Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance.SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays.Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.The online version contains supplementary material available at 10.1186/s12915-023-01782-0. Genomic prediction is a method of using genome-wide DNA markers to assess the genetic merit of an individual and relies on the accurate genotyping of these DNA markers. As a result of their low cost  and imprIn RR-GBS, restriction enzymes are used to reduce genome complexity . This rer2) from high-density (HD) SNP array genotypes to whole genome SNP are regularly greater than 0.9 in humans [Skim-GBS refers to calling genotypes from low coverage (< 1 × coverage) whole genome sequence data. Genotype imputation is often incorporated into skim-GBS pipelines to account for missing genotypes. Skim-GBS requires no additional laboratory steps, however, adds more computational complexity to account for the reduced likelihood of sampling all alleles at heterozygous loci in non-inbred species. Skim-GBS does not suffer from the same biases as RR-GBS methods however, the sequencing depth, marker density and population structure can impact imputation error rates significantly . Imputatn humans  and liven humans . Similarn humans .A number of genotype imputation packages have been developed and refined for accuracy and decreasing computational complexity. Popular genotyping packages include IMPUTE2 , Beagle4Recently, a number of imputation packages targeted specifically at GBS data have been developed. These second-generation imputation packages have been developed to handle larger SNP panels and use genotype likelihoods or physical linkage information from the sequence reads themselves, in order to maximise the use of available information for imputation. Examples of these packages include QUILT  and GenoTo date, short-read sequencing methods, such as Illumina, have been favoured for GBS due to their well-characterised error profile, high throughput and competitive cost. These platforms produce highly accurate short (200–400 bp) DNA reads . Long-reGiven the current price of ONT sequencing, to compete with SNP array technology, sequencing coverages less than 0.5 × for a human-sized genome will be necessary to allow adequate multiplexing. This raises the question; can we accurately predict genetic merit from less than 0.5 × ONT sequencing coverage? To add additional complexity, turnaround time must also be considered when evaluating pipelines, as lengthy posterior bioinformatics would undermine the advantage of in situ genotyping.Our objective was to investigate the optimum imputation strategy for skim-GBS from ONT sequence data for the purpose of in situ genomic prediction. We evaluated the performance of Beagle5.2, QUILT and GLIMPSE using five different SNP reference panels and five different sequencing coverages. We hypothesised that genomic prediction accuracy would be improved if all available SNP in sequence sets were used for imputation, not only the subset used in the genomic prediction. That is, multiple SNP flanking the target SNP should give additional information for imputation from sequence data.Bos indicus breeds (Brahman and Droughtmaster). The reads were aligned to the Bos taurus reference genome ARS-UCDv1.2 [ONT sequence data from 62 samples sequenced in Hayes et al.  on a Min-UCDv1.2  using Mi-UCDv1.2 .Table 1Two different genotyping methods were used to genotype the samples at 48,203,338 loci across the genome. These SNP were derived from the 1000 Bull Genomes project , with thThree methods were used to impute missing SNP from the genotype calls Fig. . GenotypFive different SNP reference panels were used for imputation Table . The anicorpus luteum score (CL score) and body condition score (BCS). The SNP effects used to calculate the GEBVs were derived from 28,000 animals with genotypes and phenotypes as described in Hayes et al. [. Correlations between the ONT GEBVs and GEBVs derived from the 35 K GGP TropBeef SNP array  genotypes imputed to the bovine HD array using Fimpute [The imputed genotypes at the 641,163 SNP loci that corresponded to the bovine HD array were used to calculate genomic estimated breeding values (GEBVs) for four traits, body weight (BW), hip height (HH), s et al.  and Lambs et al. . Correla Fimpute  and esti Fimpute  were calWhen genotypes for each animal were imputed using only SNP from the bovine HD array in the reference SNP panel , correlations between the ONT GEBVs and GEBVs from the 35 k array were between 0.74 and 0.92 for sequencing coverages less than 1 × , using MAC-Beagle5.2 and Q-score-Beagle5.2. However, GEBV correlations using QUILT and GLIMPSE with the HD size reference panel were greater than 0.9 for all four traits at 0.5 × sequencing coverage.When the 48 million flanking SNPs were used to help impute the 641 k SNP genotypes from the sequence data, correlations using QUILT and GLIMPSE were much higher (greater than 0.82 for 0.05 × and greater than 0.93 for 2 × sequence coverage), and higher than the other two imputation methods, particularly at low coverages .No difference between GEBV correlations for the two Beagle5.2 imputation methods was observed. Both methods demonstrated a similar decrease in correlations for sequencing coverages below 0.5 × . Even using the largest SNP panel for imputation, the correlations between the ONT BW GEBVs and 35 k SNP array BW GEBVs at 0.1 × sequencing coverage were 0.81 and 0.83 for the two Beagle5.2 methods, compared to 0.94 and 0.95 using QUILT and GLIMPSE, respectively.The regression coefficient of the 35 k SNP array GEBV on ONT GEBV was used to measure prediction bias for each imputation strategy. Predictions using QUILT and GLIMPSE had much less bias than predictions derived from the other two methods .For both of the Beagle imputation methods, significantly more variation in prediction bias was observed across traits and SNP panel size, with both SNP reference panel size and sequencing coverage having a negative directional effect on prediction bias (P < 0.05). Using all four methods BW prediction bias was consistently higher than the other three traits. This was particularly evident in the MAC-Beagle5.2, Q-score-Beagle5.2 and GLIMPSE predictions, as well as at low sequencing coverages.Across the four traits, no difference in prediction bias was observed except for in BW .For GLIMPSE and both Beagle5.2 methods, sequencing coverage had a relatively small effect on the imputation time compared to reference panel size. While for QUILT both coverage and SNP panel size had a significant effect on imputation times , as compared to genotyping and imputing from low-density SNP arrays. Genotyping accuracy was calculated and GEBVs for the four traits were also derived. The imputation accuracy was calculated for QUILT and GLIMPSE by comparing the imputed genotypes at the HD SNP array loci to the genotypes from the bovine HD SNP array. Genotypes imputed using QUILT had the highest accuracy across all sequencing coverage and imputation reference panels, except at 0.1 × and 0.05 × sequencing coverage with the smallest imputation reference panel Fig. . The higP > 0.05). While below 0.1 × sequencing coverage the QUILT GEBV correlations were worse than the 35 k SNP array correlations for some, but not all of the traits and reference panel designs. Although QUILT performed better than GLIMPSE for the majority of sequencing coverages and larger imputation reference panel sizes, the GEBV correlations decreased significantly when using the smallest imputation reference panel size at low coverages. For example, the GEBV correlations using QUILT with the Bovine HD SNP reference panel at 0.1 × were between 0.63 and 0.69 for the four different traits.Next, correlations between ONT GEBVs (imputed using QUILT and GLIMPSE) and the HD SNP array GEBVs were calculated. For sequencing coverages as low as 0.5 × , the ONT GEBVs had higher correlations than the 35 k SNP array GEBVs  were randomly centred around 1, indicating no systematic prediction bias. Sequencing coverages greater than or equal to 0.5 × had little prediction bias Fig. , while aA major benefit of using GEBVs from a producer’s perspective is the ability to quantitatively rank animals for a particular trait based on their genetic merit. To investigate the ability for low-coverage ONT sequence data to accurately rank animals, we compared the rankings of animals based on ONT GEBVs across coverages to GEBVs calculated from the bovine HD array genotypes of 19 animals. Minimal re-ranking of animals was observed down to a coverage of 0.5 × . Below 0.5 × , ONT GEBVs regressed towards the mean, creating more re-ranking of animals and less apparent difference between the performance of animals Fig. . HoweverThe final question we looked to answer was, how reproducible are ONT-derived genotypes across sequencing runs? The twelve animals which were sequenced twice at two different time points in the study by Hayes et al.  were useNear real-time, portable genomic prediction has a number of applications in agriculture and healthcare. Here, we have demonstrated in cattle that when combined with an appropriate imputation strategy skim-GBS using portable ONT sequencing is a suitable method for genomic prediction, with coverages as low as 0.1 × . Using the imputation packages QUILT and GLIMPSE, GEBV correlations were higher than or equal to correlations from low-density SNP arrays for sequencing coverages as low as 0.1 × and, at this coverage, the performance of animals relative to each other could accurately be separated into quartiles. We have also demonstrated that using large SNP reference panels with many more SNP than used in the genomic prediction equation significantly increases the accuracy of genomic prediction from low-coverage GBS. Overall, QUILT  in combiWhile a number of genotype specific accuracy metrics are available to compare imputation strategies, this study focused primarily on the accuracy of genomic prediction from the imputed genotypes. This approach was chosen as the study primarily aimed to investigate the optimum imputation strategy for the purpose of in situ genomic prediction. Imputation specific metrics such as genotype accuracy and concordance were used as a secondary means to evaluate the differences between QUILT and GLIMPSE, as they performed similarly based on genomic prediction metrics alone. The relative performance of the different strategies with regard to GEBV correlation and bias, highlighted the robustness of genomic prediction using a GBLUP model to a modest degree of imputation errors. This is in line with other literature that has reported little to no significant decrease in GEBV accuracies when imputation errors are introduced by decreasing SNP panel density from 50 k to as low as 3 k . NonethePLAG1 on chromosome 14 has previously been identified to have a significant effect on heifer fertility in Australian Brahman cattle [Calculating the prediction bias in the ONT predictions revealed a general trend of underpredicting GEBVs for CL score, BCS and HH, while over-predicting GEBVs for BW. This pattern was seen across all of the methods; however, QUILT still had the least prediction bias of the four methods across all coverages. A possible explanation for the trends in prediction bias observed could be systematic genotyping errors in SNP loci with a large phenotypic effect. For example, a SNP in the genomic region encoding n cattle , and if Another possible explanation is that the bias is actually introduced by the process of imputing 35 k SNP array genotypes up to HD SNP array density. This implies that the prediction bias observed in body weight, for example, is actually caused by the low-density SNP array genotyping and not by the ONT GBS method. This is supported by the fact that when the ONT GEBVs are compared to the HD SNP array GEBVs, less prediction bias is observed with a more random spread, than when they are compared to the 35 k SNP array GEBVs. Furthermore, when comparing the 35 k SNP array GEBVs to the HD SNP array GEBVs a reasonable degree of bias was observed in body weight and hip height. This would suggest that some systematic genotype errors, which predominantly affect hip height and body weight GEBVs, are being introduced when imputing from the low-density 35 k array to the HD SNP array density. Further research should be carried out to verify if this is the case and investigate why the errors are being introduced.QUILT’s ability to more accurately genotype and impute SNP from long-read sequence data is likely a result of the way in which it assigns haplotypes. Imputation packages that use VCF genotypes such as Beagle and GLIMPSE use SNP panels to construct known ancestral haplotypes in order to haplotype phase the genotypes. Missing loci are then imputed using the ancestral haplotypes. This approach is suitable for SNP array genotypes, where the genotypes themselves are independent observations. However, when using sequence data this approach ignores the fact that a single sequence read can span multiple SNP, meaning nearby SNP no longer need to be considered independent , 37. QUIAlthough GLIMPSE was overall not as accurate as QUILT, it performed well even when used in combination with the smallest imputation reference panel (the HD SNP array) and was significantly more accurate than either Beagle method. This is likely a result of GLIMPSE using genotype likelihoods rather than hard-coded genotypes, such as the case with Beagle5.2. Genotype likelihoods allow for some of the individual read information to be maintained through to imputation when using GBS, however, are more computationally demanding to calculate and impute. Genotype likelihoods do not maintain haplotype of origin information which is available when a single read spans multiple SNP. This likely explains why GLIMPSE is relatively unaffected by decreasing imputation reference panel size. This means that in circumstances where very large imputation reference panels are available, QUILT is the better option as it can use the higher SNP density to accurately phase reads. When only small imputation reference panels are available, GLIMPSE may offer some increase in accuracy over QUILT. Overall, however, using QUILT with very large imputation reference panels was the most accurate for genomic prediction.B. indicus origin, such as Brahman and Droughtmaster, which were used in this study. This finding may suggest that low-coverage ONT genomic prediction may be particularly advantageous in mixed breed populations with a larger effective population size, such as Australia’s northern beef industry.When comparing the accuracy of the QUILT and GLIMPSE imputed GEBVs to GEBVs derived from the bovine HD SNP array, there was no difference between 35 k SNP array GEBVs and ONT sequencing coverages as low as 0.1 × for QUILT and 0.5 × for GLIMPSE. This suggests for the purpose of genomic selection, 0.1 × sequencing coverage with ONT is as informative as low-medium density SNP arrays in beef cattle. Interestingly, no trend in prediction bias was observed when comparing the QUILT ONT GEBVs to the HD SNP array GEBVs. This suggests the underlying variation in GEBVs between the QUILT ONT and bovine HD SNP array predictions is random, unlike the variation in GEBVs between the ONT and 35 k array predictions. This means the long ONT sequence reads are potentially better able to account for decay in linkage disequilibrium (LD) than imputation from 35 k SNP array density up to HD array density. For example, at 0.1 × sequence coverage, theoretically, almost 5 million SNPs had sequence coverage from which to assign haplotypes using the whole genome SNP panel with no filter. Assuming random sampling of the haplotypes, we get 2.5 million SNPs for each parental haplotype, which is significantly more information than low-density SNP arrays for populations with greater LD decay. Examples of such populations include cattle breeds of Previous studies have also found 0.5 × sequencing coverage suitable for skim-GBS for the purpose of genomic selection. Using Illumina sequencing data, Zhang et al.  reportedWhile the sequencing coverages for accurate imputation reported here are not as low as those reported for RR-GBS methods, the additional wet-lab procedures necessary for RR-GBS methods limit their portability. Additionally, given the low complexity of the genomes of most economically important livestock species, reduced-representation GBS methods are likely not necessary. For economically important species with highly complex genomes, such as wheat and sugar cane, reduced-representation GBS may still be the best option .A potential solution to help handle complex genomes without the additional laboratory steps could be to use ONT’s adaptive sequencing technology  as a typIn this study, we compared ONT GEBVs to SNP array GEBVs using the same number of SNP markers for predictions. Future work should look to investigate the accuracy of ONT GEBVs against true breeding values and compare these results to SNP array GEBVs against the same true breeding values. Using this comparison, other methods to further increase the accuracy of ONT GEBVs could be better investigated. Such methods include using Bayesian-derived SNP effects to better estimate the distribution of effects, and incorporating more SNP into the prediction, rather than imputing to the whole genome sequence and then sub-sampling back to 641,000 SNPs. Both these methods have been shown to slightly increase prediction accuracies , 43 and Reducing the size of the SNP reference panel had a significant effect on the imputation speed for all methods. Beagle5.2 was significantly faster than QUILT and GLIMPSE, which is a result of the increased computational complexity of using genotype likelihoods. Browning et al.  reportedIn general, increasing the size of the reference panel increased imputation accuracy when using QUILT. However, this was not the case when increasing the panel size from the second largest panel (MAF > 0.1) to the largest reference panel (no MAF filter). Instead, a small decrease in imputation accuracy and GEBV correlations was observed across all coverages. It is believed this is related to the increased likelihood of sequencing errors appearing at rare variant loci, possibly causing non-reference SNP to be called at these loci and consequently introducing errors in the haplotype phasing and imputation. Given 33,202,930 rare SNP (MAF < 0.1) were removed from the unfiltered reference panel to create the second largest reference panel, it could be reasoned that up to 0.1% or 33,202 of those variants may overlap a sequencing error at 0.05 × , using a conservative ONT error rate of 2%. Therefore, it seems reasonable to assume sequencing errors may be the cause of the decrease in accuracy.Despite, increasing the accuracy of genomic prediction and imputation accuracy, the removal of rare variants from the reference panels represents a limitation to the wider application of the imputation strategies evaluated here. For example, rare variants are useful for GWAS and genetic disease screening of some monogenic traits. Therefore, the filtered SNP panels in this study are only suitable for genomic prediction of polygenic traits. Incorporating rare disease loci, such as Pompes disease , 45 or hThe accuracies achievable with QUILT at 0.1 × sequencing coverage suggest multiplexing of up to 40 human genome size samples on a single MinION (or up to 200 on a PromethION) flow cell could be possible. This would bring the reagent cost of this method down to below USD $40 per sample, while providing results in under 24 h . With fuHere we illustrated that genomic prediction can be accurately performed from ONT sequence data using sequencing coverages as low as 0.1 × with an appropriate imputation strategy. The resulting values are as accurate — if not more accurate — than low-density SNP array genotyping. This study illustrates that the computational requirements for in situ genomic prediction are available, and the sequencing coverages reported here make ONT genotyping economically viable ultimately making on-farm genotyping a possibility in the near future.B. indicus origin. An additional 14 tail hair samples were collected at a different time point from a subset of the original animals making a total of 62 samples.Tail hair samples from 48 heifers spread across three properties were collected under the ethics approval number QAAFI/269/17. These heifers were Brahman and Droughtmaster breed animals, which are of predominantly n = 41) were genotyped on the 35 k GGP TropBeef SNP array  and the genotypes were imputed up to the bovine HD array density using Fimpute [All animals  was used to base-call the raw fast5 files and the subsequent fastq files were subsampled down to 2 × , 1 × , 0.5 × , 0.1 × and 0.05 × for each animal. Each subsampled fastq was aligned to the sion 1.2  using Mision 1.2  and the sion 1.2  was usedsion 1.2 . This mesion 1.2  to calcuThis second method also accounted for methylation errors in ONT sequence data by only genotyping potential methylation sites using the non-methylated strand. For example, to genotype a cytosine flanked by a guanine on the forward strand, only reads on the reverse strand were considered.Two imputation packages, QUILT (version 1.0.1)  and BeagGenomic estimated breeding values s et al.  as descrs et al.  using:\dThe estimated phenotypes from the imputed ONT genotypes  from the mean of 1.79 × and were therefore dropped from the analysis for this coverage. The actual sequencing depth was calculated as bases mapped to the reference genome while the original read subsampling for each coverage group was done according to the raw fastq files. This therefore accounts for the difference between actual sequencing depth and coverage group and provides a more accurate estimate of GEBV accuracy for a given coverage calculated in situ.The genotype concordance between the 14 duplicate animals was calculated at the HD SNP array loci using the package Bcftools. For each duplicate sample, the vcf files were firstly filtered to remove SNP not on the HD SNP array and then compared using the bcftools stats function from Bcftools. Overall genotype concordance was recorded for each sample.The imputation accuracy for the imputed ONT genotypes and imputed 35 k genotypes was calculated by comparing the imputed genotypes to the available HD SNP array genotypes at the same loci used for the genomic prediction. Imputation accuracy was defined as the total number of matching genotypes divided by 641,163 (the number of SNP used for genomic prediction).Additional file 1: Table S1. Summary statistics of the ONT sequencing data for each sample used. Table S2. Breed composition breakdown for the SNP reference panel used for imputation. Figure S1. Principal component plot for a subset of the 1,208 animals in the SNP reference panel and 62 animals sequenced using Oxford Nanopore Technologies’ (ONT) MinION as part of this study. Figure 2. Correlations between hip height (HH) genomic estimated breeding values (GEBV) derived from 35k SNP array genotypes and HH GEBVs derived from Oxford Nanopore Technologies (ONT) data. ONT GEBVs were imputed using four different imputation strategies and across five sequencing coverages. SNP reference panel size is indicated by the minor allele frequency (MAF) filter on the right-hand side in descending order of size from top to bottom. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high density (HD) SNP which had only the 641k SNP used to calculate the GEBVs. Error bars indicate 95% confidence of the Pearson correlation. Figure S3. Correlations between corpus luteum score (CL score) genomic estimated breeding values (GEBV) derived from 35k SNP array genotypes and HH GEBVs derived from Oxford Nanopore Technologies (ONT) data. ONT GEBVs were imputed using four different imputation strategies and across five sequencing coverages in descending order of size from top to bottom. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high density (HD) SNP which had only the 641k SNP used to calculate the GEBVs. SNP reference panel size is indicated by the minor allele frequency (MAF) filter on the right-hand side. Error bars indicate 95% confidence in the Pearson correlation. Figure S4. Correlations between body condition score (BCS) genomic estimated breeding values (GEBV) derived from 35k SNP array genotypes and HH GEBVs derived from Oxford Nanopore Technologies (ONT) data. ONT GEBVs were imputed using four different imputation strategies and across five sequencing coverages. SNP reference panel size is indicated by the minor allele frequency (MAF) filter on the right-hand side in descending order of size from top to bottom. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high density (HD) SNP which had only the 641k SNP used to calculate the GEBVs. Error bars indicate 95% confidence in the Pearson correlation. Figure S5. A) Correlations between genomic estimated breeding values (GEBVs) derived from Oxford Nanopore Technologies (ONT) sequence data and GEBVs derived from bovine HD SNP array genotypes for hip height (HH). ONT derived GEBVs were imputed using QUILT and GLIMPSE and calculated across five coverages and five SNP panels. The different SNP reference panels were created using minor allele frequency (MAF) filters to reduce the size of the panels down from whole genome sequence SNP. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high definition (HD) SNP panel and featured only the 641k SNP used to calculate the GEBVs. SNP array genotypes were from the Illumina bovine HD SNP array. The correlation for each trait between GEBVs calculated from the 35k GGP SNP array imputed to 700k and GEBVs calculated from the Illumina bovine HD SNP array are indicated by the dashed line. The colour of each bar indicates how well the ONT derived GEBV accuracies compare to the 35K SNP array accuracies. Error bars indicate 95% confidence interval of the Pearson correlation. B) Genomic prediction bias for body weight (BW), defined as> Figure S6. A) Correlations between genomic estimated breeding values (GEBVs) derived from Oxford Nanopore Technologies (ONT) sequence data and GEBVs derived from bovine HD SNP array genotypes for corpus luteum score (CL score). ONT derived GEBVs were imputed using QUILT and GLIMPSE and calculated across five coverages and five SNP panels. The different SNP reference panels were created using minor allele frequency (MAF) filters to reduce the size of the panels down from whole genome sequence SNP. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high definition (HD) SNP panel and featured only the 641k SNP used to calculate the GEBVs. SNP array genotypes were from the Illumina bovine HD SNP array. The correlation for each trait between GEBVs calculated from the 35k GGP SNP array imputed to 700k and GEBVs calculated from the Illumina bovine HD SNP array are indicated by the dashed line. The colour of each bar indicates how well the ONT derived GEBV accuracies compare to the 35K SNP array accuracies. Error bars indicate 95% confidence interval of the Pearson correlation. B) Genomic prediction bias for body weight (BW), defined as Figure S7. A) Correlations between genomic estimated breeding values (GEBVs) derived from Oxford Nanopore Technologies (ONT) sequence data and GEBVs derived from bovine HD SNP array genotypes for body condition score (BCS). ONT derived GEBVs were imputed using QUILT and GLIMPSE and calculated across five coverages and five SNP panels. The different SNP reference panels were created using minor allele frequency (MAF) filters to reduce the size of the panels down from whole genome sequence SNP. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high definition (HD) SNP panel and featured only the 641k SNP used to calculate the GEBVs. SNP array genotypes were from the Illumina bovine HD SNP array. The correlation for each trait between GEBVs calculated from the 35k GGP SNP array imputed to 700k and GEBVs calculated from the Illumina bovine HD SNP array are indicated by the dashed line. The colour of each bar indicates how well the ONT derived GEBV accuracies compare to the 35K SNP array accuracies. Error bars indicate 95% confidence interval of the Pearson correlation. B) Genomic prediction bias for body weight (BW), defined as


Oxidative stress makes it difficult to preserve food and negatively affect the applicability of polymeric packaging. It is typically caused by an excess of free radicals, and it is dangerous to human health, resulting in the onset and development of diseases. The antioxidant ability and activity of ethylenediaminetetraacetic acid (EDTA) and Irganox (Irg) as synthetic antioxidant additives were studied. Three different antioxidant mechanisms were considered and compared by calculating bond dissociation enthalpy (BDE), ionization potential (IP), proton dissociation enthalpy (PDE), proton affinity (PA), and electron transfer enthalpy (ETE) values. Two density functional theory (DFT) methods were used, M05-2X and M06-2X with the 6–311++G basis set in gas phase. Both additives can be used to protect pre-processed food products and polymeric packaging from oxidative stress related material deterioration. By comparing the two studied compounds, it was found that EDTA has a higher antioxidant potential than Irganox. To the best of our knowledge several studies have been carried out to understand the antioxidant potential of various natural and synthetic species, but EDTA and Irganox were not compared and investigated before. These additives can be used to protect pre-processed food products and polymeric packaging and prevent material deterioration caused by oxidative stress. Oxida longa)  and it ha longa) . Ascorbia longa) . There aa longa)  and othea longa) , 16, terle (BHA) , santowhle (BHA) , propyl EDTA is an efficient metal chelating compound. It is also considered an efficient antioxidant and it can improve the oxidative stability, and some studies indicate that EDTA has the ability to protect against oxidation during storage . The effIrganox is a family of compounds which are commercial hindered phenolic antioxidant additives . There aAlthough several studies have been carried out to understand the antioxidant potential of various natural and synthetic species, to the best of our knowledge, EDTA and Irganox were not compared and investigated before. In the present work, the antioxidant potential of EDTA and an Irganox model (Irg)  will be 2To calculate the antioxidant potential of any molecules, three antioxidant mechanisms including hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET-PT), and sequential proton loss electron transfer (SPLET) can be employed as these are described by the following equations (1-3) .(1)HAT: •, AH, and A• are radical, antioxidant, and antioxidant radical, respectively. AH+• is radical cation created when an electron transferred from the antioxidant, A‾ is an anion which is formed after a proton loss has been occurred. By using the above equations, parameters such as bond dissociation enthalpy (BDE), ionization potential (IP), proton dissociation enthalpy (PDE), proton affinity (PA), and electron transfer enthalpy (ETE) can be determined and these can be used in the assessment of antioxidant potential of molecules [Rolecules , 31, 32.olecules . Two synolecules  and M06-olecules  in combiolecules , 40, 41.2ρ, measured in bond critical points . Additionally, we analyse the delocalisation index (DI) estimated for X–H bonds and being the number of electrons shared by given two atomic centres . Delocalisation index can be interpreted as quantum-theory derived bond order, with that difference in respect to the classic bond order that it can be estimated for any possible atomic pair. In further discussion we analyse and comment only those DIs which were estimated for formally bonded atomic pairs of X–H type ; thus, the bond-order-interpretation is fully allowed in this case.In order to get insight into the bonding between individual atoms within molecules under investigation, we use the analysis of electron density according to Quantum Theory of Atoms in Molecules (QTAIM) , 43. For33.1EDTA has four carboxyl groups which have very similar O–H bonds, in terms of their length which are between 0.965 to 0.970 Å where the shortest one is O1–H and the longest one is O2–H while O3–H and O4–H are both equal to 0.966 Å . The carAs for the Irganox model, the structure contains only one hydroxyl group with a bond length equal to 0.956 Å while the C–H bonds cover a range from 1.080 to 1.091 Å at the M06-2X/6–311++G level of theory . The sho3.2The antioxidant properties of the two synthetic antioxidant additives including EDTA and the Irganox model have been studied by using two global hybrid functionals M05-2X and M06-2X in combination with the 6–311++G basis set in gas phase. The antioxidant potential has been measured by comparing the two species in HAT, SET-PT and SPLET mechanisms.3.2.1The HAT mechanism involves a hydrogen atom transfer from hydrogen donor sites, such as methyl group, hydroxyl group and others, of the antioxidant compound to the free radical. From a thermodynamic point of view, the antioxidant potential can be measured by the bond dissociation enthalpy of the corresponding X–H bond, where the lower the BDE the greater the antioxidant capacity of the compound , 16, 29.BDE values of the C–H bonds of EDTA computed at the M06-2X/6–311++G level of theory, which cover a range between 316.7 to 367.2 kJ/mol. The weakest bond is C5–H while the strongest one is C3–H. In case of the M05-2X , the weaAs for BDE values of O–H bonds, special situations arise in case of the carboxyl groups of EDTA. When the hydrogens were removed from the carboxyl groups large structural changes occurred  as it inTherefore, by comparing the X–H bonds within EDTA, the order of antioxidant potential is the following: C3–H < C4–H < O1–H < O2–H < O3–H < O4–H < C1–H < C2–H < C6–H < C5–H. Thus, C–H bonds have higher antioxidant potential than O–H bonds within EDTA. At the same time, C3–H and C4–H have lower antioxidant potential and these bonds are located between two nitrogen atoms.In the case of the Irganox model, the BDE values of the studied X–H bonds have also been calculated and compared , and theBy comparing the results of the two synthetic antioxidant additives, it can be concluded that EDTA has a higher antioxidant potential than Irganox. To assess the applicability of these compounds as antioxidant additives, experimental BDE values of regularly used polymers (from 393.7 to 406.2 kJ/mol) , 14, 47 3.2.2The second studied mechanism was SET-PT which includes two steps. First an electron is donated from the antioxidant to the radical which is followed by proton transfer , 14. In The IP of the Irganox model is higher compared to EDTA and it is just above 730 kJ/mol . Further3.2.3The last antioxidant mechanism to consider is SPLET. It also includes two steps, a proton transfer followed by an electron transfer from the antioxidant to the radical. The values of proton affinity (PA) and electron transfer enthalpy (ETE) define the antioxidant potential for the studied compounds, the lower PA and ETE the better antioxidant. The computed PA and ETE values of the X–H (X = O or C) bonds of EDTA has been collected and compared .In the case of EDTA, during the first step of SPL, in case of the deprotonation from O2–H and O3–H, the process leads to an interesting situation. Both deprotonated species rearrange in a way that the nearby proton holding carboxyl group will stabilize the system by forming a bridge with the group which just lost its proton  and thusDue to the special situation (O2 and O3 in SPL), the order of the antioxidant potential (PA + ETE) in this mechanism is different compared to the HAT and SET-PT mechanisms. The antioxidant potential of C3–H and C4–H were the lowest among all bonds in case of HAT and SET-PT, but in this mechanism O2–H and O3–H are the least potent antioxidant sites.In case of the Irganox model, PA values cover a range between 1406.9 (O–H) and 1701.7 (C4–H) kJ/mol calculated at the M06-2X/6–311++G level of theory. As for ETE results of Irganox, the values start from 31.8 to 238.0 kJ/mol . From th3.3The electron density (ED) distribution obviously gives a valuable information on various molecular properties, with the chemical bonding at the first place , 43, 48.2- groups. However, if referring to ρ at BCP as a measure of bonding strength, the lower value of that parameter corresponds to relatively weaker chemical bond. Therefore, the relatively weakest C–H bond in EDTA is that formed by C1 carbon atom (C1–H*). The C–H bonds with contribution of C4 and C5 are also close to C1 in this ranking. In opposite, relatively strongest ones are those with contribution of C6 and C2. Nevertheless, all C–H bonds are very similar to each other if ρBCP is considered, and it is rather difficult to indicate the one which clearly differs from others. A slightly different situation appears when analysing the delocalisation index. In this case the most effective electron sharing is observed for C4–H and C3–H in case of which C atoms structurally differs from other carbon atoms in EDTA due to their chemical neighbouring. Anyway, values of DI are in general close to each other due to chemical similarity of –CH2- groups. Analogous situation occurs in case of O–H bonds. Again, they all belong to carboxyl groups and have relatively similar electron density characteristics. Nevertheless, if comparing their QTAIM properties, the O2–H shall be considered relatively the weakest while O1–H seem to be the strongest  and thus, the intrinsic characteristic of the O–H bonds slightly differ from those results. The reason behind is the stability of the formed radicals which prefers the hydrogen removal from Irganox over EDTA and that makes the process slightly more preferable for the former.Whist for Irganox system we have four types of C–H bonds if considering the chemical environment of parent carbon atoms. Thus, there is a terminal –CHrisation . There i4The target for this work is to study the antioxidant additives which can be used to protect polymers and prevent material deterioration caused by oxidative stress. In the future, the most active antioxidant additive will be selected, and its potential will also be explored in polymer synthesis. The antioxidant potential of two synthetic antioxidant additives including EDTA and Irganox model have been studied. The ability of these additives to donate hydrogen atoms to free radicals was compared by using computational tools. Based on DFT calculations employing two global hybrid functionals, the most likely radical scavenging sites were investigated. From the result, we noticed that an interesting situation happened including the hydrogen atom abstraction from a carboxyl group of EDTA initiated a successive C–C bond break, resulting in decarboxylation. Furthermore, the proton loss from O2–H and O3–H leads to both deprotonated species rearranges in a way that the nearby proton holding carboxyl group will stabilize the system by forming a bridge with the group which lost its proton and thus, the proton will be shared between the two groups. O–H bonds are more potent radical scavenging sites, but some C–H bonds also have significant antioxidant potential depending on their position in the structures. By comparing the lowest values of the computed BDE, IP, PDE, PA, and ETE of the studied structures, it can be concluded that EDTA has a higher antioxidant potential than Irganox. The most probable antioxidant mechanism of the studied species is the hydrogen atom transfer. Various additional molecular properties including the electron density (ED) distribution and others were studied by using Quantum Theory of Atoms in Molecules (QTAIM). While, when comparing its QTAIM parameters with O–H bonds in EDTA, it seems to be stronger and less available for dissociation than O–H bonds in EDTA, which is the case if only the intrinsic characteristic of the bonds are considered. But to compute BDE, the corresponding radical species have to be studied and thus, their stability also important. To fully understand the antioxidant activity, both, BDE and BCP are important to analyse and compare. The ability of these antioxidant additives has been compared by the experimental BDE values of regularly used polymers and it was found that bond dissociation enthalpy values of the polymers are higher than the lowest BDE values of EDTA and Irganox. Therefore, regardless of the order of the antioxidant potential of the different X–H bonds, the two studied species include such units which makes them excellent additives and thus, we are thinking about the future research directions will be about that, these additives can be used to protect pre-processed food products and polymeric packaging and prevent material deterioration caused by oxidative stress. Moreover, these additives also can be used as stabilizers during the polymerisation with the common polymers such as PVC.Dalal K. Thbayh, Marcin Palusiak, Béla Viskolcz, Béla Fiser: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Data included in article/supplementary material/referenced in article.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The Brangus cattle were developed to utilize the superior traits of Angus and Brahman cattle. Their genetic compositions are expected to be stabilized at 3/8 Brahman and 5/8 Angus. Previous studies have shown more than expected Angus lineage with Brangus cattle, and the reasons are yet to be investigated. In this study, we revisited the breed compositions for 3,605 Brangus cattle from three perspectives: genome-wise (GBC), per chromosomes (CBC), and per chromosome segments (SBC). The former (GBC) depicted an overall picture of the “mosaic” genome of the Brangus attributable to their ancestors, whereas the latter two criteria (CBC and SBC) corresponded to local ancestral contributions. The average GBC for the 3,605 Brangus cattle were 70.2% Angus and 29.8% Brahman. The K-means clustering supported the postulation of the mixture of 1/2 Ultrablack (UB) animals in Brangus. For the non-UB Brangus animals, the average GBC were estimated to be 67.4% Angus and 32.6% Brahman. The 95% confidence intervals of their overall GBC were 60.4%–73.5% Angus and 26.5%–39.6% Brahman. Possibly, genetic selection and drifting have resulted in an approximately 5% average deviation toward Angus lineage. The estimated ancestral contributions by chromosomes were heavily distributed toward Angus, with 27 chromosomes having an average Angus CBC greater than 62.5% but only two chromosomes (5 and 20) having Brahman CBC greater than 37.5%. The chromosomal regions with high Angus breed proportions were prevalent, tending to form larger blocks on most chromosomes. In contrast, chromosome segments with high Brahman breed proportion were relatively few and isolated, presenting only on seven chromosomes. Hence, genomic hitchhiking effects were strong where Angus favorable alleles resided but weak where Brahman favorable alleles were present. The functions of genes identified in the chromosomal regions with high ( Brangus beef cattle were developed to combine the desirable traits of Angus and Brahman cattle. Angus cattle are well known for their superior carcass qualities, and Angus cows have excellent fertility and milking capability. The Brahman cattle have developed disease resistance, overall hardiness, and outstanding maternal instincts thanks to rigorous natural selection. The crossbreeding to create the Brangus breed dated to 1932, according to the USDA 1935 Yearbook in Agriculture. Yet, Brangus registration by the International Brangus Breeders Association (IBBA) started in 1949. For official registration, a Brangus animal needs to be genetically stabilized at 3/8 Brahman and 5/8 Angus by pedigree, be solid black or red, and be polled . Both siinter-se mating and selection have been conducted with Brangus over time. For example, the United States IBBA has developed expected progeny differences (EPD) for quantitative traits, such as birth weight, weaning weight, yearling weight, milk production, total maternal calving ease, and intramuscular fat. Likely, artificial selection pressure employed at varying levels on these traits of interest in the past decades could have resulted, to some extent, in the deviation of the ancestral genomic proportions in Brangus from the previously targeted ratios. Previous studies showed significantly elevated Angus genomic breed composition (GBC) in Brangus cattle  bovine 50 K V1 (version 1) chip, except 349 Brahman cattle were genotyped with an Illumina 777 K bovine SNP chip . The genp < 1.0E-8) were also excluded. For SNPs with greater than 0.99 correlations on each chromosome, only the one with higher or the highest minor allelic frequency was kept. When there were ties in allelic frequencies, a random one was taken. The final SNP set retained 41,672 common SNPs for the subsequent analyses. In each ancestral population, outlier individuals were excluded as those with (−2)log (likelihood) exceeding a given cutoff value   . This tedefault) .Breed compositions were estimated for individual Brangus animals using the admixture model by j) genotyped on an animal (say i). Assume that allele frequencies on this locus are known for each ancestry breed. The admixture model postulates that a progeny’s genotype at this locus is determined according to an allelic frequency as a weighted average of T reference (ancestry) breeds: jth SNP in the tth reference population,; Consider one biallelic locus  breeds, are obtained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method under the restrictions that T ancestral breeds to each animal.The admixture coefficients, 1TwiT=1 . BFGS isR2 LD per segment decreased as the window size increased, ranging from 0.107 to 0.164 on 1 Mb chromosomal regions, from 0.062 to 0.104 on 5-Mb chromosomal regions, and from 0.050 to 0.081 on 10 Mb chromosomal regions  than those on 5-Mb and 10-Mb regions (0.02).Breed compositions for the Brangus animals were estimated genome-wide, per chromosome, and per chromosomal segment, respectively. The number of reference SNPs per chromosome varied from 712 (chromosome 25) to 2,568 (chromosome 1), and the average distance between SNPs ranged from 0.05 to 0.07 Mb. SBC were obtained on three window sizes: 1 Mb, 5 Mb, and 10 Mb, respectively. The widow sizes were taken arbitrarily yet still based on two factors, the average length of gene in the bovine genome and the minimum number of SNPs to give stable estimates. To minimize the errors in the estimated SBC due to insufficient SNP coverage, chromosomal segments with less than five SNPs were excluded from computing SBC. There were 2,522 1-Mb segments, 518 5-Mb segments, and 269 10-Mb segments, respectively. The average number of SNPs per segment ranged from 15.2 to 18.8 (1 Mb), 72.0 to 91.1 (5 Mb), and 132.0 to 169.1 (10 Mb), respectively . Overall regions . The R2K = 2 and 3, respectively. Initially, the number of clusters K was specified a priori, and randomly assigned all the animals to each of K distinct, non-overlapping clusters as their initial clusters. Then, the K-means algorithm computed the cluster centroid for each cluster, which is a vector of genotype means for the M reference SNPs, and it re-assigned each animal to the cluster whose centroid was the closest. The last two steps proceeded iteratively till the total within-clustering variation, defined by the sum of all the pairwise squared Euclidean distance in each cluster and summed over all the K clusters, was minimized as much as possible  (http://www.genome. jp/kegg/)  (Gene ontology (GO) term enrichment analysis, or gene set enrichment analysis, was conducted using the “gprofiler” R package with genes identified in 1-Mb chromosomal regions featuring either ancestral breed. This R package performed functional gene enrichment analysis on the input gene lists, mapped genes to known functional resources, and detected statistically significantly enriched terms. Briefly, chromosomal segments satisfying Angus SBC ≥0.75 or Brahman SBC ≥0.50 were extracted. Both cutoff thresholds represented equal upward GBC deviations  from their expected values. Then, the gene information was extracted by querying in the Ensembl database according to their chromosomal locations on the selected chromosomal segments. Finally, GO clustering analysis was performed on the gene list generated with the different filters. The annotation databases included Gene Ontology–biological processes, cellular components, molecular function (gy.org/) , and Kyop/kegg/) . Other rp/kegg/) , and “clp/kegg/) . Gene liT/index) .p < 2.2e-16). The 95% confidence intervals were 61.1%–85.8% Angus and 14.2%–38.9% Brahman. There were 459 (12.7%) Brangus cattle with Angus GBC ≥80.0% and 19 (0.5%) Brangus cattle with Angus GBC ≥90.0%. Similarly, elevated Angus GBC for the Brangus animals were documented in some previous studies. For example, The estimated GBC for the 3,605 Brangus cattle, on average, were 70.2% Angus and 29.8% Brahman , which sK-means clustering was conducted to partition the 3,605 animals into two and three clusters, respectively , the cluster (C-1) with the highest average Angus GBC (82.1%) had 640 (17.8%) Brangus animals. The cluster with the lowest average Angus GBC (67.2%) included 2,584 (71.7%) Brangus animals. In both sets of clustering results (K = 2 versus K = 3), the average Angus GBC for animals in the top Angus composition clusters (B-1 versus C-1) agreed approximately with each other (81.6% versus 82.1%), and they corresponded roughly to the expected Angus breed proportion (81.25%) for the ½ UB animals. We thus suspected that these animals could be the ½ UB animals. Using a path analysis approach, versus 67.2%). These were likely Brangus cattle without the mixture of ½ UB animals. The average Angus breed proportion for non-UB Brangus was 67.4% based on the clustering analysis with K = 2. The 95% confidence interval of Angus breed proportions in Brangus was between 60.4% and 73.5%. Hence, there was, on average, an approximately 5% deviation of GBC toward Angus breed proportions in non-UB Brangus cattle, possibly resulting from selecting Brangus cattle for phenotypes where Angus has advantages. Without selection, genomic-estimated breed compositions would agree approximately with the expected ratios  agreed roughly with the average GBC in the 3,605 Brangus cattle. The mean (standard deviation) of Angus C_GBC for the 3,605 Brangus cattle was 70.9% (6.7%). The mean (standard deviation) of Brahman C-GBC was 29.1% (6.7%). The correlation between C_GBC and GBC for the 3,605 Brangus animals was 0.99 See . Within versus 29.1% Brahman; versus 29.8% Brahman; Ancestral breed compositions were evaluated on 1-Mb, 5-Mb, and 10-Mb windows on each chromosome . Like CBAncestral breed proportions per segment varied considerably on each chromosome . For exavice versa, exemplifies the successful complementary of favorable alleles for the traits of interest from both ancestral breeds.The variability of ancestral breed compositions per chromosome or segment could be a direct effect of selection. For example, Bos taurus UMD 3.1.1 assembly, the genome size of domestic cattle is approximately 2,670 Mb, which contains 26,815 genes (https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Bos_taurus/105/#FeatureCountsStats). Hence, the average spacing between neighboring genes is around 0.1 Mb. In contrast, the chromosome regions defined on 5-Mb or 10-Mb intervals could be too broad. Nevertheless, SNPs on 1 Mb intervals had higher LD than those on 5-Mb or 10-Mb intervals. The average R2 LD per segment on each chromosome ranged from 0.11 to 0.16 in 1-Mb intervals, from 0.06 to 0.10 in 5-Mb intervals, and from 0.05 to 0.08 in 10-Mb intervals . For example, peptidoglycan muralytic activity (GO:0061783), which contributes to the degradation of peptidoglycan, is a major structural component of bacterial cell walls , with 81.6% Angus genomic lineage. The non-UB Brangus animals (B-2), on average, were 67.4% Angus and 32.6% Brahman. It was likely that selecting Brangus for traits where Angus had advantages led to an approximately 5% average deviation in the estimated GBC toward Angus lineage. Further deviation of Brangus breed compositions from the officially expected ancestral breed ratios is worth paying attention to because it could diminish the complementarity of Brahman and Angus over time.The ancestral breed proportions varied substantially by chromosomes and by chromosomal segments, likely shaped by cross-breeding and subsequent inter-se mating and selection over time. There were strong hitchhiking effects on genomic regions where favorable Angus alleles resided. The functions of genes identified in the chromosomal regions with high Angus proportions were diverse, yet many were responsible for biological processes related to growth and body development. The genes identified in the regions with high Brahman compositions were primarily responsible for biosynthetic-related processes, molecular biological functions related to enzyme activity, diseases, and immunity. The co-presence of segments of high-Angus and high-Brahman lineage, respectively, on the same chromosomes, exemplified the successful complementary of favorable alleles from both ancestral breeds.While genomic-estimated breed compositions depicted an overall picture of the “mosaic” genome of animals attributable to their ancestors, local ancestry genomic compositions were visualized through chromosomal- and segmental-estimated breed compositions. Precisely, the admixture coefficients based on the admixture model inferred the probability of alleles identical in state when estimating the ancestral breed compositions for individual animals. Nevertheless, they corresponded approximately to the probability of alleles identical by descent when confined to long chromosome chunks. We noted that alternative approaches existed, such as path analysis  and the


Introduction: Muscle glycolytic potential (GP) is a key factor affecting multiple meat quality traits. It is calculated based on the contents of residual glycogen and glucose (RG), glucose-6-phosphate (G6P), and lactate (LAT) contents in muscle. However, the genetic mechanism of glycolytic metabolism in skeletal muscle of pigs remains poorly understood. With a history of more than 400 years and some unique characteristics, the Erhualian pig is called the “giant panda” (very precious) in the world’s pig species by Chinese animal husbandry.Methods: Here, we performed a genome-wide association study (GWAS) using 1.4M single nucleotide polymorphisms (SNPs) chips for longissimus RG, G6P, LAT, and GP levels in 301 purebred Erhualian pigs.Results: We found that the average GP value of Erhualian was unusually low (68.09 μmol/g), but the variation was large (10.4–112.7 μmol/g). The SNP-based heritability estimates for the four traits ranged from 0.16–0.32. In total, our GWAS revealed 31 quantitative trait loci (QTLs), including eight for RG, nine for G6P, nine for LAT, five for GP. Of these loci, eight were genome-wide significant (p < 3.8 × 10−7), and six loci were common to two or three traits. Multiple promising candidate genes such as FTO, MINPP1, RIPOR2, SCL8A3, LIFR and SRGAP1 were identified. The genotype combinations of the five GP-associated SNPs also showed significant effect on other meat quality traits.Discussion: These results not only provide insights into the genetic architecture of GP related traits in Erhualian, but also are useful for pig breeding programs involving this breed. Meat quality significantly influences consumers’ preference and purchasing behavior, thus improving meat quality has been being a major goal of pig breeding . In receThe degree of post-mortem pH decline in porcine skeletal muscle is an important factor accounting for the occurrence of inferior meat, as it significantly affects meat color development, water-holding capacity and tenderness . The ultPRKAG3 is the first causal gene proved to affect GP level, and its R200Q mutation significantly increases muscle glycogen content by up to ∼70%, consequently causing acidic meat in Hampshire pigs , a smaller extent of pH decline (pHd) from post-mortem 45 min to 24 h, and a greater variation in the two pH index  and then stored at −80°C until assay.The Erhualian population was established as described previously . Brieflyi) and 24 h (pHu) after slaughter using a Delta 320 pH meter, and their mean values were calculated separately. The difference between pHi and pHu was taken as pH decline (pHd). Drip loss was assayed using an EZ-Drip Loss method. Three color parameters L*, a* and b* on the surface cuts of LT were objectively evaluated with a CM-2600d/2500d Minolta Chroma meter , glucose-6-phosphate (G6P) and lactate (LAT) in LT muscle were determined using the glycogen assay kits (E2GN-100) BioAssay Systems, the glucose-6-phosphate assay kits (EG6P-100) BioAssay Systems and the lactic acid assay kits (A019-2) from the Nanjing Jiancheng Bioengineering Institute, respectively. GP were calculated as the sum of: 2 × (RG + G6P) + Lactate  and exprma meter . The corGenomic DNA was extracted from pig ear tissue using standard phenol/chloroform extraction. In order to meet the genotyping requirements, all DNA samples were standardized to a final concentration of 50 ng/μL and quality controlled. Totally, 301 Erhualian pigs were genotyped using the 1.4M SNP Beadchips, which is a customized Affymetrix Axiom chip. The SNPs on the chip were found from whole genome sequence data of 150 Chinese indigenous pigs and 38 International commercial pigs, and were evenly distributed across the pig genome . PLINK vY is a vector of residual phenotypic values that were corrected for fixed effects (sex and slaughter batch) using lm function in R program. S is the incidence vector for a, and a is the additive genetic effect of the SNP under test; u is a vector of random polygenic effects that is assumed to follow a multivariate normal distribution MVN method for genetic association analyses and SNP-based heritability estimation, which were conducted using the “−lmm 1” and “−gk 1” commands of GEMMA software . GEMMA er2 < 0.3) by the command “indep-pairwise 50 5 0.3” in PLINK v1.9. Therefore, a SNP was considered to be genome-wide significance at p < 3.8 × 10−7 , and to be suggestive significance at p < 7.6 × 10−6 . The impact of population stratification was estimated by the quantile-quantile (Q-Q) plot and genotype data PCA analysis (reduce - Vfull)/Vreduce, where Vfull and Vreduce are residual variances of models for association analysis with and without the SNP term, respectively. Haplotype block or LD analysis was performed for the chromosomal regions with multiple significant SNPs clustered around the peak SNP. Haplotype blocks were identified using the HAPLOVIEW v4.2 software with default settings (Bonferroni correction method was used to set the genome-wide significant (0.05/N) and suggestive (1/N) association thresholds, where N is the number of independent association tests or SNPs . Consideanalysis . The phesettings .The descriptive statistical results of four GP-related traits  in the longissimus muscle of 301 Erhualian pigs were given in p < 0.001; d) and a lower ultimate pH (pHu) value  but strongly associated with pHd , which is located in the intron of SLC8A3 gene on Sus scrofa chromosome 7 (SSC7), exceeded the genome-wide significance threthold (r2 = 0.63) (SLC8A3–SYNJ2BP gene region (93.78–93.95 Mb) on SSC7  , and a c on SSC7 . The min on SSC7 . In addip < 3.8 × 10−7) with the peak SNPs rs320783325, rs330779127 and rs333704759 on SSC8, 9 and 13, respectively. Among them, the rs320783325, an intron variant in LIMCH1 gene, had the most significant association with G6P (p = 9.81 × 10−8). In addition, we found that on SSC14, the GWAS peak SNP rs337801210 for G6P was located 92.7 kb proximal to the GWAS peak SNP rs331183308 for RG (r2 = 0.21) were observed between rs337801210 and rs331183308, they and nine other significant SNPs (2 for G6P and seven for RG) linked to them were concentrated in the 99.52–99.64 Mb region, which only contains the MINPP1 gene. Moreover, the minor alleles of rs337801210 and rs331183308 were both associated with increased phenotypic values , and another on SSC12 with the peak SNP rs345106152 (p = 3.27 × 10−7). Moreover, these two peak SNPs were also significantly with GP. Notably, the pleiotropic locus rs345106152 explained the largest portion of phenotypic variance for LAT (10%) and GP (12.2%)  .p = 3.06 × 10−7) and rs318410870 (p = 3.77 × 10−7) represented the genome-wide significant QTLs on SSC5 and SSC7, respectively. For rs697205060, an intron variant of SRGAP1, its minor allele was associated with increased concentrations of GP and LAT, while the minor allele at rs318410870, an intron variant of the RIPOR2 gene, was associated with reduced concentrations of GP, RG and LAT . The GWAS peak SNPs rs697205060 ( and LAT . FurtherOR2 gene . In addi2) that increase GP values at the five peak SNPs in each individual’s genotype combination, we divided all individuals into three groups: group A with 0–2 alleles2, group B with three to seven alleles2, and group C with 8–10 alleles2. Analysis of variance showed that there were significant differences in GP, pHi, pHu, pHd, drip loss, redness (a*) and yellowness (b*) of meat between the three groups, but no significant differences in lightness (L*). Moreover, the GP, pHd, drip loss, a* and b* values tended to increase in the genotype combination groups with more allele2, while their corresponding pHi and pHu showed a significant downward trend  in breeding. To this end, we first identified the genome combination types of these five SNPs in the Erhualian population. 73 genotype combinations were found in this population . Accordird trend .p < 0.001) correlated with pHu, which is consistent with the notion that higher glycogen or GP at slaughter leads to greater pHu decline   . In addis. ∼5.5) , which m. Unlike most pig GWAS using 50 K or 60 K SNP chips, this study used 1.4 M SNP chips, which improved the statistical power and QTL mapping resolution. In fact, we not only detected 31 QTLs (8 loci per trait on average), but also found QTLs in some regions with small LD.By comparing with the QTLs for GP deposited in pig QTL database (pigQTLdb), we found that all QTLs detected in this study, except for two, had not been previously reported. On SSC7, the GWAS peak locus (rs332736034) we identified for LAT at 109.12 Mb is close to the QTL for the same trait detected in a Meishan × Pietrain F2 family . SimilarMINPP1 gene. The protein encoded by MINPP1 is multiple inositol polyphosphate phosphatase, which can remove 3-phosphate from inositol phosphate substrates and convert 2,3 bisphosphoglycerate  to 2-phosphoglycerate that is part of the glycolytic pathway  for RG and G6P found on SSC14 was adjacent to the  pathway . The reltabolism . Thus, Mtabolism , and SLCeostasis . TherefoRIPOR2 gene. RIPOR2 can inhibit the activity of small GTPase RhoA and regulate myoblast differentiation  in cellular differentiation and proliferation  for G6P was present in the LIMCH1 gene. This gene encodes calponin homology domains-containing protein 1 which enables myosin II head/neck binding activity. The absence of LIMCH1 can affects the formation of actin stress fibers as well as the stability of focal adhesions, which are basic structures to ensure benign contraction and expansion in skeletal muscle (The most significant GWAS signal (rs320783325 with l muscle . Fiuza-ll muscle . HoweverPRKAG3 and PHKG1, two known major genes in pig breeding (It is very important to identify genetic loci affecting GP phenotypes for genetic improvement of meat quality traits, as demonstrated by the application of mutations in breeding . In thisbreeding , indicatbreeding . TherefoSCL8A3, MINPP1, SRGAP1, RIPOR2, and LIFR near the pleiotropic QTL peaks are highlighted as novel candidate genes related to glucose metabolism in muscle, which are worthy of further study. We also demonstrate the potential application of GP-associated SNPs in improving a variety of meat quality traits. Therefore, this study not only advances our understanding of the genetic architecture of GP related traits but also provide genetic markers for improving the pork quality of Erhualian.We found that the GP content in the skeletal muscle of Erhualian pigs was low and had a wide range of phenotypic variation, which may play a role in the formation of specific meat quality traits of this breed. Further, our GWAS analysisin the Erhualian population identified 31 loci significantly associated with the GP related traits, including 29 novel loci. Among them, six loci exhibited pleiotropic effects on these GP related traits.


Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement.E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS).We investigated the genetic basis of MLD resistance in a breeding population of miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants.The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in  A commoEucalyptus species, in which marker number and density have been the limiting factors for a long time, dense sets of Single Nucleotide Polymorphism (SNP) genotyping platforms are now available . Recently, for the same fungal disease, 33 highly significant SNPs were detected in E. obliqua, identifying candidate defense response genes, one of them located within the Ppr1 locus  of Uruguay, in Lavalleja . The trial contain 194 open-pollinated families with a total of 4601 individuals. The experimental design was a randomized complete block design, with 3 replicates and eight-tree row plots. When the trees were ~ 1 year old, there were several consecutive days of rain and high humidity, causing a severe infection of MLD  within each of the 11 chromosome. The LD decay was estimated using the R script by r2 against the physical distance with ggplot  using PREGSF90 of BLUPF90 family of programs . Pairwish ggplot  R packagh ggplot . The thr2.42.4.1Linear mixed models were used to estimate the variance components of proportion of adult foliage, leaf spot severity and defoliation, considering the observed phenotypes and transformed data into normal score within each block . The liny is the vector of phenotypes; β is the vector of fixed effects including the overall mean and block, with incidence matrix X; W; Z; and A is the pedigree relationship matrix and where Heritability of each trait was estimated as:where 2.4.2The most common single-marker models were compared, with different combinations of population stratrification and genetic relatedness correction. First, a naive model, without any correction for population structure or relatedness was fitted independently for each SNP following the linear mixed model:y is the vector of phenotypes; β is the vector of fixed effects including the overall mean and block, with incidence matrix X; W; and ix is a vector that contains the genotype for the thi SNP for each individual and ib is the thi SNP effect considered as fixed. A second model was fitted to account for population structure (model P), where the vector of fixed effects (β) of equation 3 include the first three principal components identified.where Alternatively, to account for family structure, a linear mixed model including a polygenic effect was tested (model K). This association model was fitted using the same naive model by adding a random effect utilizing a genomic relationship matrix as follow:Z is a design matrix and u is the vector of polygene background effects (random), G matrix was estimated using the first method of where Z is the matrix of gene content adjusted for observed allele frequencies and ip is the allele frequency of the thi SNP. To account for family and population structure simultaneously, a model K + P was fitted, where the vector of fixed effects (β) of equation 4 include the first three principal components. All Single-SNP models were fitted using the Sommer R package  that follow a G is the genomic relationship matrix calculated using all the SNPs (equation 5).where every element is defined as before (equation 1), except for the vector of random effects of the individual trees  was considered. Thus, the H-1) was derived by The inverse of the relationship matrix that combines pedigree and genomic information , with an alpha level of 0.05. Quantile-quantile (Q-Q) plots of observed and expected p-values were used to evaluate the control of population structure and genetic relationships in the different GWAS models. These plots were generated with  package .2.5E. grandis (Phytozome Version 1.1 available on https://phytozome-next.jgi.doe.gov/) since the genome of this species is thoroughly annotated. Candidate genes were searched within 30 kb upstream and downstream of the significant SNPs. The size of this interval was chosen based on the typical LD present in the Eucalyptus populations and the SNPs density in our study (1 SNP per 28kb). The sequence of the candidate genes were extracted from the annotation file of E. grandis v1.1. Particular miRNA related to heteroblasty, and their targets, were searched based on the annotation of The physical position of the SNPs was defined according to the reference genome of 33.1T. nubilosa affected all the trees, which showed high variability for all the diseased-related traits. The mean and standard deviation for all the traits increased along with the increasing age of measurement, indicating a high prevalence of infection   resulted in the detection of a large number of associations for all traits Table 2.G matrix were included in the association models (models K and K+P), more p-values follow the uniform diagonal line in the QQ-plots (G matrix resulted in a drastic reduction in the number of significant SNP associations. No significant associations were detected for SEV and DEF after correcting for multiple testing (Bonferroni and FDR at 5%) . For disease damage traits (SEV and DEF), no significant associations were declared using the multiple test correction (Bonferroni and FDR at 5%)  at the same measurement age Figure 4Associations for the proportion of adult foliage at 14 months were detected on chromosomes 2 (1 SNP), 3 (3 SNPs) and 11 (3 SNPs) (3.3.3The number of significant SNP markers detected in the Single-step GBLUP association approach for response and escape to disease traits was larger than in the Single-SNP (model K and K + P) or GBLUP methods, which indicate a better performance of ssGWAS model Table 2.For ADFO, significant SNPs were associated across all chromosomes, except for chromosomes 1, 4 and 10. However, the most significant SNPs were identified in chromosomes 3 and 11 in the three measurement ages, indicating the presence of few genomic regions controlling the precocity of adult foliage change Figure 5The first peak identified on chromosome 3 spans a region of ~ 2 Mb, defined in accordance with SNPs overlapping at the three measurements ages. The high number of significant SNPs was associated with the first age of measurement (14 months), namely nine SNPs located in this region, the most significant with a -log10 of 9.86 (position 47 949 673). Inside this area, the six SNPs significantly associated at 21 months, were also detected at the measurement age of 14 months. Additionally, the five SNPs found at the 26 months, were also detected in the two previous measurement ages. The SNPs detected in this region explained the 1.14, 1.64 and 0.44 of the total genetic variance for each age at measurement of 14, 21 and 26 months, respectively.A small region of ~ 200 kb was also identified in chromosome 11 significantly associated with this trait. This region encompasses 7, 5 and 3 SNPs for measurement at 14, 21 and 26 months, respectively. Once again, the higher number of SNPs was identified at the first measurement age (14 months), and all the markers identified at 21 and 26 months were also detected in the previous measurement age. In this region, the SNP in the position 29 564 447 was the most significant, with -log10 values of 9.69, 11.07 and 9.36 for 14, 21 and 26 months, respectively. The SNPs inside this region represented 1.14, 0.94 and 0.73 of the genetic total variance at the three measurement ages, respectively.Additionally, other significant signals were identified in chromosome 2, with one SNP (position 54 236 535) detected at the conservative threshold of Bonferroni correction at 14 and 21 months, and with the FDR threshold at 26 months. For chromosome 6, two SNPs located 100 kb apart were identified with the less stringent FDR correction for the three measurement ages Figure 5The ssGWAS model was able to detect 12 SNPs in chromosomes 3, 6, 8 and 11 associated with the defoliation measured at 21 months. However, no significant associations were detected for the previous measurement age (14 months). As expected by the correlation between defoliation and the proportion of adult foliage traits, six common SNPs between these traits at the same age (21 months) were observed. Thus, two SNPs identified in chromosome 3 and four in chromosome 11, correspond to the significant regions detected for ADFO.3.4miRNA genes, miR156.5 and miR157.4, were found in chromosome 3 and 11, respectively  for ADFO and SEV, whereas moderate values were observed for DEF. These estimates were similar or higher to those reported in previous studies carried out for equivalent resistance or damage traits of Teratosphaeria fungal diseases in E. globulus. For example, heritabilities for severity to T. nubilosa and T. cryptica ranging from 0.12 to 0.60  to control for close family relatedness. As expected, when the background genetic structure is not properly accounted for in the model, a high rate of false positive marker-trait associations are observed  efficiently captures the variations caused by genetic structure. The adjustment by using the PCA scores may be not adequate, and would be only necessary in populations with high levels of subrace differentiation or with different geographic origins  as a descriptor of the impact of the MLD infection. For this trait, assessed at 21 months of age, we found 12 significant associations, although only one was significant after Bonferroni correction (Most studies addressing disease resistance in forest trees have mainly assessed the disease’s severity as the presence of leaf spots and/or the necrotic lesions present on the leaves. The high heritability found for severity of leaf spots (SEV) in agreement with previous reports, broadens the probability of detecting a gene of large effect. However, large heritability does not imply a direct relationship with the number, effect or proportion of the genetic variance of the genomic regions identified . Our outrrection Table 1.E. globulus. The mechanism to the juvenile to adult transition is under a strong genetic control with the same regulatory mechanisms in both annual herbaceous plants and perennial trees  in a natural coastal population  gene family. The miR156 has been recognized as a master regulator of vegetative change in plants, promoting juvenility due to high expression in seedlings that decrease during development  in Populus trichocarpa infected with stem canker pathogen , suggesting a main role in the genetic control of plant resistance  in various plant species, increasing antifungal responses esponses . Lastly,esponses .Vitis vinifera)  in different plant species in order to regulate defense responses  has been reported to be pivotal in signaling basal defense responses , whereasinifera) . LRR recrasitica . TMV resbioticum , and in bioticum . F-Box/Lesponses .5E. globulus only partially genotyped, and assessed the advantage of including phenotypes from relatives without genotypes in a single-step procedure. We compare several GWAS models that differ in the correction for population structure, the statistical approach to fit the markers and the impact of including combined pedigree, genomic and phenotypic information. Several SNP-trait associations were detected for the precocity of vegetative phase change (proportion of adult foliage). The same SNPs were identified at different measurement ages for the different strategies, providing validation of the results for these specific loci. Additionally, our results suggest that ssGWAS is a powerful model in detecting regions associated to the escape to MLD, showing a greater number of significant SNP associations than Single-SNP and GBLUP-GWAS models. Associations were detected near the miRNAs that regulate the timing of vegetative phase change, consistent with results from other studies about heteroblastic transition in trees and annual species. Thus, our research supports the hypothesis that this is a complex trait determined by a large number of genes with diverse biological functions, that can also include a mechanism of response to biotic stress, such as fungal resistance. Although the identified putative candidate genes will require future validations, they provide a better understanding of the genetic architecture of vegetative change in Eucalyptus. Overall, our results represent a foundational step in marker-assisted selection for a tree breeding program. For example, significant markers or validated genes identified by GWAS can be incorporated into prediction models, to accelerate breeding progress. However, since this is an association discovery study, validation in independent populations is necessary.In the present study, we carried out GWAS for traits related to response and escape to MLD in a breeding population of gbalmelli@inia.org.uy.The phenotypic and genotypic data analyzed in this study are not publicly available. The data are available upon reasonable request to the corresponding author and with permission of INIA. Requests to access these datasets should be directed to GB, MQ, IA, and GB contributed to conception and design of the study. GB designed the field experiment, collected the phenotypic data and provided the marker data. MQ performed the statistical analysis under the supervision of IA. FG and CS performed the candidate gene analysis and wrote this section of the manuscript. MQ wrote the final version of the manuscript. All authors contributed to the article and approved the submitted version.