Bemisia tabaci exhibits an impressive response to different temperatures. This study aims to identify important transcriptional changes of B. tabaci occupying different temperature habitats by performing RNA sequencing on populations originating from three regions of China. The results showed that the gene expression of B. tabaci populations inhabiting regions with different temperatures was altered and identified 23 potential candidate genes that respond to temperature stress. Furthermore, three potential regulatory factors’  response to different environmental temperatures were identified. Among these, the glucuronidation pathway is a notable regulatory pathway. A total of 12 UDP-glucuronosyltransferase genes were found in the transcriptome database of B. tabaci obtained in this study. The results of DEGs analysis suggest that UDP-glucuronosyltransferases with a signal peptide may help B. tabaci resist temperature stress by sensing external signals, such as BtUGT2C1 and BtUGT2B13, which are particularly important in responding to temperature changes. These results will provide a valuable baseline for further research on the thermoregulatory mechanisms of B. tabaci that contributes to its ability to effectively colonize regions with considerable temperature differences.Exposure to extreme temperatures can hinder the development of insects and even reduce their survival rate. However, the invasive species  Bemisia tabaci Mediterranean (MED) cryptic species, or Q-biotype whitefly, is an invasive and destructive pest, which originated from the Mediterranean Sea [B. tabaci MED has rapidly occupied most provinces in China due to its ability to respond quickly to different temperature conditions in the short term [B. tabaci cryptic species. For example, the native species of B. tabaci (AsiaII3) has existed in China for much longer than the B. tabaci MED, but its distribution is still limited to selected areas in southern China [B. tabaci MED differs notably [B. tabaci populations from different regions for successive generations [B. tabaci MED are unlikely to have been achieved by simple temperature domestication. Furthermore, previous studies have revealed that some genes in B. tabaci MED undergo significant changes in their expression under short-term extreme temperature stress [B. tabaci MED to deal with temperature stress. However, the specific regulatory mechanisms’ responses to temperature in B. tabaci MED are still unclear.The nean Sea ,2. It wanean Sea . So far,ort term ,5, showirn China ,7. Previ notably . Additioerations , indicate stress . These fTenebrio molitor and Ostrinia furnacalis [Liriomyza trifolii and sea buckthorn moth [RNA-Seq can be applied to the study of insect responses to extreme temperatures . We can rnacalis ,15. Furtorn moth ,17 and porn moth . AlthougB. tabaci field population in Turpan exhibited a stronger heat response, whereas that in Harbin exhibited a stronger cold response [B. tabaci MED from Haikou (N), Turpan (T), and Harbin (H) City in China with distinct temperatures and used high-throughput transcriptome sequencing of three geographically distinct populations of B. tabaci MED from regions with different environmental temperatures to identify key transcriptional differences. This work can help us understand the response mechanisms to the environmental temperature of invasive species when they occupy different habitats by exploring transcriptional responses to temperature stress in natural populations.Therefore, it is necessary to investigate the mechanisms of temperature response under natural temperature stress from a specific species. Studying natural populations can provide mechanistic insights that accurately reflect specific response mechanisms of a species in the wild. Additionally, a previous study reported that a response , which aB. tabaci MED field populations were obtained from Haikou City , Turpan , and Harbin  in China in July 2015. In these cities, the highest temperature was recorded in July  in Turpan City, 93 °F (32.7 °C) in Haikou City, and 81 °F (28.9 °C) in Harbin City. The daily average temperature information was extracted from ©WeatherSpark.com on 1 July 2022 and is shown in  in July . There at plants . PreviouB. tabaci adults in each treatment were subjected to RNA extraction. Total RNA was extracted using TRIzol reagent . The RNA quality was checked by electrophoresis on 1% agarose gels and measuring optical density values using a NanoPhotometer® spectrophotometer . RNA concentration was measured using a Qubit® RNA Assay Kit on a Qubit® 2.0 Fluorometer . RNA integrity was assessed using an RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100 system .Two hundred ® UltraTM RNA Library Prep Kit for Illumina®  following the manufacturer’s recommendations, and index codes were added to attribute sequences to specific samples. Briefly, mRNA was purified from total RNA using poly T oligo-coated magnetic beads. Fragmentation was conducted using divalent cations under elevated temperatures in NEBNext First-Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using random hexamer primers and M-MuLV reverse transcriptase (RNase H−). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. The remaining overhangs were converted to blunt ends via exonuclease/polymerase activity. The 3′ ends of the DNA fragments were adenylated and ligated to NEBNext adapters with a hairpin loop structure to prepare for hybridization. Library fragments with preferential sizes of 150–200 bp were purified using the AMPure XP system . Then, for each sample, 3 μL USER Enzyme  was mixed with size-selected, adaptor-ligated cDNA and incubated at 37 °C for 15 min, followed by 5 min at 95 °C before polymerase chain reaction (PCR). PCR was performed using the Phusion High-Fidelity DNA polymerase, universal PCR primers, and index (X) primers. The final PCR products were purified using the AMPure XP system, and library quality was assessed using an Agilent Bioanalyzer 2100 system.For each sample, 1.5 μg of RNA was used as input material for mRNA purification. RNA sequencing (RNA-Seq) libraries were generated using the NEBNextphred ≤ 20 accounts for over 50% of the total read length.). To ensure the quality of information analysis, the above reads were filtered to obtain clean reads. Clean reads were assembled using Trinity (version 2.1.1) to obtain a high-quality unigene library. The databases of Nr , Nt , Pfam , COG , Swiss-Prot , KO , and GO  were used to annotate all assembled unigenes using the BLAST algorithm with an E-value cut-off of 10−5 [The sequenced reads (raw reads) contained some reads with adapters and low quality  to perform an alternative splicing analysis on our RNA-Seq data. Taking each comparison for differential variable splicing analysis as a unit, we first counted the types and quantities of variable splicing events that occurred and then calculated the expression levels of each type of variable splicing event. Shearing events were also analyzed to determine differences between populations. In the quantitative process, rMATS adopts two quantitative methods: (1) junction count only and (2) reads on target and junction counts.We used rMATS software  is used to estimate expression levels . For repGene ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented using the GOseq R package based on a Wallenius non-central hyper-geometric distribution , which chttp://smart.embl-heidelberg.de/, accessed on 11 June 2022). Signal peptides and cross-membrane domains were predicted using SignalP 5.0 Server  and TMHMM Server v.2.0 . Multiple protein sequences were aligned using DNAMAN 8.0 and implemented in the MEGA 7 software package to evaluate the molecular evolutionary relationships between target genes in B. tabaci and various other insects. A phylogenetic tree was constructed using the maximum likelihood method in MEGA 7. Bootstrap majority consensus values for 1000 replicates are indicated at each branch point (%).Sequence alignment and identity analyses were performed using DNAMAN . The functional domains of the deduced protein sequences of target genes were identified using SMART software  million in Haikou samples, 34.90 ± 8.80 million in Turpan samples, and 50.08 ± 4.95 million in Harbin samples  and mutually exclusive exons (MXE)  . Over 28B. tabaci MED cryptic species from three geographical populations, and the two-dimensional hierarchical clustering was performed using the “heatmap” package in R to analyze 228 DEGs  terms were used to functionally classify the predicted mponent” , which sB. tabaci. In addition, the pathways of cytochrome P450 and UDP-glucanosyltransferase catalyzing oxidation and glucuronidation in lipophilic small molecule metabolism are notable regulatory pathways for temperature responses in B. tabaci.Then, a KEGG pathway analysis was performed to identify the pathways in which DEGs are involved. Subsequently, by comparing the KEGG analysis results of two high-temperature and low-temperature field populations, it was found that the DEGs were mainly involved in the following pathways: starch and sucrose metabolism, retinol metabolism, porphyrin and chlorophyll metabolism, pentose and glucuronate interconversions, metabolism of xenobiotics by cytochrome P450, and ascorbate and aldarate metabolism . The resBtUGTs, in B. tabaci MED encodes a 510–540 amino acid protein. However, the amino acid sequences of the 12 enzymes in the UGT family do not have identical structures. Some enzymes contain an N-terminal signal peptide, the Pfam domain , 1–2 transmembrane domains, and the C-terminal domain (Glyc0_tran_28_C) with the UDP-N-acetylglucosamine (UDP-GlcNAc) binding site, while other enzymes lack at least one of these structures. The structural characteristics of UDP-glucuronosyltransferases in B. tabaci are shown in B. tabaci are membrane-bound microsomal enzymes. In addition, UDP-glucuronosyltransferases catalyze the transfer of glucuronic acid to a wide variety of exogenous and endogenous lipophilic substrates, which suggests their importance in eliminating exogenous substances and toxic secondary metabolites [B. tabaci suggested that UDP-glucuronosyltransferases with a signal peptide contain only one C-terminal transmembrane domain, whereas those without a signal peptide have two transmembrane domains (one at each terminus)  . These rBtUGT2B13) and UDP-glucuronosyltransferase 2C1 (BtUGT2C1) were overexpressed in two B. tabaci high-temperature populations. The sequence structures of BtUGT2B13 and BtUGT2C1 were identical, and both contained a signal peptide, a UDPGT domain, a Glyc0_tran_28_C domain, and a single C-terminal transmembrane domain  family, the splicing factor, and the transposable element P transposase. First, splicing factor 27 is required for the activation of pre-mRNA splicing [B. tabaci to temperature stress [B. tabaci.The results showed that the gene expression of splicing ,29, and e stress . Third, e stress . In thise stress ,32; regue stress . Cytochre stress ,38,39,40B. tabaci obtained in this study. Bioinformatics analysis of 12 genes in UGT family revealed that UDP-glucuronosyltransferases in B. tabaci are membrane-bound microsomal enzymes. Additionally, structural analysis of UGT family enzymes detected in B. tabaci suggested that some enzymes in the UGT family are responsible for transmembrane transport, and others may be receptors for extracellular signals in response to external stress. Differential expression analysis showed that UDP-glucuronosyltransferases with a signal peptide (BtUGT2B13 and BtUGT2C1) were overexpressed in two B. tabaci high-temperature populations. The results further confirmed that a UGT enzyme with a signal peptide may help B. tabaci resist temperature stress by sensing external signals.UDP-glucuronosyltransferases catalyze oxidation and glucuronidation during the metabolism of small lipophilic molecules. A total of 12 UDP-glucuronosyltransferase genes were found in the transcriptome database of B. tabaci still needs to be explored in the future. Studies on the response mechanisms to the environmental temperature in invasive species will help to understand why they continue to invade and colonize low-temperature areas.This study provides some transcriptional regulation information for further research on response mechanisms to temperature from the perspective of alternative splicing and chromatin remodeling. In addition, an important regulatory pathway involving the UGT family response to external stress has been discovered. The function of the UGT family in response to temperature stress in B. tabaci MED isolated from three geographical populations with different temperature ranges . Although there are many transcriptome data associated with temperature stress in insects, we found that the number of DEGs and their levels of upregulation varies in different insects, which may be related to limitations of transient stress and species. Therefore, the comparative transcriptomics of natural populations of B. tabaci may shed new light on the response mechanisms to temperature underlying the successful invasion of this species into different regions. Our data intuitively show changes in gene expression after the invasion of new environments with different temperatures. This temperature-bound selection pressure also partly explains why a single genetically uniform population of B. tabaci dominates farmscapes with relatively uniform temperature patterns [B. tabaci, involved in the process of alternative splicing and changes in chromatin structure. These results indicate that the response mechanisms to temperature may require complex biological processes that involve numerous changes in gene expression. Three potential regulatory factors  were differentially expressed at different environmental temperatures. Among these, the glucuronidation pathway is a notable regulatory pathway response to temperature in B. tabaci. Some UDP-glucuronosyltransferases catalyze oxidation and glucuronidation during small lipophilic molecule metabolism following the response to external stress signals by other UDP-glucuronosyltransferases with signal peptides, such as BtUGT2C1 and BtUGT2B13. Results in the current study vastly improve contemporary knowledge about B. tabaci responses to temperature variations and provide a framework to study the connection between temperature response mechanism and its ability to effectively colonize regions with considerable temperature differences.In this study, RNA-Seq was used to unravel the transcriptional landscapes of patterns . We have


Triticum aestivum L.) is a staple food crop for the global human population, and thus wheat breeders are consistently working to enhance its yield worldwide. In this study, we utilized a sub-set of Indian wheat mini core germplasm to underpin the genetic architecture for seed shape-associated traits. The wheat mini core subset (125 accessions) was genotyped using 35K SNP array and evaluated for grain shape traits such as grain length (GL), grain width (GW), grain length, width ratio (GLWR), and thousand grain weight (TGW) across the seven different environments . Marker-trait associations were determined using a multi-locus random-SNP-effect Mixed Linear Model (mrMLM) program. A total of 160 non-redundant quantitative trait nucleotides (QTNs) were identified for four grain shape traits using two or more GWAS models. Among these 160 QTNs, 27, 36, 38, and 35 QTNs were associated for GL, GW, GLWR, and TGW respectively while 24 QTNs were associated with more than one trait. Of these 160 QTNs, 73 were detected in two or more environments and were considered reliable QTLs for the respective traits. A total of 135 associated QTNs were annotated and located within the genes, including ABC transporter, Cytochrome450, Thioredoxin_M-type, and hypothetical proteins. Furthermore, the expression pattern of annotated QTNs demonstrated that only 122 were differentially expressed, suggesting these could potentially be related to seed development. The genomic regions/candidate genes for grain size traits identified in the present study represent valuable genomic resources that can potentially be utilized in the markers-assisted breeding programs to develop high-yielding varieties.Wheat ( Triticum aestivum L.) is an important staple food crop, serving as the main source of energy, protein, and fiber for much of the world’s human population  has become a popular approach to identify the QTL associated with complex traits in crops. In contrast to QTL mapping, this approach enables the exploration of a large number of alleles for any locus from a natural population of diverse individuals. This approach facilitates high-resolution mapping of traits because the individuals used for the association analysis might have undergone several rounds of historical recombination . Severala sativa , Zea mayZea mays , Hordeum vulgare , Avena sa sativa , Brassicca napus , Glycinecine max , Sorghum bicolor , and in  bicolor . Over th bicolor , FASTmrM bicolor , ISIS EM bicolor , pKWmEB  bicolor , FASTmrE bicolor , and pLA bicolor .Among the GY-associated traits, grain size contributes the most, making it a key selection target in wheat breeding programs for developing high yielding varieties. Thousand grain weight (TGW) is the main component of GY and is determined by grain size traits such as grain length (GL), grain width (GW), and grain length width ratio (GLWR) . Thus, iRabi 2015-16 to Rabi 2019-20). The GWAS panel was evaluated at the ICAR-National Bureau of Plant Genetic Resources (NBPGR), Issapur Farm, Delhi located 28.3748° N, 77.0902° E, 228.6 m AMSL, for five consecutive years; during the fifth year, the trial was also taken up at the ICAR-NBPGR, Regional Station, Jodhpur located at 26.2389° N, 73.0243° E, 263 m AMSL) and the Zonal Agricultural Research Station, Powarkheda, Madhya Pradesh located at 22.4154° N, 77.4442° E, 229 m AMSL. In total, these made up seven environments: Delhi (2015-16)-E1, Delhi (2016-17)-E2, Delhi (2017-18)-E3, Delhi (2018-19)-E4, Delhi (2019-20)-E5, Powarkheda (2019-20)-E6, and Jodhpur (2019–20) - E7.The experimental material for the GWAS study comprised of 125 diverse wheat accessions, a subset of a mini core developed from the composite wheat core set  of the NEach genotype was grown in a three-rows plot of 2 m length each, with a row-to-row distance of 0.25 m. Pests and diseases were controlled chemically, whereas weeds were controlled manually. The wheat GWAS panel was evaluated for GL (mm), GW (mm), GLWR, and TGW (gm) from the harvested grain samples. The measurements of these grain parameters were performed by selecting the main spike of five random individual plants in the middle of the row for each accession. Grains per spike were estimated by hand-threshing the mature spike. TGW of each genotype was recorded by weighing all the seeds from a sample, dividing it by the total seed number measured, and multiplying the result by 1000. For GL and GW analysis, ten seeds from each five spikes were measured using digital vernier caliper and average value of the plot accessions was taken up for analysis. Grain length/width ratio (GLWR) was calculated by dividing the grain length mean by the grain width mean for each genotype.https://www.jmp.com/en_in/software/data-analysis-software.html).The phenotypic data was analyzed using ACBD-R (Augmented Complete Block Design with R) version 4.0 software . The mea® array (Genomic DNA was isolated from one-week-old wheat seedlings using the CTAB method  with a f® array . The SNPhttps://www.maizegenetics.net/tassel). STRUCTURE software was used to estimate the level of genetic differentiation in the population using the Bayesian model-based approach; the parameter burn-in period and Monte Carlo Markov Chain (MCMC) replication number were set to 10,000 and 20,000 respectively for ten independent runs to estimate the number of subpopulations (k) in a putative range of k = 1 to 5. The optimal subpopulation number was estimated using an ad hoc statistic delta k (2) between SNP markers was used to estimate linkage disequilibrium (LD) using TASSEL v5.2 (https://www.maizegenetics.net/tassel).A total of 23,874 SNPs were used to perform principal component analysis (PCA) and generate kinship matrix using TASSEL 5.2 program ( delta k . The squhttps://cran.r-project.org/web/packages/mrMLM/index.html). These five models are mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISISEM-BLASSO. All the parameters were set at default values. The critical thresholds of significant association for all the five methods were set as logarithm of the odds (LOD) score ≥3.00. The most significant SNPs, detected in at least two methods, were considered as reliable SNPs.All 125 accessions were genotyped using 35K SNPs array. We used five ML-GWAS methods which were included in the R package mrMLM v4.0.2 (https://plants.ensembl.org/Triticum_aestivum/Info/Index) using bwa-mem software (https://sourceforge.net/projects/bio-bwa/files/). The differential gene expression analysis was performed using edge R package and genes with p-value <0.05 were considered as significantly differentially expressed genes. Heat maps of differentially expressed genes were generated by MeV software (https://sourceforge.net/projects/mev-tm4/).We utilized RNA-seq data of two wheat genotypes with contrasting seed size i.e., IC111905 (large-seeded)and EC575981  at 15 days and 30 days post anthesis (DPA) with three biological replicates to check the expression profile of putative candidate genes located in the identified genomic regions. Illumina sequencing was performed, which generated approximately 177 Gb raw data. Approximately 98.5% of reads passed the quality control and clean reads were mapped back on to the reference genome (IWGSC v2.0) (1-E7). The descriptive statistics of the investigated traits in seven environments are presented in 1, 5.09 to 8.17 mm in E2, 4.83 to 8.15 mm in E3, 5.04 to 8.09 mm in E4, 4.43 to 7.79 mm in E5, 4.43 to 8.41 mm in E6, and 4.47 to 7.87 mm in E7. GW ranged from 2.29 to 3.95 mm in E1, 2.26 to 3.71 mm in E2, 1.89 to 3.73 mm in E3, 1.87 to 3.69 mm in E4, 1.41 to 3.67 mm in E5, 1.41 to 3.58 mm in E6, and 1.48 to 3.81 mm in E7. GLWR ranged from 1.49 to 2.65 in E1, 1.29 to 2.70 in E2, 1.64 to 3.12 in E3, 1.64 to 3.52 in E4, 1.62 to 3.12 in E5, 1.69 to 3.12 in E6, and 1.61 to 3.02 in E7. TGW ranged from 18.00 to 64.54 in E1, 13.51 to 67.56 in E2, 7.91 to 51.93 in E3, 9.87 to 61.39 in E4, 12.17 to 56.66 in E5, 13.75 to 67.21 in E6, and 14.26 to 55.51 in E7. The coefficients of variation for GL, GW, GLWR, and TGW ranged from 8.32% to 24.00%, indicating considerable variability for these traits. The CV percent was highest for TGW in E6 (24.00%) followed by E4 (21.62%) and E2 (20.39%). The frequency distribution of four traits   in seven different environments  to 82.4% (E4), whereas heritability for GW ranged from 22.3% (E7) to 60.2% (E4). Similarly, heritability for GLWR ranged from 22.0% (E1) to 62.3% (E5), whereas TGW was found to be heritable in the range of 21.8% (E6) to 74.1% (E3).The environment-wise heritability and variance components based on BLUP value are presented in 1, E2, and E3 respectively. Contrarily, GW showed a negative correlation with GLWR  and significant positive correlation with TGW  under E1, E2, and E3 respectively. Under the environment E4, GL showed significant positive correlations with GW, GLWR, and TGW that ranged between 0.225 (GW) to 0.457 (GLWR), while GW showed highly a significant negative correlation with GLWR (-0.748) and significant positive correlation with TGW (0.488). Similarly, under E5, a significant positive correlation was observed with GW (0.406), GLWR (0.361), and TGW (0.643), while GW showed a significant negative correlation with GLWR (-0.686) and significant positive correlation with TGW (0.723). A similar correlation pattern among seed traits was also observed under E6 and E7  against wheat genome to find out their physical location, which revealed only 31,926 SNPs with known positions. Furthermore, SNPs were also filtered on the basis of minor allele frequency (≥0.05), missing threshold of < 10%, and call rate ≥97%. Finally, a total of 23,874 SNPs were retained for genetic diversity, population structure, and GWAS analysis. Mapping of 23,874 filtered SNP markers provided a whole genome-wide coverage along the 21 chromosomes of wheat  markers. The SNP probe sequences of wheat array were BLASTn  which demonstrated the presence of two sub-groups within the association panel Figure 32) for all the SNPs. The LD decay for the whole genome was 1.9 Mb. Further, it was found that the decay was most rapid in the A sub-genome (1.63 Mb), followed by the D sub-genome (1.93 Mb) and B sub-genome (2.28 Mb)  Figure 4GWAS was performed using 23,874 SNPs filtered on various parameters to identify genomic loci associated to four different grain yield traits  independently for the seven environments and also based on the BLUP values derived from data of grain size traits of all the seven environments. Here, we have used five multi-locus models  to conduct GWAS. A total of 752 significant SNPs were predicted for four grain size traits using ML-GWAS models with LOD score ≥ 3. Manhattan plots for GL drawn using various ML-GWAS models that depict marker trait associations are presented in QTNs for the respective traits. Furthermore, distribution of these 160 significant SNPs was also analyzed across the environments. Out of these 160 QTNs, 87 were confined to only one environment that included 13, 19, 27, and 25 QTNs for GL, GW, GLWR, and TGW respectively, and 3 QTNs that were associated with more than one trait , followed by chr2D (6 SNPs), and chr7D (6 SNPs) while chr1D, chr6B, and chr6D had only one SNPs.On the basis of redundancy of SNPs in the models and locations, we combined the identified SNPs and found a total of 160 SNPs were simultaneously detected in two or more multi-locus models. These SNPs were designated as reliable ne trait Table S6QTNs on respective phenotype, we only analyzed those QTNs that were detected in more than two environments and revealed R2 value ≥10% with at least one GWAS model. Association panel genotypes were divided into two groups according to allele types in order to test whether the mean phenotypes of the two groups were significantly different (QTNs had significant effect (P ≤ 0.01) on their respective traits. Among these six QTNs, four QTNs (Q.GL-GW-3D (AX-95008504), Q.GL-5D (AX-94482861), Q.GL-5D (AX-95020206), and Q.GL-TGW-6A (AX-95238912)) demonstrated significant effects on GL(mm) whereas one, QTN Q.GW-4B (AX-94878781), had significant effect on GW and another, Q.GLWR-2A (AX-94736090), showed significant effect on GLWR. The QTNs with significant phenotypic effects on seed traits might contribute to their genetic variations.To evaluate the allelic effects of ifferent Figure 7https://wheat.pw.usda.gov/GG3/iwgsc-2.0), available at Plant Ensemble. Of these 160 QTNs, annotation was only detected for 136 QTNs. The detailed analysis of annotated SNPs showed that Q.TGW-5D (SNP-AX-95234313) was located within a gene encoding cytochrome 450 and was identified in E4 environment using mrMLM and FASTmrMLM models with LOD score ranging between 3.8 to 3.92. Another, Q.GW-3D (SNP-AX-94540502), for grain weight identified at the E6 environment was annotated as ABC transporter and detected by mrMLM and ISIS EM-BLASSO models. We also checked the annotation of QTNs that were identified at multiple environments and found that Q.GL-1B (SNP-AX-94699549), Q.GL-GLWR-4B (SNP-AX-94879134), Q.GL-3D (SNP-AX-95074739), and Q.GW-5A (SNP-AX-94657794) were located within genes encoding ABC transporter, WRKY transcription_factor, Glucan endo-1,3-beta-glucosidase, and Zinc_finger_protein respectively. Among these four QTLs, Q.GL-1B (encoding for an ABC transporter) was located at 585,331,222bp on chromosome 1B. It was identified in two different environments, E1 and E4, using ISIS EM-BLASSO model with LOD scores 4.17 and 5.65 and R2 3.05% and 6.42%, respectively. Q.GL-GLWR-4B (encoding for a WRKY transcription factor) was located on chr4B at 63,231,309bp position and identified in four different environments: E1 with ISIS EM-BLASSO model, E2 with ISIS EM-BLASSO and FASTmrEMMA models, E3 with pLARmEB model, and E4 with ISIS EM-BLASSO model. It had an LOD score ranging between 3.52 to 8.12. This QTN was associated with GL in all locations except E2. Further, in E2, this Q.GL-GLWR-4B was also determined by three different models, namely FASTmrMLM, pLARmEB, and ISIS EM-BLASSO, but associated with GLWR trait.All the 160 significantly associated QTNs with grain size traits that were detected in two or more models were searched for their annotation in the wheat reference genome assembly cv. Chinese Spring  and EC575981, was performed at two time intervals during the seed development  to quantify expression of all annotated genes within associated genomic regions.TraesCS7B02G462900 , TraesCS2D02G132600 , TraesCS1A02G187000 , TraesCS2B02G260200 , and TraesCS6A02G379200 , were downregulated at both the time points while TraesCS1A02G427400 , TraesCS3D02G002700 , TraesCS7A02G111200 , and TraesCS3A02G180200  genes were upregulated at 15 as well as 30 DPA. Result also showed that Q.GLWR-5B (SNP-AX-94915493) associated with GLWR was located within gene TraesCS5B02G552400 , which was only expressed in small grain cultivars with 10 fold upregulation, which showed it has some specific role in small seed cultivars. Another gene, namely TraesCS7D02G463100, located within the QTNQ.GW-7D and identified in E1 and E5 location was upregulated in large grain cultivars. TraesCS7D02G463100 is nuclear transcription factor associated with GLWR.Expression analysis demonstrated that only 123 genes were expressed in both stages (15 and 30 DPA) of small and large seeded genotypes, of which 23, 33, 27, and 28 were uniquely associated with GL, GW, GLWR, and TGW respectively. Among the identified genes, those with foldchange ≥1 and p-value<0.05 were considered as significantly differentially expressed genes. A total of 18 and 12 genes were significantly differentially regulated in large seeded cultivars at 15 and 30 DPA respectively. At 15 DPA, 11 genes were upregulated and 7 genes were downregulated, while 6 genes were significantly upregulated and downregulated in large seed cultivars at 30 DPA Figure 82 and thousand grain weight (TGW)  . In the er spike . In the er spike . The TGWer spike . Thus, ier spike . Therefo5 and E6. These environments fall in stress prone areas affected by less rainfall and high temperatures, which might have caused low heritability of the traits. Promising accessions for grain parameters were identified. Among them, EC578134, IC539313, IC535217, EC464070, and EC578152 were promising for GL as well as TGW. EC339611, EC578134, and IC535217 were promising for GL as well as GLWR, whereas IC335715 was promising for GW and TGW. These accessions can be used in breeding programs for trait introgression, genetics, and genomics study. The significant positive correlation of grain length and width with thousand grain weight revealed that the selection of grains with increased width and length can greatly contribute to grain weight and indirectly to grain yield. Earlier studies have reported moderate to strong correlations between TGW and size , followed by FASTmrMLM (202), ISI EMBLASSO (177), mrMLM(132), and FASTmrEMMA(62).The Genome-wide association study (GWAS) has been found to be a powerful tool to investigate genetic bases of complex traits in many plant species such as rice, maize, soybean, and wheat . There aQTL for grain yield component traits have been extensively studied and reported on all the 21 chromosomes of wheat . In our 2 ≥ 10% at least in one GWAS method), of which Q.GL-4A (SNP-AX-94839917), Q.GL-7D (SNP-AX-94872194), Q.GL-7A (SNP-AX-94760450) and Q.GL-6D (SNP-AX-94647721) were strongest because their R2 value were ≥20%. The Q.GL-4A on the chr4A with highest R2 = 31.94% may explain a significant proportion of the variation for GL in the wheat mini core germplasm. Moreover, this QTN was identified simultaneously in two environments i.e., E1 and E2, and with four different models. The Q.GL-7D is located within a gene encoding Thioredoxin M type protein with R2 ranging between 4.82% to 21.88%. This QTN was predicted in three different environments i.e., E1, E3, and E4, and using three different models, suggesting this could be a reliable QTN contributing to GL variation in wheat. In an earlier study, Thioredoxin has been shown to play an important role in preventing sprouting of developing grains in cereals (wheat and barley) by reducing the intramolecular disulfide bonds of storage proteins and other proteins in the starchy endosperm, and thereby affecting grain yield . Among these 27 QTNs, 10 QTNs were major , Q.GW-5B (SNP-AX-94547840), Q.GW-3A (SNP-AX-94741529), Q.GW-4D (SNP-AX-95213549), and Q.GW-2B (SNP-AX-94519462) were predicted as major QTNs as the phenotypic variance explained by these QTLs was ≥10% of at least one of the ML- GWAS models. Q.GW-3D and Q.GW-5B were annotated as unnamed protein product and hypothetical protein respectively. Additionally, Q.GW-2B and Q.GW-4D had R2 values ranging from 7.7 to 12.23 and 0.72 to 17.72, respectively. Q.GW-2B was identified at two environments E3 and E4, while Q.GW-4D was identified at E2. Interestingly, both intragenic SNPs showed higher expression in large grain wheat cultivars than small seed cultivars. This suggested that these QTNs might have important roles in determining variation for GW in wheat.For grain width, 36 QTNs were identified that were distributed on 17 wheat chromosomes. Among these QTNs2≥10 and were considered as major genomic regions for this trait. Q.TGW-1A (SNP-AX-94605845) was annotated as TTL1 protein (TETRATRICOPEPTIDE-REPEAT THIOREDOXIN-LIKE 1) with R2 = 11.78% and highly expressed in large grains. Studies have reported that TTL1 positively regulates the stress response regulated by ABA  was located within a gene encoding cytochrome 450 and it was only identified at E4 environment with R2 = 21.45. In a previous study on cytochrome family protein, CYP78A3 on chr7 has been shown to play an important role in wheat seed development by promoting integument cell proliferation (Q.TGW-5D (cytochrome 450) identified in our study might also have some role in seed development. A total of 13 QTNs were associated with GLWR and were considered as strong QTNs explaining ≥10% phenotyping variance of the trait. Most of the QTNs were annotated to be either hypothetical proteins or intergenic SNPs. Three QTNs for TGW, namely Q.GLWR-2D (SNP-AX-94922377), Q.GLWR-1A (SNP-AX-95213485), and Q.GLWR-6A (SNP-AX-94722285), were simultaneously identified in three different environments and located on chr2D, chr1A, and chr6A respectively.Thirty-seven and thirty-five QTNs were predictive for GLWR and TGW traits respectively. The GLWR-associated QTNs were distributed on 17 chromosomes  while QTNs for TGW were spread over 16 chromosomes . In TGW, a total of fourteen SNPs had Rd by ABA . The loselopment . So, it feration . Thus, iTaGS  for grain length trait at the same region on chr4A and identified in two environments (E1 and E2). Further, LOD (4.92~6.13) and R2 value (15.33~31.94) of this QTL demonstrated its importance in regulating GL trait. qTKW.6A.1 associated with TGW on 6A at the interval 166.64-596.18 Mb (Q.TGW-6A (SNP-AX-95240001), identified in our study appears to correspond to qTKW.6A.1. Interestingly, Q.TGW-6A was identified at two locations, E2 and E4, which showed that it is a stable genomic region for TGW. Further, we found that Q.GL-TGW-6A (SNP-AX-95238912) and Q.GLWR-6A (SNP-AX-94722285), which are located on chr6A at 362.7Mb and 307Mb, overlapped with the grain shape QTLs identified by Q.GL-TGW-6A was identified in three environments . LOD score and R2 value of Q.GL-TGW-6A and Q.GLWR-6A ranged from 3.46 to 8.23 and 2.6 to 23.8 respectively. On the other hand, Q.GLWR-6A was present at 307Mb on chr6A with LOD score (3.47~7.59) and R2 value (5.03~18.87). Since the two QTNs on chr6A were also identified in the previous studies, these appear to represent major genomic regions for the grain shape traits.In wheat, several candidate genes underlying grain size and weight have been identified including TaGS , TaGW2  gene in our data showed relatively higher expression in large seeded genotypes as compare to small seeded genotypes. So, we examined nearby QTNs around the gene and we found two QTNs, Q.GLWR-7D and Q.TGW-7D, located in the vicinity of TaGS-D1 and positioned at 54.9Mb on chr7DS and 100.1 Mb on chr7D respectively. The presence of these two QTNs indirectly suggested a major locus which corresponds to either TaGS-D1 or an additional novel gene for grain shape trait on the short of chr7D. A second grain weight locus cytokinin oxidase/dehydrogenase (TaCKX6-D1) gene is physically located on chromosome 3D and played a key role in controlling cytokinin levels and affects grain weight in wheat , in our analysis at 151.4 Mb and 239.3 Mb respectively. Q.GL-GW-3D associated with GL was identified in four environments  with LOD value from 3.05 to 6.33 and R2 value from 4.09 to 14.97, which showed the significance of SNP. The second, Q.TGW-3D, demonstrated association with TGW with LOD (3.16~8.2) and R2 (7.21~11.48) and was identified at E2, E3, and E4 environments. Both the QTNs were annotated as hypothetical proteins and expressed in our transcriptome data. Q.TGW-3D was highly expressed in large seed cultivars while Q.GL-GW-3D also showed expression in both cultivars. In conclusion, in this study we have comprehensively phenotyped wheat mini core germplasm accessions for grain shape traits and identified promising accessions for large grain size and length which can be incorporated in breeding programs. Further, integration of phenotyping and genotyping data has enabled us to identify genomic regions/candidate genes, some of which are novel. Comparative study also showed that many QTNs identified in our study represented novel genomic regions that can be further validated for their role in determining grain size and can be potentially exploited in breeding programs to develop high-yielding varieties.A few other underlying genes influenced grain size and weight have been reported by in wheat . TaCKX6-https://www.ncbi.nlm.nih.gov/, PRJNA906296.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: JK planned and handled the field experiments, conducted formal analysis, and assisted in writing the manuscript. DL contributed to the data curation, investigation, formal analysis, and original draft writing. MY and ST planned and executed DNA extraction and SNP data generation. PJ, SS, ST, SM, and HA performed data analysis, writing, reviewing, and editing, NS, KS and KM recorded phenotyping data and reviewed and edited the manuscript, RS, MY, and GS contributed resources and participated in the writing, reviewing, and editing. AS contributed to the conceptualization, supervision, writing, reviewing, and editing of the manuscript. All authors contributed to the article and approved the submitted version.


Growth performance and growth-related traits have a crucial role in livestock due to their influence on productivity. This genome-wide association study (GWAS) in Pakistani dromedary camels was conducted to identify single nucleotide polymorphisms (SNPs) associated with growth at specific camel ages, and for selected SNPs, to investigate in detail how their effects change with increasing camel age. This is the first GWAS conducted on dromedary camels in this region.Two Pakistani breeds, Marecha and Lassi, were selected for this study. A genotyping-by-sequencing method was used, and a total of 65,644 SNPs were identified. For GWAS, weight records data with several body weight traits, namely, birthweight, weaning weight, and weights of camels at 1, 2, 4, and 6 years of age were analysed by using model-based growth curve analysis. Age-specific weight data were analysed with a linear mixed model that included fixed effects of SNP genotype as well as sex.q-value method for false discovery control, for Marecha camels, five SNPs at q<0.01 and 96 at q<0.05 were significantly associated with the weight traits considered, while three (q<0.01) and seven (q<0.05) SNP associations were identified for Lassi camels. Several candidate genes harbouring these SNP were discovered.Based on the These results will help to better understand the genetic architecture of growth including how these genes are expressed at different phases of their life. This will serve to lay the foundations for applied breeding programs of camels by allowing the genetic selection of superior animals. Camelus dromedarius) from south Asia, the Middle East and North/East Africa, the Bactrian camel (Camelus bactrianus) from central Asia, and wild Bactrian camel (Camelus ferus) from China and Mongolia ). The Manhattan plot for associations with Marecha birth weight at is shown in −9, q = 0.00057), with an allelic substitution effect (b) of 144.3±25.0 kg (regression parameter estimate±SE). The Manhattan plot for this is shown in For Marecha camels, there was one significant association for birth weight, with SNP 27264_39 located on Chromosome 9 . Two other highly significant associations were detected on Chromosome 2 and another on Chromosome 13. No significant associations were detected at six years of age, likely due to fewer observations in this age category.A large number of highly significant associations were mapped for four-year old Marecha camels four with q<0.01 . The mosq<0.01, and 25 were mapped at q<0.1 . One highly significant association was mapped for weaning weight in Lassi camels .For birth weight in Lassi camels, one highly significant association was detected at at q<0.1 . The mosi camels , namely −6, q = 0.021, b = 50.6±10.4 kg). Another SNP less than 1 Mb upstream (141357_76) also has an association with body weight at age two years . The same SNP mentioned above, 40507_65, was also highly significant at two years of age , as seen in −8, q = 0.0023, b = 69.9±13.0 kg). No significant associations were detected at four years of age for Lassi camels, although the smaller number of Lassi camels at this age should be taken into consideration.There is one highly significant SNP associations for Lassi weight at one year , namely q<0.10), there were ten SNPs with association with two age classes, as summarised in An analysis was conducted to investigate the effect of specific SNPs across the age span of camels. While most of the SNP associations were at a specific age class  while the right-hand panels show the estimated effects expressed as regression coefficient with SE bands. SNP 2072_39 on Chromosome 7 was the most significant association for Marecha noted, and has strong effects from around three to five years of age. SNP 10626_65 on Chromosome 21, though only initially detected to have an association at two years, has strong effects on body weight from about one to five years of age. For Lassi camels, SNP 40507_65 on Chromosome 13 had significant associations at ages 1 yr and 2 yr , there were five SNP associations detected for Marecha  and three associations detected for Lassi camels , as shown in q<0.05), particularly for Marecha camels at 4 yr, although the limited number of Marecha in this age group may influence this result.At the most stringent level of testing,  for significant SNP associations ranged up to −4.43±1.02 kg (Marecha) and −5.06±1.00 kg (Lassi), i.e. up to ~10% of average birthweight. For weaning weight, effect sizes ranged up to 20.9±3.8 kg (Lassi), i.e. ~20% of mean weaning weight (No significant associations were detected for weaning weight in Marecha camels). Maximum effect size of significant SNPs associated with weight at specific ages tends to increase with increasing age: for Marecha: 144.3±25.0 kg at 2 yr, and −159.2±25.1 kg at 4 yr, and for Lassi: 50.6±10.4 kg at 1 yr, and 107.4±19.4 kg at 2 yr. One explanation for the relatively large number of associations at the older groups may be that the effect sizes in these older cohorts are relatively larger, increasing their power of detection. However, the small sample sizes means that these results need to be treated with caution.Also, of note was the existence of age-specific associations. The overwhelming majority of associations were found to be age-specific. Similarly, a study by Hadjipavlou and Bishop  in ScottDEXI), testis-specific Y-encoded-like protein 4 (TSPYL4) and Class II transactivator (CIITA). The current study provides a GWAS for body weight at a range of specific ages, and also tracks the effects of selected SNPs across ages of the camel. The novelty of this work has been enhanced by using the output from the growth curve analysis as additional weight phenotypes to detect effects of SNPs at different ages of animals Nevertheless there are some limitations to the current study, one major issue being the relatively small number of animals in the study. While adequate for the genetic diversity and population structure reported in Sabahat et al [The current work builds on the study by Bitaraf Sani et al  who repoat et al , this isCamelus dromedarius. Based on growth models, it was possible to evaluate genetic variation in weight across different ages, and it is apparent that genes are expressed at different ages during the growth of camels. Several genes harbouring the significant SNPs linked with growth traits were detected and some of them have interesting biological functions. However, future studies are required to confirm and target these areas that could provide further knowledge about genetic architecture of growth traits. These findings should facilitate and contribute to marker-assisted selection breeding program of the camel.In summary, our GWAS identified SNPs associated with body weight of


This aim of this study was to evaluate the effect of 3% Diquafosol Ophthalmic Solution (DQS) on children with dry eye from wearing overnight orthokeratology (OrthoK) lenses.Myopic children aged 8–18 years with dry eye syndrome were enrolled in this prospective observational study, and they were grouped according to their OrthoK treatment history for at least 1 year. All participants received DQS 4 times per day for 1 month. The following indicators were measured at baseline 1 month after treatment: the Dry Eye Questionnaire-5 (DEQ-5), non-invasive tear meniscus height (TMH), non-invasive tear film break-up time , meibomian gland score (MG score), conjunctival hyperemia redness score (R-scan), and blink pattern analysis.t = −3.36, p = 0.00). TMH increased from 0.20 ± 0.05 mm to 0.21 ± 0.05 mm , NIBUT-F and NIBUT-A were prolonged from 6.67 ± 4.71 s to 10.32 ± 6.19 s and from 8.86 ± 5.25 s to 13.30 ± 6.03 s , respectively. R-scan decreased from 0.69 ± 0.28 to 0.50 ± 0.25 . Upper MG scores decreased from 1.04 ± 0.32 to 0.97 ± 0.36 . Lower MG scores, partial blink rate, partial blinks, and total blinks did not change significantly. Both break-up time (BUT) and R-scan improved significantly after DQS treatment for 1 month  in OrthoK candidates and OrthoK wearers. Among the OrthoK wearers, TMH and dry eye symptoms increased significantly  but did not increase in OrthoK candidates (p > 0.05). There were no adverse events related to DQS.A total of 104 participants (189 eyes) including 40 OrthoK wearers (72 eyes) and 64 Orthok candidates (117 eyes) completed the study. Of all, after DQS treatment for 1 month, DEQ-5 scores reduced from 5.54 ± 3.25 to 3.85 ± 2.98 (Diquafosol Ophthalmic Solution was effective for children wearing overnight orthokeratology in relieving dry eye symptoms and improving ocular surface parameters, which may help improve children's OrthoK wearing tolerance and compliance. The prevalence of myopia in children has increased markedly worldwide, especially in East and Southeast Asia , 2. The Studies have highlighted that there is an interplay between tear film stability and effect, that is, the safety of OrthoK wearing. The hydrostatic pressure or negative pressure generated by the accumulation of tears in the reversing arc area of the OrthoK lens forms a strong positive pressure around the base arc area, while the tears in the surrounding arc are continuously sucked into the reversing arc area by a similar siphon effect to supplement the tear loss caused by the positive pressure, forming a circulating system . The qua®) exhibited effects similar to and superior to those of sodium hyaluronate in the treatment of adults with dry eyes . All procedures were conducted following the principles of the Declaration of Helsinki.The inclusion criteria were as follows: (1) age between 8 years and 18 years; (2) myopia from −1.00 Diopter to −5.50 D, with-the-rule astigmatism of up to −1.75 D or against-the-rule astigmatism of less than −0.75 D with keratometry from 41.00 to 46.00 D; (3) participants were first-time users of OrthoK or had worn OrthoK for at least 1 year; (4) participants with a potential risk of mild-to-moderate dry eye based on TMH <0.20 mm or BUT <10 s from Keratograph 5M examinations adopted by the Dry Eye Workshop  4 times per day for 1 month. The usage frequency of DQS (4 times daily) and follow-up time (1 month) in this study were based on the studies conducted by Hwang et al.  and the OrthoK candidates group (new wearers without a history of using any contact lens). All participants received DQSg et al.  and Hollg et al. . It is mSubjective symptoms were assessed using Dry Eye Questionnaire-5 (DEQ-5) . DEQ-5 wQ1: During a typical day in the past month, how often did your eyes feel discomfort?Q2: When your eyes felt discomfort, how intense was this feeling of discomfort at the end of the day, within 2 h of going to bed?Q3: During a typical day in the past month, how often did your eyes feel dry?Q4: When your eyes felt dry, how intense was this feeling of discomfort at the end of the day, within 2 h of going to bed?Q5: During a typical day in the past month, how often did your eyes feel excessively watery?TMH, BUT-F, BUT-A, R-scan, and MG score were conducted with the Keratograph 5M  to evaluate the ocular surface status and tear film stability at baseline and 1 month after DQS treatment. Participants were instructed to look straight ahead, and TMH, R-scan, and MG scores were measured in TMH quantitative photography mode, R-scan quantitative photography mode, and MG photography mode, respectively. Meibomian glands loss scores were graded as 0 (no loss), grade 1 (dropout < 1/3), grade 2 (dropout 1/3–2/3), and grade 3 (dropout >2/3). BUT was generated by automatic detection and calculation as follows: (1) BUT-F, the time at which the first distortion in the reflected Placido ring occurred and (2) BUT-A, related to the localized TBUTs and calculated based on the average time of all detected perturbations.Partial blink rate (PBR), partial blinks, and total blinks were recorded using LipiView Ocular Surface Interferometer . During the examination, the patients were asked to look at the front lightspot to ensure that their pupils were directly in the center of the interferometer camera with natural blinking, and all the measurements were conducted by the same experienced examiner. Participants with an outcome conformance factor of < 0.8 were asked to repeat the measurement.At every visit, the occurrence of systemic adverse events was checked. If adverse events were found, the findings were reported.t-test. We used the paired t-test to obtain overall and intergroup differences of continuous data between, before, and after DQS treatment responses throughout the study period. The significance level was set as α = 0.05.Statistical analysis was performed with IBM SPSS Statistics 26.0 . The continuous measurement data subjected to normal distribution were presented as the mean ± SD, and the enumeration data were presented as the ratio (%). The missing data incurred during examinations in this study were at random, and the proportion was very small ( ≤ 7%); in addition, relevant missing data were not included in the statistics. The chi-squared test was used for baseline enumeration of variables such as sex, parental smoking, daily outdoors (h), and daily electronic screen time (h). Baseline continuous data, such as DEQ-5 scores, TMH, NIBUT, R-scan, MG Scores, PBR, partial blinks, and total blinks, were compared between the two groups using the independent samples p = 0.26), parental smoking (p = 0.68), daily outdoors (p = 0.33), and daily electronic screen time (p = 0.62) between OrthoK candidates and OrthoK wearers, except for age .A total of 104 participants (189 eyes) completed the Keratograph 5M and LipiView examination at baseline and 1 month after DQS treatment. The OrthoK wearers group consisted of 40 participants (72 eyes), and the OrthoK candidates group consisted of 64 participants (117 eyes). The baseline demographic data are summarized in p = 0.06), TMH (p = 0.07), BUT-F (p = 0.84), BUT-A (p = 0.50), and R-scan (p = 0.13), Upper MG Scores (p=0.88), and total blinks (p = 0.62) between OrthoK candidates and OrthoK wearers at baseline. However, there were significant differences in lower MG scores , PBR , and partial blinks  between OrthoK candidates and OrthoK wearers at baseline.The baseline characteristics of the ocular evaluation are summarized in t = −3.36, p = 0.00). Among OrthoK wearers, the DEQ-5 scores , Q1 , Q2 , and Q4  decreased from baseline to 1 month, whereas among OrthoK candidates, there was a significant decrease only in Q5 .t = 2.59, p = 0.01), BUT-F , BUT-A , R-scan , and upper MG scores  improved significantly after 1-month DQS treatment. However, lower MG scores did not change significantly (p = 0.10). Of OrthoK candidates and OrthoK wearers, compared with the baseline, BUT-F , BUT-A , and R-scan  improved after 1-month DQS treatment; however, Upper MG scores did not change significantly . Among the OrthoK wearers, TMH increased significantly , but not in OrthoK candidates (p = 0.32). However, among the OrthoK candidates, lower MG scores  decreased significantly but not in OrthoK wearers (p = 0.42).t = 1.00, p = 0.31), partial blinks , and total blinks  after DQS treatment. Of OrthoK candidates and OrthoK wearers, no significant changes were observed in PBR (p = 0.11 and p = 0.42), partial blinks (p = 0.88 and p = 0.93), and total blinks (p = 0.15 and p = 0.26) from baseline to 1 month.All participants adhered to the DQS treatment for 1 month, and no adverse reactions or events developed during the observation period.We investigated the efficacy and safety of DQS on OrthoK lens-related dry eye symptoms and measured the ocular surface parameters in children with OrthoK wearing history or candidates for OrthoK. Owing to the poor coordination with the examination of children, we used non-invasive Keratograph 5M  and LipiAt baseline, DEQ-5, TMH, BUT, R-scan, and total blinks were similar between OrthoK wearers and OrthoK candidates; however, the DEQ-5 score and TMH were slightly worse in OrthoK wearers, suggesting that wearing OrthoK lenses may impact a wearer's subjective symptoms as well as tear quantity. We also found that lower MG scores were higher in OrthoK candidates than OrthoK wearers at baseline, this may be due to meibomian gland growth with aging  considerIn this study, through subjective symptoms analysis, we found that the frequency of eye discomfort, the intensity of eye discomfort, and the intensity of dry eye sensation were diminished more significantly in OrthoK wearers than in OrthoK candidates. OrthoK wearers had significant improvement in TMH than OrthoK candidates. Ocular surface subjective parameters were significantly improved in children with topical DQS in this study, which is consistent with the results of Kojima et al.  for adulOur study showed no significant difference in blink patterns, including PBR, partial blinks, and total blinks, after 1-month DQS treatment. However, we ascertained that the blink pattern was similar or slightly improved after DQS use for a month. However, in the baseline, the blink pattern was worse in the OrthoK wearers than in the OrthoK candidates. However, other investigators found that the topical application of DQS alleviated meibomian gland dysfunction , 54. We This study inevitably has some limitations. First, it was a single-center, single-arm research project that lacks a control population, and a large sample randomized controlled trial is recommended. Second, this study was only a short-term observation, and long-term prospective studies need to be further explored. Meanwhile, further studies are needed to evaluate tear composition and distribution and how these factors are influenced by tear film stability and meibomian gland status.Diquafosol Ophthalmic Solution was effective and safe for children wearing overnight orthokeratology in alleviating dry eye symptoms and ocular surface parameters, which may help improve children's OrthoK wearing tolerance and compliance.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Institutional Ethics Committee of Changsha Aier Eye Hospital. All procedures were conducted following the principles of the Declaration of Helsinki. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.XL, YY, QW, and ZY contributed to the study's conception and design. Material preparation and data collection were performed by YT, HW, ZL, WG, ZH, LH, and MW. Data analysis was performed by XL, YY, and ZY. The first draft of the manuscript was written by XL and YY. All authors commented on previous versions of the manuscript and read and approved the final manuscript.


As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52–35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region . The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats. Applications of molecular-assisted markers have greatly improved the efficiency and accuracy of livestock selection and breeding . The traThe accuracy of GS relies on SNP loci covering the whole genome; therefore, the high-throughput variant locus detection technology has been one of bottlenecks in GS. The development of the SNP chip array has advanced the application of GS. More recently, a liquid SNP chip panel based on genotyping by targeted sequencing (GBTS) technology has been developed to reduce the cost further and improve the accuracy of genomic selection. Compared with traditional SNP chips using magnetic beads, liquid-phase chips have many advantages, including low cost and high flexibility ,12. It hDairy goats are widely bred worldwide. Traditional breeding methods for dairy goats rely on manual experience and observation, which have low efficiency, long cycle time, and susceptibility to environmental factors. In contrast, genetic selection is a well-established method for a more objective and accurate assessment of production performance. However, this method has not yet been utilized in Chinese dairy goat breeding due to the small number of phenotype records involved and the lack of commercial SNP chips. Although GoatSNP50 Bead Chip  from IllThis article presents the development and validation of a 54K genome-wide liquid SNP chip panel for targeting dairy goat sequencing. The chip contains SNP-associated production traits from genomic regions. The chip was validated by genotyping 200 dairy goats and comparing their liquid SNP chip panel and WGS data. Developing a 54K genome-wide liquid SNP chip panel using target sequencing is a significant step forward in dairy goat breeding.We are committed to ensuring that the ethical principles of animal experimentation are fully observed to ensure that the welfare and rights of animals are fully protected. All experiments were performed under the approval of the Ethics Review Committee of China Agricultural University (permit number: AW12401202-1-32).N = 25), Toggenburg dairy goats (N = 27), and Alpine dairy goats (N = 16), and two Chinese indigenous dairy goat breeds, Guanzhong dairy goats (N = 19) and Laoshan dairy goats (N = 23). Genomic DNA from these 110 goats was extracted from ear punches  using Qiagen DNeasy columns  following the manufacturer’s instructions. The extracted DNA was quantified using a Qubit fluorometer, and the quality was assessed using a Nanodrop spectrophotometer. Sequencing libraries were prepared and barcoded using the TruSeq sample preparation kit. The libraries were sequenced on the Illumina HiSeq X Ten platform using the 150 bp paired-end sequencing strategy.The resequenced animals used in this study consisted of five dairy goat breeds, including three European dairy goat breeds, namely Saanen dairy goats  ,20,21 waAfter this, PLINK1.9  was usedTo begin with, we conducted selective sweeps according to differences between Chinese and European dairy goat breeds in milk production, milk quality, and other production traits. Selective signal sweep regions were detected according to combinations of three parameters: the XP-CLR approach implemented in the XP-CLR (version 1.1.2) , fixatioAfter scoring according to the Targeted Capture Sequencing Probe Design System , the final 54,188 loci were obtained. A total of 53,990 probes were designed based on the 54K loci, and all probes were 120 bp in length.To get a more comprehensive overview of the panel and SNP performance, 200 additional dairy goat samples were genotyped using the 54K liquid SNP chip panel, of which 15 individuals were randomly selected for 30× whole-genome resequencing. The sequencing process and analysis of the raw data for the liquid SNP chip panel and resequencing are the same as in y is the vector of the response variable (DEBVs in this study), m is the vector of SNP marker effects, and a is the vector of residual polygenic effects with a normal distribution G is the realized relationship matrix constructed with markers and e is the vector of residual errors with a normal distribution I being an identity matrix and X and W are incidence matrices of y related to m and a, respectively.To eliminate the contribution of information from relatives, the de-regressive estimated breeding value (DEBV) calculated according to the method proposed by Garrick et al.  was usedThe sequencing of 110 dairy goats generated 72,083.59M reads, representing an average of 32.45 × coverage per individual . After alignment, on average, 99.84% of reads were mapped to the ARS1 assembly. In total, 33,716,044 SNPs were eventually obtained for subsequent chip development. After a series of filtering steps in the material approach A, the fiTo validate the genotyping performance of the liquid SNP chip panel, 200 dairy goats were genotyped. The average number of reads obtained from targeted capture sequencing reached 14,927,888, of which the percentage of reads localized to the reference genome was 99.97% on average  Table S1The detection efficiency of the 54K target loci was judged according to the sequencing depth of the loci. The average detection rate was 98.82% for sequencing depths greater than 0 (dp0%), the average detection efficiency decreased to 98.41% for sequencing depths greater than 5 (dp5%), and the average detection efficiency was 97.22% for sequencing depths of 20 (dp20%) as the threshold  Table S2As a characteristic of GBTS, the flanking region of the target locus has a high sequencing depth and can capture variants beyond the designed target locus. In this experiment, a total of 100K high-quality SNPs were detected, which was twice as many as the target loci. Meanwhile, the results showed that the average sequencing depth of the target loci reached 91X, and the two flanking loci still had 60X and 26X sequencing depths at distances of 100 and 250 bp, respectively  Table S3To investigate the accuracy of the liquid SNP chip panel detection effect, we randomly selected 15 individuals for simultaneous liquid SNP chip panel detection and whole-genome resequencing at a sequencing depth of 30X. It was shown that 98.02% A and 97.To evaluate the effect of the liquid SNP chip panel, we conducted GWAS to detect genetic loci that affect coat color in dairy goats. In our sequenced population, the Toggenburg dairy goat had a signature brown pattern, while the Saanen dairy goat was pure white. We used the 54K SNP and 100K SNP datasets for detection . GWAS foUsing reliable and low-cost genotyping strategies is essential in animal breeding, including dairy goats. In this study, using resequencing data from Chinese and European dairy goat breeds, we developed a dairy goat liquid SNP chip panel. The main purpose is to set the scope using this chip, mainly for Chinese dairy goat breeding. In the process of SNP selection, trait relatedness was considered an important indicator. The specific traits covered by the liquid SNP chip panel included milk yield, fat yield, protein yield, fore udder, feet and legs, general appearance, rear udder, suspensory ligament, and teats. In addition, SNPs associated with melatonin synthesis capacity were also included. This is because in our previous work, melatonin synthesis capacity was found to play a vital role in milk-producing livestock and can be a critical selection indicator ,28,29.This 54K genome-wide liquid SNP chip panel is based on GBTS. After testing and verification, the designed capture efficiency of 54K SNPs can reach 98.41%, and the consistency of capture loci and resequencing can also reach 98.02%. Among other breeding chips based on targeted capture sequencing, the capture efficiency of the current chip can be considered highly satisfactory ,31,32. IBy performing GWAS analysis on dairy goat hair color using the captured 54K SNP dataset and 100K SNP dataset, respectively, TYRP1, a gene associated with goat and other animals’ coat color ,36,37,38Of course, the liquid SNP chip panel-based GBTS had some shortcomings during the analysis. By comparing liquid SNP chip panel detection to resequencing of genotypically inconsistent SNP, it was found that most errors came from GBTS, where variants were not detected, or heterozygous alleles were considered to be homozygote alleles. The reason for this phenomenon is most likely related to the allele drop-out (ADO) that occurs during the PCR process. This occurs when only one of the two alleles present in a sample is amplified to a detectable level . This phCompared with SNP chips based on the magnetic bead principle, liquid SNP chip panels based on GBTS have obvious advantages, including low cost and a higher number of detection SNPs than design SNPs. In addition, the quantity of liquid SNP chip panels can be produced according to the required testing quantity, which further reduces costs. However, due to the longer time for data analysis and processing with liquid SNP chip panels, this is considered a disadvantage compared with the bead SNP chip. This disadvantage may have its benefit, because as the detection population expands, the SNPs that are not originally detected as polymorphic may be re-identified, resulting in a richer set of variant SNP datasets.Overall, a low-cost, high-accuracy dairy goat whole-genome liquid chip based on resequencing data was developed. This chip can be widely used in GWAS and GS for large-scale genotyping, offering a new tool for the molecular breeding and genetic improvement of dairy goats.