Moreover, one SNP was of near-significance threshold for the dairy capacity composite, the feet and legs composite, and body depth.The ideal body conformation is an important part of the breeding objective of Czech Holsteins to allow high production while improving health and fitness. In this study, we aimed to identify loci that influence these traits. The genome-wide association study (GWAS) was performed using the weighted single-step best linear unbiased prediction (wssGBLUP) method. The multiple comparison test was performed with the Bonferroni correction. Composite traits  and partial linear traits  each showed associations with one single nucleotide polymorphism (SNP) that were either statistically significant or approached the significance threshold. The association analysis without the Bonferroni correction (based on the significance level of 1.00 × 10The aim of this study was a genome-wide association study (GWAS) on conformation traits using 25,486 genotyped Czech Holsteins, with 35,227 common SNPs for each genotype. Linear trait records were collected between 1995 and 2020. The Interbull information from Multiple Across Country Evaluation (MACE) was included for bulls that mostly had daughter records in a foreign country. When using the Bonferroni correction, the number of SNPs that were either significant or approached the significance threshold was low—dairy capacity composite on BTA4, feet and legs composite BTA21, total score BTA10, stature BTA24, body depth BTA6, angularity BTA20, fore udder attachment BTA10. Without the Bonferroni correction, the total number of significant or near of significance SNPs was 32. The SNPs were localized on BTA1,2,4,5,6,7,8,18,22,25,26,28 for dairy capacity composite, BTA15,21 for feet and legs composite, BTA10 for total score, BTA24 stature, BTA6,23 body depth, BTA20 angularity, BTA2 rump angle, BTA9,10 rear legs rear view, BTA2,19 rear legs side view, BTA10 fore udder attachment, BTA2 udder depth, BTA10 rear udder height, BTA12 central alignment, BTA24 rear teat placement, BTA8,29 rear udder width. The results provide biological information for the improvement of body conformation and fitness in the Holstein population. Complex traits are an enigma  that conTwenty years ago, the fundamentals of genomic selection (GS) were described . It was Stature is affected by many polymorphous genes of small effects in human beings. On the contrary, variation in dogs, even within breeds, has been suggested to be largely due to variants of a low number of genes . The autBTRC gene, which codes a member of the F-box protein family. The gene is involved in Wnt signaling, which plays a critical role in development and has been shown to be associated with process of limb development [XKR4, TMEM68, TGS1, LYN, RPS20, MOS, PLAG1, and CHCHD7 genes, which are well-known candidates for feed intake, carcass-related traits, and growth [PLAG1 gene (or PLAG1-CHCHD7 region) is a strong candidate liable for body size, including stature, height, and body mass in many cattle breeds [PLAG1 gene, is regarded a causal mutation responsible for stature [In a multitrait meta-analyses performed in Brown Swiss cattle, the most significant SNPs for body size were found on BTA1, 3, 11, and 26; on BTA3, 13, and 26 for leg conformation; on BTA3, 5, 6, 17, and 19 for mammary gland morphology; on BTA5 and 25 for body conformation; and on BTA10 for growth and carcass quality [elopment . Anotherelopment . SNPs aselopment . An et aelopment . One of d growth . The PLAA GWAS conducted in Chinese Holsteins revealed 11 SNPs associated with body-shape traits on 9 chromosomes, namely, BTA3, BTA4, BTA6, BTA7, BTA12, BTA13, BTA20, BTA22, and BTA29 . A mediuThe objective of this study was to identify loci determining conformation traits utilizing a genome-wide association study in Holstein cattle in the Czech Republic. There are two main dairy breeds in the country, namely Czech Spotted, which is a part of Simmental group, and Holstein. Because the GWAS was not conducted in the Czech dairy cattle, this paper should fill up the gap.Animal Care and Use Committee approval was not required for this research because the data were obtained from an existing database supplied by the Czech-Moravian Breeder´s Corporation (Czech Republic) and the Holstein Cattle Breeders Association of the Czech Republic.The analyzed dataset contains linear type trait records from 699,681 primiparous Holstein cows collected between 1995 and 2020. Linear-type trait evaluation was assessed according to the World Holstein Friesian Association on a 9-point scale by qualified classifiers. Cows are evaluated only once in the period 30 to 210 days after first calving, with their age at first calving between 600 and 1004 days.The body score was evaluated as a part of a national cattle breeding program in the Czech Republic. There are 20 linear type traits, i.e., stature, chest width, body depth, angularity, rump angle, rump width, rear legs rear view, rear legs side view, foot angle, fore udder attachment, front teat placement, teat length, udder depth, rear udder height, central ligament, rear teat placement, rear udder width, bone quality, locomotion, and body condition score. These traits were also grouped into five composite traits. These composites are body conformation , foot and leg conformation , udder conformation , dairy capacity , and total score. The total score is a weighted sum of the composite traits compiled as follows: 25% body conformation, 15% foot and leg conformation, 20% udder conformation, and 40% dairy capacity).The Czech Holstein population is strongly connected to the populations from other countries. To increase the amount of information for breeding bulls used in the domestic population, Interbull information from Multiple Across Country Evaluation (MACE) was added according to the methodology from Přibyl et al. . The effThe Czech Holstein population was genotyped throughout the whole country. From the population, all bulls used in reproduction were genotyped. In the past, the number of genotyped females was low. The milestone was the year 2018 when extensive genotyping of heifers began. For this reason, 98% of included genotypes are from young animals and these animals have a very high genetic connectedness in the population.A total of 5419 bulls and 20,067 cows included in this study were commercially genotyped by various SNP chips . All avaIn our analysis, the wssGBLUP method was used to predict genomic breeding values for all 25 linear-type traits using the BLUPF90 software suite . Our stuy is the vector of performances ; β is a vector of fixed effects including the contemporary group (herd-year-month of classification and classifier) with at least two cows in the group, a classifier (fixed class effect from 1 to 4), linear and quadratic regression for cow´s age, and linear and quadratic regression of DIM (days in milk at linear classification); u is a vector of random additive genetic effects ; X is the incidence matrix relating phenotypes to the fixed effects; Z is the design matrix relating phenotypes to breeding values (u); and e is a vector of random residual error.Genomic breeding values were predicted based on the model:H was created as a combination of the relationship matrix A  and the relationship matrix G , which is also combined with the relationship matrix A22 :The relationship matrix We assumed the normal distribution of the additive genetic effect p-values. The genome-wide association study (GWAS) was performed using the wssGBLUP method. The weights were estimated for all 35,227 SNPs using postGSf90 software [(1)D = I) where id is the ith diagonal element of D which represents the variance of the SNP effect:Creation of an identity matrix of SNP weights ((2)G matrix [Calculation of the G matrix :Z is a matrix of centered genotypes, D is a diagonal matrix of weights, and i(3)û)GEBV calculation to obtain the direct additive effect of individuals ((4)û) into the SNP effect (â)Decomposition of the GEBV (5)SNP variance estimation for each SNP. The non-linear A method was used ,39:i, and σ(â) is the standard deviation of the vector of the estimated SNP effects.Genomic control of the population structure was performed by the genomic inflation factor (λ). Quantile-quantile (Q-Q) plots and Manhattan plots were created using R software version 4.2.2. (snpStats and qqman package) . The gensoftware . There asoftware ,37:(1)Cr(6)D and construction of matrix D based on the estimated SNP weights.Normalization of matrix (7)Running of the next iteration or stopping the loop. Only two iterations were used in our procedure to maximize accuracy . The estThe multiple comparison test was performed by the Bonferroni correction. The significance threshold was determined as 0.05 divided by the number of SNPs used for the significance threshold and 0.01 divided by the number of SNPs used for the highly significance threshold.The basic statistics of the predicted genomic breeding values are shown in The genomic inflation factor (λ) was lower than 1.1 for the most of traits . Howeverp-value. The statistical significance was performed on the two levels, 1.00 × 10−6 for the suggestive significance threshold and 1.00 × 10−8 level for the genome-wide significance threshold. Manhattan plots are shown in the The significant SNPs were visualized using Manhattan plots. The level of significance was expressed by –log10 of the SNP´s A total of twenty linear traits and five linear composite traits were evaluated in the GWAS. After the Bonferroni correction, only four SNPs had statistically significant associations with the linear type traits, namely, fore udder attachment , stature (BTA24), angularity (BTA20), and the total score (BTA10) . Body deEW = α/k, where k was 35,227, and α was 0.05 or 0.01, representing the significance level (p ≤ 1.42 × 10−6) and high significance level (p ≤ 2.84 × 10−7), respectively. The computation of the Bonferroni correction is simple, but the correction is conservative, and the portion of the false null hypotheses that are correctly rejected is significantly reduced [−5 level.Multiple testing was performed by the Bonferroni correction with the critical values as αel p ≤ 1. × 10−6 ael p ≤ 1. × 10−6 a reduced . DespiteThe single-step GBLUP approaches are widely used for GEBV predictions. The advantage of the single-step methodology is the ability to include genotyped and non-genotyped individuals together in GEBV prediction, which allows for the inclusion of more information to provide better GEBV prediction accuracy ,44. BiolWhen only a few QTLs are included, the GEBV prediction accuracy increases with the increasing number of genotyped individuals, but the differences between weighted and unweighted analysis become negligible when the number of QTLs and genotyped individuals is high . Neverthp-value threshold using the Bonferroni correction or chosen SNPs did not cover most of the significant SNPs in the genome.Even though our number of genotyped individuals exceeded 25,000 and the number of SNP exceeded 35,000, only four significant SNPs were found for 25 linear-type traits. The low number of significant SNPs may be due to the polygenic character of linear-type traits, the strict A somewhat less stringent method of testing without the Bonferroni correction did not show more significant associations with SNPs . Again, p < 1.00 × 10−6, and three SNPs were observed on the significance threshold of p < 1.00 × 10−5. Thirteen of these SNPs were associated with the dairy capacity composite; the SNPs were located on twelve chromosomes, and two SNPs with the feet and legs composite. Six SNPs were associated with udder conformation traits , four with foot and leg conformation (rear legs rear and side view), and three with body conformation . The dairy capacity composite is composed of linear-type traits that are related to an assumption of adequate feed intake and high milk production. Milk production is a polygenic trait, but the presence of major genes has been confirmed by many authors [The next 25 SNPs were observed on the statistical threshold of  authors ,51,52.Our SNPs for the linear composite traits were found on different chromosomes compared to other studies. Wu et al.  reportedIndividual linear traits were associated significantly or near the significance threshold with SNPs located on eleven chromosomes . The chrEven when using a less strict method for evaluation, the number of significant SNPs is substantially low compared with other analyses ,28,53. GThe genes related to the SNPs or within close proximity are presented in Finally, of the five linear composite traits and the twenty linear traits being evaluated, just total score, stature, angularity, and fore udder attachment were found to be significantly associated with SNPs located in BTA10, 20 and 24.The objective of this study was to identify the genomic regions involved in the control of body conformation and fitness. Four significant SNPs associated with one linear composite trait and three linear traits were identified. The results of our study can be used to search for causative genes, as well as genomic regions and mutations within the bovine genome that are associated with linear traits to improve the overall body conformation of Czech Holstein cattle.


As a results, a total of 15,069 ROHs were found in the three Kazakh meat–wool sheep populations. The mean number of ROH per animal across populations varied from 40.3 (POP1) to 42.2 (POP2) in the category 1+ Mb. Furthermore, the number of ROH per animal in ROH1–2 Mb were much higher than ROH2–4 Mb and ROH8–16 Mb in the three sheep populations. Most of individuals had small number of ROH>16 Mb. The highest and lowest genomic inbreeding coefficient values were observed in POP2 and POP3, respectively. The estimated FROH presented the impact that recent inbreeding has had in all sheep populations. Furthermore, a set of interesting candidate genes , which are related to the productive traits, were found. Collectively, these findings will contribute to the breeding and conservation strategies of the Kazakh meat–wool sheep breed.Increasing the fertility of sheep remains one of the crucial issues of modern sheep breeding. The Kazakh meat–wool sheep is an excellent breed with high meat and wool productivity and well adapted to harsh conditions. Nowadays, runs of homozygosity (ROHs) are considered a suitable approach for studying the genetic characteristics of farm animals. The aims of the study were to analyze the distribution of ROHs, describe autozygosity, and detect genomic regions with high ROH islands. In this study, we genotyped a total of 281 Kazakh meat–wool sheep using the Illumina iScan A herd of sheep with high fertility was created as a result of targeted selection and breeding. In 2011, an intrabreed multiparous type of the Kazakh meat–wool breed was approved [Sheep breeding is a traditional branch of livestock breeding in Kazakhstan . The avaapproved .growth differentiation factor 9 (GDF9), bone morphogenetic protein receptor IB (BMPRIB), and bone morphogenetic protein 15 (BMP15) genes that influence reproductive qualities are of great interest [One of the most promising methods of improving productivity livestock is the use of molecular genetic analysis, the significance of which has been proved by numerous studies . There iIt is known that close inbreeding is happening during breed formation. It is a natural and inevitable phenomenon in animal populations . MeanwhiBMPRIB in famous high-prolific Hu sheep [IGF1, TGFBR2, etc. genes that affect muscle development have been reported in Chaka sheep based on the ROH analysis [Selection traits are the result of genotypic changes in populations subjected to some form of selection pressure . These cHu sheep . The IGFanalysis . Also, Ranalysis .Therefore, the objective of this research was to detect ROH features in Kazakh meat–wool sheep population and select candidate genes within the ROH islands that are related to breed-specific traits in tested populations.A total number of 281 Kazakh meat–wool sheep bred in “Kuatzhan” ) and “Dukeev”  and 71 male (POP3)) farms were used in this work. The two named farms have been working on improving sheep fertility for a long time in the animal husbandry sector of our country. Therefore, it is of interest to breeders to study their productivity and fecundity. Animals were selected for the study on the basis of their fecundity. Ewes with twin or more twin births and lambs were selected for this study. The blood samples were collected by the veterinarian into EDTA-containing tubes to prevent blood clotting. All animal experiments were approved by the Local Ethics Committee of the RSE Institute of Genetics and Physiology SC MSHE RK . The specimens were transported to the Laboratory of Animal Genetics and Cytogenetics of the RSE Institute of Genetics and Physiology using the blood bank refrigerator and stored in a freezer (at −25 °C) until DNA extraction.g for 1 min. Wash Buffer I and Wash Buffer II in 500 μL were used to clean the purification column from unnecessary cellular debris. It is crucial to centrifuge tubes at high speed  after every step of the washing process. In the final step, 200 μL of Elution Buffer was added to the center of the column membrane to elute genomic DNA. To increase the output DNA concentration, it is necessary to reduce the amount of Elution Buffer added. The presence of DNA was checked by 1% agarose gel electrophoresis and the quality of genomic DNA was assessed by Nanodrop One  spectrophotometer. The DNA concentration was diluted to 50 ng/µL before genotyping. Genotyping was performed using the Illumina iScan system , which supports the rapid, sensitive, and accurate imaging of Illumina BeadChips for genetic analysis results, and the OvineSNP50 BeadChip microarray, consisting of more than 54,000 SNPs to deliver the densest coverage available for the ovine genome. Further, the ped and map format files were generated using the PLINK Input Report Plug-in parameter implemented in GenomeStudio v2.0 software. Here, the ped files contained the information on the family and sample IDs, paternal and maternal IDs, sex of the animals, and SNPs information, while the map files contained the chromosome ID, unique SNP identifier, genomic distance, and SNP position information.Genomic DNA was extracted from the whole blood using a GeneJET Whole Blood Genomic DNA Purification Mini Kit, #K0782 . This kit utilizes silica-based membrane technology in the form of a convenient spin column. The standard procedure takes less than 20 min following cell lysis and yields purified DNA more than 30 kb in size. 200 μL of whole blood was mixed with 20 μL of Proteinase K Solution by vortexing. After adding 400 μL of Lysis Solution and vortexing, the tubes were incubated at 56 °C for 10 min using a thermomixer. Then, 200 μL of ethanol (96–100%) was added to the tubes and mixed by pipetting. After transferring the prepared mixture to the spin column, the tubes were centrifuged at 6000× p < 0.05, and Hardy–Weinberg equilibrium p < 0.0001.The primary processing of genotyped data was conducted in the GenomeStudio software. Further quality control of the data was carried out using Plink v1.07 software. The SNPs that unmapped contigs, located on sex chromosomes and mtDNA, were excluded. For the quality filtration of genotyped data, the following parameters were applied: missing genotypes >10%, SNPs with call rate <90%, the minor allele frequency For the identification and statistical analysis of runs of homozygosity, Plink v1.07 software and the detectRuns package were used. The following parameters and thresholds were applied for the identification of ROHs: the minimum ROH length was set to 1 Mb with 15 SNP, the window threshold to call a ROH was 0.05, one missing call was allowed per window, the sliding window size in SNPs was set to 15, the required minimum SNP density in an ROH was 1 SNP per 100 kb, and the maximum gap between two consecutive homozygous SNP was 250 kb.ROH) for each individual was evaluated as follows:ROH is the total length of all ROHs, and Laut is the specified length of the autosomal genome covered by SNPs on the chip [ROH for Kazakh meat–wool sheep. The literature review show that ROHS above 1 Mb are the most recurrently used to evaluate of genomic inbreeding coefficient and recent animal relatedness for farm animals.Runs of homozygosity is one of the common methods to evaluate genomic inbreeding coefficient in animals. The inbreeding coefficient on the basis of ROHs  in different ROH length classes was estimated  to 0.013 (>16 Mb) for POP2, while these values were between 0.036 (>1 Mb) and 0.008 (>16 Mb) for POP3. FROH coefficient rates rapidly declined at >2 Mb in all populations.To evaluate of genomic inbreeding level in the three populations, the inbreeding coefficients on the basis of runs of homozygosity , carcass, or meat traits , with the inclusion of one novel TIAM1 gene. Next, to investigate effect of selection in the sheep populations, the occurrence of ROHs across genomes were explored. The Manhattan plot showed that the highest frequency of ROH occurrence was observed on 1, 2, 6, 10, and 25 autosomes. Further, the top of 1% of SNPs were selected for the studied sheep populations . We annoControlling the level of inbreeding and maintaining adequate genetic diversity is crucial for the robustness of sheep population. The estimation of the inbreeding coefficient based on pedigree information was not accurate due to incomplete pedigrees, the small number of generations, and errors. One of the strengths of ROH analysis is the ability to reliably identify long homozygous segments even at relatively low marker densities. This makes ROHS attractive for studying and understanding the role of autozygosity and as a possible tool for managing inbreeding levels for breeding programs in livestock production . TherefoROH results. The lowest values of inbreeding coefficients (0.008–0.036) in POP3 may indicate a relatively short breeding history in this population. This is in accordance with reality; after weaning, the rams usually are separated to temporary stock to buy and slaughter. Similar outcomes were found in Spanish sheep breeds [A total of 15,069 ROHs were found in the three Kazakh meat–wool sheep populations, and the high frequency of ROHs in these populations may be due to their restricted geographical location. Furthermore, POP2 had the highest number of short ROH fragments, indicating more ancient inbreeding events. Numerous research studies have also reported the abundance of ROHs in the short-length category in other sheep breeds ,26. In tp breeds ,28.ROH1Mb, FROH2Mb, FROH4Mb, FROH8Mb, and FROH16Mb) was estimated. The number of segments longer than 16 Mb is much smaller, indicating that the level of inbreeding in the studied sheep population is low, and that they are unaffected by recent inbreeding events. In general, these results are consistent with previously observed results in other sheep breeds [ROH1Mb indicates inbreeding that occurred in ancient times. Ancient and recent inbreeding emphasize the relevance of measures to maintain high fertility and limit inbreeding in Kazakh meat–wool sheep.In our study, significant homozygous regions were found in Kazakh meat–wool sheep, which may indicate that some homozygous regions are contributing to fertility complications due to inbreeding depression. The inbreeding coefficient for each of the categories  plays an important role in bone formation and regeneration. BMP-2 protein is one of the most effective inducers of osteogenesis that activates the expression of osteoblast markers [BMP2 gene were associated with tail length in Chinese Hu and Tibetan sheep [Bone morphogenetic protein receptor type 2 (BMPR2) encodes the bone morphogenetic protein receptor type II, which belongs to the transforming growth factor receptor (TGFR) superfamily. The BMPR2 receptor regulates the number of cells in tissues by triggering and controlling their proliferation and apoptosis [BMPR2 in cumulus-oocyte complex and ovary tissues were related to the sheep antral follicle number [BMPR1B gene is well known to be play an essential role in sheep reproductive traits [Clock gene, which is a transcription activator, was highly expressed in the cerebellum, hypothalamus, and uterus in Small-tail Han sheep [Lysine demethylase 2B (KDM2B) has been reported to play a role in spermatogenesis [Although not all identified genes are proliferation-associated genes, some important genes have been identified in this study. It has been well documented that bone morphometric proteins (BMPs) are multifunctional growth factors belonging to the transforming growth factor β (TGF-β) superfamily, whose role in sheep reproduction has been widely studied in recent years . Bone mo markers . Of intean sheep . Bone mopoptosis . Importae number . The BMPe traits . Interesan sheep . In addiogenesis .T lymphoma invasion and metastasis protein (TIAM1) encodes the invasion of T-cell lymphoma, and it is critical for the integrity of adherent junctions and cell–matrix interactions [Myosin regulatory light chain-2 (MYBPC1) was associated with lamb meat quality characteristics [Voltage-dependent calcium channel subunit α-2/delta-1 (CACNA2D1) encodes the CACNA2D1 protein, which is cleaved into the α 2- and delta-subunits of a potential-dependent calcium channel. These channels play an important role in coupling muscle excitation and contraction. The SNP mutations in this gene was significantly associated with carcass traits in cattle [Threonine aspartase 1 (TASP1) gene was related to the fertility and precocity in Nelore cattle [Myomesin-1 (MYOM1) was found to be differentially methylated by gene ontology enrichment analysis in sheep [ractions . Myosin eristics . Voltagen cattle ,41. Recee cattle  as well e cattle . Furtherin sheep .In this study, we investigated the use of selection signature analysis within the Kazakh meat–wool sheep populations based on homozygosity. To the best of our knowledge, this is the first study identifying the genomic regions of a Kazakh meat–wool sheep breed. Moreover, genomic regions associated with reproductive traits as well as carcass and meat traits have been identified in tested population. The results obtained can be used in practice to improve the genomic characterization of this breed.


Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study. Dairy sheep and goat account for about 21% of all sheep species and most of them are distributed in subtropical areas of Asia, Europe, and Africa . The goaMelatonin was believed to be mainly produced by the pineal gland in vertebrates . CurrentSNP mainly refers to DNA sequence polymorphism at the genome level, which is the most ideal marker for DNA inheritance and molecular breeding . It can In this study, BSA method will be used to analyze the genotypes of individual dairy goats to match up their endogenous melatonin levels. The focus will be given to the SNP polymorphisms of AANAT/ASMT as well as MT1/MT2. Our purpose is to identify whether SNPs among these genes are associated with melatonin production in dairy goats. If this is the case, the SNPs can be used as molecular markers to select dairy goats with the naturally high melatonin production.A total of 103 healthy, 18-month-old dairy goats in their peak lactation period were selected. 5 mL of blood were collected from subcutaneous vein at the neck, centrifuged for 8 min at 3,000 r/min, and serum was collected and stored at −20°C for future use. The breast was cleaned with wash, and 5 mL of milk sample was collected and stored at −20°C for future use.Melatonin was dissolved in methanol (chromatographic grade) to make 1 mg/mL melatonin stock solution. The stock solution was successively diluted into 100, 50, 20, 10, and 5 ng/L to set up the melatonin test standard curve. 200 μL of blood or milk were mixed into 800 μL of cold methanol, vortexed for 30 min, centrifuged at 14,000 r/min at 4°C for 20 min. The supernatant was filtered with a filter size of 0.22 μm and stored in a brown sample bottle at −20°C for future use. Melatonin was detected using Liquid chromatography tandem mass spectrometry (LC-MS/MS)  in the Central Laboratory of Beijing Institute of Animal Science, Chinese Academy of Sciences. The C18 column was used to separate the melatonin in the samples.® Ultra™ DNA Library Prep Kit for Illumina®). For each sample, 1 μg genomic DNA was randomly fragmented to <500 bp by sonication (Covaris S220). The fragments were treated with End Prep Enzyme Mix for end repairing, 5′ Phosphorylationand dA-tailing in one reaction, followed by aT-A ligation to add adaptors to both ends. Size selection of Adaptor-ligated DNA was then performed using AxyPrep Mag PCR Clean-up (Axygen) and fragments of ∼410 bp were recovered. Each sample was then amplified by PCR for eight cycles using P5 and P7 primers, with both primers carrying sequences which can anneal with flow cell to perform bridge PCR and P7 primer carrying a six-base index allowing for multiplexing. The PCR products were cleaned up using AxyPrep Mag PCR clean-up (Axygen), validated using an agilent 2,100 bioanalyzer  and quantified by Qubit2.0 fluorometer Goat blood DNA was extracted following the instructions of the manufacturer’s protocol . Sequencing was carried out using a 2 × 150 paired-end (PE) configuration; image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on the HiSeq instrument.For the original image data of sequencing results, the software BCF2FASTQ (version 2.17.1.14) was used for Base calling and preliminary quality analysis to obtain the original Data of sequencing samples (PF), which is stored in FASTQ (FQ) file format. The data of Pair End sequencing consists of two FQ files, one storing read segment 1 (Read1) and the other storing read segment 2 (Read2). Cutadapt (version 1.9.1), the second-generation sequencing Data quality statistics software, was used to remove connectors and low-quality sequences from the original sequencing Data (Pass Filter Data) to obtain Clean Data for subsequent information analysis. Using Dragen Genome Pipeline, Clean data were aligned to the reference Genome sequence, and the resulting BAM (binary SAM file) was compared. Picard and GATK were used for PCR duplicate removal, local realignment, and base quality recalibration. The corrected genome alignment results were obtained. The coverage and coverage depth of the genome were calculated according to the comparison results.Variant detection was performed using the Haplotype Caller module of the software GATK (version 4.0.4.0) based on the comparison results of Clean Reads in the reference genome. Filtering was performed using the Variant Filtration module with the filtering parameter: -filter expression “QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR >3.0 || MQ Rank Sum < −12.5 || Read Pos Rank Sum < −8.0.” ANNOVAR software was applied for functional annotation of the detected gene variants. The genotyping information of SNPs was extracted from the above comparison results and variant results. When the coverage depth of a sample for an SNP is <5X, the locus is treated as deletion, which is indicated as NA in the table; when the genotypic mutation frequency of a sample at an SNP is ≥ 0.8 or ≤0.2, the purity of locus is confirmed; if the mutation frequency is between 0.2–0.8 and each allele in the heterozygous genotype should be supported by at least four reads, the SNP is heterozygous mutation. Otherwise, the locus was treated as deletion and expressed as NA in the table; Finally, the genotype results of samples at all SNPs were summarized.mut is the frequency of A base in the mutant pool, Awt is the frequency of A base in the wild-type pool. The Cmut is the frequency of the C base in the mutant pool, and Cwt is the frequency of the C base in the wild-type pool. Gmut is the frequency of the G base in the mutant pool, and Gwt is the frequency of the G base in the wild-type pool. Tmut is the frequency of the T base in the mutant pool, and Twt is the frequency of the T base in the wild-type pool.Euclidean Distance (ED) algorithm was used to find markers with significant differences between mixed pools and evaluate the regions associated with traits. The calculation formula of the ED method is as follows:(1) Filter out SNP of non-secondary allele;(2) If the frequency of A, T, C, and G in the wild-type mixing pool is greater than or equal to 95%, it will be filtered;(3) SNPs with sequencing depth lower than 10X in wild-type or mutant pools were filtered.In the process of ED analysis, the MMAPPR software package will process SNP as follows.In the study, the fourth power of the original ED was taken as the correlation value to achieve the function of eliminating background noise, and then local polynomial regression fitting was performed to obtain the fitting curve. Median+3SD of the fitted values of all sites was used as the association threshold for analysis. The associated region is determined based on the association threshold. Candidate SNPs were selected from the associated regions, namely, those with mutation frequency >0.75 and Euclidean distance >0.5. For the candidate SNPs, the annotation results of ANNOVAR were extracted.http://www.geneontology.org/) and the gene numbers of each term were calculated to obtain the list and the statistic number of a certain GO functional gene. Hypergeometric tests were then applied to find GO entries that were significantly enriched in genes compared to the whole genomic background.The GO function of candidate genes was analyzed for significance enrichment. The candidate genes were mapped to each term of GO database  statistical software. The distribution of different levels of melatonin in milk and blood samples of 103 dairy goats were shown in Whole-genome resequencing was performed on blood samples of six goats, and the original Data (PF) was shown in Based on the results from comparison, SNPs were detected by Dragen Genome Pipline. After identification of the functional changes caused by mutation sites, SNPs were divided into synonymous and non-synonymous mutations. The results showed that the proportion of non-synonymous mutations was higher than that of synonymous mutations . Based oAssociation analysis of 5,534,640 SNPs was performed using ED analysis and 2,740,786 SNPs were selected for further analysis . The disThe abscissa is the chromosome name, and the dot plot represents the ED value of each SNP locus. The line graph shows the ED value after fitting. The higher the ED value, the better the correlation effect of the point, and the blue shadow represents the interval to be located.After annotation for the GO function of candidate genes, it was found that these genes were enriched into Molecular Function, Cellular Component and Biological Process, respectively, as shown in A total of 22 terms were enriched in Biological Process and 498 genes were enriched in cellular process. To identify significantly enriched GO items, the GO function of candidate genes was analyzed for significance enrichment with hypergeometric test. The results indicated that the anchoring junction was the most enriched differential genes in Cellular Component . In MoleIn organisms, different genes coordinate with each other to perform biological functions. For example, the different genes can share the same pathway to produce same effect. Pathway analysis is helpful to further understand the function of genes. Based on the principle of hypergeometric distribution, the genes of the whole genome were served as background genes, and the candidate genes were analyzed by KO, as shown in SNPs of AANAT/ASMT and MT1/MT2 exon mutations are shown in Dairy products are important human non-staple food with high nutritional value . Among tA total of 5,534,640 SNPs were obtained by preliminary screening and the mutations mainly occurred in the Intronic and ncRNA regions of the genome. Further ED association analysis was conducted on these SNPs and a total of 34,921 SNPs were selected, involving 1,177 genes. GO enrichment analysis showed that the lipid binding is one of the most significant pathways. The lipid binding pathway involves in genes set related to lipid structure, which is the main component of cell membrane and involves in signal transduction . It is wAs mentioned above, melatonin synthesis depends on the expression and activities of AANAT and ASMT and its biological functions are mediated by MT1 and MT2 receptors. Therefore, we specifically analyzed the SNPs in the exon regions of these four genes . Since alterations of SNPs in these genes will significantly affect the level of melatonin as well as its functions.


Immunohistochemical microvessel density (MVD) is an early indicator of angiogenesis and it could be used to evaluate the therapeutic efficacy of non-small cell lung cancer (NSCLC). We sought to identify the ability of contrast-enhanced ultrasound (CEUS) in evaluating MVD of subpleural NSCLC.We prospectively collected CEUS data of NSCLC confirmed by ultrasound-guided transthoracic needle biopsy from October 2019 to February 2021, The MVD of NSCLC counted by CD34-positive vessels of immunohistochemical staining. Microflow enhancement (MFE) of CEUS was divided into “dead wood”, “cotton”, and “vascular” patterns. Pathology subgroup and MVD between different MFE patterns were analyzed, respectively. The arrival time, time to peak, peak intensity (PI), and area under curve (AUC) derivefrom time-intensity curve of CEUS with MVD in NSCLC and its pathological subgroups (adenocarcinoma and squamous cell carcinoma) were subjected to correlation analysis.–1. There was a significant statistical difference in MFE patterns between two pathological subgroups (p < 0.05). Besides, the MVD of “cotton” and “vascular” patterns were significantly higher than that of “dead wood” pattern (both of p < 0.05), whereas there was no significant difference in MVD between “cotton” pattern and “vascular” pattern. PI and AUC of CEUS were positively correlated with the MVD of NSCLC . Besides, PI and AUC of CEUS were positively correlated with the MVD of squamous cell carcinoma . Only the PI was positively correlated with the MVD of lung adenocarcinoma .A total of 87 patients were included in this study, consisting of 53 cases of adenocarcinoma and 34 cases of squamous cell carcinoma with a mean MVD of 27.8 ± 12.2 mmMFE patterns and quantitative parameters of CEUS had good correlation with MVD of NSCLC, especially in squamous cell carcinoma. Despite a recent decline in mortality rate due to the widespread application of new treatment methods, such as targeted therapy and immunotherapy, lung cancer still has one of the highest morbidity and mortality rates among malignant diseases worldwide , 2. EarlThe microvessel density (MVD) is considered an early indicator of angiogenesis, and quantitative assessment of MVD is an effective means of determining the angiogenesis of a tumour, which could be used to evaluate the therapeutic efficacy of treatment regimens for malignant tumours , 9. HoweTherefore, we sought to identify the ability of CEUS in evaluating MVD of NSCLC.This study was approved by the Scientific Research Ethics Review Committee of the First Affiliated Hospital of Guangzhou Medical University. Informed consent was obtained from each patient, and the study was conducted in accordance with the Declaration of Helsinki, as revised in 2013.From October 2019 to February 2021, we performed B-mode ultrasound and CEUS before ultrasound-guided transthoracic needle lung biopsy (ultrasound-guided TNLB) in 102 patients who presented to our hospital with subpleural lesions. We then collected and analysed the ultrasound and pathology data of patients with subpleural NSCLC. The inclusion criteria were as follows: all lesions confirmed to be NSCLC by pathology; age > 18 years; and all lesions could be detected by CEUS. The exclusion criteria were as follows: history of antineoplastic therapy; other types of lung cancer, such as small cell lung cancer (SCLC), CEUS images of insufficient quality for analysis, such as unstable breath result in the difficult to depict the time-intensity curve (TIC); or incomplete data.® contrast agent  was used in this study. Bolus injection of microbubbles (2.4 mL) was performed through the antecubital vein, followed by a saline bolus of 5 ml. At the same time, a CEUS quantitative analysis program was used to record and save video for 5 minutes to the local disc. After reviewing the video and initiating a microflow enhancement (MFE) analysis program that comes with the machine, the acquisition time for each MFE sequence was 10-15s  with a low-frequency (2–5 MHz) convex transducer was used to scan the lesion and collect basic information, including size, shape. SonoVues 10-15s . The MFEs 10-15s . And theUltrasound-guided TNLB was performed by two sonographers with at least 5 years of interventional operation experience. The sampling area was consistent with the ROI in the CEUS examination. Sonographer 1 was responsible for real-time ultrasound guidance, and sonographer 2 obtained samples of the lesion using an 18 G automated core cutting needle . Generally, 3–5 needle punctures were performed.All samples were fixed in 10% formalin and sent immediately for histopathological examination and immunohistochemical (IHC) staining for CD34. A single endothelial cell or a cell mass that brown-color stained were considered as CD34-positive expression, and counted as a microvessel regardless of whether the lumen was formed or not. Microvessels with a lumen diameter greater than that of eight red blood cells were not counted. We first looked for high-density areas of CD34-positive vessels (hotspots) under low magnification (×100), and then switched to a higher magnification (×400) to count the number of microvessels. The mean value of three hotspots was defined as the MVD for each sample. All samples were counted independently by two pathologists , 15.post hoc test were used to compare the difference of MVD between different MFE patterns. The correlation between MVD and quantitative parameters of CEUS was performed by Spearman correlation analysis. Subgroup analysis by pathological type (adenocarcinoma and squamous cell carcinoma) were also conducted. In all analyses, p < 0.05 was taken to indicate statistical significance.All analyses were performed using the SPSS 25.0 statistical analysis software . Chi-square test was applied to compare the difference of MFE patterns between different subgroups of NSCLC. One-way ANOVA with Tukey’s –1, and the mean size of 87 lesions was 62.0 ± 24.2 mm. 87 cases of NSCLC included 53 case of adenocarcinoma and 34 cases of squamous cell carcinoma.A total of 102 patients with subpleural lung leisons underwent CEUS examination of during the study period. 15 of these were excluded (p = 0.005)  = 5.142 (p < 0.05), Turkey post hoc test showed that the MVD of “cotton” pattern and “vascular” pattern were significantly higher than that of “dead wood” pattern , whereas there was no significant difference in MVD between “cotton” pattern and “vascular” pattern (p = 0.639).The MVD of “dead wood” pattern, “cotton” pattern, “vascular” pattern were 20.3 ± 8.4 mmp < 0.001, and r = 0.367, p < 0.001, respectively), whereas AT and TTP of CEUS had no significant correlation with MVD of NSCLC .MVD of lung adenocarcinoma was significantly higher than that of squamous cell carcinoma (30.8 ± 12.9 p = 0.037), whereas AUC, AT and TTP of CEUS had no significant correlation with MVD of lung adenocarcinoma , whereas AT and TTP of CEUS also had no significant correlation with MVD of squamous cell carcinoma  CEUS and an accumulative imaging technique to show blood vessels after a flash with high–transmission power ultrasound exposure. It can reveal the trajectory of contrast agent flowing in microvessels with a diameter > 0.1 mm in real time, to depict the structure and perfusion of microvessels within tumours through maximum-holding image processing , 19. In p < 0.05), but value of r was only 0.290. However, PI and AUC had good correlation with MVD of squamous cell carcinoma . In addition, “dead wood” pattern of MFE was a diagnostic character for squamous cell carcinoma and low grade MVD. Therefore, CEUS had good predictability for squamous cell carcinoma and good correlation with MVD of NSCLC, especially in squamous cell carcinoma. Additionally, antivascular endothelial growth factor therapy was frequently applied to patients with NSCLC  for the publication of any potentially identifiable images or data included in this article.Contributions: (I) Conception and design: All authors. (II) Administrative support: LH, QT. (III) Provision of study materials or patients: WC, JT, YZ, HL. (IV) Collection and assembly of data: WC, DW. (V) Data analysis and interpretation: LH, YZ, JT. All authors contributed to the article and approved the submitted version.


Mangifera indica L.) is an important fruit crop in tropical and subtropical countries associated with many agronomic and horticultural problems, such as susceptibility to pathogens, including powdery mildew and anthracnose, poor yield and quality, and short shelf life. Conventional breeding techniques exhibit significant limitations in improving mango quality due to the characteristics of long ripening, self-incompatibility, and high genetic heterozygosity. In recent years, much emphasis has been placed on identification of key genes controlling a certain trait through genomic association analysis and directly breeding new varieties through transgene or genotype selection of offspring. This paper reviews the latest research progress on the genome and transcriptome sequencing of mango fruit. The rapid development of genome sequencing and bioinformatics provides effective strategies for identifying, labeling, cloning, and manipulating many genes related to economically important traits. Preliminary verification of the functions of mango genes has been conducted, including genes related to flowering regulation, fruit development, and polyphenol biosynthesis. Importantly, modern biotechnology can refine existing mango varieties to meet the market demand with high economic benefits.Mango ( Mangifera indica L.) is a juicy drupe of Mangifera of Anacardiaceae. It is the world’s third most planted tropical fruit after banana and pineapple (http://www.fao.org/faostat/), widely grown in tropical and subtropical marginal areas [Mango  mostly belongs to this type [There are more than 1000 mango cultivars worldwide , 6. Accohis type –9. With Evaluating and protecting natural mango germplasm resources is essential, and new varieties are warranted for the modern market and commercial needs. Most mango plants are heteroecious and cross-pollinated. According to literature records, the Indian Agricultural Research Association (IARI) first conducted breeding research to improve mango varieties in 1961. At present,seed selection, cross-breeding, and mutation breeding remain the main breeding ways. Due to the long juvenile phase of perennial fruit trees, the selection of mango breeding offspring by theabove methods is time-consuming and requires a lot of screening work, and the characteristics of hybrid offspring are often significantly different. Plant biotechnology provides a new approach forimproving varieties and developing stable and efficient genetic transformation methods. It has become an important meansfor genetic improvement and germplasm resource innovation of mango fruit.Over the years, the mango hybrid offspring of different parent varieties have been used to construct genetic populations. Twenty-seven hybrids of Tommy Atkins, Haden, Palmer, Coquinho, Kent, and Van Dyke varieties have been used to estimate quality genetic parameters by modeling  and evalde novo transcriptome data [et al. conducted high-depth whole genome sequencing of mango and documented the whole genome data at the chromosome level [Early trait prediction is essential to shorten the breeding process. Molecular markers are genetic markers based on the polymorphism of biological macromolecules, especially the genetic material (nucleic acid) of organisms. They are directly expressed in the form of DNA and are not interfered with by tissue types, development periods, environmental conditions, etc. Compared with the morphological traits studied by traditional genetics and breeding, they exhibit obvious advantages, mainly reflected in the fact that the stage of plant development, gene expression and environmental changes do not affect the selection of target traits, easier and faster to overcome undesirable trait linkage and introduce distant superior genes . Over thome data –22 and tome data , 23 of met al. first sequenced, assembled, and annotated the mango chloroplast genome. The chloroplast genome of mango was 151, 173 bp in size, comprising a pair of reverse repeats of 27, 093 bp, separated by large and small single copies. A total of 91 out of 139 genes in the mango chloroplast genome were protein-coding genes. Sequence analysis showed that the chloroplast genome of citrus was closest to that of mango [et al. reported four mango varieties – Cv. Langra, Cv. Zill, Cv. Shelly, and Cv. Kent – and, moreover, conducted a comparative analysis of the transcriptome [Before second generation sequencing technology was first used to sequence mango in 2014, mango genome sequence resources were very scarce, with only 684 highly redundant sequence entries in the GenBank. Azim of mango . To bettcriptome . This pacriptome .et al. analysed the nucleus DNA content of mango in their study using flow cytometry with propidium iodide staining of isolated nuclei, it was proposed that the genome size of mango is about 4.39 × 108 bp [et al. conducted the first study using fluorescence in situ hybridization (FISH) technique with 5S and 45S ribosomal DNA (rDNA) as probes on the mid chromosomes of mango somatic cells and discriminated 8 out of 40 chromosomes [Although mangoes are highly heterozygous, the current evidence suggests that the mango germplasm is diploid (2n = 2x = 40 chromosomes) , 26. In × 108 bp . Besides× 108 bp , 29. The× 108 bp . Yonemoromosomes . This inet al. obtained a 371.6 Mb genome from Hong Xiang Ya mango by SMRT sequencing, which contained 34 529 predictive protein-coding genes, providing the genetic basis for understanding special phytochemical compounds related to fruit quality [et al. sequenced Irwin varieties and obtained a high-quality genome sequence of 396 Mb. After transcriptome analysis, they found that the transcriptional regulation of the MiPSY1 gene was related to β-carotene biosynthesis during mango fruit ripening, which provided a genome platform for studying the molecular basis of mango flesh color [https://mangobase.org/easy_gd b/index.php) sequenced, recombined, analysed, and annotated the genome of the monoembryonic mango variety Tommy Atkins and used the hybrid between Tommy Atkins and Kensington Pride to generate phased haplotype chromosomes and a high-resolution phased single nucleotide polymorphism map, beneficial to identify quantitative trait loci (QTL), gene and haplotype related to fruit weight [et al. determined the whole genome sequences of three Philippine mango species  for identifying genome-wide specific markers for these Philippine native mango varieties [In 2020, the first mango genome was published, providing a fine-scale mango genome map  with a s quality . Ma et ash color . The Manarieties . This geet al. used the short-read assembly program Trinity for de novo transcriptome assembly of Langra mango, which was the first report on the transcriptome of Rhizaceae plant members. More than 13 500 unigenes were assigned to 293 KEGG pathways. In addition to the main pathways related to plant biology, KEGG pathway analysis revealed significant enrichment in a series of biochemical pathways involving (i) biosynthesis of bioactive flavonoids, flavonoids and flavonols; (i) biosynthesis of terpenoids and lignin; and (iii) plant hormone signal transduction, providing novel insights into exploring key regulatory genes in mango growth and development by transcriptome technology [As shown in chnology .Fruit-specific secondary metabolites and aroma volatiles are important markers to distinguish between immature and mature stages, such as carotenoids, anthocyanins, and aroma in mango fruits. Transcriptome analysis of Alphonso mango fruit was used to analyse the unique transcription profile characteristics affecting fruit flavor, color, ripening time, ripening pattern from peel to the core and long shelf life . The traSweetness is an important trait that determines fruit quality. It is well-established that during mango flesh ripening, starch is hydrolyzed into sucrose, fructose, and glucose with different concentrations catalyzed by invertase and β-glucosidase, which account for the unique sweet taste of mango varieties. Transcriptome analysis of differences in sugar accumulation between the high-sweet mango Tainong-1 and low-sweet Mango Renong-1 found that the key genes exerted a synergistic effect in sucrose transport, metabolism, and biosynthesis through regulating transcription factors such as MYB and NAC was the main reason for high sugar content, but no specific regulatory gene was identified .et al. obtained the first reference transcriptome data of Colletotrichum gloeosporioides in postharvest mango by high-throughput next-generation sequencing technology [et al. discovered a new M. indica latent virus (MILV) virus sequence for whole transcriptome sequencing of mango fruit. Although no virus-related symptoms were detected, the differential gene analysis of mango peel transcriptome showed significant stress in mango peel, and the gene expression related to plant immune response to pathogen and virus infection increased [Abiotic and biotic stresses are important factors affecting fruit ripening. Environmental stress conditions such as drought, salinity, high temperature, and flood can significantly interfere with the development and yield of tropical fruit trees and affect the fruit quality. Ripening can change the hardness of fruits, making them vulnerable to pests and pathogens in the final stages of ripening or during storage . Gene exchnology . In the ncreased .et al. used Illumina sequencing technology for the first time to sequence Keitt and Tommy Atkins cultivars. A total of 332 016 single nucleotide polymorphisms (SNPs) and 1903 SSRs were found, and polymorphism in Israeli mango was assessed, indicating that transcriptome data analysis can significantly broaden the genetic variation data of mango fruit [de novo sequence assembly generated 27 528, 20 771, and 35 182 transcripts, respectively, and further assembled into 70 057 non-redundant unigenes for SSR and SNP identification and annotation [et al. using the ddRAD-Seq method on Illumina HiSeq 2000 platform, and a total of 1.25 million SNP data were found. In addition, Warschefsky et al. analysed 158 mango samples, of which 106 were from known mango varieties and 52 were from related species or unknown varieties for developing high-density linkage maps, QTL discovery, variety differentiation, traceability, genome collation and SNP chip development, which provided a reference for the GWAS genome selection program [et al. screened 500 000 SNP markers from RNA sequencing and transcriptome comparison [Sequencing technology ranging from genetic diversity analysis and DNA fingerprinting with molecular markers to high-throughput sequencing based on SNP, target region amplification polymorphism (TRAP), and SSR markers has led to the development of the fruit tree genome. The development of genetic markers based on mango genome or transcriptome is shown in go fruit . Using nnotation . The disnotation , 54. A t program , 56. Kuhmparison , and furUsing omics data, researchers have focussed on studying functions at the molecular level, identifying genes through annotation, studying expression regulation mechanisms and functions in metabolic pathways of organisms, analysing the relationship between genes and products, and predicting and discovering protein functions. Current transcriptomics research of mango fruit has mainly focused on the fruit, while the functional verification of genes at physiological and biological levels has focused on flowering regulation , fruit dMiGI in mango is controlled by photoperiod and biological clock and forms a complex with the MiFKF1 protein to induce MiCO expression. The MiGI-MiFKF1 complex degrades MiCDFs, which inhibits the transcription of MiCO and MiFT genes [MiCO, MiCOL1A, MiCOL1B, MiCOL16A, and MiCOL16B significantly delayed the flowering time of transgenic Arabidopsis thaliana and enhanced the drought tolerance of transgenic A. thaliana, which may be due to inhibition of AtFT and AtSOC1 expression [Flowering and fruit parameters play a critical role in growth and development events, and many genes and proteins related to flowering regulation have been isolated and identified in mango . The GIGFT genes . Thirty-FT genes . Overexppression ,63,69.A. thaliana can delay flowering time by promoting AtFLC expression and inhibiting AtFT and AtSOC1 levels, while MiSVP2 overexpression can promote the expression of AtFT and AtSOC1, inhibit AtFLC expression and accelerate flowering [MiFTs homologous genes in mango were only increased in leaves under optimal flower induction conditions, and treatment with 250-ppm gibberellin 3 (GA3) completely inhibited flowering and MiFT expression under heavy crop load and no crop load conditions [MiFT1 expression increased sharply in leaves and was significantly higher than the other two MiFTs during flower bud development. Overexpression of three MiFTs in A. thaliana showed that MiFT1 yielded the most potent effect on promoting flowering [SVP gene is involved in mediating the environmental temperature, autonomic regulation and the vernalization pathway to regulate flowering . Moreovelowering . Three Mnditions . Tissue-lowering .MiTFL1–1 and MiTFL1–3 in A. thaliana can affect the development of flower organs [MiAP1–1, MiAP1–2, MiAP1–3, and MiAP1–4. MiAP1–1 and MiAP1–2 are highly expressed in the flowers, and overexpression in Arabidopsis significantly promotes flowering [MiAP1–2 has been associated with an early flowering phenotype in transgenic tobacco [MiTFL1 is involved in the regulation of flowering mediated by the aging pathway, and overexpression of r organs . There alowering . MiAP1–2 tobacco .MiLFY expression could be downregulated by exogenous gibberellin (GA3) and upregulated by paclobutrazol (PPP333). Bimolecular fluorescence complementation (BiFC) experiment showed that MiLFY protein could interact with zinc finger protein 4 (ZFP4) and CONSTANS overexpression inhibitor 1 (MiSOC1) to promote the early flowering of A. thaliana [MiSOC1 has been isolated and identified from mango. Low ethephon concentration could upregulate MiSOC1 expression, but a high concentration inhibited MiSOC1 expression. Overexpression of MiSOC1 promoted the flowering of A. thaliana [MiSPLs genes were highly upregulated during the early flowering stage. Overexpression of MiSPL13 promoted the early flowering of transgenic A. thaliana and the expression of AtAP1, AtSOC1, and AtFUL, which significantly improved the tolerance to drought, abscisic acid (ABA), and GA3 and was sensitive to Pro-Ca treatment [It has been reported that thaliana . Moreovethaliana . SEPALLAthaliana . Analysireatment .MiErpA1, MiCel1, MiERS1, MiETR1, and MiExpA1 play a role in fruit ripening and softening [MiErpA1 expression was triggered within 90 min of ethylene treatment, and maturation-related transcription accumulation peaked on the third day after ethylene treatment. Importantly, 1-MCP treatment inhibited ripening/softening and MiExpA1 transcript and protein accumulation [MiExpA1 expression may be ethylene-dependent, and its expression increases during maturation. During maturation, the accumulation of MiCel1 transcripts gradually increases, related to increased EGase activity and decreased cellulose/hemicellulose content. The fruit ripening of control (ethylene treatment) and 1-MCP treatment was delayed by about 3 days, associated with a delayed increase in MiCel1 expression and EGase activity [MiETR1 and MiERS1 mRNA levels are upregulated with the prolongation of storage time, peaking on day 6. 1-MCP treatment significantly decreased MiETR1 expression on days 4, 6, and 10 and inhibited MiETR1 expression on days 2, 4, 6, and 10. These results indicate that MiETR1 and MiERS1 play an important role in ethylene signal transduction. The 1-MCP treatment effectively inhibited ethylene biosynthesis and ethylene-induced maturation and senescence [Mango fruit ripening often starts at an early stage, and oftening –75,99. Amulation . MiExpA1activity . Oxalic MiWRKYs genes correlated with mango malformation traits [PGs cDNA were detected in the Kent mango peel transcriptome, while a total of 48 PGs genes were found in the mango genome, among which MiPG21–1, MiPG14, MiPG69–1, MiPG17, MiPG49, MiPG23–3, MiPG22–7, and MiPG16 were highly expressed during post-harvest fruit ripening, which may promote softening [MibHLHs genes [MiWD40s genes were identified in the mango genome. Among them, MiTTG1 interacted physically with MiMYB0, MiTT8, and MibHLH1 in tobacco leaves [The ripening and softening process of fruits involves the production and transport of cell wall polymers and enzymes. It has been established that Rab guanosine triphosphatases (GTPases) are the main regulators directing traffic in the endomembrane systems. Twenty-three genes encoding RabA protein were identified using the existing mango transcriptome , and then traits . Compareoftening ,101. A tHs genes  and 315 o leaves , suggestMi9LOX and MiEH2 participate in lipid biosynthesis. The concentrations of δ-valerolactone and γ-decalactone significantly increased when Mi9LOX was overexpressed, and the concentrations of δ-valerolactone, γ-hexalactone, and δ-hexalactone increased when MiEH2 was overexpressed, which further indicated that these genes might be involved in the biosynthesis of biogenesis of lactones from Alphonso mango [MiCHS, MiANS, and MiUFGT1, play an important role in the anthocyanin biosynthesis of mango peel. The MYB transcription factor regulates the expression of these genes. Compared with red mango (Guifei), green mango (Guiqi) and yellow mango (Jinghuang) produce fewer anthocyanins during maturity, secondary to the decrease in MiPAL activity at the translational level. It has been reported that the related transcription factors MiWRKY1, 3, 5, 81, and 84 are upregulated during light-induced anthocyanin accumulation, indicating that these genes may regulate the biosynthesis of anthocyanins in mango [MiPSY, MiZDS, MiBCH, and MiZEP regulated the synthesis of carotenoids, and the transcripts were positively correlated with the total carotenoid content, but there was no significant difference in the expression of CRTISO among varieties. In addition, the differentially expressed carotenoid catabolism genes may explain the heterogeneity in carotenoid content among the three mango varieties. The expression of carotenoid catabolic genes  decreased faster in ‘Kaituki’, resulting in higher carotenoid content in ‘Kaituki’ than in the other two varieties [With the development of metabonomics, secondary metabolites can undergo quantitative and qualitative analysis, emphasizing genes involved in metabolite synthesis. Current evidence suggests that so mango . It has in mango –79. MiPSarieties ,81. HoweMiAUX1–4, an early auxin response gene, and MiPIN1, an auxin polar transport carrier element, promote root formation in Arabidopsis transgenic plants [et al. verified the response of MiLAX2 and MiPIN1 to growth. In A. thaliana, MiERS1a and MiERS1a responded positively to the ethylene signal [MiACO and MiACS in the ethylene biosynthesis pathway and ethylene signal-related transcription factor MiERS1 could respond to salicylic acid and nitric oxide signals [MieIFs gene sequences were obtained through transcriptome data, and their expressions under salt stress, low-temperature stress and low-temperature stress were analysed. It was found that MieIF1A-a, MieIF5, and MieIF3sB might be candidate genes for improving the salt tolerance of mango [Mi14–3-3 gene family were identified from the ‘SiJiMi’ mango genome database. By analysing their expression patterns under drought, salt stress and low-temperature stress, it was found that the Mi14–3-3 gene family played an important role under such stress conditions in mango [Few studies have been conducted on other tissue parts of the mango fruit. c plants . In 2017e signal . However signals . Withoutof mango . Sixteenin mango .A. thaliana and tobacco. Accordingly, it is necessary to find an effective transformation system for mango.At present, validation of gene function and the genetic transformation system of mango fruit have not been conducted due to low transformation efficiency and genotype dependence. Only some flowering regulatory and carotenoid synthesis genes have been validated in plant models, including It benefited from the progress in sequencing technology, deciphering the mango genome and analysing fruit-related transcriptomes provide valuable data for variety identification, genetic diversity and transcriptional regulation of biological processes. In particular, flowering regulation, refining mango fruit appearance and growth characteristics has gained momentum. Future genome-wide association using multi-omics data, together with genetic population, natural population analysis and even accessions constructed pan-genomes will pinpoint regulatory genes for key traits, such as disease resistance genes, allowing easy and accurate breeding of new high-quality varieties in a short period of time, based on the focus on developing efficient mango tissue culture regeneration and gene editing technologies. These efforts may eventually bring a paradigm shift for mango breeding, which has substantial economic importance in tropical and subtropical regions.M.S. drafted the original manuscript. Critical inputs and corrections were successively provided by H.W., Z.F., and H.H. during the preparation process. H.M. is the project leader and helped in the conception and structure design of the manuscript and final proofing of the manuscript for submission.Authors confirm the availability of data and that any required links or identifiers for data are present in the manuscript as described.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.


The findings of the present study may be useful for improving yield potential, and stability under drought stress conditions.Understanding the genetic architecture of drought stress tolerance in bread wheat at seedling and reproductive stages is crucial for developing drought-tolerant varieties. In the present study, 192 diverse wheat genotypes, a subset from the Wheat Associated Mapping Initiative (WAMI) panel, were evaluated at the seedling stage in a hydroponics system for chlorophyll content (CL), shoot length (SLT), shoot weight (SWT), root length (RLT), and root weight (RWT) under both drought and optimum conditions. Following that, a genome-wide association study (GWAS) was carried out using the phenotypic data recorded during the hydroponics experiment as well as data available from previously conducted multi-location field trials under optimal and drought stress conditions. The panel had previously been genotyped using the Infinium iSelect 90K SNP array with 26,814 polymorphic markers. Using single as well as multi-locus models, GWAS identified 94 significant marker-trait associations (MTAs) or SNPs associated with traits recorded at the seedling stage and 451 for traits recorded at the reproductive stage. The significant SNPs included several novel, significant, and promising MTAs for different traits. The average LD decay distance for the whole genome was approximately 0.48 Mbp, ranging from 0.07 Mbp (chromosome 6D) to 4.14 Mbp (chromosome 2A). Furthermore, several promising SNPs revealed significant differences among haplotypes for traits such as RLT, RWT, SLT, SWT, and GY under drought stress. Functional annotation and  Triticum aestivum L.) is an important staple food crop that supplies more protein (approximately 23%) than all animal sources and accounts for more than 20% of human calorific intake . During the previous century, there was annual progress of 0.3% to 1.0% in the genetic improvement of wheat’s grain yield . However, it generally produces low-resolution maps and mostly uses few polymorphic markers with less genome coverage, which results in a non-significant association of the identified markers with the target traits, creating ambiguity in selecting for marker assisted selection programs. As a result, current genomic mapping attempts are shifting from conventional bi-parental mapping to linkage disequilibrium (LD) based mapping including GWAS which offers two unique advantages- (i) it eliminates the costs and time associated with population development, and (ii) it effectively utilizes the numerous historical crossover events that occurred in the diverse association panel used, and as a result, provides high mapping resolution  for drought tolerance at seedling (in hydroponics: PEG 8000 induced drought) and at the reproductive stage (in field conditions) and to find important haplotypes associated with drought stress tolerance at the both developmental stages. GWAS was performed for different seedling traits, including chlorophyll content (CL), shoot length (SLT), root length (RLT), shoot weight (SWT) and root weight (RWT) and several adult plant traits, such as days to heading (DTH), days to maturity (DTM), plant height (PHT), thousand kernel weight (TKW), grain yield (GY), normalized difference vegetative indices at heading (NDVIH), grain filling (NDVIGF), and maturity (NDVIM) stages, and canopy temperature depression at heading (CTDH), grain filling (CTDGF) and maturity (CTDM) stages recorded under both optimum and drought stress conditions.We evaluated 294 diverse wheat genotypes, a subset from the Wheat Associated Mapping Initiative (WAMI) Panel under field conditions. This WAMI panel was obtained from CIMMYT international nurseries, which are distributed globally each year . The gen-2s-1 with 10/14hrs dark and light duration was maintained using fluorescent tubes  during the cropping season 2021-22 was also considered for the current study. These morpho-physiological traits included the following: DTH, DTM, PHT, TKW, GY, NDVIH, NDVIGF, NDVIM, CTDH, CTDGF and CTDM (the details on observations recorded and the phenotypic data for the field experiments are provided in one of our earlier study .th edition (32 bit). Further, stress indices were estimated for all the traits at the seedling stage. Stress indices and correlation was calculated using OriginPro 2022 (https://www.originlab.com/). Details on different stress indices calculated are provided in Following the testing of the data for normal distribution, the analysis of variance (ANOVA) was performed. All descriptive statistics were calculated using Microsoft Excel 2016, and ANOVAs were performed using Genstat 18https://wheat-urgi.versailles.inra.fr/) based on IWGSC ref seq v1.1.The WAMI panel was previously genotyped using a high-density illumina 90K infinium SNP array which led to the identification of a total of 26,814 polymorphic SNPs. The missing data points in the genotypic data file were imputed using LD-KNN imputation algorithm available in TASSEL. Only 20,713 SNPs were considered for the analysis out of the 26,814 SNPs that had physical positions available from the Wheat URGI database (ad hoc statistic (DeltaK) was used, which utilized the rate of alterations in the log probability between runs utilizing successive K-values  of the imputed genotypic data was then performed in R to examine population structure using the Genomic Association and Prediction Integrated Tool (GAPIT) version 3.0 . PopulatK-values  with STRK-values . Then, GK-values .2 values for all pairwise marker comparisons with a sliding window size of 50 SNPs. The LD decay over genetic distance was calculated by fitting a nonlinear model with the modified Hill and Weir approach  was used in the single-locus method to account for population stratification (K-PC model) , whereasAll three models  were implemented in the R environment using the GAPIT version 3.0 (with default parameters) and involved the first two PCs to compensate for population structure, as determined by evaluation of the scree plot and the DeltaK statistic obtained from STRUCTURE analysis . The BonAlleles of some significant SNPs identified post-GWAS analysis were utilized to evaluate their effects on the traits of interest. For each selected stable MTA, trait values for two groups of alleles (favourable versus unfavourable) were analyzed and visualized using a R package ‘ggstatsplot’  which uthttp://plants.ensembl.org/index.html; https://www.ebi.ac.uk/interpro/; The gene models available in the overlapping region and/or within 1 Mb upstream and downstream of some of the selected stable MTAs were extracted using BioMart tool of EnsemblPlants database . The first RNA-seq dataset consisted of data on differential gene expression in a drought and heat-tolerant wheat cv. TAM107 was grown under drought and drought + heat stress conditions, with leaf samples collected separately at 1 and 6 hours after treatments (http://www.wheat-expression.com) consisted of differential expression of genes available from the plants treated with PEG, with leaf samples collected separately at 2 and 12 h after treatment.eatments . Expresseatments . Anotherhttps://biit.cs.ut.ee/clustvis/; The IDs of different genes available from the significant genomic regions associated with the target traits were uploaded as input to the expVIP-powered wheat expression browser and relevant datasets (as discussed above) were chosen for the expression analysis. Following analysis, expression data for each gene recorded under stress and control conditions [available as TPM (transcripts per million) values] were downloaded and further used to calculate the fold change in gene expression by dividing the TPM values obtained under drought stress by the TPM values obtained under control conditions. Only genes with a fold change (FC ≥ 2 or FC ≤− 2) compared to the control were considered differentially expressed. ClustVis  reported highly significant differences among the wheat genotypes for all the studied seedling traits under drought stress Table 2.2 = 0.96), GMP (r2 = 0.90), MPI (r2 = 0.85), RDI (r2 = 0.83), STI (r2 = 0.90), and YSI (r2 = 0.83), whereas it showed positive correlations with some indices, including DI (r2 = 0.02), GMP (r2 = 0.64), MPI (r2 = 0.72), and STI (r2 = 0.64), and negative correlations with others such as RDI (r2=-0.31), YSI (r2=-0.31)] under control conditions (Different patterns of correlation were observed between the traits (recorded under drought stress and control conditions) and stress indices. For instance, chlorophyll SPAD readings(CL) recorded under water stress showed positive and significant correlations with all stress indices, including DI  conditions, six genotypes were found to be drought-tolerant, while three were reported to be drought susceptible. In contrast, five genotypes were found to be tolerant under hydroponic conditions but susceptible in the field, while six genotypes were susceptible under hydroponic conditions but tolerant in the field   and the D sub-genome having the fewest . The highest number of SNPs  were found on chromosome 5B, and the lowest number were found on chromosome 4D (149 SNPs) Figure 2GWAS using phenotypic data from seedlings grown in a hydroponic condition identified 95 MTAs , 212 , and 212  based on the exploratory threshold of -log10(P) = 3.0 Table 4.GWAS using MLM model identified three SNPs, including BS00021943_51 associated with RLT, Kukri_c12563_52 and wsnp_Ex_c12812_20324622 associated with SLT under both control and stress conditions. It also detected four pleiotropic SNPs including (i) BS00021943_51 associated with RLT (C), RLT (S), and SLT (C), (ii) BS00022072_51 associated with RLT (S) and SLT (C), (iii) Excalibur_c46878_419 associated with RWT (C) and SWT (C), and (iv) wsnp_Ex_c12812_20324622 associated with RLT (C), SLT (C), and SLT (S) Table 5.GWAS using phenotypic data collected from the field experiments conducted at three different locations during 2021-22 identified several significant MTAs based on the exploratory threshold of -log10(P) = 3.0. Among these, 585 MTAs  were detected using BHU field data 5. For iFurthermore, the 451 MTAs detected using all three models also included as many as 29 MTAs, each associated with at least two traits, either at the same or different locations Table 6.P= 2.72e-04) between the alleles of Tdurum_contig49841_618 associated with CL were observed, where favorable allele improved average CL by the value of 4.94 (P= 4.15e-04) between the alleles of BS00021943_51 associated with RLT were observed, where favorable allele improved average RLT by 2.71 cm (P= 7.98e-04 and 6.26e-04) between the alleles of RAC875_rep_c107228_92 and Tdurum_contig60421_74 associated with RWT were observed, where favorable alleles improved average RWT by the values of 0.06 and 0.05 (g), respectively  for each of the following traits-RLT (BS00021943_51 and RAC875_c1817_950), RWT (RAC875_rep_c107228_92 and Tdurum_contig60421_74), SLT (Kukri_c12563_52 and wsnp_Ex_c12812_20324622), and SWT , and one highly significant MTA (Tdurum_contig49841_618) associated with CL (detected with two models) were selected to evaluate the effects of their alleles on respective traits under stress conditions (P= 2.08E-03) between the alleles of RAC875_c8721_212 associated with DM(S) were observed, where one allele reduced the average DM(S) by 2.53 days  between the alleles of Excalibur_c3286_103, wsnp_Ex_c6142_10746442, wsnp_Ra_c7112_12318340 ; Kukri_c6274_1283, BS00009565_51, wsnp_Ex_c9618_15912364, and RAC875_c5834_235 associated with PH(S); Excalibur_c3286_103, wsnp_Ex_c6142_10746442, wsnp_Ra_c7112_12318340, wsnp_Ex_c53387_56641291, BobWhite_c25527_313, wsnp_Ra_c2063_4012957, and Excalibur_c34189_122 associated with GY(S) were selected to evaluate the effects of their alleles on respective traits (combined BLUEs) under stress conditions  with a frequency of 0.92, 0.05, and 0.02, respectively. Only first two haplotypes were compared for variations in trait means because Hap3 had a very low frequency. The analysis revealed significant differences (P = 6.26e–04) between two haplotypes (Hap1 and Hap2) for RLT, where Hap2 (8.31g) had higher average RLT compared to Hap1(5.83g)  for RWT with a frequency of 0.13, 0.84, and 0.01, respectively. Similarly, only first two haplotypes were compared for variations in trait means because Hap3 had a very low frequency. The analysis revealed significant differences (P = 7.26e–04) between two haplotypes (Hap1 and Hap2) for RWT, where Hap1 (0.21g) had a higher average RWT compared to Hap2(0.15g)  for SWT with a frequency of 0.04, 0.63, and 0.29, respectively. The analysis revealed significant differences (P = 8.65e-03) between the Hap2 and Hap3 for SWT, where Hap2 (0.36g) had higher average SWT compared to Hap3(0.29g)  with a frequency of 0.32, 0.35, 0.31, 0.02, 0.01, and 0.01, respectively. Only first three haplotypes were compared for variations in trait means because the last three haplotypes  had very low frequencies (Hap1 and Hap3 (P=0.03) and between haplotypes Hap2 and Hap3 (P=1.27E-03) for GY(S). Among these three haplotypes, Hap3 (mean = 361.16 g/plot) had maximum GY followed by Hap1 (mean =310.47 g/plot) and Hap2 (mean GY(S) =300.42 g/plot). In the second group, we identified five haplotypes  with a frequency of 0.29, 0.36, 0.27, 0.04, and 0.005, respectively. Among these five haplotypes, first four haplotypes were compared for variations in trait means because the last haplotype  had a very low frequency (Hap2 and Hap3 (P=6.01E-03) and between haplotypes Hap3 and Hap4 (P=7.96E-03) for GY(S). Among these four haplotypes, Hap3 (mean = 360.75 g/plot) had maximum GY followed by Hap1 (mean =317.49 g/plot), Hap2 (mean =305.29 g/plot), and Hap4 (mean =262.16 g/plot).Similarly, seven stable and significant SNPs  on chromosome 7A associated with GY under stress condition reported in the field experiments were utilized for the haplotype analysis. Based on the allelic distribution of these SNPs in the WAMI panel, we identified two sets of haplotypes for GY under stress conditions in field experiments. In the first group, we identified six haplotypes  underlying the nine stable genomic regions (as selected above) detected under stress conditions in hydroponic conditions. These genes encode several important proteins such as F-box-like protein, ABC transporter-like protein, GDSL lipase/esterase, glycosyltransferase, AP2/ERF TF, protein kinase, calcineurin-like phosphodiesterase, GroES-like superfamily protein, WD40 repeat and others  were divided into up-regulating (FC ≥ 2) and down-regulating (FC ≤ − 2) CGs (TraesCS2B02G519900 and TraesCS5B02G035700) with unknown protein products also showed significant differential expression under control and stress conditions. A representative heat map of these DECGs is represented in Gene annotation and in silico expression analysis revealed as many as 43 non-redundant important candidate genes underlying the 10 stable genomic regions including RAC875_c8721_212 (6A), Kukri_c6274_1283 (4A), BS00009565_51 (4A), and Excalibur_c3286_103, wsnp_Ex_c6142_10746442, wsnp_Ra_c7112_12318340, wsnp_Ex_c53387_56641291, BobWhite_c25527_313, wsnp_Ra_c2063_4012957, and Excalibur_c34189_122 (each located on 7A and associated with GY) detected under stress conditions in field experiments  and down-regulating (FC ≤ − 2) CGs , while one that is too strict will likely miss a true association (a type II error). In this study, we employed an exploratory threshold [-log10(P) = 3.00] based on independent tests to consider any SNP as significant in individual environments, as reported in some previous studies  and NDVIM(C&S); Excalibur_rep_c106790_155 was reported to be associated with DH(C), PH(S), and DM(C); IAAV4799 was found to be associated with NDVIM(C&S), CTDGF(S), and CTDM(S); wsnp_Ex_c12223_19533198 was reported to be associated with NDVIH(C&S), NDVIGF(C), and NDVIM(C), and wsnp_Ex_c12850_20377830 was found to be associated with PH(C), GY(C), and DM(S). A significant positive correlation between NDVIGF and NDVIM has been reported . AdditioOverall, there were significant differences in the distribution of MTAs associated with the seedling and adult plant traits on different chromosomes, with 45.50% of MTAs identified on the A sub-genome, 37.79% in the B sub-genome, and only 16.69% in the D sub-genome. These findings align with the previous findings of lower genetic diversity and occurrence of higher LD in the D genome . FurtherTaGS5-3A , while agronomically superior drought-tolerant genotypes could be used as donors in a breeding program. The construction of breeder-friendly Kompetitive allele-specific PCR (KASP) markers for the significant and stable MTAs identified may facilitate the deployment of these genomic regions through marker-assisted selection in the early stages of the wheat breeding process. In addition, the significant MTAs identified during the current study can be integrated into genomic prediction models to evaluate their potential for selection under drought stress conditions. Desirable haplotypes can be used for haplotype-based breeding in wheat to improve yield stability, potential, and performance under drought stress. The identified important genes may also be validated using functional genomics techniques and CG-based association mapping.The original contributions presented in the study are included in the article/SS conceptualized, designed, and monitored the research, SR and GS performed field and hydroponic experiments, DS performed genomic analysis, SR, DS, and GS prepared the initial draft, and SS, VM and AJ edited and finalized the manuscript. All authors contributed to the article and approved the submitted version.