and on the hand Nigeria farmers. The role of farmer-driven artificial selection is not negligible to fit landraces to a particular ecological condition. Limited study has been conducted on genome-wide association and no study has been conducted on genome-environment association for clinal adaptation for taro. Therefore, the objective of this study was to detect loci that are associated with environmental variables and phenotype traits and forward input to breeders. The study used 92 geographical referred taro landraces collected from Southeast (SE) Nigeria.astringency, diaminopimelate decarboxylase and MYB transcription factor. Genome-wide association also identified 45, 40 and 34 significant SNP markers associated with studied traits in combined, year 1 and year 2 data sets, respectively. Out of these, five SNP markers  were consistent in two different data sets.The result indicates that SE Nigerian taro has untapped phenotype and genetic variability with low admixture. Redundancy analysis indicated that collinear explained SNP variation more than single climatic variable. Overall, the results indicated that no single method exclusively was able to capture population confounding effects better than the others for all six traits. Nevertheless, based on overall model performance, Blink seemed to provide slight advantage over other models and was selected for all subsequent assessment of genome-environment association (GEA) and genome-wide association study (GWAS) models. Genome scan and GEA identified local adapted loci and co-located genes. A total of nine SNP markers associated with environmental variables. Some of the SNP markers (such as S_101024366) co-located with genes which previously reported for climatic adaptation such as The findings from this study improve our understanding of the genetic control of adaptive and phenotypic traits in Nigerian taro. However, the study suggests further study on identification of local adaptive loci and GWAS through collection of more landraces throughout the country, and across different agro-ecologies.The online version contains supplementary material available at 10.1186/s12864-023-09134-6. Because of worldwide environmental change, species need to adjust to the evolving climate, and this is just conceivable assuming there is adequate versatile hereditary variety at the hereditary level , 2. GenoColocasia esculenta (L.) Schott] is one of the world’s most ancient food crops, with a history of more than 2000 years of cultivation in Nigeria [Taro [ Nigeria . It is b Nigeria . It arri Nigeria .Taro is morphologically diverse with over 10,000 landraces worldwide  and abouThe bulk of Nigerian taro is produced in the humid forest and derived savannah agro-ecological zones which encompass the southwest and southeast part of the country . Even thThe field experiment was carried out at Ebonyi State University (EBSU), Abakaliki, Nigeria in two cropping seasons (2018 and 2019). The experiment was laid out using alpha lattice design with three replications.A total of 92 diverse taro germplasm landraces were used in this study Table S. The genThe 92 taro landraces were grown at EBSU during 2018 and 2019 cropping seasons. Young taro leaf samples were collected for each landrace at 2 months old stage and dried using silica gel. The dried leaf samples were shipped to Biosciences Eastern and Central Africa (BecA-ILRI) Hub, Nairobi for genomic DNA extraction and Genotyping.PstI and MseI restriction enzymes [DNA extraction was done using Nucleomag Plant DNA extraction kit. Libraries were constructed using a combination of  enzymes  and use Raster package in R [Climate variables (average from 1960 to 1990) were extracted from WorldClim 1.4 using the age in R  based onage in R . Phenotyage in R  was usedlmer function in LME4 package of R [The best linear unbiased estimate (BLUEs) was used to estimate trait values of the 2 years (2018 and 2019) combined and individual year for each landrace. The BLUE model using age of R  was as fyi = μ + Landracei + Blockj + Seasonk + (Landrace x Block)ij + (Landrace x Season)ik + εijk. Where μ is the mean, Landracei is the genotype effect of the ith landrace, Blockj is the effect of the jth Block, Seasonk is the effect of the kth year, (Landrace x Block)ij is the Landrace-Block interaction effect, (Landrace x Season)ik is the landrace-season interaction effect, εijk is the error of the jth block in the kth year. Landrace was considered as fixed effect whereas all remaining items are considered as random effects for estimation of heritability and BLUEs. The coefficient of variation , heritability and genetic advance were estimated using variability R package [ package .2) was analysised using TASSEL 5 software [LEA  version 1.8.1 in R [Pairwise linkage disequilibrium (LD)  was carried-out using R function . A multifunction . The altpcadapt R package for detecting local adapted loci [p values distribution at 0.05. Finally, a list of outlier loci obtained that were candidates for selected loci.Genome scan was performed using ted loci . This wated loci  was usedGAPIT3 R package [Genome–environment association studies (GEA) were analysed using ten environmental variables. These are annual mean temperature, mean temperature of driest quarter, mean temperature of wettest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual mean precipitation, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter and precipitation of coldest quarter. The variable data were averaged from 30 years (1960 to 1990). GEA was performed using  package .GAPIT3 package [Genome-wide association studies (GWAS) were performed using BLUEs in traits values of 2 years both combined and individual. Population structure and genetic relationships among accessions were used to minimize false-positive associations. Population structure represented by the PC was estimated with the  package .F = alpha value (P = 0.05) and Meff = effective number of markers. Meff and α* were estimated using poolr R package [We set the suggestive significant threshold using a multiple testing correction developed by Li and Ji  plots of the observed versus expected –log10(p) values which should follow a uniform distribution under the null hypothesis and genomic inflation factor (λ). In order to compare how well the models adjusted for systematic effects, the genomic inflation factor (λ) for all methods was calculated in R software as follows:Where λ is the genomic inflation factor and M is median of the resulting chi-squared test statistics and E is the expected median of chi-squared distribution .P < 0.001) in all morphological traits studied among the landraces. Number of leaves per plant varied from 7.40 to 12.40; number of suckers per plant varied from 2.50 to 14.40; petiole length varied from 16.67 to 6.00 cm; corm diameter varied from 2.62 to 11.06 cm , decayed rapidly at r2 = 0.1 with kilobase pair  = ~ 3). We demonstrated here the effectiveness of different models for performing Genome-environment association (GEA) and genome-wide association (GWAS) in taro. Performance of GEA models was evaluated using Q-Q plots of the expected versus observed –log10(p) values and genomic control inflation factors (λGC) achieved for each ‘variable x model’ combination. None of the models was suitable for all environmental variables and agro-morphological traits based on genomic control inflation factors (Table SAccording to Li and Ji 2005), a total of 137 effective SNP markers (Meff) was identified out of 9442 SNP markers. Using Meff, the suggestive threshold 005, a topcadapt R package to detect the outliers SNP markers. For further identification of the outliers, GEA was performed  and (b) which passed the set threshold are included for reporting GWAS results. Pcadapt analysis detected 2355 outlier SNPs with the threshold values alpha < 0.1 using Benjamini & Hochberg Procedure [S_100830796 and S_100913593 markers. Specifically, S_101024366 marker was significantly associated with all environmental variables except precipitation of warmest quarter. S_100991964 SNP marker significantly associated with all precipitation variables except precipitation of warmest quarter. Indeed, no genome association was detected with precipitation of warmest quarter. S_100830796 SNP marker associated with all temperature variables such as annual mean temperature, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, and mean temperature of coldest quarter. S_100583021 SNP marker significantly associated with mean temperature of annual mean temperature and warmest quarter.A genome scan was performed using ed Fig. S. Only trrocedure . Out of p-value (P < 9.05E-04) threshold, significant SNP markers were identified to be associated with studied traits on chromosomes 1, 2, 3, 4, 5, 9 and 10 based on three data set (two separate data and combined one) , identified the homologues region in Diospyros lotus (date-plum) DNA for the astringency trait with the 2e-15 E-values and 85.86% of identity. This Taro_0284712 is in the range of LD window size (35 kb). One of the most essential aspects of fruit sensory quality is astringency [Signatures of selection and local adaptation can be evaluated in populations across entire genomes or genome sampling using population differentiation approaches  or in association with environmental variables to test the influence of biotic and abiotic factors in the spatial genomic structure. A total of nine SNP markers were associated with environmental variables. Specifically, ringency , 64. Thiringency . Traditiringency .Fig. 6GrS_100991964. It was associated with all precipitation variables such as annual precipitation, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter. NMUH01002301.1 (Colocasia esculenta cultivar Niue isolate Niue_2 TARO_scaffold_002301) accession number or Scaffold contained this S_100991964 SNP marker. Seven genes were linked within NMUH01002301.1 accession which was identified as hypothetical protein in taro  is homologous with diaminopimelate decarboxylase gene in different crops  with E-values 2e-37 to 4e-19. Interestingly, this diaminopimelate decarboxylase gene is highly expressed under induced drought stress in different crops [Another significantly associated marker is aro Fig. . Again, nt crops .Fig. 7GrMQL99126.1), co-located with S_100991964 SNP marker, was found homologues with cyclin dependent kinase (CDK) gene in different plants  with the E-values ranging 5e-08 to 4e-04. CDKs are core cell cycle regulators and play important role in different aspects of plant growth and development [CDKs in the plant stress responses [CDKF-4 s and CDKG-2 s primary regulators of drought responses in cotton.Another gene  in different plants . MYB family transcription factors play crucial roles in response to abiotic stresses [TaMYB31 is transcriptionally induced by drought stress in Arabidopsis thaliana [stresses , 73. Forthaliana .S2_100587991 SNP) was associated with a climatic variable (mean temperature of warmest quarter) and phenotypic trait (yield per hectare). S2_100587991 SNP is found in scaffold of NMUH01001840.1. This scaffold contains 17 genes identified as hypothetical proteins in the taro genome. Several genes are linked to the identified five significant SNP markers that are identified as hypothetic proteins in the taro genome with the 35 kb window size. The Blast result is presented in detail in Table SMapping traits in taro population provides another opportunity to validate and use the SNP markers for further breeding programs. GWAS identified a total of 45, 40 and 34 significant SNP markers associated with studied traits in combined, year 1 and year 2 data sets, respectively. Out of these, five markers were identified in two data sets out of the three, including S1_18891752 S3_100795476, S1_100584471 S1_100896936 and S2_100587991. Additionally, single SNP marker , BIO8 = Mean temperature of wettest quarter(°C) BIO9 = Mean temperature of driest quarter (°C), BIO10 = Mean temperature of warmest quarter (°C), BIO11 = Mean temperature of coldest quarter(°C), BIO12 = Annual precipitation(mm), BIO16 = Precipitation of wettest quarter(mm), BIO17(mm) = Precipitation of driest quarter (mm), BIO18 = Precipitation of warmest quarter (mm) and BIO19 = Precipitation of coldest quarter(mm).Additional file 3: Table S3. BLUE mean values of phenotypic traits: COD = corm diameter (cm), COL = corm length (cm), CRD = cormel diameter (cm), CRL = cornel length (cm), CRW = cormel weight (g), DM = days to maturity, NCR = Number of cormels per plant, PH = plant height (cm), NLPP = number of leaves per plant, NSPP = number of suckers per plant, PL = petiole length (cm), YPH (t/ha) = yield per hectare and YPP = yield per plants (kg/plant).Additional file 4: Fig. S1. Estimates of subpopulations analysis of 92 diverse taro landraces revealed 4 subpopulation using cross-entropy values with LEA R package programAdditional file 5: Table S4. Genomic Control Inflation Factor (λGC) analyses of GWAS models as a function of three different methods a for adjusting population structure on climatic variables and agro-morphological traits.Additional file 6: Fig. S2. Q-Q plot of climatic variables  with different models .Additional file 7: Fig. S3. Q-Q plots of phenotypic traits (COD = corm diameter (cm), COL = corm length (cm), CRD = cormel diameter (cm), CRL = cornel length (cm), CRW = cormel weight (g), DM = days to maturity, NCR = Number of cormels per plant, PH = plant height (cm), NLPP = number of leaves per plant, NSPP = number of suckers per plant, PL = petiole length (cm), YPH (t/ha) = yield per hectare and YPP = yield per plants (kg/plant) using different models .Additional file 8: Fig. S4. genome-environment association (GEA) across the Nigerian taro landrace collection using 9442 SNP markers (MFA ≥ 0.01). Manhattan plots showing significant false discovery rate (FDR) adjusted P-value of < 0.05 associated with climatic variables for climatic variables . The x-axis represents the chromosomes and the y-axis the –log10  for marker–environment association. Each point represents the SNP marker. The threshold is set based on the Genetic Type I error calculator (GEC) of the P-values.Additional file 9: Fig. S5. A. Genome-wide association study across the Nagerian taro landrace collection using 9442 SNP markers (MFA ≥ 0.01) and combined data set. Manhattan plots showing significant false discovery rate (FDR) adjusted P-value of < 0.05 associated with phenotypic traits. The x-axis represents the chromosomes and the y-axis the –log10  for marker–trait association. Each point represents the SNP marker. The threshold is set based on the Genetic Type I error calculator (GEC) of the P-values.Additional file 10: Table S5. List of co-located genes around five significant markers of the traits: COD = corm diameter (cm), COL = corm length (cm), CRD = cormel diameter (cm), CRL = cornel length (cm), CRW = cormel weight (g), DM = days to maturity, NCR = Number of cormels per plant, PH = plant height (cm), NLPP = number of leaves per plant, NSPP = number of suckers per plant, PL = petiole length (cm), YPH (t/ha) = yield per hectare and YPP = yield per plants (kg/plant).


Inversion of the MME in SNP-BLUP involves computations proportional to the cube of the MME size; that is, (n + m)3, which can present a considerable computational burden. We introduce a full Monte Carlo (MC) sampling-based method for approximating reliability in the SNP-BLUP model and compare its performance to the genomic BLUP (GBLUP) model. The performance of the full MC approach was evaluated using 2 data sets, including 19,757 and 222,619 genotyped animals selected from populations with 231,186 and 13.35 million pedigree animals, respectively. Genotypes were available in the data sets for 11,729 and 50,240 SNP markers. An advantage of the full MC approximation method was its low computational demand. A drawback was its tendency to overestimate reliability for animals with low reliability, especially when the weight of the RPG effect was high. The overestimation can be lessened by increasing the number of MC samples.Calculation of individual animal reliability of estimated genomic breeding value by SNP-BLUP requires inversion of the mixed model equations (MME). When the SNP-BLUP model includes a residual polygenic (RPG) effect, the size of the MME will be at least the number of genotyped animals ( GBLUP) or random regression-based SNP-BLUP models. These 2 models are equivalent and, thus, yield equal EBV and prediction error variances (PEV) at the animal level, regardless of whether or not a residual polygenic (RPG) effect is fitted . In GBLUP models, the inverse of the genomic relationship matrix (G) has to be computed as well. The G matrix is a dense matrix of the size of the number of genotyped animals (n) without a known simple structure  of the number of genotyped animals. This is not tenable for very large values of n. The SNP-BLUP model requires no G−1 matrix and the MME size is bounded by the number of markers (m). When n > m, the SNP-BLUP model yields fewer equations to solve than the GBLUP model. However, although low-cost SNP arrays with approximately 50,000 genome-wide SNPs are often used today for most livestock species, even a 700,000-SNP chip array can be used  to O[(n + m)3]. Reducing the size of the MME can help decrease some of the computational costs. MC) approach for the RPG effect in an SNP-BLUP model, in which the number of RPG effects was reduced from n to the number of MC samples. In this article, we describe a full MC-based SNP-BLUP approach that extends the approach in When the RPG effect is included in the SNP-BLUP model, the computational cost of inverting the MME in SNP-BLUP increases from O for EBV of animal j can be calculated as:wherePEVj is the prediction error variance andjth individual. Here, PEVj can be calculated astj is equal to the jth row in U. The animal genetic variancej in the MC approximate genomic relationship matrix UU′. Note that instead of using the diagonal elements of UU′ forjF is the inbreeding coefficient of animal j.where We applied the method to 2 data sets to demonstrate the efficiency of the proposed approximation. Our first data set (Data1) comprised genomic and pedigree information in Finnish Red dairy cattle. The pedigree included 231,168 animals. Genotypes were available for 19,757 animals. Because SNP-BLUP is computationally less demanding than GBLUP when the number of genotyped animals is greater than the number of SNP markers, the number of markers in Data1 was reduced to 11,729 SNPs by taking every fourth marker. The second data set (Data2) comprised genomic and pedigree information from a multibreed Irish beef cattle carcass conformation evaluation. Genotypes for 50,240 SNP markers were available for 222,619 animals. The pedigree included up to 13.35 million animals. A summary of Data2 is given in MSE) between the exact GBLUP reliability and the full-MC-SNP-BLUP approximated reliability. We fitted the linear regression from the true GBLUP reliability on that from the full-MC-SNP-BLUP to investigate inflation (or deflation) in the approximated reliability. Computational performance was based on wall clock time at selected computing steps.Performance examination of the full-MC-SNP-BLUP reliability approximation involved 2 steps. First, a standard GBLUP model was used to calculate exact GBLUP model reliabilities. Second, the full-MC-SNP-BLUP model was used to calculate approximated reliabilities under 10 different MC sample sizes . Reliabilities for the 2 approaches were computed using 3 RPG proportions . Performance statistics included correlation, maximum difference, and mean-squared error (−5) and the largest maximum difference (0.14).The largest relative decrease in MSE was observed when the number of MC samples was increased from 10,000 to 20,000 . IncreasWe compared the MSE estimates obtained in this study to those reported in −5 vs. 81 × 10−5) reached by full-MC-SNP-BLUP  was similar to that obtained by MC-SNP-BLUP  with an RPG weight of 20% for Data1 . The relative difference was even larger when the RPG weight was 80% . These results suggest that the full-MC-SNP-BLUP model approximation is less sensitive, in terms of MSE, to changes from low to high RPG weight than the MC-SNP-BLUP approach at the same MME sizes.The MSE (66 × 10or Data1 . When th0) and slope (b1) of the regression of correct reliability from GBLUP on approximation by full-MC-SNP-BLUP for Data1. An unbiased approximation method would be expected to give a null intercept and a slope equal to 1. Although the regression of GBLUP on full-MC-SNP-BLUP gave estimates of b0 close to 0 and b1 close to 1, all scenarios led to inflated reliability because the slopes were slightly greater than 1. The inflation decreased as the MC sample size increased or the RPG weight decreased, which was also observed by MC-SNP-BLUP using the same data .We compared the analysis of Data2 between the full-MC-SNP-BLUP and MC-SNP-BLUP results at about equal MME sizes, as was done for Data1. Because Data2 had 50,240 markers, about 50,000 more MC samples were allowed in the full-MC-SNP-BLUP than in the MC-SNP-BLUP model when comparing the models. The MSE values with full-MC-SNP-BLUP were smaller than those obtained by MC-SNP-BLUP in all scenarios when the RPG proportion was greater than 20%, which was observed for Data1 as well. For example, with Data2, the MSE associated with full-MC-SNP-BLUP  was smaller than observed with MC-SNP-BLUP  using an RPG weight of 80%  and 1.18 (intercept −0.15) for RPG weights of 50% and 80%, respectively, when analyzing Data2 using the full-MC-SNP-BLUP method. However, for an MC sample size of 10,000, the slopes (intercepts) corresponding to these RPG weights were 1.26 (−0.24) and 1.70 (−0.63), respectively, for Data2 using the MC-SNP-BLUP method. Nevertheless, the full-MC-SNP-BLUP approach tended to overestimate the reliabilities (data not shown). Furthermore, the lower the exact GBLUP reliability, the larger the overestimation.The full-MC-SNP-BLUP computations used MC sampling to approximate the denominatorapproach .2. Computing time to invert the MME is roughly proportional toMCn exceeded the number of genotyped animals . For Data2, the number of genotyped animals n was always higher than the number of MC samples MCn . These 2 matrix inversions accounted for 28% (71%) of the total computing time for Data1 (Data2). With full-MC-SNP-BLUP, the computing time for matrix inversion depended on the number of MC samples and took at most 36% (12%) for Data1 (Data2). Thus, it is likely that with larger numbers of genotyped animals, the GBLUP approach will become computationally unfeasible due to the time needed for matrix inversion; in full-MC-SNP-BLUP, this time can be kept acceptable by controlling the number of MC samples.The computing times for calculating model reliability in genomic relationship matrix-based GBLUP are given in The reliabilities of the SNP-BLUP models can be approximated satisfactorily by using a full MC-based sampling method. The correlation between the correct reliabilities calculated by GBLUP and those approximated by the full-MC-SNP-BLUP model approached unity in all studied scenarios. The computing time required to invert the MME coefficient matrix was roughly in the proportion


Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem.We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002–HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×.https://github.com/huangnengCSU/NanoSNP.git.Bioinformatics online. Single nucleotide polymorphisms, commonly referred to as SNPs, are human’s most common genetic variation. SNPs are widespread in a person’s DNA, with an average of one SNP occurring in every 1000 nucleotides. Currently, more than 100 million human SNPs have been identified worldwide . SNPs caNext-generation sequencing (NGS) has the advantages of low cost and high per-base accuracy. SNP identification on Illumina short-read sequencing data has been well practiced. GATK (Third-generation sequencing technologies (TGS), including PacBio Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), have made significant breakthroughs in a series of genomics studies  to Illumina and read lengths of 15–20 kb , it will cost approximately $1 375 000 to obtain the Nanopore dataset with 50× sequencing coverage. Therefore, it is very urgent to improve the accuracy of identifying SNPs on low-coverage sequencing reads. The difficulty of SNP identification for low-coverage Nanopore sequencing is that the error rate of Nanopore reads is high, and the coverage of reads is insufficient to determine whether the base difference between reads and reference is a sequencing error or an actual variation. For the lack of SNP information in low-coverage Nanopore reads, phasing can divide the sequencing reads into different haplotypes, which provides additional information. Phasing information can help to improve the accuracy of SNP detection. Likewise, family structure data can also provide additional information for SNP detection, which can help to increase the sensitivity of low-coverage SNP detection.This article presents a neural network-based SNP caller named NanoSNP for low-coverage Nanopore sequencing data. To overcome the lack of information on SNP detection with low-coverage Nanopore sequencing data, we successively apply two prediction models to identify SNPs from different perspectives. We divided the main process into the following three steps. We first identified a set of SNP sites with the pileup model based on the read pileups of aligned reads. Then the SNP sites are phased and used to divide the Nanopore sequencing reads into different haplotypes. Finally, the high-quality SNPs predicted in the pileup model and the reads of each haplotype are used to construct the haplotype features of candidate sites. We use the haplotype model to combine the long-range haplotype feature and short-range pileup feature from each haplotype and predict the genotype of each candidate SNP. We train NanoSNP on the dataset of HG001 as Clair and NanoCaller. On the six low-coverage (∼16×) Nanopore sequencing datasets of human genomes HG002-HG007, NanoSNP has the highest precision score and second highest recall and F1 score on each dataset compared to Clair, Clair3, Pepper-DeepVariant and NanoCaller. When evaluating the SNPs in the difficult-to-map regions, NanoSNP outperforms the compared Nanopore SNP callers on each genome.The workflow of NanoSNP is shown in Search for candidate SNP. Pileup format is a text-based format for summarizing the base calls of aligned reads to a reference sequence. We use Samtools to generate the pileup data from reads-to-reference alignment. The candidate site is determined by the depth and alternative allele frequency at a specific genomic position. Given the pileup data and the reference base rB at site b, the alternative allele frequency bf is calculated as follows:d and the alternative allele frequency is greater than e, genomic position b is considered a candidate SNP site. We set the default thresholds of depth and allele frequency to 6 and 12%, respectively. Smaller values of these two thresholds will increase the accuracy of SNP calling but will also increase the number of candidate sites and thus increase computational resources.If the depth is greater than Generation of pileup image in pileup model. For a given candidate site b and a window size w, we extract the read fragments aligned to the region m forward reads. IMAXp is the maximum length of inserted fragments at position p and DMAXp is the maximum length of deleted fragments at position p. The processing of the reverse reads is similar to that of the forward reads. We construct two feature matrices from forward reads and reverse reads, respectively. Each feature matrix is in size of bP of the candidate site b has the shape of w to 16.Pileup prediction model. Given the pileup image bP of the candidate site b, NanoSNP has a two-tasks recurrent neural network to predict the zygosity and genotype. The number of zygosities is 3, including homozygous reference (0/0), homozygous variant (1/1) and heterozygous variant (0/1). The number of genotypes is 21, including ‘AA, AC, AG, AT, CC, CG, CT, GG, GT, TT, AI, AD, CI, CD, GI, GD, TI, TD, II, DD, ID’. The architecture of the neural network is shown in L1 in genotype prediction and L2 in zygosity prediction. With the probabilities, we can calculate the quality scores of the genotype prediction and the zygosity prediction as strategy . The batHaplotyping. Phasing for long-read sequencing improves SNP calling, structural variant calling and genome assembly . The training dataset is a mixture of the three datasets including the 25× HG001 dataset, 50× HG001 dataset and 75× HG001 dataset. The raw Nanopore reads are basecalled by Guppy v4.2.2. We divide the dataset into two parts, chromosomes 1–19 are used for training, and chromosomes 20–22 are used for validation. The six testing datasets are made from Nanopore sequencing reads of human genomes HG002–HG007. The details of each human dataset are given in https://github.com/Illumina/hap.py) against the GIAB truth variants dataset (v3.3.2).The datasets of two human genomes HG002 and HG003 are used to evaluate the performance of several SNP callers on low-coverage Nanopore sequencing data with different coverage values. We randomly sampled four subsets from each genome of HG002 and HG003, respectively. The coverages of the subsets are 10×, 13×, 16×, 19×, 22× and 25×. The five Nanopore SNP callers are Clair, Clair3, Pepper-DeepVariant, NanoCaller and NanoSNP. Clair, Clair3, NanoCaller and NanoSNP were all trained on the genome HG001. The Nanopore sequencing reads of HG001 were basecalled by Guppy v4.2.2. Pepper-DeepVariant was trained on HG002 Nanopore sequencing data, which was basecalled by Guppy v4.2.2. During inference, we obtained the alignment bam files by aligning the sequencing reads of HG002 and HG003 to the reference GRCh38, respectively. Then, each SNP caller took the bam file as input and detected the SNPs with the default parameters for Nanopore data. The SNP calls of each method were evaluated with the haplotype comparison tool hay.py  are shown in For the HG002 genome, we evaluate the accuracy of different SNP callers in non-diploid regions. We select the regions containing heterozygous deletions and duplications as non-diploid regions from the public structure variation dataset for HG002. We then evaluate the SNP sites falling in the non-diploid regions. From The dataset of the genome HG003 is new to these five SNP callers, each tool has not been trained on this genome. The evaluation of five SNP callers on HG003 Nanopore datasets at different coverages is shown in On low-coverage Nanopore sequencing data of HG002 and HG003, NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller, and the performance of NanoSNP is close to that of Clair3. We then perform cross-reference validation on NanoSNP. NanoSNP is trained on HG001 and GRCh38, we test the accuracy of NanoSNP on HG002, HG003 sequencing data and GRCh37 reference. Both the sequencing data and reference genome are new to the model of NanoSNP. From In real-world scenarios, SNP callers usually need to identify the SNPs in different human genomes, which requires the method to perform stably on different genomes. We used six human genomes (HG002–HG007) to perform cross-genome testing of five SNP callers Clair, Clair3, NanoCaller, Pepper-DeepVariant and NanoSNP. We used Samtools to randomly subsample the Nanopore sequencing data of each genome at low coverage 16×. We aligned the Nanopore reads to the reference GRCh38 with Minimap2 and then fed the alignment bam file into each SNP caller. The truth variants datasets are from GIAB variants v3.3.2.The evaluation of different SNP callers on low-coverage Nanopore sequencing reads of six human genomes is shown in On the dataset of Chinese Trio , NanoSNP achieves the highest precisions  in five SNP callers. The F1 scores of NanoSNP are 96.4%, 97.0% and 96.9%. Clair3 obtains the highest recalls and F1 scores. The F1 scores of Clair3 are 96.8%, 97.4% and 97.2%. The F1 socres of other SNP callers are much lower than the F1 scores of NanoSNP and Clair3. The F1 scores of Clair are 85.7%, 88.0% and 87.9%. The F1 scores of Pepper-DeepVariant are 91.2%, 92.3% and 91.8%. The F1 scores of NanoCaller are 76.1%, 80.8% and 79.9%.We also evaluated the accuracy of different SNP detection tools on exonic regions. The performance of different SNP callers on exonic regions is shown in On the cross-genome testing of the low-coverage Nanopore sequencing data, Clair3 and NanoSNP outperform other SNP callers including Clair, Pepper-DeepVariant and NanoCaller on all six genomes. NanoSNP achieves the highest precision and second highest recall and F1 score in five SNP callers. Since NanoSNP is trained on HG001 genome, the Ashkenazim Trio and Chinese Trio genomes are new to NanoSNP. The SNP calling performance indicates that NanoSNP has good robustness for SNP identification on low-coverage Nanopore sequencing data in real-world scenarios.GA4GH and GIAB have defined the difficult-to-map regions on the reference genome GRCh38. The difficult-to-map regions are the union of all tandem repeats, all homopolymers >6 bp, all imperfect homopolymers >10 bp, all difficult to map regions, all segmental duplications, GC <25 or >65%, bad promoters, and other difficult regions such as major histocompatibility complex (MHC). We tested the SNP calling performance of several SNP callers in difficult-to-map regions on Nanopore reads of six genomes HG002–HG007. The coverage of each dataset is around 16×.We downloaded the BED files of difficult-to-map regions from the public website. Each difficult-to-map region on the reference is recorded as an interval including the start position and end position. The benchmark datasets are GIAB variants dataset v3.3.2 of each genome. Each benchmark contains two files, one is a VCF file that records variant sites, and the other is a BED file that records high-confidence regions for SNP detection. The SNP sites that fall in the high-confidence regions in the VCF file have higher confidence than the SNP sites in other regions. We used Bedtools to obtain the intersection of the difficult-to-map regions and the high-confidence regions for each human genome. In the genomes HG002, HG003 and HG004 of Ashkenazim Trio, SNPs in the difficult regions account for about 18% of total SNPs in the high-confidence regions. In the genome HG005, HG006 and HG007 of Chinese Trio, the percentage is around 15%. These proportions indicate that SNPs in the difficult-to-map regions account for a significant fraction of all the SNPs in the high-confidence regions.The evaluation of SNPs identified by NanoSNP and the compared SNP callers in the difficult-to-map regions is shown in The evaluation of SNP calls identified by several SNP callers in the MHC regions is shown in When the coverage of Nanopore sequencing reads is insufficient, it is difficult to determine whether the difference between the read base and reference base is caused by sequencing error or SNP. In NanoSNP, we employed two different models to identify SNP sites sequentially. The pileup model first detected a subset of SNP sites based on the pileup image of aligned reads. This set of SNPs is phased by WhatsHap and used for the phasing of Nanopore reads. We then generated the haplotype image and the pileup image of each haplotype and used the haplotype model to identify SNPs. We evaluated the SNPs called by the pileup model and the haplotype model on the low-coverage Nanopore sequencing of genomes HG002–HG007.The evaluations of SNPs identified by the two models in NanoSNP are shown in To assess the runtime of NanoSNP, we divided the pipeline of NanoSNP into four main steps, including feature generation of the pileup model, prediction of the pileup model, feature generation of the haplotype model and prediction of the haplotype model. The prediction of the pileup model and the haplotype model ran on a graphics processing unit (GPU), and the feature generation ran on a central processing unit (CPU). We ran NanoSNP and three other SNP callers on the server with 40 CPU processors, an Nvidia Tesla A100 GPU. For human whole-genome sequencing data with 16× sequencing depth, NanoSNP took a total of 16 h, where each main step took 4, 2, 7 and 3 h, respectively. We then ran NanoCaller, Pepper-DeepVariant, Clair, Clair3 to identify the SNPs under the same computing resources. The runtime of these four tools is around 8, 8, 9 and 10 h, respectively. In SNP calling, NanoSNP has two identification steps, including calling SNPs with the pileup information and calling SNPs with the haplotype information. Compared with the methods based on either pileup information (Clair) or haplotype information , NanoSNP has one more identification step. Therefore, the running time of NanoSNP was nearly 50% longer than that of the compared SNP callers. Compare to Pepper-DeepVariant, the second step of NanoSNP verifies all the candidate SNP sites, which spends more computing time. Compare to Clair3, the full-alignment model in Clair3 directly takes phased local alignment as a feature image, while NanoSNP calculates both local features and long-range features of candidate SNP sites from reads alignment, which increases the runtime of NanoSNP.On the low-coverage Nanopore sequencing dataset, it is difficult to determine whether the difference between the read base and reference base is a sequencing error or SNP due to the high sequencing error rate and insufficient sequencing coverage. This study proposed a novel deep learning-based SNP caller, NanoSNP for low-coverage Nanopore sequencing data. In NanoSNP, we design a two-step SNP identification workflow. In the first step, the pileup model of NanoSNP takes the pileup feature of aligned reads to identify a set of SNPs. This set of SNPs is used for phasing aligned reads, which divides the sequencing reads into different haplotypes. In the second step, the haplotype model of NanoSNP extracts the long-range haplotype information and short-range pileup information of each haplotype. The haplotype model can correct part of the false SNP calls of the pileup model and identifies SNPs that are missed by the pileup model.We evaluate several Nanopore SNP callers on low-coverage Nanopore sequencing reads with different coverage values . NanoSNP performs better than Clair, Pepper-DeepVariant and NanoCaller. When the coverage is greater than 16×, the F1 scores of NanoSNP are close to that of Clair3. In the cross-genome testing on six human genomes HG002–HG007, NanoSNP achieves the highest precision, second highest recall and F1 score on each low-coverage Nanopore dataset. Usually, the SNP identification in the difficult-to-map regions and the MHC regions of the human genome is more difficult and error-prone. By benchmarking the SNPs in these regions, we find that NanoSNP has a good performance in the difficult-to-map regions and the MHC regions.NanoSNP is designed for identifying SNPs on low-coverage Nanopore sequencing data. NanoSNP cannot identify SNPs with PacBio reads and Illumina reads. We will further develop NanoSNP to support multiple sequencing platforms in future work.btac824_Supplementary_MaterialsClick here for additional data file.


Accurate prediction of open reading frames (ORFs) is important for studying and using genome sequences. Ribosomes move along mRNA strands with a step of three nucleotides and datasets carrying this information can be used to predict ORFs. The ribosome-protected footprints (RPFs) feature a significant 3-nt periodicity on mRNAs and are powerful in predicting translating ORFs, including small ORFs (sORFs), but the application of RPFs is limited because they are too short to be accurately mapped in complex genomes. In this study, we found a significant 3-nt periodicity in the datasets of populational genomic variants in coding sequences, in which the nucleotide diversity increases every three nucleotides. We suggest that this feature can be used to predict ORFs and develop the Python package ‘OrfPP’, which recovers ~83% of the annotated ORFs in the tested genomes on average, independent of the population sizes and the complexity of the genomes. The novel ORFs, including sORFs, identified from single-nucleotide polymorphisms are supported by protein mass spectrometry evidence comparable to that of the annotated ORFs. The application of OrfPP to tetraploid cotton and hexaploid wheat genomes successfully identified 76.17% and 87.43% of the annotated ORFs in the genomes, respectively, as well as 4704 sORFs, including 1182 upstream and 2110 downstream ORFs in cotton and 5025 sORFs, including 232 upstream and 234 downstream ORFs in wheat. Overall, we propose an alternative and supplementary approach for ORF prediction that can extend the studies of sORFs to more complex genomes. Arabidopsis [Annotation of open reading frames (ORFs) in genomes is one of the most important processes required for downstream analyses and the use of reference genomes. Various algorithms have been developed to predict ORFs  in genombidopsis . The recbidopsis , 11–16. bidopsis , allowinTriticum aestivum) (6X) and cotton (Gossypium hirsutum) (4X). In addition, the challenges in preparing high-quality RPFs also limit the application of this technique to a broader range of non-model organisms. Incomplete digestion of unprotected mRNAs can reduce or eliminate the 3-nt periodicity of RPFs [Although the Ribo-seq technique has been used in studies of many species, including yeast, humans, animals and plants , most of of RPFs , 22, becAlthough the 3-nt periodicity shown by high-quality RPFs is powerful in ORF prediction, the application of RPFs for this purpose is hampered by the complexity of genomes, particularly in plant studies. We noticed that the populational diversity of nucleotides in coding sequences (CDSs) also shows a significant 3-nt periodicity, as observed for high-quality RPFs, and thus propose to use this periodicity to predict ORFs. In this study, to achieve this, we developed a Python package, ORF predictor using population genomic dataset (OrfPP), which identifies ORFs using the nucleotide diversities in populational datasets of single-nucleotide polymorphisms (SNPs). We tested OrfPP and recovered an average of 83.20% of the annotated ORFs with an average accuracy of 94.77%. The performance of OrfPP is robust even when a small subset of the SNPs is used. Finally, we applied OrfPP to two polyploidy genomes, cotton and wheat and identified 4704 and 5025 novel ORFs, respectively, with reliability comparable to the annotated ORFs in the genomes. Our study suggests that predicting ORFs from SNPs can be an approach supplementary to the existing methods and can be used to identify sORFs in complex genomes, for which the existing methods are not yet workable. We believe that this approach will play a greater role in future studies of animals and plants, given the rapidly growing number of SNP datasets for different species and the application of advanced techniques of DNA sequencing and SNP calling.F-score) were chosen to represent the ORFs predicted from RPFs. The predicted ORFs identical to the annotated ORFs in the reference genome were counted as true positives and the others were considered false positives. The accuracy, recall and F-score were calculated following the formula:To simplify the comparisons, the RPF-based ORF prediction in all the tested datasets was performed using RiboCode (v1.2.11) , ORFquanbona fide ORFs, such as sORFs that were not included in the annotation of reference genomes.F-scores were used to comprehensively assess the performance of OrfPP. It should be noted that although the unannotated ORFs were counted as false positives, many of them are F test implemented in the ‘multitaper’ R package (version 1.0–14) [P-values for all the frequencies. As a frequency of 0.33 Hz (1/3) indicates the periodic appearance/increase of the diversity of every three nucleotides, a significant P-value is expected for a pattern with 3-nt periodicity.Metagene analyses have been used to illustrate the read distribution pattern of RPFs on mRNAs, which show a clear 3-nt periodicity in various organisms , 26. To  1.0–14) , which dThe RPFs used for ORF prediction in this study were also used to calculate the translation levels of the predicted ORFs. Briefly, the RPFs were mapped to the genomes using HISAT2  with defSchizosaccharomyces pombe, Arabidopsis thaliana and Oryza sativa from the PRIDE archive under the accessions of PXD015484 (S. pombe) [A. thaliana) [O. sativa) [G. hirsutum) [T. aestivum) [To validate the ORFs predicted by OrfPP, we downloaded the protein mass spectrometry (MS) datasets of . pombe) , PXD0094haliana) , PXD0198 sativa) , PXD0186irsutum)  and PXD0estivum) . The MS estivum)  with defDegree of MS support = Arabidopsis and rice, were selected to test the performance of OrfPP. These organisms usually have high-quality reference genomes, SNP datasets [http://bib.oxfordjournals.org/).Several datasets from model organisms, including fission yeast, datasets  and highdatasets , 26, allArabidopsis and rice), the population size of which varied from 148 (yeast) to as large as ~3000 in rice. Our results show highly consistent patterns for all these tested populations; only the nucleotide diversities in CDSs showed a significant 3-nt periodicity, while the diversities in other regions appeared to vary randomly . Given that the 3-nt periodicity of nucleotide diversity only appears in CDSs, as shown in To utilize the periodicity of nucleotide diversity to predict ORFs in genomes, we developed a pipeline named ‘OrfPP’, which is available as a Python package , the reference genome sequence (in fasta format) and genome annotation (in gtf format) and includes three major steps, as shown in t-tests are performed to test whether the nucleotide diversities in frame 2 (the third nucleotides of codons) are greater than those in frame 0 and frame 1 in OrfPP, and are combined to report a final P-value for the identification of ORFs. To obtain ORFs with higher reliability for codon usage training, several stringent criteria are applied in this process of preliminary prediction. For example, at this stage, OrfPP only predicts ORFs starting with the canonical initiation codon ‘AUG’ from the candidates longer than 300 bp. The codon usages are then calculated from these predicted ORFs and used to represent the genome-wide usages.To calculate genome-wide codon usages, OrfPP first predicts ORFs solely based on nucleotide diversity. The pipeline extracts the transcripts according to the genome annotation and allocates the values of nucleotide diversity onto each position on transcripts, from which candidate ORFs (start with ‘AUG’ and end with stop codons with a length multiple of three) are extracted for the prediction of ORFs. Two Student’s P-values from these four Student’s t-tests to give a final P-value for the prediction of ORFs. To also report sORFs, OrfPP reports ORFs shorter than 100 bp and the initiation codons can be customized.Transcript sequences are extracted according to the genome annotation information, and candidate ORFs were retained  for furtde novo prediction of ORFs when the annotation of ORFs is not yet known. In this situation, the predicted ORFs will all be annotated as ‘Novel ORFs’.The predicted ORFs are classified into 11 categories following previous definitions , 5. Theyhttp://bib.oxfordjournals.org/), including yeast, Arabidopsis and rice, all with high-quality reference genomes and available datasets of both RPFs and SNPs, were selected to assess the performance of OrfPP. The accuracy, recall and F-score of prediction were used to describe the performance of OrfPP in these genomes. We used the annotated ORFs in the reference genomes as a benchmark to measure the performance of OrfPP, with the predicted ORFs identical to the ORFs annotated in the reference genome considered true positives and false positives otherwise. A note of caution should be made that many unannotated ORFs may be bona fide ORFs, such as sORFs, not included in the annotation of reference genomes but considered false positives in this computation.Several species . We then used the yeast ORFs predicted by ORFquant and Arabidopsis and rice ORFs predicted by RiboCode to compare with those predicted from SNPs. Our analysis showed that 89.57, 83.82 and 79% of the known ORFs in the reference genome of fission yeast, Arabidopsis and rice were successfully recovered by OrfPP with accuracies of 98.03, 95.6 and 90.19%, respectively, from the SNP datasets. Many of the annotated ORFs in these genomes were also recovered from RPFs, but there were fewer than those found using SNPs by OrfPP , probably because the RPFs included only actively translating ORFs. RPFs from silenced ORFs under the tested conditions would have been absent in the RPF dataset. However, the accuracies of prediction are similar between these two tools for all the tested genomes, suggesting a comparable accuracy between the ORFs predicted from SNPs and those from RPFs. The comparison between the ORFs predicted from RPFs and those from SNPs indicates that most of the former were included by the latter  commonly predicted from both RPFs and SNPs, (ii) predicted only from SNPs and (iii) not predicted, and calculated their translation levels using the corresponding RPFs used for ORF predictions in this study. Our data revealed the highest translation levels for the ORFs commonly predicted from both SNPs and RPFs , suggesthttp://bib.oxfordjournals.org/). For example, for the RPFs tested in this study, the unique mapping rates of RPFs varied from 9 to 54%, while the unique mapping rates of whole-genome sequencing (WGS) reads can be as high as 92% . The usual solutions to this multimapping problem are using only the uniquely mapped RPFs or randomly assigning the RPFs to one of the potential mapping sites [RPFs have been used to predict sORFs in many studies, but a shortcoming of this method has often been ignored. Due to the short size of RPFs, only a few of them can be uniquely mapped to genomes  were also identified from SNPs by OrfPP; 289 of the 840 (34.4%) dORFs identified from rice RPFs were recovered by OrfPP from SNPs  and hexaploid wheat (T. aestivum), to test its performance in complex genomes. SNP datasets derived from either 1913 cotton accessions [http://bib.oxfordjournals.org/), which allows the identification of ORFs. As a result, 91 594 ORFs were identified from cotton SNPs, among which 86 890 (94.86%) and 4704 (5.14%) were annotated and novel ORFs, respectively, and 114 929 ORFs, including 109 904 (95.63%) annotated and 5025 (4.37%) novel ORFs, were identified from wheat SNPs. The annotated ORFs recovered from SNPs accounted for 87.43 and 76.17% of all the annotated ORFs in the genome of cotton and wheat, respectively . We compared the MS support to the ORFs predicted from SNPs and RPFs . For the ORFs predicted from RPFs, the MS support for the novel ORFs was weaker than that for the annotated ORFs . This suggests the novel ORFs predicted from SNPs are more reliable than those from RPFs, given that the former was supported to a degree nearly equivalent to that of annotated ORFs .The overall support for ORFs derived from MS data appears to be variable across these genomes; therefore, we normalized the MS support degree of novel ORFs by calculating the ratio of MS support for novel ORFs to the support for annotated ORFs (MS support of novel ORF/MS support of annotated ORF) to enable intraspecies comparison. We found a higher normalized MS support for the novel ORFs identified from the yeast and Arabidopsis and rice novel ORFs identified from RPFs .We also performed this analysis to verify the ORFs identified from the SNPs of cotton (4 X) and wheat (6 X). Our data indicated that the novel ORFs identified from both the cotton and wheat SNPs were supported by MS data to a degree comparable to that of the annotated ORFs in the genome . We coulTaken together, our data suggest that the novel ORFs identified from SNPs are as reliable as the annotated ORFs in either the simple or the complex genomes. Additionally, the reliability of novel ORFs identified from SNPs is higher than those identified from RPFs.Arabidopsis (1135 accessions) and rice (3024 accessions) were used in this test, from which 100 to 1000 accessions were randomly sampled and used for ORF prediction. Our results suggest that the performance (measured by F-score) of OrfPP was robust even when a small subset of Arabidopsis datasets was used  to hexaploid (T. aestivum) species, with genome sizes ranging from ~12 Mb (S. pombe) to ~16000 Mb (T. aestivum). OrfPP successfully recovered most (~83%) of the known ORFs annotated in the reference genomes with considerable accuracy in all these tests, suggesting that the information recorded in SNP datasets can be a powerful ORF predictor. However, the predicted sORFs from SNPs appeared different from RPFs  can be used in future works interested in the roles of sORFs in these crops.Given the important biological roles of sORFs, we applied OrfPP to identify the sORFs in the polyploid plants, cotton and wheat, for which the RPF approach is not workable thus far due to the short lengths of RPFs. As a result, ~80% of the annotated ORFs in these genomes were successfully recovered and a total of 4704 and 5025 novel ORFs, including uORF and dORFs, were identified from cotton and wheat SNPs  and S4, Solanum lycopersicum, S. pennelli and Medicago truncatula [Although RPFs have been proven powerful in predicting ORFs, several drawbacks have limited their application to a broader range of species. For example, RPFs can only be used to predict the translating ORFs, thus resulting in incomplete identification of ORFs. In addition, RPFs are too short to be correctly mapped to the loci where they originate, particularly in polyploid genomes. Lastly, the preparation of high-quality RPFs can be difficult, particularly in many non-model organisms. Although some quality-insensitive predictors have been developed to use low-quality RPFs , 23, RPFuncatula , we foununcatula , 57. It As OrfPP directly utilizes the periodicity of nucleotide diversities calculated from SNP datasets, any factors that can potentially affect the accuracy of SNPs would introduce mistakes into the final outputs.(1) Low-quality reference genomeMore than 700 plant genomes have been released in recent decades, but many are of poor quality , 20. The(2) Mixture of SNPs from different speciesIn many studies, accessions from several, instead of only one, close species were included, but the SNPs were called based on only one reference , 58. Mis(3) Autoploidy and repetitive genomesStudies of polyploid genomes are usually challenged by incorrect and multiple mapping problems caused by the short length of whole-genome sequencing reads. To address this problem, in some works, the authors remove the reads with multiple hits and use only the reads mapping to gene regions for further analyses . However(4) Young genomesORF prediction from SNPs relies heavily on nucleotide diversity in the population. Although our tests show considerable independence from population size, the prediction could be incomplete for young genomes because only a few nucleotide substitutions have accumulated since the divergence of these genomes.https://pypi.org/project/OrfPP/1.0/) and can be easily installed using pip tools. Three compulsory inputs are needed: genome sequence (—genome), genome annotation (—gtf) and populational nucleotide diversity (—pi). The diversity of nucleotides at each position can be calculated using vcftools [OrfPP has been distributed to the Python Package Index (vcftools  with thePopulation nucleotide diversity shows that a 3-nt periodicity can be used to predict open reading frames (ORFs).A python package ‘OrfPP’ is developed to predict ORFs from single-nucleotide polymorphisms datasets.Application ‘OrfPP’ in polyploidy genomes recovered ~83% of the annotated ORFs.OrfPP could help extend the studies of small ORFs to organisms with complex genomes.Figure_S1_bbac210Click here for additional data file.Figure_S3_bbac210Click here for additional data file.Figure_S4_bbac210Click here for additional data file.Table_S3_bbac210Click here for additional data file.Table_S4_bbac210Click here for additional data file.


Zebrafish elf3 is maternally deposited. At different developmental stages, elf3 expression was detected in different tissue, mainly neural tissues, endoderm-derived tissues, cartilage, heart, pronephric duct, blood vessels, and notochord. The expression levels were high at the tissue boundaries. Elf3 loss-of-function consequences were examined by using translation blocking antisense morpholino oligonucleotides, and effects were validated using CRISPR/Cas9 knockdown. Elf3-knockdown produced short and bent larvae with notochord, craniofacial cartilage, and fin defects. The extracellular matrix (ECM) in the fin and notochord was disorganized. Neural defects were also observed. Optic nerve fasciculation (bundling) and arborization in the optic tectum were defective in Elf3-morphants, and fragmentation of spinal motor neurons were evident. Dysregulation of genes encoding ECM proteins and matrix metalloprotease (MMP) and disorganization of ECM may play a role in the observed defects in Elf3 morphants. We conclude that zebrafish Elf3 is required for epidermal, mesenchymal, and neural tissue development.E26 transformation specific (ETS) family transcription factors are expressed during embryogenesis and are involved in various cellular processes such as proliferation, migration, differentiation, angiogenesis, apoptosis, and survival of cellular lineages to ensure appropriate development. Dysregulated expression of many of the ETS family members is detected in different cancers. The human ELF3, a member of the ETS family of transcription factors, plays a role in the induction and progression of human cancers is well studied. However, little is known about the role of ELF3 in early development. Here, the zebrafish  Elf3  is a member of the E26 transformation specific (ETS) family of transcription factors. ETS transcription factors share a highly conserved DNA binding domain, the ETS domain that possesses 85 to 90 amino acids rich in positively charged and aromatic residues. The ETS domain, which is localized at the C terminus of Elf3, forms three α -helixes and four-stranded β-sheets [ELF3 has been reported to play significant roles in the development and progression of human cancers , 8–11. EIn addition to its role in oncogenesis, ELF3 regulates inflammation. ELF3 expression was reportedly restricted to epithelial tissues in normal conditions , 25, 27.Elf3 expression increases after fertilization and remains high until the blastocyst stage [Elf3 knockout (Elf3-/-) caused lethality of about 30% of mice in utero. Elf3-deficent pups looked malnourished, lethargic, and had watery diarrhea [Elf3 [Elf3 expression is also high in the retinal pigment epithelium (RPE) in mice [ETS family transcription factors are involved in proliferation, migration, differentiation, angiogenesis, apoptosis and survival of cellular lineages during embryonic development , 37, 38.st stage . Elf3 kndiarrhea , 40. Thodiarrhea , 41–43. ea [Elf3 , 44. Elf in mice . These fHere we report the distribution and loss-of-function consequences of Elf3 during zebrafish development. Morpholino oligonucleotide (MO) mediated Elf3-knockdown in embryos produced short and bent body, distorted fins, which show cartilage defects. Neural defects were seen in spinal motor neurons, brain and the visual system. Dysregulation and disorganization of extracellular matrix (ECM) components were observed. Our result show that Elf3 is required for epidermal, mesenchymal, and nervous tissue development, which may, in part, be due to defective ECM remodeling during embryogenesis.Danio rerio) AB strain was raised and maintained under standard laboratory conditions following Indiana University Policy on Animal Care and Use. IUPUI School of Science Institutional Animal Care and Use Committee approved the use of zebrafish adults for breeding, embryo collection and embryo experiments.Zebrafish (elf3 5′UTR (elf3-UTR-MO: 5’ TGTGGTGGTCAAAGATTGTTTTCCA 3’) or the elf3 translation start site (elf3-ATG-MO: 5’ TTAGACTAAGTTCGCTTGACGCCAT 3’). elf3-UTR-MO targets -36 to -12 nucleotides upstream of the start codon and elf3-ATG-MO targets 25 nucleotides of elf3 coding sequence. The ENSEMBL transcript ENSDART00000113795.4/ elf3-201 (NCBI Reference Sequence: XM_002666100.3) was used to design the morpholino. A morpholino designed against human β globin pre-mRNA that targets the bases to correct a splice-generating mutation at position 705 was used as a standard control. It was shown that this morpholino has no target and no significant biological activity in zebrafish (Gene Tools). Independent control experiments were also performed using two independent guide RNAs to produce CRISPR/Cas9 knockdowns . Quantitative phenotype analysis is presented in 5’ TGAACTACTGCACCATGGAT 3’; gRNA2: 5’ GAATCTGAACTACTGCACCA 3’) targeting elf3 were injected into embryos . These elf3 CRISPR targeted embryos showed evidence of insertions or deletions in elf3 sequences after PCR amplification and sequencing their genomic DNA  were designed to target the elf3 using 2 μL of cDNA, elf3-F: ATGGCGTCAAGCGAACTTAGTC and elf3-R: TTAATAACCGGTCTCCTCTATCC primers, Taq DNA polymerase with Thermopol Buffer (NEB). Gel electrophoresis showed the specific amplified product of the expected size (~1.1 kb), which was purified using Qiagen PCR purification kit. We cloned this cDNA into the pCBA3 expression vector, producing the plasmid pCBA3-zf-elf3. The cDNA was sequenced, which was submitted to the GenBank Database (OP605767).Total RNA was extracted from about twenty 2 days post fertilization (dpf) embryos using TRIzol, and cDNA was synthesized using 1 μg of total RNA, oligo(dT) primer and M-MLV reverse transcriptase (Promega). We amplified the open reading frame of elf3 expression during embryogenesis, total RNA was extracted from 4.5, 8, 20, and 24 hours postfertilization (hpf) embryos, and cDNA synthesis and PCR were done following the same procedures as described above.To examine pCBA3-zf-elf3 was cut using Acc1 restriction enzyme and the elf3 coding sequence 500 to 1128 bases were used to make the probe. A BLAST search ensured that there were no similarities between these elf3 nucleotides and other zebrafish mRNA sequences. Digoxigenin labeled antisense probe was made using DIG RNA labeling kit and T7 RNA polymerase, and sense probe was used as a negative control, showing no staining. Whole-mount in situ hybridization of zebrafish embryos were performed as previously described [The plasmid escribed . Images In situ hybridization stained larvae were embedded in 1.5% agarose in 5% sucrose, and stored in 30% sucrose solution at 4°C overnight. Agarose embedded larvae were mounted with O.C.T. . Ten micrometer sections were cut using a Leica CM 3050 cryostat at −20°C and transferred onto Superfrost slides (Fisher). Images were acquired using a Zeiss Observer Z1 microscope (10X o.3 NA objective).2O2 and 1 M KOH, and stained overnight in 0.1% Alcian Blue solution.Alcian Blue staining was done as previously described . Brieflyrsp15 was used as internal control. Primers used to quantify the expression of mmp9, mmp13, mmp2, col2a1, Fn1, rsp15 genes and amplicon sizes are listed in S1 Table in T method (ΔΔCT) as previously described [Total RNA was extracted from 2 dpf embryos (~ 20 embryos) using the TRIzol (Sigma). One microgram of total RNA was reverse transcribed to cDNA using M-MLV reverse transcriptase (Promega) and oligo (dT) primer. The cDNA was diluted tenfold with RNase free water. Each target was amplified using 4 μL of cDNA, Power SYBR Green PCR mix (Applied Biosystems) and 0.5 μM of each primer. Ribosomal RNA escribed .For whole-mount immunostaining, embryos were fixed in 4% PFA at 4°C overnight. Embryos were placed in blocking solution  for 2 hours at room temperature. Primary antibodies against HuC/D , acetylated tubulin , Col2α1 , Fibronectin  were diluted in blocking solutions and incubated 1 to 2 days. Alexa Fluor 488-conjugated and Alexa Fluor 555-conjugated anti-mouse or anti-rabbit secondary antibodies  were used at 1:500 dilution. Nuclear staining was performed using TO-PRO-3 iodide at a 1:1000 dilution incubated for 1 hour at room temperature. Images were acquired using a Zeiss Observer Z1 LSM 700 confocal microscope (40X 1.1 NA W or 20X 0.8 NA objectives). Regions of interest in stained embryos/larvae were imaged with several confocal optical z-sections. Zen Black (Zeiss) software was used to produce maximum intensity image projections of the z-section volumes.Whole-mount acetylated tubulin immunostaining images of embryos were used to measure motor neuron axon lengths. Using ImageJ , confocal images were calibrated to pixels per micron, and individual motor neurons in the anterior trunk were measured.Ingenuity pathway analysis (IPA) software was used to predict molecular interactions based on the manually curated content from human, mouse and rat publications in the Ingenuity knowledge base. “Experimentally observed or highly predicted” setting was used to predict potential Elf3 interactions with other molecules.t-test was used for comparisons between uninjected control and morpholino injected groups using GraphPad software .Unpaired two-tailed student’s elf3 was dysregulated by ethanol exposure during early development [elf3 during zebrafish development, elf3 gene sequences were amplified and cloned and sequenced. Zebrafish Elf3 and the human ELF3 protein share 47.31% identity, and ETS domain of zebrafish Elf3 is highly conserved, sharing 91.76% identity with human ELF3 ETS domain  embryos, indicating maternal deposition  designed to block translation of elf3 mRNA. As a negative control, an equal amount of the morpholino designed against human β globin pre-mRNA was also injected into 1 to 2 cell embryos. Either elf3-UTR-MO or elf3-ATG-MO morpholino injected embryos had no significant effects on epiboly progression. At 8.3 hpf, uninjected control embryos reached 82% epiboly ; control morpholino injected embryos reached approximately 85% ; and the elf3-ATG-MO morpholino injected embryos reached approximately 76% epiboly .To determine the function of Elf3 during development, we perform Elf3 loss-of-function experiments by injecting 1 to 2 cell embryos with antisense morpholino oligonucleotide   .The Elf3 morphants were grouped as mild-, moderate- and severe-morphant groups. The mildly affected morphants  were slightly shorter than the uninjected control but otherwise looked similar to control. The moderately affected morphant embryos (~50%) had a short body with a bent tail, pericardial edema, small eye, distorted notochord, aberrant/almost absent fin-folds, and reduced pigmentation . SeverelAnterior regions of the control and morphant larvae were compared. Morphant larvae had smaller heads compared to control. Smaller forebrain was evident in 5 dpf larvae . The morTo examine the craniofacial development in the morphants, Alcian blue staining was performed. Alcian blue-stained 5 dpf Elf3 morphant larvae showed relatively normal Meckel’s and ceratohyal cartilages but ceratobranchial arches (1–5) are either lightly stained or not stained, indicating poorly developed branchial cartilages with less proteoglycans in the extracellular matrix. Neurocranium development was also severely affected in Elf3 morphants, lacking the lateral regions of the ethmoid plate, showing deformed or missing trabecula, parachordal cartilage and auditory cartilage. Compromised craniofacial cartilage development was also observed in CRISPR/Cas9 injected larvae  antibody. Pairs of trabecula cartilages with Col2α1 positive chondrocytes were detected in control embryos at 2 dpf. At 3 dpf, Col2α1 was detected in developing Meckel’s, ceratohyal cartilage, 1l larvae . Howeverl larvae . Immunosl larvae . Cadheril larvae .Proper cell-cell and cell-matrix interactions are essential for tissue formation. Epidermal cell shape changes and correct ECM assembly are essential steps in zebrafish median fin fold and caudal fin morphogenesis , 56. Theelf3 gene function was required for proper ECM organization in the zebrafish fin and notochord, and Elf3 deficiency was associated with disorganization of the matrix.Elf3 morphants were shorter, bent, and had distorted/wavy notochords. The notochord functions as a hydrostatic skeleton in chordate embryos, rigid support provided by fluid pressure in the notochord vacuoles. Mechanical properties of notochord partially depend on its external fibrous matrix, which exerts the appropriate stiffness essential for lengthening of the embryo and maintenance of a straight axis. The notochord sheath cells produce the notochord fibrous matrix, which consist of Col2α1, other collagens, laminins, and other matrix molecules , 63. TheElf3 morphants exhibited delays in the eye cup formation and had smaller eyes. To examine the visual system of the morphants, acetylated-tubulin antibody labeling was performed that stains axons, including those in the optic nerve and optic tectum. Three-dimensional renderings of the confocal sections of the stained control embryos showed tightly fasciculated optic nerve projections of the retinal ganglion axons from each retina, which crossed at the optic chiasm and subsequently, projected toward the contralateral optic tectum . HoweverAfter crossing the midline, the optic nerves extend to the contralateral optic tectum in zebrafish where the nerve fibers ramified (branching) within the neuropil of the optic tectum, which are normally confined into the neuropil region. Acetylated-tubulin stained control embryos showed extensive arborization of optic nerve axons within the tectal neuropil . HoweverIn acetylated tubulin stained embryos, we noted defects in spinal motor neuron morphology in Elf3 morphants, indicating axonal growth and fasciculation defects. The morphology of neurons examined in 3D renderings of the confocal sections of acetylated tubulin stained 2 dpf control embryos showed thick bundles of caudal motor neurons that exited the spinal cord, spanned the entire ventral musculature, and arborized within muscle . Howeverin situ hybridization and RT-PCR was performed. Sox9 plays multiple roles during zebrafish development, including cartilage, pectoral fin, eye, and neural crest development [sox9b, one of the homologues of sox9 by in situ hybridization. The probe detected sox9b in the retina, brain ventricular zone, craniofacial cartilages, and pectoral fin in the control embryos at 2 dpf  in the Ingenuity Knowledge Base. IPA predicted 89 direct relationship  and 89 nodes that are downstream of Elf3 . Those melopment –66. The at 2 dpf . Morphanpression . Severelmmp13, mmp9, mmp2, and fn transcripts (~5 to 3 fold) in Elf3 morphants over control embryos at 2 dpf. The level of col2α1 transcripts was minimally decreased (~0.7 fold) in Elf3 morphants as compared to control embryos, but this decrease was not significantly different from the control  of the mid-Carnegie stages embryos to fetus stages [High level of s stages , 68. TheXenopus [elf3, indicating potential mechanism for notochord defect. For proper notochord morphogenesis, correct deposition and organization of the ECM are essential. Loss of col8a1a or col1a1a produced notochord defects in zebrafish similar to Elf3 knockdown [Elf3-deficient embryos [The posterior axis did not develop normally in Elf3 morphants. Shorter anterior-posterior axis was also reported after ectopic expression of Ets-related gene (Erg) in Xenopus . The disnockdown , 71. It  embryos . Both moSox9 and inhibited chondrogenesis [sox9b expression. These observations, along with the missing pieces of neurocranium cartilages, suggest that the neurocranium defects in Elf3 morphants could arise early during chrondoprogenitor formation and migration. Presence of all branchial arches but reduced Alcian blue staining suggested near-normal initial steps but compromised chondrocyte differentiation step of branchial cartilage development.Elf3 morphants showed craniofacial cartilage defects. Roles of other Ets family members in cartilage development were shown previously. Overexpression of Ets-related gene (Erg) in mouse sclerotome changed cellular morphology, reduced Alcian blue staining, decreased ogenesis . Increasogenesis . Anotherogenesis . Elf3-de(hai1) morphant exhibited lateral and caudal fin defects like the Elf3 morphant, which was shown to be associated to increased mmp9 expression [mmp9 partially rescued fin defects of hai1 morphant [mmp9 and mmp13, suggesting a regulatory role for Elf3 in ECM remodeling. Our experiments detected changes in ECM protein Col2α1 expression in fin. A dose-dependent requirement for Elf3 in maintaining the tissue architecture has been recognized.Fin tissue architecture was defective in Elf3 morphants with frequent cysts or clumps of cells, which suggested problems in cell-cell and cell-matrix interactions. Altered fin organization was also reported after Mmp13 knockdown in zebrafish . Hepatocpression . Knockdomorphant . IPA ideXenopus studies showed that MMPs are important for both axon guidance and extension in the developing visual system [mmp13, mmp9, and mmp2 after Elf3 knockdown. We speculate that optic nerve and motor neuron defects seen in Elf3 deficient embryos result from ECM remodeling and pathfinding signal recognition defects caused by MMP dysregulation, affecting axon projection and target recognition.MMPs play crucial roles during axonal growth and guidance . Receptol system , 80. Inhl system . Roles fl system . Paclital system . These sIPA identified interactions between Elf3 and many proteins belonging to various functional classes, which have been implicated in important developmental processes. Elf3 deficiency during development may directly or indirectly interferes with several signaling pathways and cellular processes, leading to the observed developmental defects. Our study showed that Elf3 is critical for embryonic development, and it is required for proper morphogenesis of the anterior-posterior body axis, craniofacial cartilages, fin tissue, optic nerve retinotectal projection and motor neuron pathfinding in zebrafish. Prior studies showed that the mouse knockout affected intestinal development, and Xenopus studies used gain-of-function, overexpression to show effects on axis development. Our findings show a set of new phenotypes that result from Elf3 loss-of-function and bioinformatics analysis points to potential pathways for gene activity. We hypothesize that these defects in Elf3 morphants are associated with ECM assembly and regulation, particularly collagen, producing defects in the craniofacial cartilage, notochord, and fins. Small brains and eyes were noted, and motor neuron projections were shorter and disorganized. In conclusion, our study showed that Elf3 is required for epidermal, mesenchymal, and neural tissue formation during development. Additional research is needed to fully understand the role of Elf3, a critical regulator of developmental signaling pathways, cellular processes, and cell-matrix interactions during development.S1 File(DOCX)Click here for additional data file.


The performance of genome-wide association studies with high-density single-nucleotide polymorphism panels and next-generation sequencing is a robust approach to the identification of genetic variants that can elucidate variation in complex diseases and economically important traits in farm animals. We identified many markers associated with crucial traits, such as muscle development in Simmental beef cattle. This information can be used to pinpoint candidate genes affecting traits of interest. Our results contribute to the clarification of molecular mechanisms underlying processes associated with hind-leg meat yield and carcass depth in beef cattle.p = 0.05/number of SNPs identified) and adopting a false discovery rate (FDR), we identified many putative SNPs on different bovine chromosomes. We identified 12 candidate genes potentially annotated with the markers identified, including CDKAL1 and E2F3, related to myogenesis and skeletal muscle development. The identification of such genes in Simmental beef cattle will help breeders to understand and improve related traits, such as meat yield.Genome-wide association studies are a robust means of identifying candidate genes that regulate economically important traits in farm animals. The aim of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes potentially related to carcass depth and hind leg circumference in Simmental beef cattle. We performed Illumina Bovine HD Beadchip (~670 k SNPs) and next-generation sequencing (~12 million imputed SNPs) analyses of data from 1252 beef cattle, to which we applied a linear mixed model. Using a statistical threshold ( Farm animals, particularly cattle, are of major economic importance in many parts of the world . Beef caSimmental beef cattle, distributed in nearly all parts of the world, are bred primarily due to their high levels of growth and lean meat production under an appropriate diet ,6,7. ManThe search for candidate genes via genetic association studies, conducted for almost all species , may be PLXNA4, ASB15, SLC13A1, NDUFA5, IQUB, LMOD2, and WASL [MTPN) [The performance of genome-wide association studies (GWASs) using high-density single nucleotide polymorphism (SNP) panels is a robust approach to the identification of different parts of the genome and genetic variants that can elucidate variation in complex diseases and prominent or economically important traits in farm animals ,15,16 anand WASL , and a pL [MTPN) . HoweverL [MTPN) ,24,25.Hindquarters from beef carcasses include valuable cuts, and their properties are thus important components of meat-related traits . The hinIGF2 gene, a key gene for muscle development and growth in farm animals [In the past several years, genetic selection for carcass conformation traits has been practiced in most beef cattle populations. Carcass depth is an important carcass conformation trait that covers the principal muscle and bone tissues and thus is profitable in the beef industry. Han et al.  illustra animals . HoweverIn this GWAS, we detected SNPs for the identification of candidate genes (via QTLs and markers) associated with hind leg circumference and carcass depth in Simmental beef cattle. Our results can contribute to the clarification of the molecular mechanisms underlying hind-leg meat yield and carcass depth.All procedures were conducted in accordance with the guidelines of China’s Council on Animal Care, and the study protocol was approved by the Institute of Animal Science of the Chinese Academy of Agricultural Science, Beijing, China . Analyses associated with animals adhered to the rules of China’s Council on Animal Welfare.The Simmental beef cattle population examined in this study comprised 1346 cattle born between 2009 and 2015 in Ulgai, Xilingol League, Inner Mongolia, China. Following weaning, the cattle were transferred to the Jinweifuren Farm feedlot in Beijing. All calves were raised under standard conditions and fed a total mixed ration based on the nutritional requirements of beef cattle . At 16–18 months of age, the animals were slaughtered. Immediately after bloodletting, the target carcasses were refrigerated at 4°C until trait measurement (about 9 h after slaughter). The carcasses were hanged, and the carcass depth and hind leg circumference were measured in centimeters using a tape . Trait mp < 10−6), or >10% genotype data missing. These criteria were assessed using PLINK software (ver. 1.07) [Illumina Bovine HD SNP Beadchip (770 k) and genotype examinations were performed using Illumina Genome Studio . For quality control, individuals with high Pi-Hat values (representing sample duplication) were excluded, and animals and SNPs were excluded if the SNP call rate <90%, minor allele frequency (MAF) <5%, deviation from Hardy–Weinberg equilibrium detected (r. 1.07) . After tUsing genomic relationship data and Pi-Hat values, we chose 44 unrelated cattle for resequencing. The TIANamp Blood DNA kit  was used to extract genomic DNA. DNA concentrations and purity were quantified using the NanoPhotometer N50 device . DNA (1.5 μg) with an A260/280-nm absorbance ratio of 1.8–2 and A260/230-nm absorbance ratio of 2.0–2.2 was splintered using the Ultrasonicator S2 . Sequencing libraries were prepared using the Truseq Nano DNA HT sample preparation kit (Illumina Inc.) according to the manufacturer’s instructions of manufacturers. Each sample was given an index code for sequence recognition. The DNA samples were splintered into ≤350–base pair fragments by sonication, and DNA fragments were chosen for end polishing, addition of A-tailing, and sequencing with further PCR amplification using a full-length adapter. Sequencing libraries were prepared using the Illumina HiSeq 2500 system (Illumina Inc.). We obtained 9,621,765,847 reads and subjected them to quality control; low-quality reads with >10% unrecognized bases, >10% mismatches, or >50% poor-quality bases were removed. To facilitate down-stream analysis, SAM files were converted to BAM files. Then, we performed variant calling using samtools-0.1.19 mpileup [NSG-derived variants with MAFs > 0.05  were subjected to imputation using BEAGLE (ver. 4.1)  with they is a vector phenotypic value and μ represents the population mean. We applied two fixed-effect variables for single marker regression; b reflects noise associated with fixed effects  and bj reflects SNP effects. jm is the vector for the ith marker and u reflects the polygenic effect assumed with N , where K corresponds to the kinship matrix and 2σ is the additive genetic variance. Although all SNPs on autosomal chromosomes were qualified for inclusion in the analysis, SNPs on chromosomes with jm were excluded. X and Z are the incidence matrices connecting the phenotypic values to fixed and polygenic effects, respectively. e represents random residual effects assumed withI is an identity matrix and We employed a general mixed linear model based on the formula p < 0.05 in accordance with division by the number of SNPs. For carcass depth, dependable QTLs and putative candidate genes were identified with a stringent significance threshold of 7.45 × 10−8. To obtain p values for the SNPs, t values were calculated. Considering the fact that Bonferroni correction outcomes are too stringent with low statistical power [n is the number of p < 0.01, and m represents the total number of SNPs [Associated SNPs were identified using GenABEL (ver. 1.8-0)  in R, wial power , we dete of SNPs .http://www.genome.ucsc.edu, Bos_taurus_UMD_3.1, accessed on 2 October 2020).To detect candidate genes of interest, we used the UCSC genome browser  in candidate gene CLK1 on chromosome 2 and BovineHD0200025553 (p = 3.4 × 10−13) and BovineHD0200025550 (p = 8.3 × 10−13) in candidate gene BZW1. Exploration of the region that is about 10 kb upstream of the marker BovineHD0200025562 (p = 1.49 × 10−14) led to the identification of candidate gene PPIL3. The marker BovineHD0500023139 (p = 2.17 × 10−8) was found in the gene CCDC91 on chromosome 5. In addition, the candidate genes DIP2B and LARP4 were identified about 3 kb upstream and 24 kb downstream of the marker BovineHD0500008673 (p = 1.3 × 10−13), respectively. On chromosome 17, the gene c17h22orf31 was identified about 14 kb downstream of the SNP BovineHD1700020542 (p = 3.47 × 10−8). We also identified two significantly associated SNPs on chromosomes 10  and 26 , but no associated candidate gene. The estimated heritability for carcass depth was high (0.41 ± 0.06). With beadchip analysis, we identified several SNPs associated significantly with carcass depth , includip = 1.23 × 10−9) in BTA5 related to hind leg circumference, but no candidate gene in the vicinity of this SNP. In segment 2, several associated SNPs were identified , NXPE4 was identified about 4.7 kb downstream of the SNP rs25439719 (p = 4.14 × 10−8), and the candidate gene RBM7 was found about 20 kb upstream of the SNP rs25237349 (p = 1.12 × 10−7).The estimated heritability for hind leg circumference was high (0.52 ± 0.12). Of the 9,621,765,847 raw reads acquired, 9,584,920,309 reads were examined after the application of quality control measures. In segment 1, we identified one SNP  was located in the gene CDKAL1, which might be a crucial gene for Simmental beef cattle. About 47 kb upstream of this gene, the candidate gene E2F3 was identified. In segment 3, we found many putative QTLs on chromosomes 23, 26, and 29 . ExploraGWASs have been conducted for many animals  and plaCLK1 gene harbors the SNP BovineHD0200025561 in BTA2, associated most strongly with carcass depth. This gene codes for a protein kinase with binary specificity that phosphorylates tyrosine and serine/threonine residues [EGF and other growth elements [EGF plays a key role in cellular development by promoting cell proliferation and differentiation. CLK1 has been reported to play a role in cancer due to the associations among CLK1, ASF/SF2, and SRp20 [CLK1 inhibitor, TG693, is a splicing modulator with therapeutic potential for patients with Duchenne muscular dystrophy [CLK1 were observed in the kidney, small intestine, and longissimus dorsi tissues [CLK gene expression showed downregulation related to muscle fat during lactation [CLK1 and the intermuscular fat content in Hanwoo cattle. In another study of Hanwoo cattle, CLK1 was associated significantly with marbling  [CLK1 on meat quality and its relationship to cellular  development in Simmental beef cattle is lacking. We hypothesize that CLK1 plays a role related to meat quality and likely muscle cell development in Simmental beef cattle, but more research is needed to validate the relationship of this candidate gene to these traits.Beadchip analysis revealed that the residues . Its N-telements . EGF pland SRp20  and in mnd SRp20 . The CLKystrophy . In pigs tissues . In a raactation . In a geactation  detectedlar fat) . HoweverBZW1, which harbors two SNPs associated significantly with carcass depth (BovineHD0200025553 and BovineHD0200025550). This gene has been reported to be a proliferation regulator that promotes the progression of many aggressive cancers, such as salivary mucoepidermoid carcinoma [BZW1 plays an important role in cell cycle processes and transcriptionally manages the histone H4 gene in the G1/S phase. Chiou et al. [BZW1 in tumor growth [BZW1 has been associated with bovine endometriosis [BZW1 is associated with carcass depth in Simmental beef cattle. More research is warranted to validate this association. We also identified the gene PPIL3 about 10 kb upstream of the SNP BovineHD0200025562. PPIL3 has been associated with estrogen receptor-negative breast cancer [PPIL3 with carcass depth in Simmental cattle is novel. Further research is needed to corroborate this association.In BTA2, we identified the candidate gene u et al.  reportedr growth  and Alzhr growth  have alsetriosis , and Hoeetriosis  showed tt cancer , but infCCDC91, which harbors the SNP BovineHD0500023139, associated significantly with carcass depth. This gene is expressed in numerous cancer cell lines. CCDC91 has been identified as a candidate gene associated with production traits (fat percentage) in dairy cattle [DIP2B about 3 kb downstream of the SNP BovineHD0500008673. In cattle, this gene has been associated with fatty acid content [DIP2B has also been associated with lipid metabolism, skeletal system development, and insulin signaling [LARP4 about 24 kb upstream of the SNP BovineHD0500008673. This gene may be able to suppress cancer cell migration and invasion [LARP4 repressed the motility and metastatic development of ovarian cancer cells. This gene has also been shown to control mRNA solidity and translation [LARP4B, that influence milk protein composition traits in Holstein cattle. We hypothesize that LARP4 is associated with carcass depth in Simmental beef cattle.In BTA5, we identified the candidate gene y cattle , but few content , an impoignaling . Informainvasion . Egiz etinvasion  reportednslation . Zhou etnslation  identifiZBTB16 was located about 50 kb downstream of this SNP in segment 2. This conserved gene, broadly expressed in normal tissues such as cardiac and skeletal muscle [PLZF and Zfp145, acts as a crucial transcriptional suppressor or activator [ZBTB16 controls homeostasis and the fate of hematopoietic stem cells [ZBTB16 enhances white adipogenesis and brown-like adipocyte formation in intramuscular preadipocytes in cattle. The bovine hind leg contains important muscles and bones, and its circumference is a good criterion for the assessment of traits related to bone and muscle development. Thus, we hypothesize that ZBTB16 plays a role in bone and muscle development in Simmental beef cattle, but more research is needed to validate this hypothesis. In BTA15, about 4.7 kb downstream of SNP rs25439719, we found the candidate gene NXPE4. This gene has been recognized as a potentially promising prognostic biomarker for colorectal cancer [NXPE4 was a candidate gene located near SNPs related most closely to methane emission and fat traits in Holstein cattle. The candidate gene RBM7 was also identified in BTA15, almost 20 kb upstream of the SNP rs25237349. The literature contains little information about this gene. A GWAS revealed that it was a candidate gene associated with milk production in Iranian buffalo [NXPE4 and RBM7 are associated with hind leg circumference in Simmental beef cattle.In the NGS analysis, we found that the SNP rs25168454 in BTA15 was associated most strongly with hind leg circumference. The candidate gene l muscle ,68 and activator  that is ctivator , hind lictivator , spermatctivator , and antctivator . It is activator  and mesectivator , which iem cells . Epigenoem cells ,78, and em cells . Wei et em cells  reportedl cancer . Sarghall cancer  reported buffalo . We hypoCDKAL1, which harbors the only SNP associated with hind leg circumference in BTA23 (rs37207517). Several GWASs have revealed the role of CDKAL1 in type 2 diabetes [CDKAL1 encodes the CDK5 regulatory subunit–associated protein 1-like 1. This protein results from the translation of CDKAL1 and is homologous to cyclin-dependent kinase 5 regulatory subunit–associated protein, a neuronal protein that can hamper CDK5 activation [CDK5 can act as a myogenic protein kinase; it is required for myoblast differentiation and thus plays a key role in muscle myogenesis [CDKAL1 has also been associated with mitochondrial morphology and adipogenesis [E2F3, another important candidate gene, about 47 kb upstream of CDKAL1. As a promoter of the muscle cell cycle, E2F is related to myogenesis [E2F3 can induce myogenic differentiation [E2F3 in muscle cells. E2F3 is associated with various other processes and plays an important role in the progression of a variety of cancers, such as bladder, breast, and prostate cancers [E2F3 in Simmental beef cattle is lacking; we hypothesize that this gene is related to myogenesis in this breed.In segment 3, we identified the very promising candidate gene diabetes ,85,86. Itivation . CDK5 caogenesis . CDKAL1 ogenesis . Thus, wogenesis . Importantiation . Ma et antiation  reported cancers ,93,94. ICDKAL1 and E2F3 may be associated with myogenesis and muscle development. Although the data were obtained by analyzing the genome of the Simmental breed, they can also be extended and validated in other cattle breeds. The identification of such genes helps breeders to understand and improve traits such as carcass depth and hind leg circumference.Using the Illumina Bovine HD Beadchip and NGS dataset analyses, we identified many putative QTLs on several chromosomes that were associated with carcass traits in 1252 Simmental beef cattle. Several candidate genes related to carcass depth and hind leg circumference were identified. Among them,