The salinity tolerance of Mekong Delta accessions was independent from the previously described salinity tolerance–related locus Saltol, which encodes an HKT1-type transporter in the salinity-tolerant cultivars Nona Bokra and Pokkali. Indeed, genome-wide association studies using SES scores and shoot dry weight ratios of the 79 accessions as traits identified a single common peak located on chromosome 1. This SNP did not form a linkage group with other nearby SNPs and mapped to the 3′ untranslated region of gene LOC_Os01g32830, over 6.5 Mb away from the Saltol locus. LOC_Os01g32830 encodes chloroplast glycolate/glycerate translocator 1 (OsPLGG1), which is responsible for photorespiration and growth. SES and shoot dry weight ratios differed significantly between the two possible haplotypes at the causal SNP. Through these analyses, we characterize Doc Phung, one of the most salinity-tolerant varieties in the Mekong Delta population and a promising new genetic resource.The Mekong Delta River in Vietnam is facing salinity intrusion caused by climate change and sea-level rise that is severely affecting rice cultivation. Here, we evaluated salinity responses of 97 rice accessions (79 landraces and 18 improved accessions) from the Mekong Delta population by adding 100 mM NaCl to the nutrient solution for up to 20 days. We observed a wide distribution in salinity tolerance/sensitivity, with two major peaks across the 97 accessions when using the standard evaluation system (SES) developed by the International Rice Research Institute. SES scores revealed strong negative correlations (ranging from − 0.68 to − 0.83) with other phenotypic indices, such as shoot elongation length, root elongation length, shoot dry weight, and root dry weight. Mineral concentrations of NaThe online version contains supplementary material available at 10.1186/s12284-022-00613-0. Oryza sativa L.) is a main staple food source for about 90% of Asian people and is grown in areas with easy access to water. One of greatest threats to rice production is salinity stress  associated with this salinity-tolerance and was mapped to chromosome 1 -type sodium (Na+) transporter , Can Tho University, Vietnam 6Mo7O24. The nutrient solution was replaced every 3 days, and the pH was adjusted to 5.0 every day with 1 M NaOH or 0.5 M HCl, as needed. At 15 DAT, the salinity stress treatment was started by adding 100 mM NaCl to the nutrient solution. Immediately before treatment, the shoot and root lengths of all samples were measured. Five days under treatment (DUT), shoot and root elongation lengths were determined, as well as visible phenotypes in response to salinity stress, using the modified standard evaluation system (SES) score developed by IRRI  for 15 min for surface sterilization. Seeds were then washed with tap water three times and with distilled water two times. Surface-sterilized seeds were placed into petri dishes and germinated in dark conditions at 25 °C. Three days after germination, seedlings were transplanted to net sheets floating on distilled water in a 40-L container at 20–25 °C in the greenhouse  for 4 h; 0.5 mL of 30% hydrogen peroxide was added, and the samples were heated to 107.5 °C. The tubes were allowed to cool to room temperature, and their volumes were adjusted to 10 mL with 2% (w/v) HNO3. The mineral ion composition for Na+, K+, Ca2+, and Mg2+ was determined by inductively coupled mass spectrometry (ICP-MS)  using a blank sample to account for chemical contaminations.To perform mineral analysis, 20 accessions with varying SES scores were selected from the initial 97 MDI accessions . Subsequently, SNPs with a q-value  lower than 0.05 were selected as significant markers with R software  distributed across the 12 rice chromosomes in the 97 accessions previously obtained by restriction site–associated DNA sequencing (RAD-seq) . Linkage disequilibrium (LD) analysis was performed using SNPs located within 200-kb windows upstream and downstream of the SNP peaks based on the Haploview method according to Barrett et al. (Candidate genes (near peak SNPs) were searched in QTL and gene databases in the rice SNP seek database  in roots, stems, and leaves  and Ca concentrations in roots (r = 0.765) and stems (r = 0.64)  and 12 sensitive accessions (SES > 4)  sequences from 20 MDI rice accessions representing 97 MDI rice accessions chosen for whole-genome resequencing. In Nona Bokra/Pokkali, the OsHKT1;5 open reading frame was 1,662 bp in length and encoded a protein of 554 aa  > 4.0  < 0.1, only one SNP on chromosome 1 (S01_18015212) remained significant  ≥ 4.0, on chromosomes 1, 2, 3, 5, 7, 8, 10, 11, and 12 . After filtering, we retained 19 SNPs for LD analysis based on the Haploview method Fig. C, D. SurSaltol locus   of LOC_Os01g32830 on chromosome 1, about 6.5 Mb away from the We identified 3 possible haplotypes among 79 rice accessions at SNP S01_18015212: 61 accessions harboring the C allele (Hap 1), 10 carrying the A allele (Hap 2), and 8 heterozygous accessions   and another peak from a group with low-salinity-tolerance (SES scores between 8 and 9) Fig. . On the Saltol locus , which function in Na+ uptake, K+ concentrations, and the Na+/K+ ratio, respectively  in their root cell walls  in roots, stems, and leaves. The 20 accessions were treated with 100 mM NaCl for 20 days (20 DUT). r, Pearson’s correlation coefficient.Additional file 2: Table S1. List of Mekong Delta rice accessions examined in this study. Table S2. Phenotypic features for the standard evaluation system (SES) scores. Table S3. Preliminary test for salinity-tolerant/sensitive groups with 50 and 100 mM NaCl. Table S4. SES scores and phenotypic values in the 97 MDI accessions and their rankings. Table S5. Concentrations (mg/gDW) for mineral ions  and Na2+/K+ ratio in roots, stems, and leaves of 20 accessions exposed to 100 mM NaCl for 20 days (20 DUT) and control samples. Table S6A. SNPs above the threshold of p-value at 1.00E–04 in a Manhattan plot for SES score at 20 DUT (Fig. Table S6B. SNPs above the threshold of p-value at 1.00E–04 in a Manhattan plot for shoot dry weight ratio (Fig.


Given the high economic and qualitative values of local-breed meat products, it is not uncommon that substitution or mislabeling  occurs at the market level. Therefore, to protect the interests of both producers and consumers, a reliable traceability tool should be developed. Nowadays, traceability usually relies on physical labeling systems . These systems do not, however, have good performances when dealing with carcasses or processed meat products. Molecular markers  can be a solution, since DNA is easily extracted from a wide variety of animal products and parts, and is not degraded during processing, even at the high temperatures involved. The aim of this study was to identify a small number of DNA mutations for breed-traceability purposes, in particular of the Italian Nero Siciliano pig and its derived products. A small panel of 12 DNA mutations was enough to discriminate Nero Siciliano pigs from other pig breeds and from wild boars.Food authentication in local breeds has important implications from both an economic and a qualitative point of view. Meat products from autochthonous breeds are of premium value, but can easily incur fraudulent or accidental substitution or mislabeling. The aim of this study was to identify a small number of SNPs using the Illumina PorcineSNP60 BeadChip for breed traceability, in particular of the Italian Nero Siciliano pig and its derived products. A panel of 12 SNPs was sufficient to discriminate Nero Siciliano pig from cosmopolitan breeds and wild boars. After adding 8 SNPs, the final panel of 20 SNPs allowed us to discriminate all the breeds involved in the study, to correctly assign each individual to its breed, and, moreover, to discriminate Nero Siciliano from first-generation hybrids. Almost all livestock breeds are being genotyped with medium- or high-density SNP panels, providing a large amount of information for many applications. Here, we proposed a method to select a reduced SNP panel to be used for the traceability of pig breeds. In recent decades, consumers and producers have paid increasing attention to food quality and safety, and to traditional foods. Food authenticity has become one of the most important concerns through the entire production chain. To date, traceability has relied on a labeling system that should ensure a connection between the final processed product and the animal source itself . This syThe interest in local breeds is growing worldwide, as demonstrated by the large number of studies published in recent years ,14,15. TNero Siciliano (NS) is an Italian autochthonous breed, reared in the northeastern part of the island of Sicily. NS pigs are bred on small farms, mainly under extensive management systems , and theThe aim of this work was to identify a small panel of SNPs usable for distinguishing NS from the main cosmopolitan breeds reared in Italy  and WB, as a tool for the traceability of its meat and related products. We also simulated first-generation (F1) hybrids to check whether a small SNP panel was able to discriminate purebred animals from hybrids, as a first step in the development of a system to identify frauds intended as the use of crossbreds or meat mixtures marketed as monobreed products.Nero Siciliano (NS) is an ancient Italian autochthonous black pig breed (the presence of which is described by ancient documents and dated by fossils at least to the 7–6th century BC), reared in the northeastern part of Sicily on small traditional farms, mainly under extensive management systems. NS is characterized by slow growth, a high meat quality, and adaptation to harsh conditions . It has A total of 93 blood samples were collected from NS pigs reared on 22 farms well distributed in the natural park of the Nebrodi Mountains, in the northeastern part of Sicily. Blood-sample collection was performed via jugular vein puncture into EDTA vacutainer tubes with a 21G needle, contextually to the routine sanitary screening of the National Animal Health Services and in accordance with the general recommendations of respect for animal welfare; all efforts were made to minimize animal suffering. Sampling was performed to avoid strongly related animals, based on information obtained from both breeders and genealogy data (when available).Genomic DNA was extracted using a commercial kit  following the manufacturer’s protocol. After qualitative and quantitative checks , the extracted DNA was genotyped in outsourcing using Neogen Corporation’s GeneSeek  with the PorcineSNP60 BeadChip v2 .Genotyping data of other breeds were obtained from previous studies to obtain a more representative sample of these breeds: 65 Italian samples from 3 cosmopolitan breeds analyzed by Chessa et al. 2011) [11 [20] aF1 hybrids were simulated using HYBRIDLAB v1.1 , which cp < 0.01 Bonferroni-corrected, Wiggans et al., 2009 [The NS 60K SNP panel data was edited using PLINK software  to removl., 2009 ). Subseql., 2009  were alsWhere not specified, data analyses were conducted in the R base environment . The reli−PAj|, where PAi and PAj are the frequencies of allele A in two different populations . Pairwise comparisons were performed and subsequently analyzed.Different statistical selection methods  was used as already described by Dimauro et al., (2013) . Such anThe mean genotyping rate for the NS pigs was 98.5%. In particular, 90 subjects out of 93 had a call rate (CR) higher than 99%, and 74 were higher than 99.5%. Only one individual had a CR of 62% and was therefore removed. After data cleaning of the NS dataset, 58,232 of the 61,565 SNPs included in the PorcineSNP60 BeadChip v2 were retained. After merging this dataset with the one including the LW, LR, DU, and WB populations, and removing SNPs that were absent in v1 of the chip, 43,697 SNPs were available for the subsequent analyses.Before proceeding with SNP selection for discriminant analyses, a multidimensional-scaling approach was applied to the cleaned dataset  in order to assess group clusterization of the individuals: close or overlapping clusters involving a genetic similarity amid populations. As shown in To deeply investigate this feature, we performed an MDS analysis using only the LR, LW and NS breeds : the NS We ranked the SNP markers based on their genetic information content, and therefore they were ordered by decreasing level of delta to identify the more informative ones, as suggested in Wilkinson et al., (2011) . AccordiTo perform the CDA, the 306-sample dataset was pruned of the individuals with a missing genotype. A total of 275 animals had no missing genotypes when considering the 20-SNP panel, and were therefore used as the training population as well for the 12-SNP panel to obtain a better comparison of the performances of the two SNP panels.The 12-SNP panel was first tested to assess if it was sufficient to discriminate correctly among the analyzed breeds; 98.2% of the individuals in the training population  were correctly assigned. The incorrectly allocated individuals were one DU (assigned to WB), two LR (one assigned to LW and one to WB) and two LW (assigned to LR). All of the NS pigs were attributed to the correct cluster, with an average probability of assignation of 98.1%. Detailed results of the performances of the 12- and 20-SNP panels in the linear CDA can be found in A total of 97% of the 200 individuals in the relative PVP were also correctly assigned, along with a 100% total for the correct assignment of NS pigs. The incorrectly allocated individuals were two LR (one assigned to LW and one to WB again) and four LW . The average probability of assignation of individuals to the right cluster was 97.1% for the entire PVP and 99.8% for NS pigs (unpublished data).Using the same 12-SNP panel, a discriminant analysis was performed on the HTP dataset : 85.3% of the individuals in the training population were correctly assigned to their own cluster; in particular, 89 NS pigs out of 92 were correctly assigned, while 3 individuals were allotted to the HY cluster. The validation phase we ran on the HVP dataset resulted in an average correct assignment for 87% of the individuals, with 100% correct assignation for the simulated NS to NS cluster. The average probability of assignation per individual to the right cluster was 83.1%: 91.6% for purebred individuals, 97.1% for purebred NS, and 66.1% for hybrid individuals. Thus, hybrid identification was a more complicated task.To increase the performance when including hybrids, 8 SNPs were added, and the obtained 20-SNP panel was then analyzed. The first analysis was performed using the PTP dataset: the linear discriminant analysis and its subsequent prediction applied to the dataset resulted in a perfect attribution (100%) of all the individuals to their respective breeds. Validation-phase testing was then performed using the PVP dataset, including the simulated purebred individuals, and also resulted in a perfect assignment (100%) for all the individuals. The second analysis, conducted using the HTP dataset for the training population resulted in a correct attribution for 92.2% of the individuals; in particular, 87 out of 92 NS pigs were correctly identified as purebred, while the remaining 5 were assigned the hybrid cluster. The following validation phase correctly identified 94.7% of the individuals. All the purebred individuals in the validation dataset were correctly attributed to their breed’s cluster, while only 84 out of 100 (84%) hybrids were correctly assigned, with the others being allotted to one of their parental breeds . For theWhen dealing with traceability, the most interesting SNP to be used should be a private allele. Finding a private allele in local breeds is time-consuming and costly, and potentially requires whole-genome sequencing of enough representative individuals of each involved breed. If some potential private alleles are discovered, then they should be tested in a larger population to verify that they are actually present only in one breed. Thus, using commercial medium- or high-density SNP panels is a cheaper and simpler approach that could be used on potentially all breeds of a species. One of the most difficult and controversial aspects in selecting a reduced number of SNPs when starting with medium- or high-density SNP panels consists of choosing the most suitable way of selecting the SNPs. Many different statistical selection methods have been described ,28, but Using the 12-SNP panel, we were able to correctly assign 98.2% of the individuals in the training population and 97% of the validation population. In particular, the Nero Siciliano pig was discerned from both cosmopolitan breeds and wild boars, with a 100% attribution of individuals to the correct cluster. The incorrectly allocated individuals belonged to the cosmopolitan breeds LW, LR, and DU, and none of them were assigned to NS, thus confirming that our method of selecting the SNPs and the selected SNPs were appropriate as a traceability instrument for the NS breed. Using the 20-SNP panel, we were able to correctly assign 100% of the individuals to their breeds.Hybrid identification was obviously a more complicated task. Using the 20-SNP panel, we were able to correctly assign 92.2% of the individuals in the training population and 94.7% of the validation population, with all the purebred individuals correctly assigned in the second dataset. Moreover, when adding an assignation probability threshold of 65% to confirm the purebreds, the correctly assigned individuals totaled 97.7%, and all the Nero Siciliano hybrids were assigned to the correct cluster. This is of great value in potentially preventing frauds intended as meat mixtures that are passed off as monobreed products. Finally, when considering the hybrids and using the assignation probability threshold of 65% to reduce the assignation errors, the individuals that were still incorrectly assigned were related to cosmopolitan breeds. The higher overestimation of the expected heterozygosity for populations involved in the discovery process than for the population not included in the SNP selection for the original array  actuallyTM, MALDI-TOF-MS, target SNP-seq, etc.).The genetic authentication of animal products from local breeds is a challenging task for geneticists, but the development of an easy-to-use tool for breed traceability would protect consumers from frauds and preserve animal genetic resources, providing a higher gain to the breeders. NS, the pig breed analyzed in this study, is an endangered population to be safeguarded in its breeding area, and the authentication of its derived meat products will help to maintain the local sustainable rearing system and add value to its monobreed products. Previous studies used panels with a low number of SNPs to compare different breeds, showing that it was possible to use them to differentiate many breeds . We furtWith our approach of selecting the SNPs while considering the delta statistic, the ascertainment bias of an array design that usually negatively affects population genetic estimators when dealing with breeds not involved in the SNP selection seemed to be bypassed, allowing the correct assignation of these breeds. The SNP selection approach used in this study is therefore not limited to NS, and also could be applied to other autochthonous pig breeds to select the most informative SNPs from a commercial genotyping chip. Furthermore, this approach also could be applied to local breeds of species other than porcine ones that face the same threats.


Colletotrichum falcatum is the main disease limiting sugarcane productivity in several countries including the major producer India. The genetic basis for red rot resistance is unclear. We studied a panel of 305 sugarcane clones from the Australian breeding program for disease response phenotype and genotype using an Affymetrix® Axiom® array, to better understand the genetic basis of red rot resistance. SNP markers highly significantly associated with red rot response (≤ 10-8) were identified. Markers with largest effect were located in a single 14.6 Mb genomic region of sorghum (the closest diploid relative of sugarcane with a sequenced genome) suggesting the presence of a major-effect QTL. By genomic selection, the estimated selection accuracy was ~0.42 for red rot resistance. This was increased to ~0.5 with the addition of 29 highly significant SNPs as fixed effects. Analysis of genes nearby the markers linked to the QTL revealed many biotic stress responsive genes within this QTL, with the most significant SNP co-locating with a cluster of four chitinase A genes. The SNP markers identified here could be used to predict red rot resistance with high accuracy at any stage in the sugarcane breeding program.Red rot caused by the fungus  Saccharum spp. inter-specific hybrids) is a major food and industrial crop grown in more than 110 countries in the tropics and sub-tropics  that was released in 1918, and which quickly became dominant in northern India, succumbed to red rot within a few years after release . Genome wide association studies (GWAS) have been conducted in sugarcane research to identify specific QTL and associated DNA markers for a range of traits including fibre composition , yield tGWAS studies aim to identify individual markers correlated with traits of interest, but are constrained by limited statistical power to identifying only QTL with moderate to large effect, particularly when population sizes are small (eg. panels with<1000 genotypes). This is a potentially important limitation because it is clear that most commercially important traits in sugarcane are controlled by relatively small effects of large numbers of genes. Resolution of marker-trait association by some past studies may also be limited by the small number of available markers relative to the large size of the sugarcane genome.® Axiom® array containing over 58K single nucleotide polymorphism (SNP) (More recently genomic selection (GS) has been advocated as a potentially useful approach that may be used in sugarcane breeding programs to more accurately select for traits controlled by large numbers of small effect QTL. In sugarcane this method has been applied with encouraging results . Where ism (SNP)  allows f® Axiom® SNP array to identify specific markers linked to resistance to this disease. We examined the results from analysing data using GWAS and GS for resistance to red rot. We evaluated the accuracy of GS using pedigree, markers and a combination of both to predict red rot resistance. The population of clones studied were also characterised for tonnes of cane per hectare (TCH) and commercial cane sugar (CCS) content, and results from these traits provided a comparison for red rot. In particular, GS prediction accuracies for red rot resistance were compared with TCH and CCS which have been studied previously  sugarcane breeding program for response to red rot disease. These same clones were also genotyped using the Affymetrixeviously . We alsoThree hundred and five clones from a final stage regional selection trial of the SRA sugarcane were used for the study. These clones were representative of those routinely generated and evaluated in the Australian commercial sugarcane breeding program, apart from three clones which were commercial standard cultivars. The clones were derived from 186 different parent clones and 166 crosses, also representative of those used and generated in the Australian sugarcane breeding program.A field trial was established at Kalamia mill estate in north Queensland, Australia to measure cane yield  and commercial cane sugar  . About tC. falcatum morphology were sub-cultured regularly to produce pure isolates. Pathogen identity of isolates was confirmed by conidia, culture morphology, and red rot symptoms in sugarcane. Sporulating fungi were suspended in deionised water and mixed with a kitchen blender to produce inoculum for red rot screening trials. Conidial concentration of the inoculum was adjusted to 1 million conidia per ml.Sugarcane stalks showing typical red rot symptoms were sourced from a sugarcane farm near Mackay, Queensland, Australia. Stem cuttings were thoroughly cleaned with water, sprayed with 70% ethanol and split longitudinally with a sterile knife in a laminar-flow hood. Small pieces of infected tissues were isolated under sterile condition and cultured on potato dextrose agar in Petri dishes and stored at room temperature in dark conditions for several days. Fungal colonies with typical C. falcatum conidial suspension or water (control) around the second and third visible nodes from the top. Inoculated stalks were positioned upright with cut end inside wet sand and maintained in a growth chamber set at 30°C, > 90% humidity and constant light for two weeks. The experiment design was a randomised complete block with four replicated stalks inoculated for each clone. A highly resistant standard (negative control) and a highly susceptible standard (positive control) were also included in this experiment.The level of resistance to red rot was observed for all 305 clones in the trial following methods developed by the Sugarcane Breeding Institute, Coimbatore, India and described by After two weeks the stalks were split longitudinally and disease symptoms were scored following the metrics described previously : shoot thttp://media.affymetrix.com/support/downloads/manuals/axiom_2_assay_auto_workflow_user_guide.pdf ). DNA samples that had a dish quality control (DQC) measure of less than 0.82 or a quality control (QC) call rate of less than 97% were excluded from the analysis. Allele calling was performed using generated CEL files with Axiom Analysis Suite (1.1.0.616) . For each polymorphic marker, all genotypes were given a marker score of 1 if only the most frequent allele was present , 0 if both alleles were present (i.e. heterozygous), and − 1 if only the minor allele was present. Markers in which one of these three classes occurred for > 98% of the clones were deleted. A total of 56, 788 polymorphic markers were retained for further analysis after the above filtering. For each marker, missing values were replaced with the most frequent allele within a marker.All clones were genotyped using an Affymetrix Axiom SNP array developed for sugarcane with 58,028 SNPs, previously screened and chosen based on quality parameters and polymorphism in Australian and Brazilian parental clones . High-quTCH, CCS, red rot rating were analysed under a mixed model framework using a commercial R package, Asreml-R.For TCH and CCS, an optimal linear mixed model was first determined for each trial crop class data from fitting different fixed, random and residual effects . For fixFor red rot, the linear mixed model can be described as follows:y is the measurement of total score from each plot, μ is a grand mean and e is the residual effects. Date, replicates within date (rep/date) and clone were set as random effects. All the random effects were assumed to follow iid N. Similarly, BLUPs of clonal effects and the broad-sense heritability were estimated from the model, which were used as observed values in genomic selection.where The association between each individual SNP marker and Red Rot resistance, TCH, and CCS was analysed using ASReml-R package based on the following mixed model:y is BLUP of a trait obtained from the above analysis, a i the intercept, SNP is a fixed effect of SNP, u is a polygenic effect ~ N where A is the numerator relationship matrix and σ2α is the polygenic variance; e = random residual effects ~ iid Nwhere -7) was calculated using Haploview software . Sorghum was used as a reference genome as it is the closest diploid relative to sugarcane with a high level of synteny between the two genomes, but is smaller with more detailed gene annotation (http://matsui-lab.riken.jp/morokoshi/Home.html).The genomic location of SNPs most significantly associated with red rot resistance, TCH and CCS was determined by searching the sugarcane and sorghum genomes on the CSIRO public genome browser . The majority of clones appeared resistant or highly resistant to red rot in response to the screening method Figure 1A summary of the results of association tests between individual SNP markers and red rot resistance are given in -7, and this level of P corresponded to a near zero false discovery rate. These markers were all found to be in LD, with most in strong LD (r > 0.8), suggesting all were associated with a common QTL for resistance to red rot.Thirty-five markers were found to be significantly associated with red rot at P ≤ 10-4) was considerably less than for red rot, with no markers associated at P values less than 10-6. However, a higher number of markers than expected by random chance were associated with TCH and CCS in both the plant and first ratoon crops for p values between 0.001 and 0.01, with the ratoon crop having higher numbers. For TCH, a higher number of SNPs were also associated with the trait than expected by random chance for P values ≤ 0.0001 and ≤ 0.00001. This was also the case for CCS for P ≤ 0.0001 in the ratoon crop only. SNP associations for each of TCH and CCS traits were strongly correlated between plant and ratoon crop measurements, consistent with performance for each of these traits being correlated between crop cycles (data not shown). However, there was no correlation between SNP marker effects for TCH and CCS, indicating that these two traits appear governed by different genes.The number of markers associated with TCH and CCS at low P values  with closely located genes (< 5kb from the SNP) were listed in S. spontaneum which could lead to incomplete synteny to sorghum in this region  gave lower accuracy than the M and AM models Table 3.-7 were added as fixed effects. Except for one case, the accuracies of prediction in most cases for the model with the fixed effects added were higher (0.48 to 0.51) than without (0.41-0.46). This result indicates adding large effect markers separately in the genomic prediction models can improve prediction capacity. However, there was a single exception to this result with the Bayes A model gave a very low accuracy when the fixed effects were added, and reasons for this were unclear.In addition to “standard” genomic prediction models where markers are assumed as random effects, the 29 red rot SNPs listed in Average resistance scores for clones homozygous for each allele, and heterozygous for the alleles were determined for each of 29 markers listed in Clones were classed into those putatively with and without the QTL (or multiple QTLs) characterised by the presence of alleles of the 29 linked SNP markers in As shown in The results indicate that a major QTL affects resistance to red rot within germplasm generated in the Australian sugarcane breeding programs. The presence of this effect, indicated by a cluster of closely linked SNP markers, appears to provide a high chance of resistance, with only a very small proportion (2% or less) of clones with resistance alleles linked to this QTL showing susceptibility. However, the presence of resistance in over 50% of clones without this QTL also indicates that other genetic factors also contributed to red rot resistance in the germplasm studied, reducing somewhat the overall difference in resistance levels of clones with the QTL versus those without, across the whole population.The set of clones sampled for this study represented those routinely generated in the commercial sugarcane breeding program in Australia. The observation that less than 20% of the sampled clones were susceptible to red rot, with most clones exhibiting resistance, is consistent with the situation seen for many years in the Australian sugarcane breeding program and industry, where susceptibility to red rot is not generally an important problem. However, this situation is clearly different to other countries, such as southern Asian countries where cultivar susceptibility to red rot is a critical problem, and it is of interest to understand possible reasons for the difference. It is at present unclear if the potentially important QTL conferring resistance to red rot in the germplasm, or other genetic effects, contributes to the high proportion of resistant clones relative to the situation in other countries, or if pathogen variation is the major cause of differences. It is of interest to understand if the major QTL in the Australian germplasm exists in sugarcane cultivars and breeding programs in countries such as India. At this stage, the value of this QTL in other countries is unknown, and its effectiveness (or vulnerability) against different evolved strains of red rot in other countries should be investigated. The use of common SNP markers available for easily comparing germplasm in different programs will facilitate this investigation in the future. For the Australian sugarcane breeding program, based on the results in this study, it would be possible to screen for several of the SNP markers  whose orthologues are transcriptionally activated in response to abiotic stress in Arabidopsis  because of the relatively small number of clones observed (307). These accuracies are lower than those found with a larger number of clones (about 2500) measured in prior work (with accuracies of >0.35) . Howeverhttps://github.com/SugarAus/data.git.The data presented in the study are deposited in the GitHub repository, accession number AO’C, JD, PL, ED, XW, PJ, and KA performed the experiments and/or analysed data. PJ, PL, and RM conceived and designed the project. HG, KM, RV, and BR contributed to methodology development and related technical expertise. All authors contributed to the article and approved the submitted version.This project was supported by the Australian Government under the Australia-India Strategic Research Fund grant number 48454, and by Sugar Research Australia, Brisbane and the ICAR Sugarcane Breeding Institute, Coimbatore, India.Authors AO'C, JD, ED, XW and PL were employed by the company Sugar Research Australia Limited.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


ZmFCL, encoding a 5-formyltetrahydrofolate cyclo-ligase, accounted for 30.12% of phenotypic variation in 5-FTHF content. Further analysis revealed that two adjacent SNPs on the second exon resulting in an AA-to-GG in the gene and an Asn-to-Gly change in the protein could be the causative variant influencing 5-FTHF content. Meanwhile, 5-FTHF content was negatively correlated with ZmFCL expression levels in the population. These results extend our knowledge regarding the genetic basis of folate and provide molecular markers for the optimization of folate levels in sweet corn kernels.Folate is commonly synthesized in natural plants and is an essential water-soluble vitamin of great importance inhuman health. Although the key genes involved in folate biosynthesis and transformation pathways have been identified in plants, the genetic architecture of folate in sweet corn kernels remain largely unclear. In this study, an association panel of 295 inbred lines of sweet corn was constructed. Six folate derivatives were quantified in sweet corn kernels at 20 days after pollination and a total of 95 loci were identified for eight folate traits using a genome-wide association study. A peak GWAS signal revealed that natural variation in  Folates serve as donors and acceptors in one-carbon (C1) transfer reactions, making them essential in all living organisms (http://ods.od.nih.gov/factsheets/folate.asp). Unfortunately, the most important staple crops in folate production are poor suppliers of folates, hence, folate deficiency has become a significant public health problem, especially in developing countries  and its Crganisms . An adulountries . Folate ountries . In addiountries .GTPCHI and ADCS) have been successfully applied to enhance folate content in maize and other plants  have become a powerful tool to identify new genes associated with nutritional traits in corn ;. RecentIn a previous study, we constructed a diverse natural population of 204 sweet corn inbred lines and successfully dissected the genetics of vitamin E in this population . In the Genomic DNA of accessions were extracted using the CTAB protocol  before bTotal RNA was extracted for RNA sequencing using a Quick RNA Isolation Kit. Libraries were constructed with a 300 to 500 -bp insert size, and 150-bp paired-end Illumina sequencing. A total of 31.27 billion raw reads per sample were obtained. Reads were mapped to the B73 reference genome (RefGen_V4) using Hisat2 . Next, SFolate extraction and detection was carried out as previously reported . In brielme4 package of R as follows: iy = μ + ig+ ie + iϵ, where µ is the mean, ig represents the genetic effect, ie is the environmental effect, and iϵ is denoted as the residual error. The broad-sense heritabilities (2h) of each traits was estimated as 2h = g2σ/(g2σ + ϵ2σ/e), where g2σ represents the genetic variance, and ϵ2σ represents the residual error, and e is the number of environments  was calculated using the ronments .2r between two SNPs within 600-kb windows was calculated by PopLDdecay (v3.40). Overall, with the threshold set to 2 =r0.2, the distance of LD decay was 35 kb in the population. A GWAS regarding folates traits was conducted in TASSEL (v.5.0) using a mixed linear model by control of both population structure and kinship. For conditional GWAS, the genotype of the leading site was extracted and combined with covariates to detect additional significant sites. Considering the strong LD among all SNPs, a total of 767964 independent SNPs were determined by PLINK  (-6 = 1/767964) which followed the significance level similar to a previous study (2r ≥ 0.2). For each trait, the phenotypic variation of the population explained by all significant leading SNPs was estimated by stepwise regression, using the lm function in R  matrix values in TASSEL (v.3.0) . The pop ≥ 0.2)  and thusus study  was usedion in R .All genes within 35 kb up- and downstream of the leading SNPs were considered as acceptable associated genes, and the gene in or near each peak which were expressed in kernels at 15 DAP (FPKM > 0 in more than one line of the sweet corn population) were proposed to be the most likely candidate for association.ZmFCL from 215 sweet corn inbred genotypes  using a mixed linear model by control of both population structure and kinship as previously mentioned.According to the sequences of B73 and RC , while positive correlations were observed among most traits  to identify the novel sites for each of the nine folates in sweet corn kernels. A total of 96 sites were significantly associated with eight of the folates at a threshold of P<1.0×10-6 Table 2.P<0.05) in sphingolipid metabolism, circadian rhythm and cysteine and methionine metabolism  and analysis of the expression data from immature sweet corn kernels at 15 DAP, a total of seventy-three genes were considered to be potential candidate genes  significantly associated with its expression level was located in the promoter region of ZmFCL   , including the S5_20162982 site detected by the GWAS. Two additional SNPs were located 22-bp and 21-bp away from S5_20162982, revealing complete linkage with S5_20162982 (2r = 1). Unlike S5_20162982, these two SNPs were nonsynonymous with an AA-to-GG change resulting in an Asn-to-Gly change   was an insertion with 3/9/12-bp in the second exon, resulting in a 1/3/4 amino acid insertion in the protein. The additional sites  were all synonymous, while the final site (S6) was the functional site causing an Asn> Gly change mentioned previously. A total of 13 haplotypes were detected in 193 inbred lines. Eight of these haplotypes had a sample size of > 2 lines (frequencies > 0.01) (n = 113) resulted in lower 5-FTHF content, showing no significant disparity with Hap2, Hap3 and Hap4. In contrast, Hap5, Hap6, Hap7 and Hap8 led to higher 5-FTHF content, showing no significant differences amongst each other. When GG haplotypes (Hap5-8) were compared with AA haplotypes (Hap1-4), a highly significant difference in 5-FTHF level was observed   was present in about one third of total inbred lines in the sweet corn panel  that was significantly associated with  > 0.01) Figure 5in- vivo is largely dominated by 5-MTHF and 5-FTHF  (ZmFCL orthologs was constructed by MEGA and the results demonstrated that the protein was highly conserved within monocots with the highest level of conservation between sweet corn and field corn (th of the amino acid which was predicted in the 7th β-strand of the protein (th amino acid which near the conserved region (amino acid YN) might result in an alteration to the activity of 5-FCL  by PPVED  repository, accession numbers CNP0003213 and CNP0003294.JH and GL conceived the research and designed the experiments. YX and YY performed the experiments, population collection and wrote the manuscript. LX performed phenotypic determination. KL performed some of the data analysis. XG and GyL and JL performed the field experiments. All authors contributed to the article and approved the submitted version.This study was supported by the key area research and development program of Guangdong Province (2018B020202008), provincial rural revitalization strategy special project of Guangdong in 2022 (No. 92), construction and operation of the Food Nutrition and Health Research Center of Guangdong Academy of Agricultural Sciences (XTXM 202205), agricultural competitive industry discipline team building project of Guangdong Academy of Agricultural Sciences(202115TD), Special Fund for Scientific Innovation Strategy-Construction of High Level Academy of Agriculture Science (R2017YJ-YB1002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We obtained 12.6 Mb of single nucleotide polymorphism (SNP) data, and the three shin colors were subsequently genotyped. Genome-wide association studies (GWASs) were performed to identify candidate and potential SNPs for the three shin colors. According to the results, 2947 and 3451 significant SNPs were associated with black and yellow shins, respectively, and six potential SNPs were associated with spotted shins. Based on the SNP annotations, the MITF, EDNRB2, POU family members, and the SLC superfamily were the candidate genes regulating pigmentation. In addition, the isoforms of EDNRB2, TYR, TYRP1, and MITF-M were significantly different between the black and yellow tibiae. MITF and EDNRB2 may have synergistic roles in the regulation of melanin synthesis, and their mutations may lead to phenotypic differences in the melanin deposition between individuals. This study provides new insights into the genetic factors that may influence tibia color diversity in birds.Shin color diversity is a widespread phenomenon in birds. In this study, ducks were assessed to identify candidate genes for yellow, black, and spotted tibiae. For this purpose, we performed whole-genome resequencing of an F Skin color diversity is a common phenomenon in most birds, and the color of the feathers, coats, and skin is mainly regulated by melanin and carotenoids. Melanin is primarily synthesized in melanocytes ,2,3,4,5.TYR, MCIR, BCOD2, IRF4, SLC45A2, KITLG, MITF, and OCA2, have been shown to be associated with human skin color [CLEC12B, is highly expressed in melanocytes, and its expression is reduced in dark skin compared to white skin. CLEC12B directly recruits and activates SHP1 and SHP2 through its immunoreceptor tyrosine-based inhibitory motif structural domain and promotes CRE CLEC12B, thereby ultimately controlling melanin production and hyperpigmentation in vitro and in reconstructed human epidermal models. The identification of CLEC12B in the melanocytes suggests that the C-type lectin receptor is involved in functions other than immunity and inflammation, and also provides insights into melanocyte biology and melanogenesis regulation [With rapid advances in genetics and genomics, scientists have realized that the diversity of human skin color is due to the natural positive selection of genes that influence human pigmentation, particularly genes in the melanosome biogenesis or melanin biosynthesis pathways ,12,13. Rin color ,15. SAL in color ,17,18. Iin color . The C-tgulation .As the second-largest poultry species in the world, ducks mostly have yellow and black shins. Spotting occasionally occurs, and this phenotype directly affects the marketability of carcasses. However, the genetic factors responsible for the appearance of a spotted color remains unclear. Thus, we conducted genome-wide association studies (GWASs) on 275 ducks and crossed F2 generations of Cherry Valley and Runzhou crested-white ducks to gain insights into the influence of genetic factors on shin pigmentation. Our findings provide insights into the molecular regulatory mechanisms underlying skin color and the genetic modifications that lead to the melanin deposition in duck shins.All of the duck experiments were performed in accordance with the Regulations on the Administration of Experimental Animals issued by the Ministry of Science and Technology  in 1988 (last modified in 2001). The experimental protocols were approved by the Animal Care and Use Committee of the Yangzhou University (YZUDWSY2017-11-07). All efforts were made to minimize animal discomfort and suffering.2 resource population, which represented a cross between the Chinese crested (CC) and Cherry Valley ducks (CV), was obtained from the Laboratory of Poultry Genetic Resources Evaluation and Germplasm Utilization at Yangzhou University. The ducks were raised in stair-step cages under recommended environmental and nutritional conditions at the conservation farm of the Ecolovo Group, China. The CC duck has black shins and white plumage and represents an indigenous Chinese breed. The CV duck has yellow shins and white plumage and represents a commercial breed. In the F1 generation, 30 CC ducks and six CV ducks were randomly selected and divided into six families to interbreed. To generate F2 progeny, 30 males and 150 non-related female ducks were used as the hybrids. A total of 275 ducks were used in the re-sequencing and subsequent experiment. To identify the candidate genes associated with shin color, we classified the shin colors as yellow, spotted, or black  was extracted from the blood samples using a DNA extraction kit (QIAampR DNA Blood Mini Kit), following the manufacturer’s protocol. Two paired-end sequencing libraries with insert sizes of 350 bp were constructed, according to the Illumina protocol . All of the libraries were sequenced on the Illumina NovaSeq platform.The raw reads were filtered using the NGS QC Toolkit (version 2.3) with the default parameters. The clean reads were mapped to the duck reference genome (CC duck genome assembled in our lab (unpublished)), and a Burrows–Wheeler alignment  was performed using the default parameters . The GAT2 threshold of 0.2. Pairwise identity-by-state (IBS) distances between all of the individuals were calculated using these independent SNP markers, and the MDS components were acquired using the mds-plot option based on the IBS matrix. A relative kinship matrix was constructed using the independent SNP markers. In addition, we used the trimmed SNP data and performed a dimensionality reduction using three methods , and PCA-UMAP). First, we performed a PCA analysis of the trimmed genotype matrix, using the EIGENSTRAT software. The first two principal components are displayed in two-dimensional space. Second, we performed UMAP clustering of the genotype matrix using the umap package for python software . Third, we applied the UMAP method to the first 50 principal components of the trimmed genotypes used for the PCA-UMAP (n components = 2 and default parameters) [https://github.com/mkanai/grimon, accessed on 15 January 2022)) was used to visualize the two-dimensional graph, which was visualized as three-dimensional (3D) parallel coordinates and colored according to the color of the shin [The population structure was assessed by multidimensional scaling (MDS) using the PLINK 1.9 software . The indameters) ,26. To cthe shin .The GWAS analysis for shin color employed the linear mixed model of Effective Mixed Model Association eXpedited (EMMAX) software. The EMMAX software simplifies and saves the assumption that the effect of any given SNP on the trait is typically small; therefore, it only estimates the model variance components once per analysis to account for the population structure. The EMMAX estimates the variance components using the REML model:y is a vector of shin color; X is the incidence matrix for a random additive effect; a is the column vector of random additive effects; Z is the genotype value of the candidate SNP; b is the regression coefficient of the candidate SNP; and e is a random residual. The phenotypic variance–covariance matrix is var (y) = Var (a) + var (e) = aK σ2 + eI σ2, where K is the IBS kinship matrix; I is the identity matrix; a2σ is the additive variance; and e2σ is the variance of the random residuals.Where Based on the LD attenuation distance calculated by PopLDdecay, the related genes in a certain region upstream and downstream of the physical location of the significant SNPs were annotated . The seq−2ΔΔCt method after normalization to the levels of GAPDH  [Total RNA was extracted using the RNA Simple Total RNA Kit , according to the manufacturer’s protocol, and 1 μg of total RNA was reverse-transcribed to cDNA, using the Fast Quant RT Kit (with gDNase) . The gene expression was quantitatively analyzed by RT-qPCR in a QuantStudio™ 5 Real-Time PCR System, using the AceQ qPCR SYBR Green Master Mix , gene-specific primers , and cDNcontrol) .t-test in the stats package of R. The statistical significance was set at p < 0.05.All of the results displayed in the bar graphs are expressed as the mean ± standard error of the mean (SEM) of three independent experiments. The statistical significance was determined using the student’s The ggcorr package of R and the GGally package were used to identify the correlation between shin color and sex. The results showed no correlations between the black, yellow, or spotted shin color and sex .2 population, we initially applied four dimensionality reduction methods to the large-scale F2 population genotype data: (1) PCA, an orthogonal linear transformation that projects the genotype data into a new reduced dimensional space so that larger variances are ordered  UM ordered b; (3) PC ordered c; and ) for the three different colored shins were 0.926 (black shin), 0.985 (spotted shin), and 0.878 (yellow shin), and the points in the upper-right corner of the Q–Q plot represented significant markers associated with the traits under study . Thus, tp-value ≤ 3.94885 × 10−9 as the threshold, while one extremely significant SNPs was identified using a p-value ≤ 7.8977 × 10−10 as the threshold. Most of these SNPs were located on chromosome 14 (ALP 14)  and 3451 extremely significant SNPs were identified, using a significant p-value ≤ 7.8977 × 10−10 as the threshold. Most of these SNPs were located on ALP 11  , top. Fon ALP 14 , middle.n ALP 11 , bottom.n ALP 11 .SLC25A43, SLC25A5, BMP15, SPRY3, POU4F3, EDNRB2, and others associated with SNPs significantly associated with black shins . The tyrosinase production is regulated by TYR and TYRP1. The first stage of melanin formation is accomplished by tyrosinase, which changes the amino acid tyrosine, a component of proteins, into dopaquinone. The dopaquinone is then converted to melanin in the skin, hair follicles, iris, and retina by a sequence of chemical processes. The TYR and TYRP1 expression showed that TYR and TYRP1 were significantly expressed in the black shins  with seven transmembrane structure domains; moreover, it is a homolog of the endothelin receptor B (EDNRB), a gene involved in the formation of the melanocyte lineage [EDNRB2 genes, which affect the ability of specific skin cells to synthesize pigments [EDNRB2 is associated with white plumage phenotypes in Japanese quail, chickens, and domestic ducks [ lineage . The leupigments . The abepigments ,42. The pigments . Previouic ducks ,44. Our MITF gene is a bHLHZip transcription factor belonging to the MYC superfamily [MITF plays an important role in melanocytes and is a key regulator of melanin synthesis, and it is involved in the proliferation, differentiation, and transport of melanocytes [MITF silencing in human melanocytes, the expression of the TYR and TYRP1 proteins was significantly reduced at different levels, while the expression of the TYRP2 protein was significantly increased [TYR) is involved in the synthesis of dopa and dopaquinone during melanin synthesis [TYRP1) gene is a member of the tyrosinase-related protein family and represents a key factor in melanin synthesis [The erfamily ,46. MITFanocytes . Moreovencreased . Tyrosinynthesis . The typynthesis . The tyrynthesis .MITF [MITF; however, further experimental validation of the exact mechanism is needed. The present study provides new insights into the genetic factors that may influence the color diversity of birds’ tibiae; however, further experimental studies are needed to strengthen the hypotheses.The yellow color of the skin, beak, and feet of most birds is caused by carotenoid deposition. The results showed that most members of the POU family are clearly associated with yellow tibiae, which have typical POU structural domains . The POUMITF . TherefoMITF and EDNRB2 are candidate genes associated with shin melanosis. In addition, POU4F3 is a candidate gene associated with shin color. We speculate that pigmentation may be coregulated by the POU family, MITF, and EDNRB2 in the shins. However, the specific underlying mechanisms require further experimental verification.This study identified the candidate genes that are closely related to duck shin color.


Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D.A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D . We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets.KLKs, GRM8, SHANK2) and pathways  were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy).We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.The online version contains supplementary material available at 10.1186/s12933-022-01715-1. Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases driven by numerous additive and interacting genetic factors and in combination with the environment. The two diseases represented the most prevalent and burdensome non-communicable chronic diseases (NCDs). Making the issue more challenging, the co-occurrence of CHD and T2D is also common rather than random assortment of individual conditions . DiabeteGLUL, which is functionally related to glutamic acid metabolism, was associated with elevated cardiovascular risk specifically in diabetic individuals  or SNP sets). Phenotype was identified as different disease patterns involving CHD and T2D, which occurred naturally in the general population. Genotypic-phenotypic architecture cross-matched the SNP sets network with phenotype subtypes. This schematic drew on previous work from Zwir et al. https://doi.org/10.1176/appi.ajp.2014.14040435. Figure S2. Manhattan plot summarizing the association results for the coronary heart disease (CHD) and Type 2 diabetes (T2D). Each tested SNP is visualised as a dot with location on the genome shown on the x-axis and -Figure S3. Heatmaps of SNP sets. Abbreviations: CHD SNP: variant associated with CHD in logistic regression; T2D SNP: variant associated with T2D. Figure S4. Pie plots represents molecular consequence of SNPs within each SNP set.Additional file 2: Table S1. Mapping SNP Variants to genomic information. (Information obtained from dbSNP database and GeneCards database).Additional file 3: Table S2. Genes within each SNP set associated with multiple cardiometabolic trait.Additional file 4: Table S3. KEGG enrichment among different disease patterns.Additional file 5: Table S4. GO enrichment among different disease patterns.Additional file 6: Table S5. Replication of SNP set using Hypergeometric test.


Udder conformation traits are economically important for dairy animals in the dairy industry. Milk production loss can be reduced via a better udder structure in dairy cattle. Udder traits are related to milk production and the somatic cell count, which is a sign of mammary infections (mastitis); as such, it is vital to understand the genetic architecture underlying udder traits in Holstein Friesian cattle for genetic development and long-term selection. Through a GWAS on udder structure traits in Chinese Holstein cattle, we identified several significant single-nucleotide polymorphisms (SNPs) and candidate genes associated with udder traits. The results could provide useful information regarding the genetics architecture of udder structure traits, thus improving the genetic information, health, longevity, and production of dairy cattle.p < 5.90 × 10−7). Many candidate genes were identified within 200kb upstream or downstream of the significant SNPs, which include MGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3, and GCLC. Pathway analysis revealed that 58 Gene Ontology (GO) terms and 18 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched with adjusted p values, and these GO terms and the KEGG pathway analysis were associated with biological information, metabolism, hormonal growth, and development processes. These results could give valuable biological information for the genetic architecture of udder conformation traits in dairy Holstein cattle.Udder conformation traits are one of the most economic traits in dairy cows, greatly affecting animal health, milk production, and producer profitability in the dairy industry. Genetic analysis of udder structure and scores have been developed in Holstein cattle. In our research, we conducted a genome-wide association study for five udder traits, including anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD), in which the fixed and random model circulating probability unification (FarmCPU) model was applied for the association analysis. The heritability and the standard errors of these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. Phenotype data were measured from 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data in Holstein cattle. For GWAS analysis, 984 individual cows and 84,407 single-nucleotide polymorphisms (SNPs) remained after quality control; a total of 18 SNPs were found at the GW significant threshold ( Udder conformation traits play a vital economic role in dairy cattle . The uddESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, BTRC, and TGFBR2, across three French dairy cattle breeds [The identification of quantitative trait loci (QTL) is an essential step in identifying and understanding genetic variants associated with economically significant phenotypes. GWAS has become a widely used method for identifying QTL and genome regions associated with phenotypes in Holstein cattle . GWAS hae breeds . There aWhole processes for hair sample collection and data collection of phenotype traits were measured according to the plan proposed by the Ministry of Agriculture of the People’s Republic of China and the China Council on Animal Care. The Institutional Animal Health Care, and this study was also accepted by the Yangzhou University Animal Researchers Ethics Committee  IACUC 2012-0029). During the research, no animals were harmed.The studied experimental population consisted of 1000 Holstein cows raised from 4 dairy farms in Jiangsu Province, China . The hair samples for genotyping data were collected within two months . The five udder structure traits were measured individually by scores ranging from 1 to 9 points. The five udder structure traits consisted of the anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW) and udder depth (UD) of 1000 cows, were measured according to China National Standard (GB/T 35568-2017). Three expert technicians completed the phenotypic measurement of udder straits for each cow, and hair samples from the tail of 999 cattle were also collected for DNA extraction and genotyping.ijklmy is the phenotypes in the year jth; kth is the season; Ith is the individual parity mth from the herd of animals ith; u is the mean of the population; animal iherd is the herd effect according to a cows origin from one of the four herds; jYear is the year levels of the effects in jth; kSeason is the season levels of the effects kth and parity is the effect of parity lth; a is the additive effect of the individual mth, which was conducted by the pedigree information; and e is the residual in the jth year, kth season, and lth parity of the individual mth from the ith herd. All the effects were evaluated as fixed effects except for the overall mean. The pedigree data of the cows age could be traced back at least 3 generations (2009–2020), the parities of the cattle were from 1 to 4, and the 4 seasons represented “September–November, December–February, March–May, and June–August”.Statistical analysis and Pearson correlation of the phenotype traits were investigated using the computer software, SPSS , for data analysis . Genetic analysis was carried out with an animal model using the DMU software (v.5.6)  to deterhttp://www.neogenchina.com.cn/; accessed on 25 June 2022) based on ARC-UCD1.2/bosTau9 as the genome reference. The GGP Bovine 100K SNP chip consisting of 100,000 SNPs was used for genotyping individuals. Then, quality control was conducted using Plink software  [p < 1.0 × 10−6). After quality control, there remained 984 cattle and 84,407 markers (SNPs) variants for further analyses. The distribution of the SNPs information on 29 chromosomes within 1mb window size shown in Genome DNA was extracted and genotyped using the GGP Bovine 100k SNP Chip, by Neogen Corporation (mbridge) , to remoFor the analysis of the population structure, Plink 1.90 software (v1.90)  was usedi is the ith of an individual: Mi1, iM2,……, iMt are the genotypic of the pseudo-QTNs, started with an empty set; b1, b2,……, tb are the consistent effects of the pseudo-QTNs; ijS is the genotype of the ith individual and jth genetic marker (SNPs); dj is the corresponding effect of the jth genetic marker; ie are the residuals having a distribution with a zero mean and a variance of ϭ2e.This study used the multi-locus linear mixed model to perform the GWAS analysis between the SNPs and trails using the fixed and random model circulating probability unification (FarmCPU) method . The Farp value after substitution. The p values and the associated marker map were used to update the selection of the pseudo-QTNs using the SUPER algorithm [iy and ie are similar to in Equation (1), and iu is the individual’s ith overall genetic effect. The type I error false-positive rate was measured at the level of 5%, and the genome-wide significance of the threshold value was revealed according to this method (0.05/SNPs) after quality control, where Nsnps were the remaining number of SNPs after quality control [p < 5.90 × 10−7 (0.05/84407). Then, the Manhattan plots and quantile–quantile plots (QQ plots) were drawn using the “CMplot” package in the R 4.1.0 software [Each marker (SNP) had its own lgorithm  in a REM control . After B7 0.05/847. Then, http://genome.ucsc.edu/bosTau9, ARS-CUCD1.2) accessed on 25 June 2022 [All of the genes within a 200 kilobases (Kb) position of the significant SNPs were identified as candidate genes for udder traits. Two hundred kilobases is a common distance that has been used to annotate and find genes related to SNPs in previous association ,42. We iune 2022 .p values were adjusted for multiple tests using the Bonferroni correction. Gene network analysis was carried out among genes using online software for the identification of Interrelating Genes (STRING database software v.11.0) [In the study, to further understand the biological information among these candidate genes, we submitted all the candidate genes identified from the GWAS analysis into the DAVID database  software  for Gene v.11.0)  and Cyto v.11.0)  to visuaThe adjusted udder conformation traits of 984 cattle included the anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD) are presented for normal distribution in this study. This descriptive statistical data analysis of the traits is shown in The phenotypic and genetic correlations between these traits are shown in This research used Principal Component Analysis to visualize the family structure. The result showed that all the populations were divided into two sized clusters group. The PCA results in p-value of each SNP) by chromosomes position; each chromosome showed a different color. Significant SNPs in the Manhattan plot were strongly associated with traits  of the associated SNPs were shown relations to five udder traits. The five circles from outside to inside represent the udder conformation traits.  plots demonstrated that the genome-wide association study analysis model was reasonable in this research . The lamh traits a. A Circ traits. b.−7 for significant SNPs in the GWAS. The 18 SNPs passed the threshold and were significantly associated with five udder traits , four SNPs  positioned on chromosomes 5, 22, 27 and 9 were identified to be associated with the trait AUA. Three SNPs  were identified to be associated with the trait CSL. For PUAH traits, three SNPs were presented on chromosomes 29, 18, and 22  Moreover, three significantly associated SNPs  located on chromosomes 7, 5, and 15, respectively, were indicated to be associated with the trait PUAW. While five SNPs  detected on chromosomes 5, 6, 6, 9, and 23, respectively, were found to be associated with the trait UD ; LOC101903734, Microtubule-Associated Scaffold Protein 1 (MTUS1), PRKN (Parkin RBR E3ubiquitin protein ligase); GRID2 (Glutamate Ionotropic Receptor Delta-Type Subunit 2); LOC112447118, Forkhead Box P1 (FOXP1), SMC5-SMC6 Complex Localization Factor 1 (SLF1); Transmembrane Protein 117 (TMEM117), SET Binding Factor 2 (SBF2); Galectin 2 (LGALS2); GC Vitamin D Binding Protein (GC); ADGRB3 (Adhesion G Protein-Coupled Receptor B3); GCLC . Although, one of the SNPs on Chr6 was located near (50 kb) to the Ubiquitin-Conjugating Enzyme E2 K gene (UBE2K), three SNPs on chromosomes21, Chr29, Chr18 were located closely (100 kb) to the STXBP6 (Syntaxin-Binding Protein 6), E2F8 (E2F Transcription Factor 8), and Cadherin 11 (CDH11), respectively , and a total of 141 candidate genes were identified within the 200 kb region upstream/downstream of the significant SNPs for the udder structure traits; all of the candidate genes were used for KEGG pathway and enrichment analysis (GO). The results of the GO analysis determined 58 Gene Ontology terms .p value. A KEGG pathway analysis of the dot plot is presented in the The KEGG pathways analysis revealed 18 pathways  which are shown in The STRING database was used to perform a protein–protein interaction network using all the genes previously used in functional analysis. From the relationships between genes identified in this gene network, there were many interactions among the genes (consisting of 44 nodes related via 75 edges). The corresponding interactivity power among these genes was determined by the staining strength between the lines that linked one gene to the other .In this research, the heritability of the udder traits was low and medium between 0.04 and 0.49 as shown in The phenotypic and genotypic correlations result of the udder conformation traits were negatively low to medium at −0.37 to 0.26 and −0.62 to 0.52, respectively, for Chinese Holstein cows ; the resPopulation stratification was a significant causative factor in GWAS. When a GWAS analysis included samples with different genetic structures, the PCA procedure  could idPopulation stratification is a major challenge during GWAS work analysis. Furthermore, population stratification can lead to many false positives in GWAS results due to incorrect associations . Due to STXBP6 gene, and BovineHD0700028083, located within SLF1. The STXBP6 gene provides a syntoxin-binding protein which attaches to the SNARE complex components, preventing membrane fusions such as phagocytosis [STXBP6 gene causes rheumatoid arthritis [SLF1 gene is also important in genome stability maintenance in human cells after DNA damage [In this research, GWAS revealed 18 significant SNPs (single-nucleotide polymorphism) associated with five udder structure traits in Holstein cattle using the FarmCPU model. Among all of the SNPs, two significant SNPs were ARS-BFGL-BAC-29174, located near the ocytosis , and therthritis . It has rthritis . The SLFA damage .MGST1, MTUS1, and PRKN). Some studies have determined that MGST1 has the largest significant effect on Chr5 for fat production in U.S. Holstein cattle [MGST1 has been recognized extensively as a functional candidate gene for the QTL associated with milk composition features in Montbéliarde cows [MTUS1 gene was associated with meat quality and carcass traits [PRKN gene was associated with growth and production traits in Fuzhong Buffalo [Our research identified genes associated with the AUA trait  is associated with the CSL trait, but, in the previous study, the GRID2 was involved in the sexual maturity of Simmental cattle [The Hapmap32447-BTC-033214 SNP located on Chr6; glutamate ionotropic receptor delta type subunit two genes , are associated with udder depth trait; meanwhile, one BovineHD0600001885 SNP located on Chr6 (50kb) was also related with this trait. Zexi et al. [GC gene on milk production in Holstein cattle has already been investigated in association analysis [ADGRB3 gene is responsible for the uniformity of yearling weight in Nellore cattle [GCLC gene is consistently increased as parturition approaches and lactation began in Holstein Dairy cattle [GCLC is associated with udder depth trait.The BTA-75047-no-rs, BovineHD0600024277, BovineHD0900001933, and BovineHD2300001734 SNPs are located within genes  had seven genes involving MGST2 and HAL. Embryo development ending in birth (GO:0009792) consisted of four genes, including E2F8. Cardiac muscle tissue development (GO:0048738) consisted of two genes . All these identified genes were closely related to our significant SNPs. As mentioned above, some candidate genes that participated in the GO term may play an essential role in the biological function of body traits, and afterward, these candidate genes participate in udder traits. The PDCD5 gene regulates cell proliferation, cell cycle progression, and apoptosis in bovine cancer cells (A431) [MGST2 has been found as a potential gene in the bovine mammary gland [UGDH gene is associated with milk production traits in Chinese Holstein [In our research, GO and KEGG pathways analysis identified many GO terms and KEGG pathways. These included significant candidate genes that are associated with udder traits under analysis, for example, Proteolysis, a GO:0006508 biological process which has nine genes in s (A431) . The MGSh GO:00092 consistHolstein . These fHolstein . GlutathHolstein . HistidiHolstein . ProlactHolstein . The funMGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3 and GCLC) identified mostly to participate in biological information, metabolism, and development processes. Our findings provide useful biological information for understanding the genetic architecture for improving udder traits and will therefore contribute to the genetic selection of Chinese Holstein cattle.In conclusion, our research found 18 significant SNPs associated with udder conformation traits in Chinese Holstein cattle. Several candidate genes harbored SNPs (i.e.,


Bemisia tabaci is a known pest of cassava and a vector of cassava geminiviruses in Africa and India, but its role in the recent spread of Cassava Mosaic Disease (CMD) in Southeast Asia is not well known. This is in part due to a lack of data on the occurrence and distribution of B. tabaci in this region. We show here the first results of any country-wide survey and identification of B. tabaci colonizing cassava in Lao PDR.The whitefly species B. tabaci as potential vectors of CMD in SEA, but their occurrence and distribution in cassava fields is not well known. We conducted a countrywide survey in Lao PDR for adult whiteflies in cassava fields, and determined the abundance and genetic diversity of the B. tabaci species complex using mitochondrial cytochrome oxidase I (mtCOI) sequencing. In order to expedite the process, PCR amplifications were performed directly on whitefly adults without DNA extraction, and mtCOI sequences obtained using nanopore portable-sequencing technology. Low whitefly abundances and two cryptic species of the B. tabaci complex, Asia II 1 and Asia II 6, were identified. This is the first work on abundance and genetic identification of whiteflies associated with cassava in Lao PDR. This study indicates currently only a secondary role for Asia II in spreading CMD or as a pest. Routine monitoring and transmission studies on Asia II 6 should be carried out to establish its potential role as a vector of SLCMV in this region.Cassava Mosaic Disease (CMD) caused by Sri Lankan cassava mosaic virus (SLCMV), has rapidly spread in Southeast Asia (SEA) since 2016. Recently it has been documented in Lao PDR. Previous reports have identified whitefly species of  Manihot esculenta Crantz) is an important smallholder cash crop and a staple food in the tropics of Latin America, Africa, Asia and Southeast Asia (SEA) [Begomovirus in Africa, and two in Asia [Bemisia tabaci (Gennadius) whitefly complex (Hemiptera: Aleyrodidae) [Cassava (ia (SEA) ,2,3. How in Asia ,5. In 20 in Asia . Since t in Asia ,7,8,9,10 in Asia ,12 and trodidae) ,13.Begomovirus, Carlavirus, Crinivirus, Ipomovirus, Polerovirus and Torradovirus [Bemisia tabaci is the sole vector of begomoviruses [B. tabaci currently relies on network analysis of its mitochondrial cytochrome oxidase I (mtCOI) gene. This method has been widely used in diversity studies of B. tabaci [Whiteflies are vectors for at least 320 species of plant viruses belonging to the genera adovirus ,15,16,17oviruses ,19 and i. tabaci ,23,24,25. tabaci ,27.B. tabaci species diversity and their potential as virus vectors [B. tabaci diversity has increased in SEA due to increasing importance of cassava and the recent introduction of CMD in this region [In several Asian countries such as Bangladesh China, Nepal, Thailand, Malaysia and Vietnam, different studies using mtCOI have identified  vectors ,30,31,32s region . B. tabaci populations in Laotian cassava-growing regions and their association with CMD. B. tabaci genetic diversity does not change significantly over a farm-scape [B. tabaci complex colonizing cassava in Lao PDR and its potential relation with the recently reported occurrence of CMD in this country [B. tabaci per province (10 provinces). To identify the genetic diversity, we sequenced 867 nt of the 3′ half of the mtCOI gene using a rapid and high throughput genotyping tool based on Oxford Nanopore Technologies (ONT). Two indigenous cryptic species, Asia II 1 and Asia II 6, were identified, which were grouped into 14 haplotypes distributed throughout the country. This is the first study on whitefly diversity in Lao PDR. Furthermore, unlike previous studies relying on DNA extraction, PCR amplicon cloning and subsequent sequencing of the mtCOI gene, the current study used more efficient and less cumbersome nanopore sequencing. The methodology employed in this study will simplify future B. tabaci genetic diversity studies and monitoring of regional abundance. Such studies will be pivotal in developing effective regional management programs for B. tabaci.Lao PDR is the sixth largest exporter of cassava in the world, with an estimated annual production of 2.3 M tons and USD 116 M in exports . Howeverrm-scape , althoug country . For thiBemisia tabaci) three to six months after planting to calculate the relative abundance of adult whiteflies, in 10 different provinces of Lao PDR (Table 1).Between June and July 2020, 70 cassava fields were inspected for adult whiteflies , 0.5 μL of each primer (10 pmol) and 7 μL ultrapure water  to complete a final volume of 20 μL. PCR was carried out at 95 °C for 5 min , followed by 35 cycles at 94 °C for 40 s, 56 °C for 30 s for annealing, and 72 °C for 45 s for extension, with a final extension at 72 °C for 10 min. The PCR products were run on a 1% agarose gel in 1× TBE buffer stained with GelRedTM . DNA bands were visualized using a Gel Doc™ XR+ Gel Documentation System . As no unspecific bands were observed, libraries for sequencing were prepared directly from the PCR reaction mix.Mitochondrial COI (mtCOI) DNA amplification was carried out by removing the whiteflies from the ethanol, and quickly dropping the insects into tubes containing the PCR reaction mix; there was no need to macerate the samples or to include any additional PCR steps. For amplification we used primers 2195-Bt-F (5′-TGRTTTTTTGGTCATCCRGAAGT-3′) and C012-Bt-sh2-R (5′-TTTACTGCACTTTCTGCC-3′) . These pThe sequencing workflow is shown in Raw FAST5 files pass produced by MK1C device were basecalled under high-accuracy mode using the ONT basecaller Guppy . The FASTQ files were filtered with a quality Qscore ≥ 9 and only files with this quality were further processed using the next pipeline. The reads were assembled using reference sequences with Minimap2 long-read aligner with 99% accuracy . Then, twww.fluxus-engineering.com/sharenet.htm (accessed on 31 July 2022)) using the Neighbor-Joining algorithm. On the other hand, to determine phylogenetic relationships, we used fourteen haplotypes from Lao PDR plus 104 sequences obtained from Genbank (https://www.ncbi.nlm.nih.gov/nuccore/?term=Bemisia+tabaci+coi (accessed on 31 July 2022)) representing all whitefly (Bemisia tabaci) cryptic species reported so far (http://tree.bio.ed.ac.uk/software/figtree/ (accessed on 31 July 2022)). Additionally, to identify whether a population has undergone a recent population expansion event, the mutation frequencies in the sequences were calculated using the tests of Tajima’s D implemented in MEGA-X software [The estimation of nucleotide (π) and haplotype (h) diversities, as well as the number and allocation of haplotypes, was determined using DnaSP 6.12.03 software  for sequd so far . A phylosoftware . This issoftware .B. tabaci in those fields , suggesting a recent population expansion. On the other hand, neutrality tests for Asia II 6 were close to zero (Tajima’s D = −0.644 with p > 0.05) and the sequences in this subgroup showed the highest values of average nucleotide differences (k)  sequencer which had a coverage greater than 1000× , 37 sequnces (k) . When analyzed together with other available COI sequences detected in cassava from Asian countries (n = 45), the network confirmed the presence of haplotypes previously reported in Asia, and revealed a few haplotypes exclusively found in Lao PDR . Asia IIAsia II 1 haplotypes H3, H5, H12 and H6 were exclusively found in Lao PDR and among these, haplotype H12  had the highest number of genetic changes. In comparison, Asia II 6 sequences (n = 22) were grouped into eight haplotypes  where haB. tabaci . Interestingly, provinces in Lao PDR with the relative highest abundance of whiteflies correspond to fields located in the southern provinces where CMD also occurs  is rarely found on other hosts under field conditions, unlike other non-cassava B. tabaci in Africa such as MED, Indian Ocean and MEAM1, which do not colonize cassava under field conditions [B. tabaci in cassava in SEA [At this moment we cannot consider cassava as a major host of Asia II in SEA. In comparison, cassava-infesting nditions . In conta in SEA .Direct PCR amplification of the mtCOI region, without DNA extraction , produceB. tabaci Asia II 1 and Asia II 6 , has not yet been identified as a vector of SLCMV. Furthermore, a study in Japan found this species to be sensitive to the majority of the tested pesticides [Asia II 1 is an efficient vector of SLCMV, in comparison to MED and MEAM species , and is sticides . B. tabaci whitefly populations collected in Lao PDR, at the haplotype level (fourteen groups of B. tabaci haplotypes). The two whitefly cryptic species (Asia II 1 and Asia II 6) are found throughout the country, including the southern provinces where CMD has been reported. Cryptic species Asia II 1, a known vector of SLCMV, are widespread in the region; however, in Lao PDR, low abundances of whiteflies and the distribution of CMD symptoms in infected plants suggest, at this time, a secondary role for Asia II in the dissemination of CMD or as a pest, as compared to CMD transmission by infected planting material. However, this situation will likely evolve in the near future. Haplotypes unique to Lao PDR have been identified and are useful in studying and tracking the spread of whiteflies at a regional level in parallel with CMD. Further transmission studies with Asia II 6 should be carried out to establish its potential role as a vector of SLCMV and routine monitoring is recommended for timely detection of an increase in abundance of whitefly populations in Lao PDR.Our study presents the first report on the genetic diversity of