Understanding mechanisms of sugar accumulation and composition is essential to determining fruit quality and maintaining a desirable balance of sugars in plant storage organs. The major sugars in mature Rosaceae fruits are sucrose, fructose, glucose, and sorbitol. Among these, sucrose and fructose have high sweetness, whereas glucose and sorbitol have low sweetness. Japanese pear has extensive variation in individual sugar contents in mature fruit. Increasing total sugar content and that of individual high-sweetness sugars is a major target of breeding programs. The objective of this study was to identify quantitative trait loci (QTLs) associated with fruit traits including individual sugar accumulation, to infer the candidate genes underlying the QTLs, and to assess the potential of genomic selection for breeding pear fruit traits.PPAIV3 on chromosome 7 and a newly identified SNP on chromosome 11 had quite large effects on accumulation of sucrose and glucose, respectively. We used ‘Golden Delicious’ doubled haploid 13 (GDDH13), an apple reference genome, to infer the candidate genes for the identified SNPs. In the region flanking the SNP on chromosome 11, there is a tandem repeat of early responsive to dehydration (ERD6)-like sugar transporter genes that might play a role in the phenotypes observed.We evaluated 10 fruit traits and conducted genome-wide association studies (GWAS) for 106 cultivars and 17 breeding populations  using 3484 tag single-nucleotide polymorphisms (SNPs). By implementing a mixed linear model and a Bayesian multiple-QTL model in GWAS, 56 SNPs associated with fruit traits were identified. In particular, a SNP located close to acid invertase gene SNPs associated with individual sugar accumulation were newly identified at several loci, and candidate genes underlying QTLs were inferred using advanced apple genome information. The candidate genes for the QTLs are conserved across Pyrinae genomes, which will be useful for further fruit quality studies in Rosaceae. The accuracies of genomic selection for sucrose, fructose, and glucose with genomic best linear unbiased prediction (GBLUP) were relatively high (0.67–0.75), suggesting that it would be possible to select individuals having high-sweetness fruit with high sucrose and fructose contents and low glucose content.The online version contains supplementary material available at 10.1186/s12870-021-03130-2. Pyrus spp.) belong to the subtribe Pyrinae of the Rosaceae and are one of the most important fruit crops in temperate regions. The origin of pear is presumed to be in the mountainous regions of southwestern China and to date back to the Tertiary period (65 to 55 million years ago) [P. communis L.), whereas others that dispersed toward the east gave rise to Asian pear species. Asian pears have fruit with crisp, juicy, and sandy texture that is edible just after harvesting. Records of pear cultivation in China have been found from 2000 to 3300 years ago [P. ussuriensis Maxim., P. bretschneideri Rehder, and P. pyrifolia (Burm. f.) Nakai [P. pyrifolia is presumed to have been introduced into Japan prehistorically and became the major species in Japan [Pears (ars ago) . Pears tears ago , 2, and .) Nakai . Among tin Japan , but thein Japan .Some fruit traits that are important for pear breeding programs and have been genetically studied are fruit harvesting day, fruit weight, fruit hardness, acid content, and sweetness –11. AmonPPAIV3 and PPAIV1 were detected within the QTL intervals. The enzymes encoded by these genes are located in the vacuole, where they catalyze the conversion of sucrose to hexose. Large-effect QTLs that control the conversion of sucrose to hexose were also identified at the similar position on apple chromosome 1 [In a study of various Rosaceae species, pear had a large variation in individual sugar contents in mature fruit , whereasmosome 1 . Moreovemosome 1 , 29, thon = 2x = 34) [S haplotypes that control gametophytic self-incompatibility [Alternaria alternata [Pyrus and Malus genome sequences, pear genetic studies have been conducted using advanced apple genome information [P. betulaefolia), and European pear are available [Whole-genome duplications are suggested to have occurred in pear and apple, as their genome sequences have extensive syntenic blocks covering much of the chromosomes 22x = 34) . In additibility  and genelternata , 33 are lternata , 34–36.  . In addlternata , 37–39. ormation . The genormation . Althougvailable –44, a drSeveral useful DNA markers have been developed and applied in Japanese pear breeding programs: these include DNA markers to identify self-compatibility , 46, a mIn this study, we updated Minamikawa et al.’s  study byThe average contents of sucrose (SUC), fructose (FRU), glucose (GLC), sorbitol (SOR), and total sugar content (TSC) averaged over 1218 individuals , and GLC had positive correlations with FRU and SOR . TSC was correlated positively with SUC, FRU, and SOR  but was not significantly correlated with GLC. TSC had a very strong positive correlation with SSC (r = 0.92), indicating that the SSC of juice from mature Japanese pear was composed almost entirely of sugars.Phenotypic correlation coefficients and their significances for all trait combinations were calculated Table . SUC hadA total of 1.7 billion reads were obtained from the 1218 individuals . After trimming low-quality data and adapter sequences, 94.5% of the high-quality reads were successfully mapped onto the apple GDDH13 reference genome. After selecting SNP loci with VCFtools and removing individual SNP markers with < 25% missing data, we obtained 9011 SNPs. Missing data were imputed using Beagle 4.0, and 7463 SNPs with > 0.8 imputation accuracy were selected. To normalize the SNP density, 3484 tag SNPs were selected from the 7463 SNPs using Haploview.r2 values (estimates of LD) were plotted against marker distances (bp) in the apple genome GDDH13. The average r2 values dropped below 0.2 at a marker distance of 250 kb and below 0.1 at 1750 kb  between pairs of loci was estimated using all 7463 SNPs, before selecting the tag SNPs. The K for Bayesian clustering analysis, the standard errors of the cross-validation error were calculated and found to plateau around K = 15. Because the cluster diagrams for values of K around K = 15 were too complicated to provide a clear overview of the genetic relationships among the breeding populations, Bayesian clustering analyses at K = 3 and K = 4 are shown in Fig. K = 3 and K = 4 roughly corresponded to the classification based on PCA. Although cultivars showed admixed genetic structure, each breeding population had a simple genetic structure. Populations derived from crosses with ‘Kanta’ (proportion of “orange” cluster = 1.00) and other cultivars  presented the “orange” genetic cluster, those derived from crosses with ‘Hoshiakari’ (proportion of “light blue” = 1.00) and other cultivars  presented the “light blue” genetic cluster, and those derived from crosses with ‘Akizuki’ (proportion of “dark blue” = 1.00) and other cultivars  presented the “dark blue” genetic cluster. When K = 4, a “green” cluster was formed from the “dark blue” and the “light blue” clusters in some cultivars and populations . The average ratio of “orange”:“light blue”:“dark blue” in 1218 individuals was 0.20:0.38:0.42 at K = 3, and the average ratio of “orange”:“light blue”:“dark blue”:“green” was 0.19:0.33:0.35:0.13 at K = 4. The new “green” cluster seemed to be derived from relatively old cultivars such as ‘Okuroku’  and ‘Inagi’ (proportion of “green” = 0.60) that are genetically somewhat different from recent cultivars.Prior to setting the value of GWAS analysis was conducted for 10 fruit traits by applying two methods: one using a mixed linear model (MLM) to test the significance of association between a single SNP and each trait, and the other using a Bayesian multiple-QTL model. The latter was regarded a variational approximation version of the BayesB method  in whichr2 = 1.00 between Chr00_30710088 and Chr11_41197041 for population 589, derived from ‘Kanta’ and ‘Rinka’). Thus, the effect of Chr00_30710088 was excluded from further analysis and discussion. Also, Chr15_17923340, which was significant for SUC, and Chr15_16568005, which was significant for FRU, are close to each other, suggesting that these two SNPs are detecting the same QTL. For HarT, two significant SNPs were identified in the MLM-based GWAS. Chr03_31587739 and Chr15_16568005 showed high − log10 (p) values  and explained 16.5% and 20.3% of the phenotypic variance, respectively. The difference between the average values of the CT and TT genotypes was 14 days for Chr03_31587739, while the number of CC individuals was too low to include in the comparison. For Chr15_16568005, the average values of the AG and GG genotypes were comparable , but that of AA was 66.2. Out of the four significant SNPs associated with Aci, Chr06_20720912 and Chr16_3043313 showed relatively high percentages of variance explained . The average values for Aci at Chr06_20720912 were 4.85 for AA, 4.96 for AT, and 5.20 for TT , in which a polygenic effect included in MLM-based GWAS was regarded as the breeding value of an individual, and with the vBayesB-based method, which was also used in GWAS. We used each F1 family as a tested population and the remaining families and cultivars as the training population, where a prediction model was constructed with both observed phenotypes and SNP genotypes of a training population and the breeding values of each tested population were predicted from SNP genotypes with the constructed model. We calculated correlation coefficients between predicted breeding values and observed phenotypes over all individuals in F1 families Table . The prer =  − 0.57 and − 0.76, respectively), and FRU had a positive correlation with GLC (r = 0.39). This result was quite similar to those in previous studies of QTLs for individual sugar traits [r =  − 0.42). These results suggested that sugar conversion from sucrose to hexose or from sorbitol to sucrose had a strong influence on sugar composition in mature fruit. TSC had quite a strong correlation with SSC, suggesting that it would be possible to use SSC for the first screening of individuals: it is much easier to measure SSC (the amount of sugar in fruit) than TSC, which requires high-performance liquid chromatography. In a previous study, the broad-sense heritability of TSC was only 0.33, whereas those of SUC, FRU, GLC, and SOR were 0.64, 0.69, 0.71, and 0.76, respectively [Several phenotypic correlations among individual sugars were identified in 1218 individuals and cultivars. SUC had strong negative correlations with both FRU and GLC , those derived from crosses between ‘Hoshiakari’ and other cultivars (“light blue”), those derived from crosses between ‘Akizuki’ and other cultivars (“dark blue”), and cultivars and populations relatively distant from those three cultivars (“green”). ‘Kanta’ has high sweetness, with high TSC and FRU [In previous studies, LD values of Japanese pear had been calculated based on genetic linkage maps (centimorgan distance) , 10. In a et al.  and Kumaa et al. , but a la et al. . We expea et al. . Neverth and FRU , ‘Hoshia and FRU , and ‘Ak and FRU . Because and FRU , the genPPAIV1 and PPAIV3 were the candidate genes [In this study, we identified several candidate genes based on the genome sequence of GDDH13. All of these candidate genes were successfully mapped to the whole-genome sequences of European pear and wild pear in China, suggesting that those genes are conserved in the Pyrinae Table . We idente genes . In applte genes . Thus, iearly responsive to dehydration (ERD6)-like gene, encoding sugar transporters, are also located near two of the SNPs (Chr11_41197041 and Chr04_29871378). A single ERD6-like gene was detected near Chr04_29871378 , whereas a tandem repeat of ERD6-like genes  was detected near Chr11_41197041. Arabidopsis ERD6 was found to be sugar transporters in bacteria, yeasts, other plants, and mammals [ERD6-like family genes were found to be conserved in citrus, grape, apple, and Chinese jujube [ERD6-like genes detected in this study is also conserved in the genomes of European pear and wild pear in China  was identified as the candidate for a gene associated with low fruit acidity [ma1 is located at Chr16:3,176,495–3,179,279, which is close to Chr16_3043313 (for Aci) in this study. In apple, two major loci, Ma3 on chromosome 8 and Ma1 on chromosome 16, play an important role in fruit acidity [Several QTLs for HarT and Aci similar to those identified here were identified in previous studies of apple. The NAC18.1 transcription factor  is a promising functional candidate for fruit ripening  and is c acidity . In the  acidity . On the In this study, we focused on detecting QTLs and inferring putative candidate genes for those QTLs, whereas Minamikawa et al.  focused PPAIV3 on chromosome 7 and a newly identified SNP at chromosome 11 had large effects on individual sugar contents. The SNP on chromosome 4 that was associated with FRU would be useful for increasing the contents of FRU and TSC without decreasing the contents of other individual sugars. The candidate genes of QTLs identified in this study are conserved in the genomes of several Pyrinae species. Further studies including expression analysis of those genes and developing gene-specific markers would contribute to clarifying the mechanisms of sugar accumulation and validating the candidate genes for fruit traits. Fruit traits are complex and controlled by multiple factors, so it is important to accumulate relevant genetic information. The traits evaluated in this study covered the principal fruit traits in pear breeding programs, so the results obtained illustrate the feasibility of GS for fruit traits in pear.In this study, we collected phenotypic data for fruit traits and conducted GWAS and GS in Japanese pear. Several important QTLs for fruit traits were identified, and genes associated with sugar accumulation were predicted by GWAS using a large number of individuals. The SNP located closest to www.gene.affrc.go.jp) and the breeding populations were developed at Institute of Fruit Tree and Tea Science, NARO. The population ID numbers are indicated in Table A cultivar collection including 106 cultivars and 17 breeding populations  were used in this study  were analyzed in fruit from each individual. To do this, the juice from two fruits per sampling date was extracted and the samples were combined. Sampling was performed on two days in each of the years that the individual or cultivar was analyzed. The analysis of sugar components was based on the method described by Nishio et al. . TSC wasPhenotypic data were collected from 2014 to 2018. The majority of individuals and cultivars were evaluated for more than one year. The average phenotypic values over five years (2014–2018) were generated by the allEffects function from the package “effects”. Phenotypic correlation coefficients and their significance were calculated for all trait combinations using R (4.0.0) (R Development Core Team).2O and amplified by 20 cycles of PCR with indexed primers. Amplicons were pooled and separated on a BluePippin 1.5% agarose cassette , and fragments of 300–900 bp were purified using a Mini Elute Kit (Qiagen). The library was then sequenced using a HiSeq 4000 .Genomic DNA was extracted from young leaves with a DNeasy Plant Mini Kit  according to the manufacturer’s instructions. ddRAD-Seq libraries were constructed as described in Shirasawa et al. . A totalhttps://github.com/vsbuffalo/scythe) for filtering out contaminant substrings and Sickle (https://github.com/najoshi/sickle), which removes reads with poor-quality ends (Q < 30). Alignment to the reference apple genome  was carried out using BWA aligner [http://www.broad.mit.edu/mpg/haploview) was used to select tag SNPs using its pairwise function, with a minimum r2 of 0.8.SNPs and indels were identified according to Acquadro et al. . Illumin aligner  , wild pear in China (P. betulaefolia), and European pear (P. communis) are all currently available [Pyrus pyrifolia. There is also less information on genes and transcripts and fewer relevant genetic studies in comparison with those using apple reference genomes. Because the apple genome GDDH13 is the most frequently used reference genome in the Rosaceae and it has precise order and rich information on transcripts, QTLs, and LDs, we used GDDH13 as the reference genome in this study.The draft genomes of Chinese pear (vailable –44. Hower2) were plotted against physical distances in increments of 10 kb.LD was estimated using data after the imputation. Based on the apple GDDH13 genome, the detected SNPs were aligned according to their positions. LD between pairs of SNPs was calculated using R (4.0.0) (R Development Core Team). The SNPs mapped on the fictive chromosome 0, which contains all unassigned scaffolds, were removed to calculate the LDs. Average LD values  from 1 to 20 to estimate the cross-validation values. The cross-validation value gradually decreased as the value of K increased. The CLUMPAK online tool [K = 3 and K = 4. PCA was performed in PLINK v1.90. Prior to PCA, an input file was created by conducting linkage pruning using the –indep-pairwise option in PLINK (plink –file data –indep-pairwise 50 10 0.1). PCA was performed using the –pca option in PLINK. The plot was drawn with the R package “ggplot” [Population structure was estimated for each cross and for the cultivar collection in ADMIXTURE 1.30  using thine tool  was appl“ggplot” .For the MLM-based GWAS, the effect of a SNP and a polygenic effect affected by the genetic background of an individual were included in the model as a fixed effect and a random effect, respectively. The covariance matrix of polygenic effects between individuals was established with a kinship matrix calculated from SNP genotypes. The effects of population structure were also included as fixed effects using the first three principal components obtained from the SNP genotype data. We used the R package rrBLUP ver. 4.3  for thisFor the vBayesB-based GWAS, a Bayesian multiple-QTL model was applied in which all SNPs were simultaneously fitted in the model assuming a specific probability for each SNP included in the model, called SNP weight, and a specific variance for each SNP effect as well as the progeny effect specific to each F1 family. The progeny effect specific to each F1 family was also included in the model to control for the difference in genetic background between F1 families. The model was fitted with a variational approximation method proposed by Hayashi and Iwata  using a To validate the effect of the detected SNPs, the average values of each genotype for the SNPs and the distribution of the phenotypic values in the SNP genotypes were plotted. These plots were drawn with the R package “ggplot” . The pheTo infer the candidate genes of the detected QTLs, we thoroughly checked the QTLs and genes identified in previous apple genetic studies , 66, 67.The two methods used in GWAS, MLM-based and vBayesB-based, were also used for prediction of breeding values based on SNP genotypes. In the MLM-based method, a polygenic effect was regarded as a breeding value that was predicted from SNP genotypes through a kinship matrix. This MLM-based method was referred to as GBLUP when applied to GS. In this study, an intercept but no fixed effects were included in MLM for GS. In the vBayesB method, we obtained estimates of a SNP effect and a SNP weight for each SNP, regarded as an approximate posterior probability of each SNP included in the model; accordingly, the predicted breeding value of an individual was calculated as the sum of the estimated SNP effects multiplied by the SNP weights over all SNPs.r) between observed phenotypic values and predicted genotypic values. When the estimated r was less than 0, it was regarded as 0. The prediction accuracy for each tested family based on the training population (which excluded the target family) was calculated and averaged to understand the accuracies of predicted genomic breeding values for the breeding populations used in this study.Generally, in practical breeding programs, new elite cultivars and selections have been used as parents to create breeding populations and to select genotypes superior to those of the established leading cultivars. Thus, GS would be applied in breeding populations obtained by crossing between new combinations of parental genotypes. To evaluate the accuracy of genomic prediction for practical pear breeding programs, we conducted a cross-validation in the following way. Each F1 family was regarded as a tested family, and the set of cultivars and F1 families with the tested family excluded was regarded as a training population. A prediction model was constructed using data for both phenotypes and SNP genotypes of a training population, and breeding values of individuals of a tested F1 family were predicted with a model using only their SNP genotypes. This process was repeated until all F1 families were selected just once as a tested population and the prediction accuracy was evaluated with the Pearson’s correlation coefficient (https://ddbj.nig.ac.jp/DRASearch/submission?acc=DRA011324).The datasets supporting the conclusions of this article are included within the article and its supplementary information files. Sequence reads are available from the Sequence Read Archive (DRA) of DNA Data Bank of Japan (DDBJ) under the accession number of DRA011324  against physical distances in increments of 10 kb. Gray curves show local polynomial fits obtained using kernel smoothing regression. Dotted lines indicate the genetic distance at which r2 fell below 0.2. Figure S2 Summary of the results of genome-wide association studies using two different models. a Manhattan plots of −log10(p) values based on a MLM for 10 fruit traits. Red lines indicate a false discovery rate of 0.05. Chromosome location information is based on GDDH13 Version 1.1. The fictive chromosome 0 contains all unassigned scaffolds. b Posterior probability of having a QTL based on vBayesB, estimated for 10 fruit traits. Figure S3 Heatmap showing the percentage of the phenotypic variance explained by each QTL in each population. The percentages for the cultivar collection (designated as “Cultivar”) and for all 1218 accessions including the populations and cultivars (designated as “ALL”) are shown on the right. The prefix rrBLUP indicates the SNPs identified in the MLM-based GWAS analysis; BayesB indicates those identified in the vBayesB-based GWAS. A blank indicates that the population showed no segregation for the SNPs. The intensity of the red color corresponds to the percentage of phenotypic variance explainedAdditional file 2:Table S1 The cultivars and individuals used in this study and the performance for fruit traits.Additional file 3: Table S2. Average values of individual and total sugar contents for each genotype of three SNPs having a large and significant effect on at least one trait.


The study aims to identify risks of agrochemicals that impact farmworkers, consumers, and ecology in Vietnamese mango cultivation to enhance safety and friendly production. The study finds out the total numbers of root fertilizers (N-P-K) of the noncooperative and cooperative farmers are similar, approximately 1,400 kg/ha/year higher than those in other countries. Excessive fertilizer usage is a potential threat to soil, water, and air pollution. In addition, the findings indicate that the ecology component is undergoing the most negative impact from excessive agrochemical use in mango farming. The vast majority of agrochemicals in mango cultivation are fungicide and paclobutrazol over 90% of the total number of agrochemicals used in both noncooperative and cooperative farmer groups among the three seasons. Total field EIQ of the cooperative grower category is less than that of the noncooperative grower category. These results show that mango cultivation should consider rejecting the banned active ingredients of glyphosate, paraquat, and carbendazim as well as reducing fungicide and paclobutrazol usage and encouraging cooperative participation to safeguard the environment and human health. Moreover, science information needs to be closely linked and fed back to policy development to boost the management of the awareness of the ecological risks for farmers associated with reducing agrochemical use in mango cultivation. Nowadays, agriculture production relies on increasingly agrochemical and chemical fertilizers to increase productivity . FarmersMango is one of the most popular tropical fruits, known as the king of fruits, nutritionally rich fruits with unique flavour, fragrance, and taste. Vietnam is a tropical country with long latitude, sun, heavy rain, diverse ecology, and clear zoning. The most prominent advantage is tropical fruits such as durian, rambutan, mangosteen, mango, jackfruit, guava, and dragon fruit. Mango is the second most popular fruit in Vietnam (after banana) and is grown across many provinces. Mango is one of the most prominent tropical fruits in Vietnam, and its production generates a large amount of income for growers and stakeholders in the value chain. The production area of mango rose sharply between 2015 (83.7 thousand hectares) and 2019 (104 thousand hectares). The Mekong Delta (MD) is the key region for tropical fruit production in Vietnam. Due to its ideal natural conditions and effective farming techniques, the MD has the largest share of mango production in Vietnam. The region also has the highest overall productivity level in Vietnam and plays a key role in the government's flowering manipulation policy. In 2019, it was the largest mango production region in the country, with 48,200 ha  and 511,800 tons . Within the region, the largest mango areas are in Dong Thap, An Giang, Vinh Long, Hau Giang, Dong Nai, and Tien Giang [There are different measurements of the environmental risk of agrochemical use . HoweverIn the recent literature, various studies have determined a nexus of human health damage and air pollution , 18. SimThe environmental impact quotient (EIQ) model was developed by Kovach et al.  at CorneInformation from Calculation of total field EIQ helps producer and farm manager have overall picture of agrochemicals use in production process and identify the negative impacts of these pesticides on health and ecology. This helps them have necessary adjustments in pesticide use for pests and disease management more environmentally friendly . In thisOur data cover seven of the major mango farming provinces in southern Vietnam such as An Giang, Dong Thap, Tien Giang, Vinh Long, Tra Vinh, Hau Giang, and Dong Nai . The datAlthough there are at least fourteen elements or nutrients that are required for plant growth, the three key nutrients for mango production are nitrogen (N), potassium (K), phosphorous (P) . UnderstData from the Southern Vietnam survey  show thaApplying the N fertilizer in mango cultivation also needs to note that if applied excessively, it can have harmful impacts such as reduction of yellow skin percentage in mature fruits, increasing the severity of anthracnose . MeanwhiIn mango farming, mango gardeners usually apply the liquid fertilizer (N-P-K) to spray on mango leaves for flowering stimulation . The liq2O, NO2) emissions. The nitrogenous fertilizer used in mango cultivation only absorbs a part of the soil, the rest of the nitrogenous fertilizer is lost by evaporation and reacts with organic compounds in the clay soil, and the remaining interferes with surface and groundwater [In summary, the application of fertilizer in mango production needs strict control because it has not only an influence on humans  but also an impact on ecology . Nitrogenous fertilizer is considered one of the most important inputs in agricultural production. Its overuse causes air pollution by nitrogen oxide  that are applied in mango production, in which nineteen active ingredients  are the The results of The findings from According to , EIQ claThe field EIQ values of the farmworkers, consumers, and ecology components in the cooperative farmers are 616.08, 319.51, and 1,778.86, respectively . These fThe findings of season 3  are simiAmong the agrochemicals, fungicide has the highest EIQ percentage compared to paclobutrazol, herbicide, and pesticides. Specifically, the fungicide EIQ percentage of the cooperative grower group is 42.9%, 52.5%, and 69.6% in seasons 1, 2, and 3, respectively, and in the noncooperative grower group, it is 45.3%, 61.3%, and 75.4% in seasons 1, 2, and 3, respectively. Next is paclobutrazol that is an active ingredient of mango growth control to stimulate flowering. Paclobutrazol EIQ rate of the cooperative farmers is 49.3% in season 1, 42.0% in season 2, and 23.2% in season 3, and the figure of the noncooperative farmers is 49.1% in season 1, 31.2% in season 2, and 16.6% in season 3. The result of the study indicates that the vast majority of agrochemicals in mango cultivation is fungicide and paclobutrazol over 90% of the total amount of agrochemical used in the noncooperative and cooperative farmer groups among the three seasons.In herbicides, glyphosate is used the most popular to control weeds in both noncooperative and cooperative farmer groups among the three seasons, followed by paraquat. The glyphosate may reach groundwater, surface water, and other nontarget sites through processes of runoff, spray drift, and root uptake. The herbicide glyphosate raises the susceptibility of plants to diseases and impacts the health and nutrition of crops . In fungThe study contributes to the interpretation of threats of fertilizers and pesticides to health and ecology in mango farming in Vietnam by the EIQ model. The findings show that the cooperative farmer group uses the N-P-K fertilizers more than the noncooperative farmer group. The total field EIQ of the cooperative grower category is less than that of the noncooperative grower category. The ecology EIQ component is higher than the farm worker and consumer EIQ components in the noncooperative and cooperative farmer group in three seasons. The ecology is undergoing the most negative impact from excessive agrochemical use in mango farming. The vast majority of agrochemicals in mango cultivation is fungicide and paclobutrazol over 90% of the total number of agrochemical used in the noncooperative and cooperative farmer groups among the three seasons.To reduce agrochemical use in mango production, farmers should focus on reducing fungicide and paclobutrazol usage. To safeguard the environment and human health, mango cultivation in southern Vietnam should consider rejecting glyphosate, paraquat, and carbendazim. These active ingredients are acute toxicity characterized with high toxicity to animals and humans that caused health problems soon after exposure to UTZ classification in 2015. Moreover, the N-P-K used in mango cultivation needs to be considered carefully because overuse of the fertilizer may lead to negative effects on mango progression and soil, water, air, and terrestrial and aquatic ecosystems.Agrochemicals are widely used in mango cultivation to ensure crop quality and production. However, the use of such agrochemicals may cause adverse effects to the farmworkers, consumers, and ecology. It has long-term effects on human health. Agrochemical pollution is not easily measured, so the potential environmental risks do not evoke an immediate response, as they can occur in several years. Therefore, science information needs to be closely linked and fed back to policy development to boost the management of the awareness of the ecological risks for farmers associated with reducing agrochemical use in mango cultivation.


Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes.Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the  When ASE is parentally guided, it is named genomic imprinting, caused by epigenetic modifications with cis action, which result in the monoallelic expression of genes essential for mammal growth and development2.Allele-specific expression (ASE) or allelic expression imbalance is a pattern that reflects the expression difference between copies of a gene from each chromosome 3. The knowledge about regions with ASE and their regulatory mechanisms is relevant to improve animal breeding programs by increasing the accuracy of predictive models4. De Souza et al.5 identified SNPs in transcribed regions showing allele-specific expression patterns (ASE SNPs) in the muscle of Nelore (Bos indicus) related to meat tenderness, an important trait for meat consumer’s approval7. Many of these SNPs were located within genes responsible for essential biological functions for muscle development and meat tenderness5.Genes showing ASE in their transcripts, as evidenced by the analysis of single nucleotide polymorphisms (SNP) alleles within them, are called ASE genes. ASE genes have been shown to affect phenotypes for livestock, such as adipogenesis and lipid metabolism in pigs10. For instance, ASE can be caused by differential methylation in regulatory regions of the two parental alleles11. DNA methylation is a well-known imprinting mark in CpG islands12, DNA regions with clusters of cytosine followed by guanine dinucleotides. This type of epigenetic modification, when presented in promoters, can suppress transcription when the CpG islands are hypermethylated, while active transcription shows no methylation or few isolated methylation events11. Another mechanism involved with the ASE pattern is the presence of differences on microRNA (miRNA) binding sites between the alleles14, once miRNAs can impair the translation by degrading the target mRNA sequence15. Similarly, sequences showing an affinity for a transcription factor (TF), i.e., a transcription factor binding site (TFBS), may promote or suppress the transcription of the allele in phase with the given TFBS. Thus, in a heterozygous locus, if the regulatory region of only one allele has an affinity with the transcription factor (TF), this cis-regulatory variant can also lead to ASE16.Recent studies have explored putative regulatory mechanisms that may be responsible for ASE and genomic imprinting17. From this initial screening, aseQTLs that present any evidence of being related to interesting phenotypes and regulatory mechanisms affecting allelic expression can be prioritized for further analysis. AseQTLs can contribute to the understanding of expression regulation of genes associated with economic traits and how alleles under selection are expected to be expressed in the offspring23.To discover regulatory mechanisms causing ASE, we can execute an initial screening analysis to identify SNPs associated with a given transcript's unequal allele expression pattern: the ASE-quantitative trait loci (aseQTLs)5. We then analyzed its involvement with predicted regulatory mechanisms, such as DNA methylation, whether they contained TFBS or miRNA binding sites. This study aimed to add a new knowledge layer about genomic regulation in Nelore muscle, describing potential mechanisms involved in the allelic expression of genes related to relevant phenotypes in bovine.Herein, we investigated aseQTLs associated with allelic expression imbalance, evidenced by the number of reads corresponding to different SNP alleles in transcripts, previously described in a Nelore experimental populationP value ≤ 0.05), associated with 126 SNPs marking allelic imbalance of 85 genes. AseQTLs reference alleles have a mean frequency of 0.67, ranging from 0.49 to 0.94. Figure We retrieved a total of 82,384 SNPs after filtering the original genotype file , considering a 1 Mb sized window around the 820 SNPs with significant ASE. Despite this initial dataset, only 22,470 tests were valid due to the requirement of 10 heterozygous samples for both the aseQTL candidate and the SNP marking allelic imbalance. In this context, only 192 SNPs with ASE were tested in the aseQTL analysis. Allelic imbalance ratio values were compared between groups of homozygous and heterozygous animals for each SNP to be tested as aseQTL. AseQTL analysis resulted in 1134 significant aseQTLs (XIRP2), associated with the ASE pattern of the rs109372848 SNP; and rs136097891 (MTUS1), an aseQTL for the rs109005284 SNP (MTUS1). AseQTLs association results can be found in Supplementary Table Three SNPs marking ASE were also identified as aseQTLs: rs136209194, located in the intergenic region, which acts like an aseQTL for the rs132817153 SNP; rs109842586 . The rs134422650 marker was associated with the highest number of aseQTLs (56). The second one was the rs110850310, located in the CMYA5 gene, and the rs110663707 aseQTL was associated with three SNPs, also of the CMYA5 ASE gene. Moreover, 54 aseQTLs were associated simultaneously with different SNP pairs.Most aseQTLs affected the ASE marked by a unique SNP. Still, we found the rs132798564 aseQTL simultaneously associated with four ASE SNP markers of the An average distance of 404,983 bp (varying from 281 bp to 1.1 Mb) was observed between aseQTLs and the respective transcription start sites (TSSs) of affected ASE genes. Moreover, the average distance between an aseQTL and its associated SNP marker for ASE was 417,698 bp . AseQTLs were more frequently located close to the TSS of ASE genes and their associated SNPs (from 0 to 100 kb), becoming less frequent as the distance increases, especially in intervals larger than 1 Mb. Figure CMYA5 gene.Firstly, we evaluated the linkage disequilibrium (LD) between all aseQTLs to investigate LD blocks with associated regulatory elements. A total of 3030 aseQTL pairs showed D′ greater than 0.8, and 1813 of them were in total disequilibrium (D′ = 1). In our aseQTL data, we identified 117 LD blocks . Seven blocks consisted of two neighboring SNPs marking ASE within the same gene, i.e.,CMYA5 was identified as presenting more aseQTL/ASE SNP interactions (38), followed by EIF5 (33) and VCP (27).Lastly, we evaluated LD values between SNPs marking ASE and their respective associated aseQTLs. From 1134 SNP pairs submitted to LD analysis, 238 showed D’ higher than 0.8, and 71 aseQTL/ASE SNP pairs were in total disequilibrium (D’ = 1) (Supplementary Table 4) corresponding to 35 ASE genes. The gene P value = 0.0001). The three traits with the highest number of aseQTLs within a QTL region were body weight (81 aseQTLs), milk palmitoleic acid content (42 aseQTLs), and intramuscular fat (IMF) (28 aseQTLs). The rs136717535 aseQTL overlapped with regions of more QTL traits: milk kappa-casein percentage, milk protein percentage, shear force, calf size, and birth index.After converting the QTLs retrieved from the Cattle QTL Database to the current genome assembly build, ARS-UCD1.2, we identified overlaps of 173 aseQTLs within QTL regions for 21 traits, distributed in 215 occurrences (Supplementary Table 5). The permutation approach applied to test if the probability of the aseQTLs location within QTL regions was higher than chance, was significant . We identified 234 aseQTLs distributed in 894 overlaps with 25 traits (Supplementary Table 6). The aseQTLs that integrate with GWAS data from our research group are distributed in the blue ring of Fig. 21. Table We also integrated data with other studies produced by our research group, with a broader sample of the same experimental population. All permutation tests performed for QTL ranges data were significant (ACOT13 gene), with eight aseQTLs in QTLs for eight meat quality traits, i.e., b*FAT, BFT, CL, L*FAT, L*MUSCLE, REA, WBSF7, and WHC. Thirty-five aseQTLs associated with six SNPs marking ASE of CMYA5 overlapped with seven meat quality traits: b*FAT, CL, L*FAT, REA, WBSF0, WBSF7, and WHC.The rs109550233 SNP, marker of allelic imbalance, presented more associated aseQTLs overlapping with QTLs for meat quality traits than any other in this study, with nine aseQTLs within regions of QTLs for nine traits, b*FAT, BFT, CL, L*FAT, L*MUSCLE, REA, WBSF0, WBSF7, and WHC, followed by the rs135906938 SNP , maintenance efficiency (ME) and partial efficiency of growth (PEG)26. Additionally, 20 aseQTLs overlapped with cis-eQTL regions and four with trans-eQTL regions27.Ten aseQTLs overlapped with mineral content QTLs for Co, Mn, Zn, Ca, Cr, Ar, K, Mg, and S28 (Supplementary Table 7). AseQTLs were predominantly distributed in intronic regions (68%), followed by intergenic regions (27%). The remaining approximately 5% were distributed equally in non-coding transcription regions, 3′UTR variants, missense variants, and synonymous variants. Four SNPs, rs132671408 (in the AOPEP gene), rs135305605 (in the RUSC2 gene), rs136155631 (in the RAI14 gene) and rs43291678 (in the CCDC141 gene) were within the 5′UTR regions and three were located in splice regions: rs109091165 (inside PECAM1 gene), rs133325919 (inside FAIM2 gene) and rs137526254 (inside MYO6 gene).According to aseQTL locations in the genome, we predicted SNP consequences with the VEP softwareMSRB1 gene, was methylated in 11 of the 12 tested animals. Thirteen aseQTLs had 37 cytosines with 100% of methylation in the samples. Eight animals showed 100% of methylation in the rs134229733 aseQTL, and seven animals in the rs137496307 aseQTL, associated with an SNP marker of ASE of the VIM gene , at a distance of 51,026 bp from it. The average distance of these methylated aseQTLs and the TSS of associated ASE genes was 216,169 bp. The other ten methylated aseQTLs were associated with 9 ASE SNPs. The rs42616057 and rs109332326 aseQTLs were both methylated and associated with the allelic imbalance of rs110095343 SNP.Six methylated aseQTLs were associated with SNPs inside transcribed regions with the allelic imbalance of seven genes: 29, we found 83 TFBS overrepresented in our sequences . Of them, only 12 TFs are expressed in bovine muscle30. Considering only aseQTLs that change muscle-expressed TFBS, we found 215 aseQTLs, distributed in the pink ring of Fig. Using 50 bp flanking sequences of all aseQTLs as input in TRAP software31. These muscle-expressed miRNAs were denoted without an asterisk in Supplementary Table 10, which contains the results of miRNA binding sites prediction.We used a subset of 163 aseQTLs identified outside intergenic regions and that overlapped with our reference population’s QTL data for miRNA binding site prediction. We identified 153 miRNA binding sites corresponding to 163 aseQTLs flanking sequences. Considering miRNA binding sites affected by aseQTL, we obtained 1448 aseQTLs/miRNA binding site pairs. In silico analysis of these pairs predicted 693 miRNA binding sites with the alternative aseQTL allele, whereas 755 binding sites were predicted in the reference aseQTL allele , rs135834852 (NXN), rs136463427 (CUEDC1), rs110417686 (CUEDC1), rs132705216 (MBNL2), and rs137489440 (MBNL2), rs133710221 (CAB39). The complete list of aseQTLs affecting the binding sites of miRNAs known to target the ASE genes is in Supplementary Table 11.In the following examples, aseQTLs affected the binding sites of miRNAs known to affect the ASE gene in question. The rs42437277 aseQTL, associated with the allelic imbalance of the CMYA5, TXNIP, DNAJC21, NXN, TMBIM6, ASB5, CAB39, ACOT13, CUTC, and CUEDC1, respectively. Considering each section of results, represented in Fig. CMYA5, with 35 aseQTLs overlapping seven meat quality traits identified in our Nelore population; TXNIP, with 36 aseQTLs located in QTL regions from Cattle QTL Database; CMYA5 with 31 aseQTLs potentially changing the binding of miRNAs and EIF5 gene showed 11 aseQTLs in TFBSs. Additionally, seven genes showed one methylated aseQTL.In short, we predicted three regulatory mechanisms in our aseQTL data: methylation, TFBSs, and miRNA binding sites. Only one aseQTL, rs109540393, associated with the ASE present in the rs109550233 SNP genomic location, was methylated in six animals of our population and was potentially creating the binding site of the AHR TF. Six miRNAs expressed in Nelore muscle had binding sites created by the aseQTL rs109540393, five matching the reference allele sequence and one the alternative allele. This aseQTL overlapped with nine meat quality traits. Moreover, 33 aseQTLs were within two regulatory mechanisms, 354 were predicted to have one regulatory mechanism, and 746 did not show any regulatory evidence. Supplementary Table 12 contains the number of regulatory mechanisms or overlapping QTLs for each aseQTL. In Fig. Longissimus thoracis muscle of Nelore steers. Due to the sample requirement to associate each SNP marking ASE and aseQTL SNP, we tested 23% (192 out of 820) SNPs with significant ASE previously detected in the same population5 and identified aseQTLs for 126 (66%) of them. Thirty-seven ASE SNPs associated with aseQTLs were also described in a previous ASE study developed with bovine muscle samples8. Two ASE SNPs described by Guillocheau et al. (2019) were identified here as aseQTLs: rs109170993, associated with the rs110850310 ASE SNP, and rs109842586, associated with the rs109372848 ASE SNP. The rs109842586 was also identified marking ASE in our population as well5.In this study, we identified 1134 aseQTLs associated with the ASE pattern of 126 SNPs in the HIKESHI gene, which mediates heat stress-induced nuclear transport of Hsp70 heat shock proteins, protecting cells from heat shock damage34. Nelore cattle are widely valued by their heat tolerance36, and this aseQTL potentially regulates a heat stress-related gene’s allelic expression. Moreover, the Hsp70 protein had a lower abundance in more tender muscle37 in the Nelore population used in this study, thus reinforcing this aseQTL as a potential regulatory variant for shear force in Nelore. On the other hand, comparisons with our Nelore population QTL data revealed enrichment for meat quality traits, with 94.7% of the overlapped aseQTLs located in regions of 10 meat quality phenotypes21. For example, SPARC-associated aseQTLs overlapped with lightness and yellowness of fat, WBSF7, and WBSF14, being this gene identified as differentially expressed between contrasting samples for IMF content27 in our population, and also classified as a potential target for IMF and tenderness in swine39. Seven QTL regions, including for BFT, b*FAT, and L*FAT, overlapped with aseQTLs associated with the allelic imbalance of an SNP of the STBD1 gene, which was differentially expressed between contrasting samples for IMF content27. Additionally, CMYA5 had 245 overlaps between aseQTLs and QTLs for b*FAT, CL, L*FAT, REA, WBSF0, WBSF7, and WHC traits. In addition, ASE genes in the bovine muscle functionally enriched for meat quality traits were previously identified in our population5 and elsewhere8. Although these overlaps with QTLs might be useful for prioritizing SNPs for validation, a word of caution is necessary, as the confidence intervals for the QTLs might be considerable and could not be taken into account in our analyzes.Sixteen percent of our aseQTL data overlapped with QTL regions from the Cattle QTL Database, showing enrichment for milk quality and production traits, although we must consider a bias in the frequency of publications for each trait in that database. The rs136717535 aseQTL, which presents more overlapping QTLs from Cattle QTL Database, including for shear force, was associated with the ASE SNP rs41257152. This SNP is located in the EIF5 gene, previously associated with marbling in Nelore cattle40. The rs109922571 aseQTL had strong LD among other SNPs in these two LD blocks and was predicted to affect miRNA and TF binding sites. Another example was the rs132798564 aseQTL, associated with four SNPs in the transcribed ASE regions of the CMYA5 gene, with total disequilibrium (D′ = 1) within their block. Additionally, 15 aseQTLs were associated with the ASE of two different pairs of CMYA5 SNPs, each of them in complete LD (D′ = 1). CMYA5 was associated with IMF in pigs41. It lies within regions associated with REA in Nelore cattle40 and six other meat quality traits21. This gene encodes for a desmin-associated protein42, which can regulate vesicular/lysosome trafficking43. The autophagy-lysosome pathway is a degradation system involved in muscle wasting and muscular atrophy44. Moreover, CMYA5 protein may protect from autolysis45 the CAPN3, whose variants were associated with meat tenderness in zebu cattle46. The aseQTLs distributed in LD blocks within the CMYA5 gene may be affecting the allelic expression of this gene. The most informative SNP, displaying strong LD with all SNPs of the largest LD block, is located in a specific intronic region present in two out of three CMYA5 isoforms. Thus, its enrolment on CMYA5 splicing regulation should be further investigated. Our results indicate that LD analysis is essential for the interpretation of ASE association tests in the search for causal mutations because most of the SNPs marking ASE and their associated aseQTLs could be reduced to LD blocks, which in turn makes it difficult to pinpoint the causative mutation.As expected, most aseQTL associations with more than one SNP marking ASE were with ASE SNPs blocks that presented total LD (D′ = 1), suggesting that these aseQTLs haplotypes regulate the same transcript. For example, 14 aseQTLs located in two LD blocks were associated with 2 SNPs, displaying total LD. These SNPs marked the allelic imbalance of the cis-regulatory activity of aseQTLs. Cis-eQTLs and aseQTLs were identified more frequently near the TSS of affected genes48. AseQTL annotation showed similar results as cis-regulatory SNPs obtained in the same population27, from which twenty cis-eQTLs overlapped with our aseQTL data. The predominant distribution of aseQTLs and cis-eQTLs in intergenic and intronic regions supports the concept that the SNPs in these genomic locations tend to have a regulatory role50.Both the LD results and the presence of aseQTLs nearby the respective TSS or close to the associated ASE SNP were indicatives of potential Cis-acting variants can modify TFBSs51, resulting in the allele-specific loss of TF binding52 and leading to ASE in heterozygous animals. We found 215 aseQTLs within 12 TFBS for TFs expressed in bovine muscle30. Binding sites for five different TFs were predicted to be affected by more than one aseQTL: SMAD4, AHR, VDR, STAT3, and PPARA. All these TFs have been associated with phenotypes related to muscle tissue. SMAD4 deficient knockout mice showed muscle atrophy53, PPARA was related with lipid metabolism54, VDR is a vitamin D receptor that was suggested to regulate marbling and calcium homeostasis55, the AHR TF was associated with human aging56, STAT3 is related to muscle wasting57 and muscle regeneration59. The alternative allele of the rs135348406 aseQTL was identified within a STAT3-binding site and was associated with an SNP marking ASE of HSPA1A gene, which encodes the Heat shock 70 kDa protein 1. STATs are known to modulate heat shock proteins, including Hsp7060. The reference allele of the rs137692633 aseQTL was predicted to benefit the binding of STAT3 and STAT6 transcription factors. Besides that, SP1, SP3, VDR, and ZNF219 TFs were predicted to have a binding affinity with the alternative allele of the same aseQTL.11, can also be associated with ASE61, repressing the transcription of a specific hypermethylated allele11. To identify whether the methylation of these aseQTLs could have an important role in ASE, we compared the level of allelic imbalance ratio of the ASE SNPs with the methylation of the associated aseQTLs per animal. However, in our study, only a few animals with methylation data showed significant ASE in an SNP associated with a methylated aseQTL. The methylated aseQTL closest to the TSS of the respective SNP marking ASE (within VCP gene) was rs110407852, with methylation in the alternative allele. The corresponding ASE SNP was monoallelic in nine animals, expressing only the allele in phase with the reference aseQTL allele, thus providing an indication of methylation-mediated silencing of the allele phased with the methylated aseQTL. Similarly, three animals with the genotype 1|0 for the ASE SNP rs42719199 showed an allelic imbalance ratio of 0.5, expressing only the alternative allele. The associated rs136787973 aseQTL showed methylation rates of 32.30%, 80%, and 55.75% and the genotypes C|T, T|C, and T|C, respectively. Thus, it could be postulated that, for the two animals with higher methylation levels for this aseQTL (80% and 55.75%), the T|C aseQTL genotypes and the methylated cytosine potentially silenced the expression of the ASE SNP reference allele, which should be further investigated. We considered that analyzing the methylation pattern in individual candidate variants was of little value on indicating the causative variants, probably due to our restricted methylation data.Epigenetic mechanisms, as DNA methylation63 have been related to genomic imprinting. Some studies related the allelic imbalance with the cis-regulatory action of miRNA binding sites65, which was also described in bovine muscle8. We identified 162 aseQTLs within miRNA binding sites, potentially changing the binding affinity of 149 miRNAs, distributed in 1448 miRNA/target pairs. Among the affected, the bta-miR-423-5p and bta-miR-486 miRNAs were differentially expressed between extreme groups for IMF31 and RFI66, respectively, in our reference Nelore population. Fourteen ASE genes affected by aseQTLs within binding sites of miRNAs were predicted as targets for the corresponding miRNA. For instance, the rs42437277 aseQTL, associated with the ASE marked by an SNP in the ASB5 gene, was predicted to affect the binding of five miRNAs which, in turn, are predicted to target this gene. Thus, the integration of our data and the known miRNA-target genes information offers more reliability for these findings.As well as methylation, miRNAs21, was methylated, predicted to affect a binding site of six muscle-expressed miRNAs and the TFBS of AHR. AHR was negatively associated with aging56 and angiogenesis67, although the associated allelic imbalance SNP marker is within an unannotated transcript. Finally, the CMYA5 associated aseQTLs, which showed more overlaps with Nelore QTLs (35), contained a larger number of aseQTLs affecting miRNA binding sites (7), including sites for two differentially expressed miRNAs for IMF and RFI. The combined results demonstrate that muscle-related aseQTLs are located in regions associated with bovine traits, mainly enriched for meat quality QTLs, including REA, tenderness, and IMF. Tenderness is an essential factor for consumers’ acceptance7, while IMF is related to palatability and juiciness68, sensorial characteristics that affect consumer choice and meat price69. Some genes seem to have a strong aseQTL regulation, influenced by different and integrative cis-regulatory mechanisms, as CMYA5, which the literature indicates as functionally relevant for meat quality phenotypes. These results suggested that the aseQTLs regulating the CMYA5 gene can be candidates for the improvement of sensory traits of Nelore beef.Some aseQTLs appeared more than others among the analyzes performed in this work. The rs109540393 aseQTL, which overlapped with nine meat quality QTLs identified in our populationcis-regulatory variants whose effects remain to be explored due to the high LD and the lack of conclusive evidence about causal variants. As we were not able to address false positives due to the restrictions of sampling, these results should be interpreted along with other evidence for regulatory function for each given potential aseQTL. Future studies may prioritize SNPs based on aseQTLs for experimental validation, and methodological approaches can be designed according to the potential regulatory mechanisms and phenotypes predicted in this work.Thus, our results reinforce a complex regulation of ASE in bovine muscle, indicating potential The study complied with the ARRIVE guidelines. All experimental procedures and animal protocol were carried out following the relevant guidelines and regulations provided by the Institutional Animal Care and Use Committee Guidelines of Embrapa Pecuária Sudeste ethics committee . The Ethical Committee of Embrapa Pecuária Sudeste  approved all experiments .21 Concisely, we used a subset of the described experimental population, consisting of 190 Nelore steers. These animals were sired by artificial insemination using 34 bulls that represented the main lineages of the breed, monetarily accessible to any Brazilian producer and not closely related. The steers were raised until 18 months of age in three farms and afterward maintained in three feedlots under identical nutrition at Embrapa Pecuária Sudeste . Animals were slaughtered according to the subcutaneous fat thickness (5 mm), with an average weight of 383.2 kg and around 24 months of age.Details regarding the animal's production can be found in Tizioto et al.70. DNA concentration and purity were measured by spectrophotometry in NanoDrop. Integrity inspection was made by agarose gel electrophoresis.The DNA was extracted from blood samples by using salting out method71. Quality control was performed with PLINK software72 from the autosomal SNPs with genome coordinates corresponding to the bovine reference genome (Assembly: ARS-UCD1.2). SNPs with MAF lower than 5%, as well as samples and SNPs with a call rate lower than 95%, were discarded. In addition, we removed SNPs with a Hardy–Weinberg Equilibrium P value ≤ 0.0001. This genotype file has a total of 429,513 SNPs available for ASE analysis. The genotypes were phased using BEAGLE software73 with default configurations. We did not use imputation parameters and did not use a pedigree file, so all the samples were considered unrelated individuals.Genotyping was performed with Illumina BovineHD BeadChip  at the USDA ARS Bovine Functional Genomics Laboratory in Beltsville, MD, USA and the ESALQ Genomic Center in Piracicaba, SP, BrazilLongissimus thoracis tissue from 190 animals were collected immediately after slaughter, submerged in liquid nitrogen, and stored at − 80 °C until RNA extraction. Total RNA extraction and RNA sequencing methodology were described elsewhere27. Briefly, RNA extraction was made using TRIzol reagent , following the manufacturer’s instructions. Bioanalyzer 2100  was used to verify the RNA integrity and the TruSeq RNA Sample Preparation kit v2 guide  was used to prepare the RNA libraries from 2 µg of RNA. We also used the Bioanalyzer 2100 to determine the average size of the libraries and the quantitative PCR with the KAPA Library Quantification kit  to carry out the quantification. Clustering and sequencing were performed with Illumina HiSeq 2500 , and SeqyClean software (https://github.com/ibest/seqyclean) was used to remove sequencing adapters and low complexity reads. The quality control was performed with FASTQC software version 0.10.1 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The sequencing was performed at the Genomics Center at ESALQ, Piracicaba, São Paulo, Brazil, and archived on the European Nucleotide Archive (ENA) under accessions: PRJEB13188, PRJEB10898, and PRJEB19421.A total of 100 mg of 74, which created two diploid homozygous genomes basing on the two haplotypes from each heterozygous animal. After that, the RNA-Seq reads of the 190 steers were aligned to each in silico-created genome, resulting in the counts of reads that mapped the first or second haplotype per SNP and individual. A binomial test was applied to determine if allele counts were statistically divergent for each SNP and animal. Finally, a total of 820 SNPs in ASE (FDR < 0.05) were described and used in this study5.SNPs located in regions showing ASE were obtained from De Souza et al. (2020). In brief, the authors identified heterozygote SNPs from Illumina BovineHD BeadChip genotyping data, and the genotypes were phased and organized in individual files. Together with the bovine reference genome (ARS_UCD1.2), these files were used as inputs in the ALEA algorithm5 because this window probably encompasses cis-regulatory SNPs. To test for the presence of aseQTLs, the mean values of allelic expression imbalance, i.e., the ratio of counts from each allele, were computed and compared between heterozygote and homozygote animal groups for the candidate aseQTL, using the non-parametric Wilcoxon Rank Sum test 17. If an SNP has regulatory action on the allelic expression, we only can observe ASE when the genotype is heterozygous. If this SNP is in homozygosis, the two alleles will contribute equally to allelic expression. Thus, the identification of an aseQTL occurs if animals of the heterozygous aseQTL group had an imbalance of allelic expression (present in the SNP marking ASE) significantly higher than any homozygous aseQTL group, being always tested with the homozygous group with more animals. This analysis required a minimum of 10 heterozygous animals for each test, both in candidate’s aseQTLs and ASE SNPs.For the ASE-association test, we selected SNPs within 1 Mb upstream and downstream from each SNP marking ASE identified previously17 and, as in that reference, we did not apply any method for multiple comparisons correction.The aseQTLs identification methodology comprises a non-parametric test and requires a large number of heterozygotes, which makes it more restrictive, being more rigorous for an association to be significant. The required minimum repetition of genotypes in the population increases confidence for the results, but unfortunately, such conditions turn unviable the correction for multiple tests due to a large number of tests for few significant results. As the aseQTLs are candidate SNPs for the understanding of bovine regulation, thus to be further verified by other methods, we followed the method described previously75. To compare aseQTL positions with the TSS, we extracted from BioMart (Ensembl Release 98. Access in: https://www.ensembl.org/biomart/martview) the TSS positions for all annotated ASE genes. AseQTLs SNPs were annotated using the Variant Effect Predictor  software28.The aseQTL allele frequency was computed with VCFtools software2 and we aimed to inspect any possible LD, including rare variants. We calculated LD to investigate allele relationship among aseQTLs and SNPs located in ASE regions in three ways using the software Haploview76: (1) LD analysis between aseQTLs and their respective ASE SNPs (to verify if the high LD is relevant for ASE-association test significance); (2) LD analysis between all aseQTLs (to investigate possible ambiguous results and indicate conserved cis-regulatory elements within an LD block); and III) LD analysis among the SNPs marking ASE (to understand whether the same aseQTLs can be associated with more than one ASE SNP because of high LD).Some associations between SNPs may result from population dynamics, such as the presence of LD by calculating D’ statistics. We choose D’ statistics because it is less sensitive to allele frequency than Rhttps://www.animalgenome.org/QTLdb/cattle/ downloaded in June 2019).We intersected data from aseQTLs with QTL regions previously associated with economic traits for beef cattle. For an overview on how aseQTLs can affect phenotypes of interest in other populations, the overlap of each aseQTL position was intersected with QTL positions obtained from the Cattle QTL database (Assembly: UMD_3.1) .All the GWAS data obtained by our research group or in the Cattle QTL database in studies performed with the UMD 3.1.1 reference genome was converted for the ARS-UCD1.2 version using Lift Genome Annotations UCSC tool , according to the manufacturer's protocol. DNA concentration was quantified with Qubit dsDNA High Sensitivity Assay (Thermo Fisher Scientific). The quality was measured with Fragment Analyzer and DNF-487 Standard Sensitivity or the DNF-488 High Sensitivity Genomic DNA Analysis Kit .To determine the methylation profile in Nelore muscle, we subset 12 animals of our population for the Reduced Representation Bisulfite Sequencing (RRBS), selected based on the ranking of the estimated breeding value (EBV) for the tenderness phenotypeThe RRBS experiments were performed by Diagenode. Each sample library was prepared from 100 ng of genomic DNA using the Premium RRBS kit (Diagenode), according to the manufacturer's protocol. Bisulfite conversion efficiency independent of the CpG context was assessed by adding methylated and unmethylated spike-in controls (concentration of 0.1%). In summary, the protocol consisted of the digestion of DNA by the MspI enzyme, followed by the fragment end repair and addition of adaptors. Thus, samples were quantified by qPCR, being the Cq values used to pool samples by similarity. Bisulfite conversion was performed using the Premium RRBS kit (Diagenode) according to the manufacturer's protocol, followed by library enrichment by PCR. Adequate fragment size distributions were confirmed by Bioanalyzer High Sensitivity DNA chips (Agilent).https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and adaptors were removed by Trim Galore! Version 0.4.1. . Bismark version 0.16.179 was used to align the reads to the Bos taurus reference genome ARS-UCD1.2 and to identify methylated cytosines.Libraries were sequenced on Illumina HiSeq 3000 using single-end 50 bp reads. Sequencing read quality control was performed using FastQC version 0.10.1 . We selected aseQTLs that changed the TF binding with the reference or alternative allele.The aseQTLs with TFs affinity were identified using TRAP software (Transcription Factor Affinity Prediction) for multiple sequences80. RNAhybrid calculates the most energetically favorable hybridization between the mRNA and miRNAs sequences. Here we evaluate the interaction between aseQTL flanking sequences and bovine mature miRNAs sequences available in the MiRBase database81. Only miRNA-target interactions showing an MFE of less than − 18.0 were maintained for analysis. We compared these results with the miRNA expression data obtained in 185 animals of our population31. After that, we used MiRWalk software32 to identify if the ASE genes affected by these aseQTLs were miRNA targets identified by RNAhybrid80. For this, we selected the bovine model in the species panel on MiRWalk and searched for genes that were found in the literature as targets for these miRNAs. This list of genes was used for further comparisons with the ASE genes affected by aseQTLs within miRNA binding sites.The third approach was to find aseQTLs overlapping predicted miRNA binding sites. To filter all the aseQTLs dataset for the miRNA binding site prediction, we used the aseQTLs outside of intergenic regions that integrate with other QTL regions identified in our population. The prediction of miRNA binding sites in our aseQTLs was made with the RNAhybrid softwareSupplementary information.


Among them, 21 were high‐value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi‐parent advanced generation intercrosses and several low‐chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high‐yielding potentials.Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome‐wide association study (GWAS) using a combined population panel of  Oryza sativa L.) is one of the oldest domesticated crops in Asia, originating from the wild rice species O. rufipogon. It currently contributes to the daily energy needs of half of the world’s population  was found to affect cellular endomembrane trafficking, which impacts on PGC . In addition, the importance of the genetic regions was validated through multi‐parent advanced generation intercross (MAGIC) populations, specifically made from the intercrosses of sub‐species and lines identified as low chalk with low levels of amylose.Given that the heritable component of grain chalk is governed in a polygenic manner, understanding the epistatic interactions between the loci of the minor/major effect QTLs could unravel complex genetic interactions that impact on grain chalkiness. The present study combined whole‐genome resequencing‐based GWAS with epistatic interactions, to discover the genomic regions from a combined panel of indica and 284 japonica accessions from 71 countries. They also covered a wide range of PGC levels, ranging from 0 to 100, for both the indica and japonica populations , and the second PC distinguished the tropical japonica from the temperate japonica (4.36% of the variation)  ≥ 7.68; red colour horizontal line) and the false discovery rate (FDR)‐threshold criterion (P < 0.05)  and 6 (PGC6.1) were identified as having significant loci regulating the PGC  and QTL PGC5.3 in multiple lines , and both conferred a low‐chalk phenotype  Figure . By cond) Figure . The SNP) Figure . Intereshttp://www.gomapman.org)  Figure . Among t) Figure . In addi) Figure .PGC1.2, with a PVE of 26.7, negatively influenced the PGC. Within this region, the candidate gene LOC_Os01g11960 (histone lysine methyl transferase) was identified with snp_01_6520765 in the 5ʹ‐UTR region encoding the G allele, which confers a lower chalk level with a PGC of ~8.9  increases chalkiness. Within this genetic region, the GG haplotype that was predominantly represented in japonica, increased the chalkiness, while its alternative AC haplotype constructed from the genic region of LOC_Os01g69990 (expressed protein), caused synonymous variations and had a very low median PGC of ~1.5 , a synonymous SNP  in the genic region of the LOC_Os03g53220 (encodes ribonucleoprotein helicase) was identified. Lines with the C allele, which were abundant in japonica, had a PGC variance of ~9.5 and were clearly distinguished from the lines for the T allele, that conferred a very low PGC of ~0.64  was found to reduce the PGC to a very low level of ~0.64 in the diversity lines AGAC derived including SNP causing a missense mutation  within the candidate gene LOC_Os06g03500 (encoding the NBS‐LRR disease resistance protein) had a median PGC of 2.9 and was abundantly represented in indica  that resulted in a low‐chalk phenotype with a median of ~4.4  in influencing the lower PGC levels, to help achieve premium grade rice production. Interestingly, these haplotypes were also found to have amylose content levels of >5%  were employed to identify the target genes located within the major QTL regions conferring lower PGC with higher PVE values in the diversity panel Figure . The gen.9 Table . The altca Table . The sups Figure . Notablys Figure . The supPGC1.1, PGC1.2) and 4 (PGC4.4). Within PGC1.1, three moderate effect loci  were mapped to LOC_Os01g03720 (encodes for MYB62 TF) and showed intensive epistatic interactions with the gene family members of ent‐kaurene synthase genomic loci , and LOC_Os04g52220 (unknown expressed) of chromosome 4 (PGC4.4)  and Dof TF   from chromosome 4, with a collective PVE of 17.4 . The MAGIC population  from the PGC5.8 QTL showed epistatic interactions with the two gene family members of the ent‐kaurene synthases  Figure . Intrigu) Figure . By combs Figure . In addis Figure . Candidas Figure . In addi4 Figure . The varn Figure  confirmen Figure . Howevers Figure , likely PGC1.1), G2‐like MYB TF (PGC1.2) with GA biosynthesis genes located in PGC4.4]. The underlying dynamic relationships between the genes were identified using the derived gene regulatory networks influencing the PGC. The genome‐scale co‐expressed network construction resulted in a total of 47 modules , with significant edge weight and a higher degree of connectivity  was found to have multiple interacting partners with: (a) the key regulators of LOC_Os01g03720 (MYB62) from the PGC1.2 QTL, LOC_Os05g51150 (RNA polymerase sigma factor) from the PGC5.8 QTL, and (b) other tandemly duplicated gene family members encoding ent‐kaurene synthases  and the LOC_Os04g52220 (unknown) target genes from the PGC4.4 QTL  interacts with the G2‐like MYB TF (LOC_Os01g13740) and gibberellin synthesis genes . Module M2 contains one of the target epistasis genes, LOC_Os04g52210, encoding different gene family members of the ent‐kaurene synthase from the PGC4.4 QTL  gene. Markedly, several SNPs  within the PGC6.1 region exhibited epistatic interactions  and exhibited a number of prominent epistatic interactions with 9 SNPs on chromosome 7. The epistatic interactions were established using 9 key SNPs falling on LOC_Os07g37480 (expressed unclassified), LOC_Os07g37540 (expressed unclassified) and LOC_Os07g37550 (photosystem II protein) from PGC7.8, showing a collective PVE of 13.6  from 6 Figure . Of the 6 Figure . On LOC_6 Figure . The can6 Figure . SNPs fr5 Figure .PGC6.8 was mapped to a 0.8 Mb region (30.2–31 Mb) of chromosome 6, exhibiting a PVE of 20.6, and contained a total of 107 SNPs, that showed interactions primarily with PGC7.8 and minor interactions with PGC1.2  and PGC3.7 (snp_03_26840782) showed epistatic interactions with multiple SNPs in the PGC7.8 genetic region  have previously been reported  and PGC7.8 (22.4–22.5 Mb region), and these were found to contribute to reducing the chalkiness to less than 2%. Such novel regions need to be utilized in breeding programs to lower chalk levels, and in particularly when exploring the heterosis created from the inter‐subspecies crosses of japonica and indica.Farmers sell their harvested paddies to millers, who avoid harvests with high levels of chalky rice grains, as they are predisposed to breakage during the milling and polishing processes  chalk QTL was mapped to chromosome 6, and co‐localized with the waxy loci of GBSSI in a combined BC2F6 mapping population, made from japonica and indica crosses  locus  and PGC (qPGWC5) were overlapped , regulates grain shape  in the present study, and the fine‐mapped target gene LOC_Os07g41200 (GW7) did not signify any association with the chalk trait. In the diversity panel, the phenotypic variation for the PGC and grain width showed a weak positive correlation (~0.30)  Table . Taken tMYB62 located in PGC1.1 and G2‐like TF (LOC_Os01g13740) located in PGC1.2] that epistatically interacted with several tandemly duplicated ent‐kaurene synthase genes (located on PGC4.4) belongs to gibberellic acid biosynthesis , suggests that there was overlapping of the DNA‐binding sites for the two MYB proteins  from the PGC4.4, to impact on chalkiness was validated using the MAGIC population. G2‐like transcription factors belong to the GRAP subfamily of the MYB TF family in plants , suggesting a potential link to the complex intermolecular epistasis interactions that contribute to an increase in grain chalkiness.The genome‐wide SNP mutations impacting chalk in the diversity panels were explored using GWAS‐based epistasis interactions and further validated the genetic network through transcriptome‐based gene regulatory networks. These complementary approaches resulted in the identification of two different MYB domain TFs [LOC_Os01g13740, PGC QTLs identified in the selected breeding and MAGIC populations, exhibited a low‐chalk phenotype. This two‐pronged strategy of GWAS coupled with epistatic and gene regulatory network analyses identified the following epistatic interactions: PGC1.1 and PGC4.4, and the impacts of these QTLs on chalkiness, and the PGC6.1 (with higher PVE) contributions to lower grain chalkiness in low amylose varieties. In addition, our results distinguished the pleiotropic effects of grain‐size genes from the causal‐associated genetic factors impacting chalkiness. These genome‐wide superior and favourable alleles need to be deployed in future breeding programs to promote lower levels of the undesirable chalkiness trait, especially in hybrid breeding programs generated from intercrosses, to ensure higher economic benefits for farmers.The genome‐wide genetic interactions were identified by utilizing the synergy of statistical epistasis and association mapping and revealed high‐value genetic regions that influence grain chalkiness in diverse lines. The japonica and 318 indica accessions that were identified from 3000 re‐sequenced rice genetic resources  to determine the PGC. Using the designated sample trays, grains were scattered onto the wider wells, to ensure that the grains were proportionally represented prior to the analyses. After scanning the tray, the PGC data were estimated using the built‐in software after proper calibration, following the manufacturer’s instructions.japonica and 318 indica germplasm were utilized for SNP‐calling against the Nipponbare reference genome (MSU version 7). We used variant call format (vcf) files generated from the pipeline of the 3K‐resequencing data for processing the genotyping data. We filtered individual samples, ensuring a Phred score of ≥30, and then merged all samples together. Subsequently, we identified SNPs from the merged vcf files. Genotypic files were subjected to a filtration criterion as follows: (a) alleles possessing a missing rate of not more than 1% at the individual genotype and SNP level, and (b) minor allele frequencies of ≥1% were retained. A filtering step based on pLink 2 resulted in the identification of a total set of 868 941 and 2 401 369 quality‐assured SNPs within the 284 japonica and 299 indica accessions. The underlying genetic variations of both sets of the germplasm panels were estimated using PCA, by accounting for the 868,941 SNPs in japonica and 2 401 369 SNPs from the combined germplasm panel, using the SNPRelate package in the R platform . The genetic variations that were captured utilizing the first two principal components (PC1 and 2), were plotted in the PCA plot.The re‐sequenced genomic resources that were generated from the 307 et al., et al., japonica panel  by associating the PGC phenotype with the genotyping data of the assured 868 941 SNPs, to reveal the associated significant genomic loci, with the criteria of the more conservative Bonferroni correction, set at −log10 (P) ≥ 7.24, , and the FDR of P ≤ 0.05 , with the genotyping data of the 2 401 369 high‐quality SNPs, with the Bonferroni correction set as −log10P ~ 7.68, and the FDR at P ≤ 0.05.In order to fulfil the data normalization requirements for the phenotypic data, the warped linear mixed models function was applied , which involves genetic variation, mainly due to additive genetic values, was calculated using a heritability R package  were considered a significant pair of interactions. The Bonferroni cutoff was calculated using the formula of 0.05/m, where m = number of tests . Furthermore, the Cytoscape  and SNPs were further called using the BWA‐GATK pipeline . Paired‐end reads that were 150 bp in length, were generated at ~30× coverage. Low‐quality reads and adapter sequences were removed from the raw sequence reads. Furthermore, the raw sequence reads were mapped to the PGC4.4  and PGC1.1 . Sixteen days after fertilization, the seeds of 76 unique lines  were subjected to RNA preparation, using the Qiagen RNeasy Plant Mini Kit. Samples with an integrity number of 7.0 or above were subjected to cDNA synthesis and cRNA labelling, using a single‐colour Low Input Quick Amp Labeling Kit. The labelled samples were hybridized to 60 K indica microarrays in a SureHyb chamber, and microarrays were scanned with an ozone barrier slide using a SureScan Microarray scanner, as described by Butardo et al.  performed in Japonica and combined germplasm panel.Figure S3 Mining haplotypes in the 92 breeding lines set for low chalkiness trait and their distribution across breeding lines.Figure S4 Distribution of 8 parental genotypes of MAGIC population along with 3000 germplasm lines.Figure S5 Genome‐wide epistatic interactions involved in regulating the PGC, established between key genomic regions.Figure S6 GWAS for PGC revealed the significant association on chromosome 4 within combined germplasm panel.Figure S7 GWAS conducted for PGC in the MAGIC population identified genomic loci on chromosome 4.Figure S8 Gene regulatory network created using the transcriptome data generated from contrasting chalk haplotype containing lines.Figure S9 GWAS for PGC revealed the significant association on chromosome 6 genomic regions within combined germplasm panel.Click here for additional data file.Appendix S1 Discovery of chalk genetic regions through GWAS in Japonica panel.Click here for additional data file.Appendix S2 Epistatic interactions that regulate PGC.Click here for additional data file.Table S1 Significant QTLs (showing PVE≤10) identified for regulating PGC along with their physical position on chromosomes, underlying topmost SNP, and further details.Table S2 Details of total significant SNPs identified under chalk‐regulating QTLs with their significance level, effect, and their functional relevance.Table S3 Genome wide epistasis interaction targets (mentioned as region 1 and 2) identified from the GWAS genetic variants associated with grain chalkiness.Table S4 Co‐expressed gene regulatory networks of developing seeds derived from diversity lines containing contrasting chalk haplotypes. The interactions among target‐QTL containing epistasis genes located in Module (M1) and Module (M2).Table S5 Detail of germplasm used for the study with their phenotypic values for grain chalkiness and grain‐size parameters, along with their countries of origin.Click here for additional data file.