Germination of soybean seed is the imminent vital process after sowing. The status of plumular axis and radicle determine whether soybean seed can emerge normally. Epicotyl, an organ between cotyledons and first functional leaves, is essential for soybean seed germination, seedling growth and early morphogenesis. Epicotyl length (EL) is a quantitative trait controlled by multiple genes/QTLs. Here, the present study analyzes the phenotypic diversity and genetic basis of EL using 951 soybean improved cultivars and landraces from Asia, America, Europe and Africa. 3VmrMLM was used to analyze the associations between EL in 2016 and 2020 and 1,639,846 SNPs for the identification of QTNs and QTN-by-environment interactions (QEIs)”.A total of 180 QTNs and QEIs associated with EL were detected. Among them, 74 QTNs (ELS_Q) and 16 QEIs (ELS_QE) were identified to be associated with ELS (epicotyl length of single plant emergence), and 60 QTNs (ELT_Q) and 30 QEIs (ELT_QE) were identified to be associated with ELT (epicotyl length of three seedlings). Based on transcript abundance analysis, GO (Gene Ontology) enrichment and haplotype analysis, ten candidate genes were predicted within nine genic SNPs located in introns, upstream or downstream, which were supposed to be directly or indirectly involved in the process of seed germination and seedling development., Of 10 candidate genes, two of them (Glyma.04G122400 and Glyma.18G183600) could possibly affect epicotyl length elongation. These results indicate the genetic basis of EL and provides a valuable basis for specific functional studies of epicotyl traits. EL exhit stages . EL alsot stages . As a tyt stages . EL was t stages . Severalt stages . It requt stages .Molecular marker could effectively improve traditional selection efficiency by increasing the allele’s frequency of desirable quantitative trait loci (QTLs). Presently, linkage analysis and association analysis, were two major strategies utilized to identify QTLs of important traits in crops . SegregaSince the establishment of mixed linear model (MLM) method in genome-wide association studies (GWAS) , these mArabidopsis, we supposed a similar model, It has been proved in 1998 that the transcription of Arabidopsis CYP90A1 was negatively controlled by exogenous brassinolide  mutant of pea shows many of the characteristics normally associated with light-grown seedlings when grown in complete darkness, such as expanded leaves, a short epicotyl and partially developed chloroplast . Chory esinolide .To address above mentioned issues, 951 landraces and cultivarsselected from Chinese primary core collection in the Chinese National Soybean GeneBank (CNSGB), were phenotyped for EL in 2016 and 2020, and genotyped by 1,639,846 SNPs in order to identify QTNs, QEIs, and their candidate genes for EL in soybean.To construct a diversity panel of EL, a total of 951 landraces was selected from more than 20,000 samples, which delegated much of the representatives of diversity of the collection at the Chinese National Soybean GeneBank (CNSGB). These tested materials were planted with the single row plots (3-m long and 0.35-m between rows), which was performed with the completely randomized design and three replications in Sanya, Hainan China in 2016 and 2020.A total of 3 randomly selected plants from each plot were phenotyped for EL by measuring the distance between the cotilenodary knot and the unifoliate leaves pair knot using vernier caliper.® DNA HT sample preparation Kit (Illumina USA), and index codes were added to attribute sequences to each accession according to the method described by (via Short Oligonucleotide Alignment Program 2 (SOAP2) software. The SNPs were calling based on MAF ≥ 0.05. The genotype was regarded as heterozygous if the depth of minor allele/the total depth of the sample was more than 1/3.The genomic DNA of each tested samples were isolated from fresh leaves of a single plant, and then resequenced. Sequencing libraries were constructed based on TruseqNanoribed by . The IllThe population structure of GWAS panel were evaluated based on principle component analysis (PCA) programs of Software package GAPIT . LD was https://phytozome-next.jgi.doe.gov/info/Gmax_Wm82_a2_v1).A total of 1,639,846 SNPs from 951 landraces samples were utilized to detect association signals of EL in soybean. Imputed genotype of total sample panel was first transformed in to *.fam, *.bed, and *.bim format, ELS and ELT in two different environments were adopted as phenotype, evolutionary population structure encoded as B (Landrace) and C (Improved cultivar), and kinship were employed as covariates for multi-environment joint analysis with significant level of 0.01 using IIIVmrMLM software of ST) was calculated by published genome sequences data of 2214 soybeans  and the Kyoto Encyclopedia of gene and genomes (KEGG).Then SNP variations in the coding region of candidate genes were analyzed to screen candidate genes with mutation type of nonsynonymous, stoploss, stopgain, or alternative splicing. To further screen candidate genes, fixation index (Fsoybeans  using vcion gene . Subsequion gene . FunctioP<0.01)  . HaplotyP<0.01) . GeograpGlobally, the improved cultivars selected for the experiment mainly comes from America and Asia, with few from Europe and Africa. Landraces were all obtained from Asia Figure 1The EL of 951 landraces in Sanya, Hainan China in 2016 and 2020, were evaluated, respectively. The skewness and kurtosis of EL the three environments were less than ±1, which exhibited a continuous variation and the near normal distribution Table S3Based with the frequency > 0.05 as the minor allele and the missing data less than 0.03, a total of 1,639,846 single nucleotide polymorphisms (SNPs) were unevenly distributedon 20 chromosomes of soybean genome. with a density of578.8 bp per SNP on average, and varied from 337.3bp~1334.4bp per SNP. In detail, there were 168,498 SNPs on Chr1 with the highest density (337.3bp/SNP), 31,650 SNPs on Chr5 with lowest density (1334.4bp/SNP). Figure 3QTN (Q) and QTN-by-environment interaction (QEI) detection method in the 3VmrMLM was used to analyze SNP-trait associations in two EL two-environment datasets, ELS (2016 and 2020) and ELT (2016 and 2020). A total of 180 QTNs and QEIs associated with epicotyl length were detected. Among them, 74 QTNs (ELS_Q) and 16 QEIs (ELS_QE) were identified to be associated with ELS, and 60 QTNs (ELT_Q) and 30 QEIs (ELT_QE) were identified to be associated with ELT. Arabidopsis annotation information. site contribution rate, Transcription abundance of candidate genes in epicotyl of two representative soybean germplasms including cultivar Williams 82 with a long epicotyl of 3.93 cm and cultivar Jack with a short epicotyl of 2.13 cm were analyzed using publicly available soybean transcriptome integration dataset . GO enrichment analysis showed all genes were assigned to one of three GO categories: biological process (BP), molecular function (MF), and Cellular component (CC)  Figure 8Further, haplotype analysis was performed for 103 potentially candidate genes screened by the above analysis. epicotylP=0.0039) and ELT (P=0.039)showed extremely significant differences (P<0.01). The Hap1 and Hap3 of Glyma.18G183600, Glyma.18G185300, exhibited extremely significant differences (P<0.01), while the Hap1 and Hap3 of Glyma.01G050100, Glyma.04G122400, Glyma.18G183600 in different years of ELS had a very significant difference in 2016 (P<0.01), but there was no significant difference in 2020. The candidate gene Glyma.18G185300 showed a very significant difference in the two years of EL (P<0.01), and the ELT revealed a significant difference in 2016(2016_ELT P=0.02) and showed a very significant difference in 2020(2020_ELT P=0.0031)  Figure 9Glyma.03G142200 , Glyma.04G122400 (DCD domain protein), Glyma.04G145000 , Glyma.10G0319000 (indole-3-acetic acid 7), Glyma.10G056000 (SAUR-like auxin-responsive protein family), Glyma.13G270800 (ubiquitin-conjugating enzyme 35), Glyma.17G005900 , Glyma.17G18500 (NAC domain containing protein 83), Glyma.18G183600 (far-red elongated hypocotyl 1), and Glyma.18G255300 (thioredoxin H-type 5). These results suggest that soybean epicotyl length may be regulated by multiple signaling pathways  and cultivar “Erimo167” (ordinary ectomorph) with EL associated SNP) . There aGlyma.03G142200 is a Ribosomal protein S10p/S20e family protein, proteins involved in photosynthesis  domain protein, thought to be involved in the hypersensitive response and programmed  transcription factors are heterotrimeric complexes composed of NF-YA and histone-fold domain (HFD) containing NF-YB/NF-YC , NF-Y subunits are emerging as transcriptional regulators with essential roles in diverse plant processes . playing key roles in development and in response to adverse environmental conditions AtNF-YB6 (L1L) and AtNF-YB9 (LEC1) are involved in embryo development in seeds  transcription factors constitute one of the largest transcription factor families in plant genomes b. Roles Glyma.18g183600 far-red elongated hypocotyl 1, Phytochrome A (phyA) is the primary photoreceptor for mediating the far-red high irradiance response in Arabidopsis thaliana.FAR-RED ELONGATED HYPOCOTYL1 (FHY1) and its homolog FHY1-LIKE (FHL) define two positive regulators in the phyA signaling pathway . Most abGlyma.18G185300 one helix protein, The cellular functions of two Arabidopsis  one-helix proteins, OHP1 and OHP2 , OHP1 and OHP2 play an essential role in chloroplast development as well as in vegetative growth, The photosynthetic capacity of ohp1-1 and ohp1-2 mutants also was decreased significantly .The protWe speculate that traits during soybean domestication are gradually selected, and the priority traits are yield-related traits, such as seed size, oil content, and protein content . The epiArabidopsis. Cellular function of Glyma.18G185300 with sequence similarity to light-harvesting chlorophyll a/b binding protein, Glyma.03G142200 is a protein involved in photosynthesis, and the analysis results show that they are all involved in the growth and development of soybean epicotyl. This is consistent with the results that soybean epicotyl length is greatly affected by different environments. These results can be reflected from the haplotype analysis of ten candidate genes, which can be reflected in the significant differences in different environments  gene, which is the main photoreceptor mediating the far-red high-irradiation response in ronments Figure 9All whole genome sequencing data in this study have been deposited in the NCBI Sequence Read Archive under ac-cession number PRJNA681974.HH, and ML conceived the study and contributed to population development. YC, HW, JW, and BG contributed to phenotypic evaluation. HG, and HR analyzed the data. MY, and HG contributed to genotyping. LQ, and YH contributed to experimental design and writing the paper. All authors contributed to the article and approved the submitted version.This study was financially supported by the National Natural Science Foundation of China (32172005), Agricultural Science and Technology Innovation Program (ASTIP) of Chinese Academy of Agricultural Sciences.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A pangenome aims to capture the complete genetic diversity within a species and reduce bias in genetic analysis inherent in using a single reference genome. However, the current linear format of most plant pangenomes limits the presentation of position information for novel sequences. Graph pangenomes have been developed to overcome this limitation. However, bioinformatics analysis tools for graph format genomes are lacking.qPH8-1) associated with plant height on chromosome 8 undetected by the SNP-based genome-wide association study (GWAS).To overcome this problem, we develop a novel strategy for pangenome construction and a downstream pangenome analysis pipeline (PSVCP) that captures genetic variants’ position information while maintaining a linearized layout. Using PSVCP, we construct a high-quality rice pangenome using 12 representative rice genomes and analyze an international rice panel with 413 diverse accessions using the pangenome as the reference. We show that PSVCP successfully identifies causal structural variations for rice grain weight and plant height. Our results provide insights into rice population structure and genomic diversity. We characterize a new locus (Our results demonstrate that the pangenome constructed by our pipeline combined with a presence and absence variation-based GWAS can provide additional power for genomic and genetic analysis. The pangenome constructed in this study and the associated genome sequence and genetic variants data provide valuable genomic resources for rice genomics research and improvement in future.The online version contains supplementary material available at 10.1186/s13059-023-02861-9. Oryza sativa L.) is one of the most important staple crops, feeding nearly half of the world’s population. As the population expands to 10 billion people [Rice , translocations, and inversions, that can recover the genomic position of sequence variations. We then applied this pipeline to construct a rice pangenome using 12 diverse accessions representing major subpopulations of Asian rice, and identified SVs from an international rice mini core panel consisting of 413 accessions . Our resIn this study, we developed a pangenome construction and SV genotype calling pipeline (PSVCP)  accessions selected from 33 representative accessions based on their subpopulation [Oryza glaberrima)    of PAVs within genic and intergenic regions. A higher MAF was observed within genic regions , with fewer genes in Indica  and Aus . The difference in average gene number hides a complex pattern of increases and decreases in the frequency of specific genes  genes are absent from the Nipponbare reference. In contrast, of the 978 genes exhibiting higher frequency in Indica, 513 (52.5%) genes are absent from the Nipponbare reference, with 482 genes derived from the Indica rice genomes. This reflects differences in gene content between sub-species at the population level.We observed a significant difference in average gene numbers between Aus Fig. a. JaponiJaponica-Indica: 0.476 ±0.207, Japonica-Aus: 0.525 ±0.205, and Indica-Aus: 0.304 ±0.158) is higher than that calculated using PAVs   using the SNPs  and plant height (PH), using SNPs genotyped from the Nipponbare reference genome and PAVs genotyped across the pangenome.GW5, a known functional gene controlling rice grain weight [GW5, which are two PAVs (950 bp and 1212 bp) in this region [GW5  of 7.47×10−5 and 6.02×10−9 respectively , while the 1212 bp deletion disrupts the coding region of qSW5, leading to TGW phenotype variations [For TGW, the SNP-GWAS identified 354 significant associations  [sd1 gene , further validating the accuracy and efficiency of GWAS using PAVs genotyped from our pangenome.SNP-GWAS identified 37 SNPs associated with PH in rice  . SimilarqPH8-1) controlling PH in rice on chromosome 8 by PAV-GWAS  that was not identified by SNP-GWAS , located 1 kb upstream of LOC_Os08g07400  than those with the sequence. Expression analysis showed that the presence and absence of this 13-kb sequence significantly correlated with the expression level of LOC_Os08g07400, located 1 kb downstream from the 13-kb sequence  and aimed to overcome the bottleneck of other linear pangenome strategies. A major challenge for current linear pangenome construction strategies is the ability to accurately embed the newly identified PAV sequences into the linear reference genome. In several recent pangenome studies, including the 3010 rice pangenome , the tomngenomes , novel sngenomes .To address this issue, PSVCP was designed to place novel sequences into the correct genome position, providing an accurate genomic map for functional genomic studies. The accuracy of the placement of the novel sequences by PSVCP was confirmed in the present study by successfully identifying the existence of the novel sequences and the sequence surrounding them by PCR amplification followed by sequencing. The advantage of our strategy was further demonstrated by GWAS analysis using PAVs genotyped in our pangenome. Our PAV-GWAS successfully captured the casual structural variations of TGW and PH, which are not available in the Nipponbare reference genome or are difficult for functional characterization without the sequence information surrounding them. Furthermore, the pangenome constructed using PSVCP benefits from its linear format, which can directly integrate with currently available bioinformatics pipelines such as GATK  for genoPSVCP is robust in placing novel PAV sequences into the linear pangenome; it may be limited to display more complex SVs such as translocations and inversions, which is a challenge in current pangenomics studies, even for the advanced graph-based pangenome. In addition, due to its read length, short-read sequencing data may have lower sensitivity for SV detection compared with long-read sequencing data.Most population structure studies are currently performed using SNPs ; howeverJaponica and Indica than in SNP-based analysis. Higher frequencies of novel sequence insertions and more haplotype diversity were discovered in this region using PAVs than SNPs, which may cause by transposon movement. This suggests that SNPs may underestimate genetic differentiation in some highly diverse genomic regions containing PAVs. These results demonstrate that PAV genotypes in our pangenome can provide additional power and information in analyzing genomic divergence and evolution.The rice pangenome constructed in this study contains novel genome sequences and annotated genes from comprehensive comparative genomic analysis. Our results indicated that compared to SNPs, PAVs provided novel insights into rice evolution when used to identify genetic differentiation regions using Fst and phylogenetic inferences. In most cases, we found that SNP- and PAV-based population structure analyses shared similar patterns of Fst value changes along the chromosomes. However, in some genomic regions, PAV-based analysis has significant different Fst values compared with SNP-based results, providing higher resolution to differentiate the population structure in these regions. As an example, the present study identified a 1.6-Mb interval in chromosome 8 displaying much higher Fst values in PAV-based analysis between Indica accessions clustered with the Aus subpopulation, which is consistent with the TE-insertion phylogeny analysis using the 3000 rice accessions [Aus and Indica contain more common TE-insertions, since the divergence of the Indica/Aus lineages occurred more recently  than the divergence of Indica/Japonica  [Indica and Aus subpopulations based on the PAV data, consistent with previous studies showing that Indica accessions contain Aus introgressions [Indica and Aus show closer genetic affinity [Arabidopsis thaliana [Amborella trichopoda [Setaria viridis [Brassica oleracea [Detailed analysis of the PAVs identified in the present study indicated that the majority of the newly inserted PAV sequences are transposable elements. Compared with SNP-based phylogeny, PAV-based phylogeny shows that some cessions . This rears ago) . Additioressions  and Indiaffinity . The topthaliana , Amborelichopoda , green m viridis , and Braoleracea , showingBrassica napus showed that a PAV-based pangenome-wide association study could directly pinpoint the causal SVs for silique length, seed weight, and flowering time [Most GWAS analysis uses SNPs identified from a single reference genome as markers to detect marker-trait associations. However, recent studies suggest that SVs, including PAVs, contribute to and explain more phenotypic variation than SNPs for many traits . Phenotying time .GW5 for TGW and sd1 for PH. Surprisingly, the peak PAV-GWAS signals are directly and accurately located in the functional PAVs causing the phenotypic variations. In contrast, the most significant signal for SNP-GWAS can only identify the approximate location of the causal variants.In this study, PAVs were genotyped from the pangenome constructed by the PSVCP pipeline and used for GWAS analysis of TGW and PH in an international rice panel. Both PAV- and SNP-GWAS methods can identify previous characterized QTLs, such as LOC_Os08g07400, whose expression was significantly associated with the presence and absence of the 13-kb sequence. These results suggest that retrotransposon movement in this locus may lead to phenotypic variation by affecting the promoter region of LOC_Os08g07400.More importantly, PAV-GWAS can identify new candidate causal variations that SNP-GWAS cannot discover. In the present study, a 13-kb PAV containing two retrotransposons was found to be strongly associated with plant height using PAV-GWAS, and this variant was not identified using SNP-GWAS. Transposon movements are important sources of phenotypic variations. A GWAS study in tomatoes based on TE-insertion polymorphisms revealed that transposon movement was associated with leaf morphology and fruit color . FurtherTo unravel why SNP-GWAS cannot identify this locus, we investigated the candidate variant region at the population level. Our results showed that no SNPs were found in the 13 Kb PAV sequence, while SNPs located near the 13-kb PAV sequence show a poor correlation with the PAV, with no association between SNPs and the plant height phenotype. TEs having a low LD with nearby SNPs were observed in other genomic studies in rice and tomato . Akakpo A new strategy and pipeline to construct a linear pangenome by whole genome comparison were developed in the present study. This strategy supported the construction of a linear pangenome that can solve the problems of preserving the location information of SVs and facilitates downstream pangenomic analysis. A rice pangenome was constructed using 12 complete genomes spanning all rice subpopulations. Downstream population analysis demonstrated that using the pangenome provided insights into the rice population structure and evolution, which are not available by analysis using SNPs from a single reference. GWAS analysis using PAVs genotyped from the pangenome revealed a significant improvement in power, especially in characterizing causal PAVs. The new pangenome construction pipeline and the rice pangenome provide a novel framework for future pangenomic studies in rice and other plants.http://hannonlab.cshl.edu/fastx_toolkit) was used to remove adaptor and low-quality reads. All raw sequence data have been deposited in the NCBI sequence read archive (BioProject accession PRJNA820969). Plant height and thousand grain weight were assessed at the mature growth stage with three biological replicates.The RPD panel  containshttps://ricerc.sicau.edu.cn/) [Data for twelve assembled genomes were downloaded from the Rice Resource Center (edu.cn/) , represeedu.cn/) , with paedu.cn/)  to detechttps://github.com/broadinstitute/picard) [Paired-end short-read sequencing data for each accession was aligned to the pangenome using BWA-MEM with default settings . Mapping/picard)  and filt/picard) , retaini/picard)  with thePAVs were called based on the mapping coverage of sequencing reads to the pangenome for each accession. The breakpoint positions of each accession were recorded, and a complete set of all breakpoint positions (the breakpoints’ union) were generated. Every segment with two adjacent breakpoints from the breakpoints’ union is defined as a new PAV region, named by the adjacent left breakpoint position. Each accession was re-called using the integrated PAV genotypes. All accessions’ new PAV genotypes were combined by row, generating a PAV genotype matrix with accession names as row labels and the adjacent left breakpoint position as column labels. The PAV genotype matrix was further filtered by MAF>0.05.P-values were adjusted for multiple comparisons using the Bonferroni method as implemented in p.adjust from R v3.5.0. Genes with an adjusted p-value<0.001 and difference frequency between groups ≥10% [A gene was considered missing when the horizontal coverage across the CDS is less than 95% and the vertical coverage less than two, as used in the 3K-RG study  using Moups ≥10%  were defDuring pangenome construction, the PAV positions were recorded and updated in an annotation file in “gff” format. We used seqkit v1.2  with theShort-read sequencing data were aligned to the Nipponbare reference genome using BWA-MEM . The res10(p) <5. Manhattan plots were produced using the CMplot package (https://github.com/YinLiLin/R-CMplot) in R v3.5.0.To construct the PAV genotype map for GWAS, we used “A” to represent “Absent” and “C” to represent “Present” in the HapMap genotype file. PAVs and SNPs were selected for GWAS analysis based on the criteria of missing data <15% and MAF >0.05. GWAS was performed using a mixed linear model (MLM) with kinship matrix and principal component analysis in GAPIT version 2 . The sigE-values <1 × 10−5). Gene ontology (GO) analysis was conducted using topGO [Functional annotation was performed using Blast2GO v2.5 . Genes wng topGO , and FisFiltered PAV and SNP data were used for the population structure study. SNP-based and PAV-based phylogenetic trees of 413 rice accessions were constructed by IQ-tree  using a −5).A de novo transposable element (TE) library was generated for the rice pangenome using EDTA v1 (--sensitive 1 --anno 1 --species Rice) . Using BqPH8-1, primers were designed (Additional file For validation of the 13-kb insertion in Additional file 1:Supplemental figures 1-28.Additional file 2:Supplemental tables 1-25.Additional file 3:Supplemental notes.Additional file 4. Uncropped images for the blots in Figure S3 and Figure S28.Additional file 5. Review history.


Capra hircus genome using assembly ARS1. We have identified a number of highly significant SNPs and respective candidate genes associated with growth and body conformation traits. The functional aspects of these candidate genes suggested their potential role in body growth. Moreover, pleiotropic effects were observed for some SNPs for body weight and conformation traits. The results of current study contributed to a better understanding of genes influencing growth and body conformation traits in goat.Pakistan is third largest country in term of goat population with distinct characteristics of breeds and estimated population of 78.2 million. Punjab province has 37% of country’s total population with seven important documented goat breeds namely Beetal, Daira Din Pannah, Nachi, Barbari, Teddi, Pahari and Pothwari. There is paucity of literature on GWAS for economically important traits i.e., body weight and morphometric measurements. Therefore, we performed GWAS using 50 K SNP Chip for growth in term of age adjusted body weight and morphometric measurements in order to identify genomic regions influencing these traits among Punjab goat breeds. Blood samples were collected from 879 unrelated animals of seven goat breeds along with data for body weight and morphometric measurements including body length, body height, pubic bone length, heart girth and chest length. Genomic DNA was extracted and genotyped using 50 K SNP bead chip. Association of genotypic data with the phenotypic data was performed using Plink 1.9 software. Linear mixed model was used for the association study. Genes were annotated from  Single nucleotide polymorphisms (SNPs) are DNA sequence variations that occur when a single nucleotide such as adenine (A), thymine (T), cytosine (C) or guanine (G) in the genome sequence is altered. Two main strategies for SNPs genotyping are traditional and high throughput methods. Moreover, SNPs genotyping technologies have become increasingly important in animal breeding programs. Thus, genomic selection using SNPs is a tool for choosing the best breeding animals. Moreover, high density maps using SNPs can provide valuable genetic tools for genetic variation studies of quantitative traits2. Eventually technology has been developed which in turn provides valuable tools for SNP discovery and genotyping in non-model organisms3.Molecular markers, which reveal polymorphisms at the DNA level are now a days key players in animal genetics. However, the choice of molecular markers according to the purpose is crucial viz. which depends upon various molecular biology techniques and their implications4 and release of Caprine SNP50 chip5 developed by the International Goat Genome Consortium (IGGC). It has also opened a new window of opportunity for genome wide association studies (GWAS). GWAS is widely used to identify genetic variations and candidate regions that are associated with quantitative traits8. The developments of Illumina Goat 50 K SNP Bead Chip has also provided the opportunity to explore genomic regions that might be under the influence of selection9. Moreover studies have been conducted in goats using SNP arrays i.e. native Italian and Moroccan breeds10. SNP arrays have also been used for exploring genomic regions, selection signatures and selection history in other worldwide renowned breeds i.e. Alpine, Boer, Cashmere, and Saanen11.The status of goat genetics has entirely changed since 2010 after the sequencing of goat genome12. Body weight is also an important trait for successful farming of small ruminants because of its economic value. Therefore, identification of genomic regions for understanding variations in body weight and other morphological traits is quite valuable for selection purpose13.Morphological traits are of importance for breed identification, classification and are also positively correlated with body weight. The variations in these traits might be due to selection of breeders for these important traits and environmental effects in different production systems and geographical regions. Consequently the selection process led to goat breeds specialized for milk, meat, fiber or dual purposes14. Punjab is the largest province of Pakistan with variety of goat breeds. According to the Livestock Census; Punjab has the highest goat population which is 37% followed by Sindh (23%), Balochistan (22%) and KPK (18%) respectively15. Beetal, Daira Din Pannah, Nachi (Bikaneri), Barbari, Teddi, Pahari (Kajli) and Pothwari are the documented goat breeds of the province16. Goat rearing is commonly meant for meat production as primary objective while milk is also consumed domestically and hair of goats are used for making rugs by poor families. In Pakistan, only fresh meat is usually sold. Hundred thousands of goats are sacrificed on Eid-ul-Azha where Beetal and Kamori breeds are preferred due to their larger size while Teddi which is one of the smallest goat breed is also common because of its highly prolific nature. Moreover, goat rearing is also encouraged by religious customs in certain areas of the country i.e. in Kafiristan goat sacrifice is a custom to celebrate deaths, therefore large flocks of goat are common in these areas17.Pakistan has 78.2 million goats making it third largest goat producing country in the world. Goats play a vital role in uplifting the economy of poor farmers in Pakistan. Milk and meat production obtained from goats in Pakistan is 965 and 748 thousand tonnes respectively18. Current study was aimed to fulfill the gap by finding significant genomic regions or SNPs and relevant genes influencing body weight and morphometric measurements among goat breeds of Punjab province.Despite of having rich diversity of goat breeds in the country; no GWAS has been carried out in Pakistani goats regarding growth and conformation traits. However, Punjab goat breeds have been recently documented using 50 K SNP chipGenotyping of DNA samples was performed using 50 K SNP Bead Chip which coverd 53,347 SNPs distributed across the whole Caprine genome. After quality control filters; 36,861 SNPs were left while 11,181 SNPs were removed due to Hardy Weinberg Equilibrium, 3963 removed due to minor allele threshold and 1342 removed due to missing genotypes. Numbers of SNPs present before and after quality controls with average distance in kb (1 kb = 1000 base pairs) at each chromosome are given in Table P value was determined using quantile–quantile (QQ) plots  gene while snp2733-scaffold1079-1194419 was located within Rho-related BTB domain-containing protein 3 (RHOBTB3) gene.Likewise, GWAS analysis for body height trait generated two additional significantly affecting SNPs other than two aforementioned SNPs on chromosome 8 and 16 viz. snp28833-scaffold310-5443065 located on chromosome 5 and snp2733-scaffold1079-1194419 located on chromosome 7 given in Table P values.Finally, there were five SNPs identified as result of GWAS analysis for pubic bone out of which two on chromosome 8 and 16 as detailed above and mentioned in Table Both chromosome 6 and 8 are harboring two SNPs in the same region with 786 and 914 genes in a window of 100 MB respectively given in Table Among 786 genes in the window of 100 MB on chromosome 6; some of these play important role in growth and development i.e. Apelin Receptor Early Endogenous Ligand (APELA) is required for heart development, PR Domain Zinc Finger Protein 5 (PRDM5) is involved in cell differentiation, LIN-28 family RNA-binding protein (LIN28) is responsible for developmental timing and self-renewal in embryonic stem cells, Dickkopf WNT Signaling Pathway Inhibitor 2 (DKK2) is having a role in embryonic development, TBC1 Domain Containing Kinase (TBCK), Epidermal Growth Factor (FGF) and Ankyrin 2 (ANK2) are engaged in the regulation of cell proliferation and growth. Similarly, there were 914 genes in the window of 100 MB on chromosome 8 while genes are involved in embryogenesis i.e. GATA Binding Protein 4 (GATA4), cellular processes such as cell growth and differentiation (Protein Tyrosine Phosphatase Receptor Type D), brain development (Nuclear Factor I B), development of pancreatic beta cells, thyroid, eye, liver and kidney (GLIS Family Zinc Finger 3) and male sex determination and differentiation by controlling testis development and male germ cell proliferation (Doublesex and Mab-3 Related Transcription Factor 1).Polygenic risk score (PRS) is given in Supplementary file that ranged between − 0.340 to 0.136, − 1.240 to 0.441, − 0.570 to 0.212, − 1.023 to 0.428, − 1.502 to 0.444, − 1.068 to 0.359 for pubic bone length, heart girth, chest length, body length, body weight and body height respectively.19. Body weight and morphometric measurements are economically important traits for genetic improvement of meat type goats20. Keeping in view, the rapidly increasing demand of goat meat and importance of growth trait current study was performed using Illumina 50 K SNP Bead Chip to identify SNPs and genetic variations influencing growth in term of age adjusted body weights and body conformation traits.Demand of goat meat is increasing due to ever increasing human population with improved living and national standards21.GWAS is widely applicable in identification of casual genes using single nucleotide polymorphism. GWAS is an ideal technique for discovery of major genes especially for complex traits and a novel way to study the genetic mechanism of these traits. Moreover, identification of genomic regions and their genes regarding economically important traits in livestock paved the way for selection of genetic markers and candidate genes which influence the trait of interest. Marker assisted selection is faster, more reliable and economical as compared to traditional selection as it enables selection of younger animals with desirable traits on the basis of highly significant genomic regions and their candidate genes. Such an approach may accelerate the breeding progress and enhance the economic prospects for selection of best breeding animals22. We identified two highly significant SNPs influencing body weight. Moreover, 2, 3, 4, 4 and 5 highly significant associations were observed for heart girth, height, body length, chest length and pubic bone length respectively. Almost more than half of these SNPs were found in the coding regions of the genes. Generally different SNPs affect different traits but this study explored two of the nine SNPs affecting more than one trait as previously stated byWu et al.23 in GWAS of body conformation traits in Chinese Holstein cattle population. We observed that there were two SNPs (45231-scaffold617-879437 on chromosome 16 and snp24590-scaffold25-1223464 on chromosome 8) which were significantly associated with all the six traits under investigation in the current study. Moreover, candidate genes identified for growth were also influenced body conformation traits and vice versa which might be due to genetic correlation among studied traits and also because of a pleiotropic effect of these SNPs and their respective genes as previously suggested in GWAS of body conformation traits in the Chinese Holstein cattle population23.It is quite evident from the previous studies that body weight appears to be the function of linear body measurementsIn the current study, it was observed that more than half of highly significant SNPs were located in the coding regions of the genes influencing growth in term of age adjusted body weight and morphometric measurements. Moreover, current study identified a number of the most promising candidate genes which might play a critical role in growth and body conformation traits on the basis of highly significant SNPs. The genes identified in the current study may broadly be categorized as those involved in 1. cell growth and development; 2. cellular transportation, transcription and translation and 3. fertility.26. RhoBTB3 is proposed to have a role in the tissue development during embryonic life27 while THEGL is involved in all stages of male gonads development such as sperm maturation, its fertilization and subsequent embryonic development. Moreover it has also been reported to have a high level of expression from adolescence to adulthood suggesting key role in the organs development and onset of puberty28. TLE4 supresses Pax7-mediated Myf5 transcriptional activation through inducing Myf5 enhancer to continue latency. Loss of TLE4 function results into Myf5 upregulation thus, showing its role in growth through transcription suppression29. TLE4 is also a critical regulator in haematopoiesis30 and bone development31.The candidate genes involved in cell growth and development mechanism are EPH Receptor A5 (EPHA5), Rho-related BTB domain-containing protein 3 (RhoBTB3), Spermatid protein-like (THEGL) and Transducin-like enhancer protein 4 (TLE4). EPH Receptor A5 (EPHA5) was a candidate gene for body length in the present study. EPHA5 along with EFNA5 mediates communication between pancreatic islet cells to regulate glucose-stimulated insulin secretion. Insulin plays a key role in utilizing sugar in the body which is needed for proper growth, metabolism and tissue repair in the body secretionsThe candidate genes involved in cellular transportation, transcription and translation mechanism were RhoBTB3, WASH Complex Subunit 4(WASHC4), Plasma membrane calcium-transporting ATPase 4-like (ATP2B4) and Translation initiation factor IF-2-like (IF2) as appeared in current GWAS analysis.32. WASHC4 is a gene encoding a component of WASH complex that is responsible for transport of endosomes within the cell33; moreover, a mutation of this gene resulted in remarkable developmental disorders and skeletal muscles dysmorphism34. ATP2B4 is an enzyme that is calcium/calmodulin-regulated and magnesium-dependent. It catalyzes the hydrolysis of ATP along with the calcium transportation out of cell as well as has a role in sperm motility35. Translation initiation factor 2 (IF2) is required for GTP/GDP-binding protein whose principal role is to interact with initiator fMet-tRNA and to position it correctly in the ribosomal P site, thus enhancing the rate and fidelity of translation initiation36.RhoBTB3 is involved in cell cycle thus has a role in mitosis and eventually also in development as mentioned in the last paragraphThe candidate genes involved in fertility mechanism are Spermatid protein-like (THEGL), Plasma membrane calcium-transporting ATPase 4-like whose role is discussed previously.The functional aspect of aforementioned genes have been predominantly studied in human beings. However, it is suggested that these genes or their variants most probably have had more or less similar functions in other mammals i.e., goats. Moreover, in the current GWAS analysis number of highly significant SNPs were identified representing eight the most promising candidate genes which might play a key role in the growth in term of age adjusted body weight and morphometric measurements in goat. Moreover, these candidate genes seem to play a key role in growth, metabolism, cellular transportation, and fertility. This study also suggested pleiotropic effects for body weight and conformation traits of couple of SNPs and their candidate genes.This study involved a questionnaire-based survey of farmers as well as blood sampling and phenotypic data recording from their animals. Participants provided their verbal informed consent for animal blood sampling as well as for the related survey questions. Collection of blood samples and all methods were performed in accordance with the relevant guidelines and regulations for animals by veterinarians adhering to the regulations and guidelines on animal husbandry and welfare as per international norms as per approved experimental protocols by the institutional Ethics Committee of PMAS-Arid Agriculture University, Rawalpindi, Pakistan. Moreover questioning of farmers were performed in accordance with the relevant guidelines and regulations for human subjects as per approved experimental protocols by institutional Ethics Committee of PMAS-Arid Agriculture University, Rawalpindi, Pakistan.N = 631), Teddi (N = 114), Daira Din Panah (N = 21), Nachi (N = 33), Barbari (N = 23), Pahari/Kajli (N = 41) and Pothwari (N = 16). Data were collected from various districts of Punjab i.e., Fateh Jhang, Bakhar, Liyyah, D.G. Khan, Rajanpur, Faisalabad and Jhang as mentioned in Table 37. The pictures of seven documented goat breeds of Punjab and five strains of Beetal are given in Figs. Blood samples were collected by jugular vein puncture in EDTA (Ethylene Diamine Tetra Acetate) containing vacutainers. Total of 879 samples were obtained from all the Punjab goat breeds of Pakistan namely Beetal breed including all five strain under study  as described previously39. Briefly, 1 mL of blood was taken and 2 mL of lysis buffer was added. It was then vortexed and mixed well and samples were centrifuged for 5 min at 1500 g (2690 RPM). Previous step was repeated with 3 mL of Lysis buffer. Supernatant was discarded and 300 uL of DNA extraction buffer was added followed by addition of 5 µL of 10 mg/mL Proteinase K and 20 uL of 10% SDS. Samples were vortexed gently and kept at 65 ℃ in the incubator overnight. After that, samples were placed in − 20 ℃ freezer until completely frozen. Samples were thawed after freezing and 120 uL of 5 M NaCl was added and mixed. Samples were centrifuged for 15 min at the rate of 6000 RPM and 1 mL of 100% Ethanol was added in new conical tube while clear solution was added to get DNA in 50 uL of DNase/RNase free water. Biological samples of DNA were kept at 4 ℃ to resuspend overnight and then frozen at − 80 ℃. Concentration and optical density of DNA samples were measured using Nanodrop (Quawell Q 5000).DNA was extracted using organic method of DNA extraction as previously reported5.All 879 samples of DNA having optimal concentration and density were then genotyped by GeneSeek USA using illumina 50 K SNP Bead Chip which is a medium density chip as it covers 53,347 SNPs spread across the whole Caprine genomeP value ≤ 0.001)40. This retained 827 individuals out of total 879 after applying quality control filters.Quality control measures were performed in Plink by setting call rate of 0.95 and SNPs with minor allele frequencies (MAF) lower than 0.05 or that do not conform to the Hardy–Weinberg expectation ; a significance level was defined as 10e−8 for the traits under study. The identified SNPs were further carried to Stage 2 of analysis on all animals. Identified SNPs were used to calculate polygenic risk score (PRS) using Genome-wide Complex Trait Analysis (GCTA)42 and Plink 1.943.After pruning and quality control measures two stage SNPs association analysis was carried out as methodology adapted from Shi et al.(QQ) plots were made in R program using CMplot package (https://github.com/YinLiLin/CMplot). For SNPs with P values less than 10e−8, the Q–Q plot represented highly significant deviations from the distribution under the null hypothesis indicating a strong association of these SNPs and respective regions with body weight and morphometric measurements.Manhattan and Quantile Quantile Capra hircus ARS1. We identified genes listed on www.genecards.org that were functionally responsible for the phenotypic effects.Genes annotation was performed with latest goat genome assembly Morphometric measurements data i.e. body weight, body length, body height, pubic bone length, heart girth and chest length were analyzed using Proc mixed of SAS University Edition. Any wrong and biologicaly impossible information was not included in the final analysis. Raw data mean, standard deviation, minimum and maximum values for the studied traits are given in Table 44 and model equation narrated as under.ith individual, The mixed linear model was used to describe the data as previously reportedSupplementary Information.


The lower N95 amplitude was associated with the presence of MvD  and wider angular width of MvD . Old age (p = 0.001), average ganglion cell’s inner plexiform layer thickness (p = 0.003), and the presence of MvD (p = 0.020) were significantly related to low N95 amplitude. Association between the presence and extent of the MvD and PERG amplitudes suggests that the presence of MvD has relevance to the generalized dysfunction of retinal ganglion cells.This study aims to investigate whether parapapillary choroidal microvasculature dropout (MvD) is related to visual function measured by pattern electroretinogram (PERG) in glaucomatous eyes with β-zone parapapillary atrophy (PPA). A total of 79 patients with open angle glaucoma and preperimetric glaucoma with β-zone PPA was included in this cross-sectional study. Through the deep layer of the Swept-source optical coherence tomography angiography image, the angular width and the area of MvD were measured. Visual function was evaluated with a standard automated perimetry and PERG. N95 and P50 PERG amplitudes in eyes with MvD were noticeably decreased compared to those without MvD ( Glaucoma diagnosis is mostly based on “structural tests”  and macular was investigated ,23,24,25Regarding PERG, it mainly measures the electrical activity of the inner retina including retinal ganglion cells (RGC) . AccordiBased on recently reported findings using OCTA and PERG ,40, we hThis cross-sectional study was approved by the Institutional Review Board (IRB) of the Catholic University of Korea, Seoul, Korea . It was performed following the tenets of the Declaration of Helsinki. The requirement of informed consent was waived by the IRB of Seoul St. Mary’s Hospital, Korea, due to the retrospective design. Data of patients who visited the glaucoma clinic at Seoul St. Mary’s Hospital from January 2017 to November 2020 were collected using electronic medical records. Patients were included when they met the following inclusion criteria: a best-corrected visual acuity (BCVA), ≥20/40, an open angle glaucoma or preperimetric glaucoma with PPA, and OCTA images with quality scores greater than 60. Exclusion criteria were: poor OCT images , a history of retinal disease including diabetic and hypertensive retinopathy, a history of eye surgery except cataract operation, a history of a systemic or neurological disease that might affect VF, or axial length longer than 26.5 mm to minimize the impact of myopia in optic disc change. If both eyes were eligible, one eye per individual was selected on a random basis.Each participant underwent a full ophthalmic assessment, including measurement of BCVA, slit-lamp examination, Goldmann applanation tonometry, gonioscopy, central corneal thickness using pachymetry , determination of axial length (AL) using ocular biometry , dilated stereoscopic examination of the optic disc and fundus, color disc photography, red-free RNFL photography , optical coherence tomography (OCT) , and Humphrey VF examination .p < 5%, or a Glaucoma Hemifield Test result outside the normal limits in a consistent pattern on two qualifying VFs) confirmed by two glaucoma specialists (K.I.J and C.K.P.), and an open-angle on gonioscopy. Preperimetric glaucoma was defined with normal visual field accompanied with either glaucomatous optic disc appearances  or preexisting RNFL defects.Open-angle glaucoma diagnosis was defined based on glaucomatous optic disc appearance , VF consistent with glaucoma  using a laser with a wavelength of 1050 nm and a speed of 100,000 A-scans per second. The OCTA provided En Face images through automated layer segmentation around the optic nerve head in four layers. Among those, the radial peripapillary capillary (RPC) mode was chosen to estimate a 70 μm thick layer below the internal limiting membrane (ILM) to calculate superficial parapapillary VD. To measure superficial macular VD, images of the superficial macular layer lying from 2.6 μm below the ILM to 15.6 μm below the junction of the inner plexiform layer and IPL/INL were obtained. To measure VDs of both superficial parapapillary and macular layers, we used a published method ,41,42. ITo assess MvD, the image of the deep layer was retrieved by manually resetting the starting point as retinal pigment epithelium (RPE) while keeping the original endpoint 390 μm below Bruch’s membrane. Since the original choroidal/disc mode was intended to measure from 130 μm below the ILM to 390 μm below Bruch’s membrane, the manual customization setting was arranged to avoid and minimize the effect of the superficial layer.MvD was identified when the width of the dropout appeared to be greater than twice of that of visible juxtapapillary microvessels . The areIn terms of the angular width of MvD, the same tool for measuring the area was applied. Two lines were drawn from the center of the optic disc to points where the disc margin and the border of MvD met. The angle was then measured. The area and angular width of the MvD were calculated by two authors (J.L and K.I.J) who were masked to clinical data. These values were determined as the mean of measurements performed by the two authors.2. Participants fixed their views at the center of the monitor, where a red-colored fixed point was placed. The PERG was measured as binocular recordings with an appropriate refractive correction. Responses were band-pass filtered (1–50 Hz) and sampled at 10,000 Hz. At least 100 readings were recorded and averaged. To investigate the reproducibility of ERG parameters, test-retest variability was determined with 34 randomly selected measurements.An electroretinogram (ERG) stimulator  that was commercially available was adopted. Before applying a total of four electrodes, participants with non-dilated pupils were placed in a dim room with a background illumination of 50. Two 35 mm Ag/AgCl skin electrodes were taped to the lower lids and two ground electrodes were taped to both earlobes. Black and white checkerboards with a check size of 1.81° were presented on a 24 inch-monitor with a 48° × 33° visual angle at a distance of 60 cm. Stimuli were modulated in counterphase at 4 Hz. These checkerboards had a mean luminance of 105 cd/mLatencies for N35, P50, and N95 were measured from the onset of checkerboard reversal to the peak of each component. P50 amplitude was estimated from the trough of N35 to the peak of P50. N95 amplitude was determined from the peak of P50 to the trough of N95. t-test was adopted to compare differences between groups. Chi-square test was applied to compare frequencies. A p-value of less than 0.05 was considered statistically significant. Logistic regression analysis was used to ascertain factors associated with the presence of MvD. Linear regression analyses were applied to explore meaningful factors affecting either angular width of MvD or amplitudes of PERG. Variables with significance at p < 0.10 in univariate analysis were included in the multivariate model. p < 0.05 was considered to represent statistical significance.All statistical analyses were performed using SPSS version 17.0 . Student’s A total of 96 eyes of 96 OAG patients were initially recruited. Due to the absence of β-PPA, 17 patients were excluded from further analysis. The remaining 79 eyes with β-PPA were analyzed, including 46 (58.2%) eyes with MvD and 36 (41.8%) eyes without. The characteristics of patients are shown in p = 0.007 and p = 0.004, respectively). Comparison between eyes with MvD and those without MvD was performed. Results are shown in p = 0.017). Logistic regression analysis was carried out to find factors associated with presence of MvD . Declinep = 0.014; B = −0.796, p = 0.027; and B = 73.736, p = 0.065, respectively). Ultimately, in multivariate analysis, the N95 amplitude was substantially correlated with angular width of MvD . Although the presence of MvD showed a significant association with N95 amplitude , its association with P50 amplitude only showed a borderline significance .Representative cases are shown in Another representative case demonstrating the association between the angular width of MvD and the N95 amplitude is shown in p = 0.004). The presence of MvD and thinner average GCIPL thickness were associated with lower N95 amplitude in multivariate linear regression analysis . There was a negative correlation between the angular width of MvD and N95 amplitude (p = 0.014). In this study, we found that in patients with PPA and similar degree of VF damage, eyes with MvD showed significantly lower N95 amplitudes than those without MvD . According to reports of the angular width of MvD [Quantitative analysis of MvD revealed that the angular extent of MvD was associated with the PERG N95 amplitude (h of MvD ,48,49, aOne report has recently demonstrated relationships between OCTA findings and PERG parameters . Lee et Regarding the relationship between the presence of MvD and PERG parameters, univariate analysis showed negative associations of the presence of MvD with P50 and N95 amplitudes. However, in multivariate analysis, only the N95 amplitude maintained its significant association. These findings are based on previous studies showing that the P50 amplitude stems from the soma of RGC, while the N95 amplitude originates from its axon . ConsideThis study showed noticeable associations of the N95 amplitude with the presence of MvD and the angular width of MvD. However, it has several limitations. First, due to the nature of a cross-sectional study, we could not follow longitudinal changes in patients with MvD and reduced N95 amplitude to determine whether they truly experienced faster glaucoma progression than others. Moreover, changes in areas and angular widths were not followed. Several studies have shown that MvD is strongly associated with glaucoma progression ,45,53,54In summary, we investigated the probable impact of parapapillary deep-layer MvD on PERG parameters either qualitatively or quantitatively. The presence of MvD, and also its angular width, had robust associations with decreases of the N95 amplitudes. Since eyes with MvD would possess more dysfunctional RGCs than those without MvD, even if they have similar glaucoma severity, the presence of MvD could possibly mean that a fair number of RGCs are under mechanical or vascular stress in ONH. Our results indicate that more vigilance might be needed for glaucoma patients presenting with MvD.


Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5%  more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.To improve the efficiency of high-density genotype data storage and imputation in bread wheat ( Triticum aestivum, created varieties adapted to diverse environments and cultivation practices  and complexity of the wheat genome present a substantial challenge for sequence-based analysis of genetic diversity. Alignment of short sequence reads to the wheat genome is complicated by high levels of sequence redundancy resulting from two rounds of recent whole genome duplication , and theHigh-quality reference genomes and a reduction in the cost of sequencing presented opportunities for the characterization of genetic diversity by direct sequencing of either whole genomes or genomic regions targeted by sequence capture  was proposed to improve the efficiency of sequence-based genotyping data storage and imputing genotypes in low-coverage sequencing datasets  database tool in allohexaploid wheat with the complex genome. Our study combines five datasets that were created using different sequencing approaches. A summary table describing the datasets and their usage is provided in Supplementary Table S1.The primary dataset used in this study includes 65 wheat accessions and breeding lines that were subjected to whole exome capture as part of the WheatCAP, henceforth referred to as WC65. Many of these lines are used as parents in the US university/academia-associated wheat breeding programs, and information about these lines is found in Supplementary Table S2.DNA was extracted from the leaves of 2-week seedlings grown under greenhouse conditions. DNA was extracted using Qiagen DNeasy kit following the manufacturer’s protocol. DNA was quantified with Picogreen (Sage Scientific) and wheat exome capture was performed on each sample targeting the non-redundant low-copy portion of the genome. Briefly, wheat exome captures designed in collaboration with Nimblegen targeted 170 Mb of sequence covering about 80,000 transcripts  were calculated using TASSEL v5.2.65 , and conThe second dataset used in our study includes another set of US breeding lines subjected to exome capture at KSU Integrated Genomics Facility. Information about these lines is found in Supplementary Table S2. This dataset was used to test the imputation efficiency and accuracy of the PHG database at reduced genome coverage depths.DNA was extracted from leaf tissue as stated above for the WC65. The samples were subjected to whole exome capture and sequenced on the NovaSeq  platform using 2 × 150 bp read runs, generating ∼30× depth of coverage.seqtk  than Beagle imputation. Imputation accuracy comparisons between genomes and SNPs with different minor allele frequencies (MAFs) were performed using ANOVA from car and lme4 R packages.The concordance of imputed genotypes was assessed in relation to the HQ-SNPs using a custom Perl script. The script compares the SNP positions and alleles between the imputed and HQ-SNP datasets for each accession, and divides the number of matching genotype calls by the total number of overlapped genotype calls. On average, the estimates of accuracy were based on nearly 550,000 genotype calls per accession for DS75. The imputation accuracy in DS75 between the Beagle v.5.0 and PHG imputation methods for 0.01× and 0.1× coverage levels was compared using a paired in silico digestion of wheat genome RefSeq v.1.0 detected nearly 3 million PstI recognition sites, of which 1.96 million are located within 250 bp of an MspI recognition site . These lines were not included into the PHG database construction. An PHG imputation below).Raw fastq files (1 × 100 bp) were quality filtered, separated by barcode, and barcodes trimmed from reads, as described  were processed as previously described . The accuracy of PHG imputation was assessed by calculating the concordance of imputed genotypes and genotypes from the HQ-NAM dataset. On average, the estimates of accuracy were based on nearly 5000 genotype calls per accession. Paired t-tests were used to compare the imputation accuracy between NAMgbs and NAMskim for matching accessions.Demultiplexed fastq files were quality trimmed and used for PHG imputation . In this case, “informative” reference ranges were chosen by extending the high confidence gene model coordinates from Chinese Spring RefSeq v.1.1  generated by GATK . Pre-processed exome capture g.vcf files for the WC65 accessions, including 58 The third PHG construction step creates consensus haplotypes for the reference ranges, using the diversity data from the WC65 accessions . This step collapses the raw haplotypes into consensus haplotypes using a user-defined maximum divergence (mxDiv) parameter, which was set to 0.0001 for wheat. This parameter results in the clustering of raw haplotypes that contain <1 variant within 10,000 bp into a common haplotype. The value of the mxDiv parameter was based on prior diversity estimates in wheat  was aligned to the consensus haplotypes stored in the PHG database  using minmap2  program.r2 ≥ 0.6 within a 50 SNP window, stepping 10 SNPs at a time. The resulting set of 9806 markers with no missing data was used for stepwise regression (SR) mapping performed with the ICIM software v.4.1.0.0  and the distal CrossOvers (dCO) were taken from the previous analyses of the spring wheat NAM population for family NAM1 (lmer package in R:We used a family of 75 RILs from the spring wheat NAM panel (www.triticeaecap.org). This set of accessions was selected from a larger panel of nearly 250 wheat cultivars assembled in coordination with the US wheat breeding programs to build a genomic resource to be used as a reference panel for genotype imputation. This diverse set of 65 accessions is comprised of mostly spring and winter bread wheat cultivars, but it also included three accessions of the diploid ancestor of the wheat D genome, A. tauschii , and four accessions of tetraploid wheat .A Wheat PHG database was created using whole-exome capture data from a set of 65 wheat accessions, WC65 (Supplementary Table S2) contributed by the major US wheat breeding programs and the parental lines used for the genetic analyses of the yield component traits in WheatCAP  sites in the D genome were monomorphic among the bread wheat cultivars. Similarly, we found that 31,486 SNPs (7%) in the A genome and 32,228 SNPs (6%) in the B genome are contributed by the domesticated emmer and durum lines, and are monomorphic in hexaploid wheat. These private SNPs explain the high levels of divergence between the domesticated emmer and A. tauschii accessions from the hexaploid wheat lines , and (4) LD drops to half of its initial value (r2 ≤ 0.33) at 20 Mb in the D genome, whereas in the A and B genomes LD drops to the same level at 12 and 10 Mb, respectively . The inclusion of three diploid at lines . The patat lines : (1) divectively .r = 0.64) observed between the genetic distance and IBD. However, the estimates of the total length of IBD segments among cultivars were quite variable . Pairwise comparisons among these lines showed that, on average, they share 416 Mb of IBD segments, with an average IBD segment length of 13 Mb, and nearly 83% of all shared IBD regions are located in the D genome (Supplementary Table S5). This finding is consistent with the lack of diversity among breeding lines in the D genome . First, we used a set of 75 spring and winter wheat lines, DS75, from the US wheat breeding programs sequenced using the whole-exome capture approach  < 2 × 10−16] than imputation in the A (90.3%) and the B genomes (89.9%), respectively  = 2.0 × 10−16; P-value 0.01×(t-test) = 1.9 × 10−14).We compared the performance of the Wheat PHG to one of the commonly used low-coverage imputation methods implemented in Beagle5.0 . For thiP-value (ANOVA) < 2 × 10−16] than both the A (85.4%) and B (85.5%) genomes  than for other cultivars at all levels of sequence coverage . No similar relationship between the presence of specific haplotypes in the reference panel and imputation accuracy was observed for Beagle. We further explored this relationship by analyzing genotype imputation results in the cultivar Jagger, which showed a substantial reduction in imputation accuracy in the low sequence coverage datasets (0.1× and 0.01× coverage) imputed using Beagle . We assumed that one of the likely factors contributing to the decreased imputation performance of Beagle in the cultivar Jagger was the presence of the wild-relative introgression from Aegilops ventricosa on chromosome 2A  in the DS75 panel that were included in PHG database construction, with respect to the other 71 accessions not included in the database construction, and found the four cultivar’s imputation accuracy was statistically higher  < 4.2 × 10−09]. However, the imputation accuracy of 2.5× coverage GBS reads, which represents nearly 500× more sequencing reads per sample than DS75 at 0.01× was still reduced by 3.1%  as was used to populate the PHG database. We also evaluated the utility of the developed PHG database for imputing genotypes using two cost-effective complexity-reduced sequencing approaches, GBS  < 2 × 10−16] in mean imputation accuracy for lines with both parents in the database, vs. those with one parent, all lines having one or two parents represented in the database were imputed more accurately  than the 18 independent lines from GBS70 with the same depth of coverage, whose accuracy was 86.9%  < 2.7 × 10−13] imputed from the NAMgbs dataset. This lower accuracy likely is associated with a lower proportion of skim-seq reads, mostly represented by reads from the repetitive regions, uniquely mapped to the wheat genome compared to the proportion of uniquely mapped reads from the exome capture and GBS datasets, which are enriched for the low-copy genomic regions . The genomic libraries generated for this set of RILs from the spring wheat NAM population . In fact regions . The acc regions .Vrn-A1 and Vrn-D1 loci, which play a major role in the regulation of flowering in wheat  and maize (92–95%) PHGs  compared to PstI sites with higher sequence coverage, ∼1× in the GBS database (89%), even for accessions that are included into the PHG database. One possible approach to improve imputation accuracy for GBS datasets could be to create reference ranges around the GBS-associated PstI sites. However, this may also increase the proportion of ranges located within the repetitive portion of the wheat genome and increase the chance of read misalignment, reducing imputation accuracy.et al. 2017; A. ventricosa in cultivar Jagger  than those previously detected using lower density marker sets. The increase in the number of significant loci also resulted in a higher proportion of genetic variance (80–91%) in recombination rate and HD being explained, suggesting that the imputed genotypes are better at capturing the genetic architecture of these traits, and have the potential to identify more adaptive and beneficial genetic targets in breeding programs.The application of imputed genotypes to the genetic analyses of trait variation in the wheat NAM population showed that an increase in marker density increases the number of loci associated with trait variation and detects alleles that have smaller effects on phenotypes (http://wheatgenomics.plantpath.ksu.edu/phg/ (Accessed: 2021 November 23) Phenotypic datasets for NAM Family 1 associated with the paper can be downloaded from the wheat NAM project website: http://wheatgenomics.plantpath.ksu.edu/nam/ (Accessed: 2021 November 23). Supplemental Material available at figshare: https://doi.org/10.25387/g3.14770974.The raw sequence data for previously published accessions can be accessed from the NCBI Short-Read Archive database (BioProject SUB2540330 and PRJNA381058). Newly generated exome capture data can be accessed from NCBI Short-Read Archive database (BioProject PRJNA732645). Genotypic datasets used in this study are available from the website:


T-Box Transcription Factor 3 (TBX3) on Sus scrofa chromosome (SSC) 14 and Wnt signaling pathway are revealed to be associated with teat number-related traits, with important roles in mammary gland morphogenesis and development.Teat number is important for the mothering ability of a sow, but this trait has seldom been investigated by high-depth genomic data-based genome-wide association study (GWAS). Here, we performed GWAS for the teat number-related traits in 100 Chinese native Qingping pigs by recording their left and right teat numbers and analyzing their genetic variations through 10-fold whole-genome sequencing. TRIML1, TRIML2, ZFP42, FAT1 and MTNR1A. We also highlighted TBX3 as an interesting candidate gene for SSC14. Enrichment analysis of candidate genes suggested the Wnt signaling pathway may contribute to teat number-related traits. This study expanded significant marker-trait associations for teat number and provided useful molecular markers and candidate genes for teat number improvement in the breeding of sows.Teat number plays an important role in the reproductive performance of sows and the growth of piglets. However, the quantitative trait loci (QTLs) and candidate genes for the teat number-related traits in Qingping pigs remain unknown. In this study, we performed GWAS based on whole-genome single-nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) for the total number of teats and five other related traits in 100 Qingping pigs. SNPs and Indels of all 100 pigs were genotyped using 10× whole genome resequencing. GWAS using General Linear Models (GLM) detected a total of 28 SNPs and 45 Indels as peak markers for these six traits. We also performed GWAS for the absolute difference between left and right teat number (ADIFF) using Fixed and random model Circulating Probability Unification (FarmCPU). The most strongly associated SNP and Indel with a distance of 562,788 bp were significantly associated with ADIFF in both GLM and FarmCPU models. In the 1-Mb regions of the most strongly associated SNP and Indel, there were five annotated genes, including  Porcine teats are located from the anterior to posterior limb bud and symmetrical to the abdominal midline, and teat number is an important trait for the mothering ability of a sow, which can affect the piglets’ weight gain and mortality. It has been shown that teat number is a quantitative trait with a medium level of heritability 0.32) . Using g . Using VRTN (located at 103.4 Mb on SSC7 on Sscrofa10.2) was a credible candidate gene in this major QTL for teat number on SSC7. These studies also indicated that QTLs for teat number-related traits are distributed across the genome on every chromosome. Several other genes have also been annotated as teat number-associated genes, such as Lysine Demethylase 6B (KDM6B) [TOX3) [ESR1) and Nuclear Receptor Subfamily 5 Group A Member 1 (NR5A1) [In 2000, Wada et al. revealed two QTLs for teat number on SSC1 and SSC7 by QTL analysis of 265 F2 offspring of the Meishan and Göttingen miniature pig . Since t (KDM6B) , TOX Hig) [TOX3) , Estroge (NR5A1) . HoweverThe purpose of the current study was to detect genome-wide associations for teat number-related traits in Qingping pigs using whole-genome SNPs and insertions/deletions (Indels) based on whole-genome resequencing.In this study, all 100 pigs were raised indoors in the Qingping pig Conservation Farm in Yichang, Hubei, China. Ear tissues were collected and stored in liquid nitrogen until further analysis. Genomic DNA samples were extracted from ear tissues, using a standard phenol–chloroform method. Sequencing libraries were constructed and sequenced by the Novogene Bioinformatics Institute . High-throughput sequencing was performed as paired-end 150 sequencing using a HiSeq 4000 sequencing system .In this study, three independent persons observed all 100 pigs and counted the teat number of the left and right lines. A total of six teat number-related traits were obtained: (i) the number of teats on the left side (LTN); (ii) the number of teats on the right side (RTN); (iii) the total number of teats (TNUM = LTN + RTN); (iv) the maximum number of teats in LTN and RTN (MAXAP); (v) the difference between the two sides (L-R = LTN − RTN) and (vi) the absolute difference between left and right teat number (ADIFF = |LTN − RTN|). The mean, standard deviation, minimum value, maximum value, and coefficient of variance for each trait were calculated using R (version 3.6.0) .Sscrofa11.1 reference genome by the BWA software (version: 0.7.8)  [p > 1 × 10−6) and excluding SNPs and Indels located on the sex chromosomes).Clean reads from all 100 pigs were aligned to the ton, UK) . To reduton, UK) . SNPs anton, UK) . SNPs anton, UK) . SNPs anton, UK) . Missington, UK) , followeThe phenotypic variance explained by genome-wide SNPs (SNP-based heritability) was estimated using GREML in Genome-wide Complex Trait Analysis (GCTA) . BrieflyPrincipal component analysis (PCA) was used to explore the population structure of Qingping pigs and determine whether principal components (PCs) should be added to the GWAS. PCA was performed using MVP.Data.PC in rMVP R (version 3.6.0)   package In the present study, GLM in rMVP R (version 3.6.0) package was used to perform the GWAS for teat number-related traits based on high-quality common SNPs and Indels . Principp-value (2.16 × 10−9 for SNPs and 2.44 × 10−8 for Indels). These markers can be used to define kinship in the REM step of FarmCPU to avoid the model over-fitting problem in FEM. The FEM is used to test each genetic marker, one at a time. Pseudo QTNs are included as covariates to control false positives. Specifically, the FEM could be expressed by the following equation:The Fixed Effect Model (FEM) and the Random Effect Model (REM) are used iteratively in FarmCPU , and Farp-values (2.16 × 10−9 for SNPs and 2.44 × 10−8 for Indels) and positions by using the SUPER algorithm [The REM is used to optimize the selection of pseudo QTNs from markers with whole genome significant lgorithm . The REMSscrofa11.1 were downloaded from the PigQTL database. The information of QTLs for teat number-related traits was obtained using R (version 3.6.0), followed by comparing the significant SNPs and Indels with these QTLs using R (version 3.6.0). For the strongest significant SNP (rs322863105) for ADIFF on SSC17, SNPs with suggestive significant p-values  around rs322863105 were used for haplotype block analysis to evaluate the linkage disequilibrium (LD) patterns of selected SNPs within this region using Haploview version 4.2 [Pig QTLs based on sion 4.2 . The effSscrofa11.1. Candidate genes located in 1-Mb regions of significant SNPs and Indels were also annotated. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses and visualizations were performed using the clusterProfiler R (version 3.6.0) package [Candidate genes, including or close to the significant SNPs and Indels, were annotated using the biomaRt  R (versi package .Sscrofa11.1), with 36,482,281 SNPs and 4,859,001 Indels being called with GATK. After filtering, 23,193,931 SNPs and 2,053,221 Indels, with a distribution roughly proportional to autosomal chromosomes of pigs, were retained for subsequent analyses  for SNPs and 1.75 × 10−6  for Indels.PCA results indicated that Qingping pigs could not be clustered into groups , so GWASCompared with known QTLs for teat number-related traits in the PigQTL database, 14 significant SNPs and 24 significant Indels overlapped with known QTLs . InteresSNP = 1.37, λIndel = 1.36) indicated possible false positives  were improved by FarmCPU . However, Q-Q plots and genomic inflation factors  also showed significant association with ADIFF in FarmCPU . The effect of rs322863105 on the ADIFF was estimated by genotyping Qingping pigs for this SNP. Individuals with the TT genotype had a lower ADIFF, suggesting LTN and RTN were more symmetrical (tripartite motif family like 1 (TRIML1), tripartite motif family like 2 (TRIML2), and ZFP42 zinc finger protein (ZFP42). Moreover, a peak Indel (rs700363122) close to this SNP showed significant associations with ADIFF in the results of both GLM (1.13 × 10−8) and FarmCPU (9.10 × 10−16), with two annotated genes in the 1-Mb region around this Indel, including FAT Atypical Cadherin 1 (FAT1), and Melatonin Receptor 1A (MTNR1A).The strongest significant SNP in GLM for ADIFF on SSC17 , epidermal cell differentiation (p = 4.61 × 10−9), and skin development (p = 1.12 × 10−7). In p = 1.27 × 10−3), ECM-receptor interaction (p = 4.79 × 10−3), and Glycine, serine and threonine metabolism (p = 5.43 × 10−3). Furthermore, we also paid attention to the Wnt signaling pathway  significantly associated with RTN, is known to play a role in breast cancer [TRIML2 and MTNR1A were close to SNP (rs330045817) and Indel (rs700363122), respectively, on SSC17. As mentioned above, these two different types of variants were close to each other and significantly associated with ADIFF in both GLM and FarmCPU models. TRIML2 was significantly associated with prognosis, with a higher expression in triple-negative breast cancer cell lines than in normal mammary cell lines [MTNR1A may contribute to breast cancer susceptibility [TBX5, encoding T-Box Transcription Factor 5, was close to the significant Indel (rs701717756) for ADIFF on SSC14. In a large German family, TBX3 and TBX5 duplication was reported to be associated with Ulnar-Mammary syndrome [TBX3 was the placode marker required for the formation of mammary placodes and the development of fetal mammary glands in all mammals [TBX3 was close to this Indel with a distance of 625,516 bp.The nd STAT3 . FAM3C, er cells . WWOX, it cancer . TRIML2 ll lines . Common tibility . TBX5, esyndrome . Importa mammals . AlthougVRTN on SSC7, which was reported as a credible candidate gene for the teat number and the vertebra number [VRTN may exist in different populations and VRTN may not be a strong or the only causal gene for teat number based on their finding that VRTN mutation was significantly associated with the teat number in Canadian Duroc pigs, but not in American Duroc pigs [VRTN mutation on SSC7 was also not significantly associated with the teat number in Chinese pig breeds, including Beijing Black pig and Sushan pig [VRTN was reported to modulate somite segmentation [VRTN plays a more important role in vertebra number and varies in its role in teat number among populations with different genetic backgrounds. Therefore, the teat number in Qingping pigs is speculated to involve other genes or pathways.Unlike earlier studies, the present study failed to detect significant association signals in genome regions around a number ,44. Zhuaroc pigs . Moreoveshan pig ,9, but sWNT11, WNT16 and FZD3, were located at key positions in this pathway (WNT16 and FZD3 were also involved in GO terms, skin development and epidermis development. The Wnt signaling cascade is implicated in almost all stages of mammary development and is pivotal for the specification and morphogenesis of the mammary gland [WNT11 was expressed in stromal cells and basal cells in the adult mammary gland [WNT11 and FZD3 were expressed in mammary buds at E12.5 and E15.5 [TBX3 in mammary placode development [The GO enrichment results showed significant enrichment in skin development, epidermis development, and epidermal cell differentiation. The mammary gland is an epithelial organ, and epithelial–stromal crosstalk is a key aspect of mammary morphogenesis . In KEGG pathway c. WNT16 ry gland . WNT11 wry gland . Chu et nd E15.5 . Importaelopment . These rTBX3, was detected as a mammary placode marker. These findings contribute to our understanding of the genetic architecture of teat number and provide genetic markers for genetic improvement of teat number in Qingping pigs.In this study, GLM and FarmCUP GWAS were carried out to detect associated SNPs and Indels for 6 teat number-related traits. We found a total of 33 SNPs and 50 Indels for teat number. The most significant SNP and Indel were located on SSC17. Six candidate genes were enriched in the Wnt signaling pathway with an important role in mammary gland morphogenesis and development. A novel candidate gene on SSC14,


The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world’s population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder’s interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, −log Oryza sativa) is a staple food source for more than half of the global population  . In addiHigh-throughput SNP genotyping is an attractive genotyping tool for identifying sequence polymorphisms . It is tOryza genus. McCouch et al. constructed a high-density rice array consisting of 700 K SNPs surpassing the largest publicly available genotyping platform for any crop species , 3) High-Density Rice Array  array reported from Cornell university (http://qtaro.abr.affrc.go.jp/) (https://plantreactome.gramene.org/) 7) Chloroplast (ftp://ftp.ensemblgenomes.org/pub/plants/release-36/fasta/oryza_sativa/dna) (http://rice.hzau.edu.cn/rice/download_ext/MH63RS2.LNNK00000000.fasta.gz) (O. rufipogon (https://www.ncbi.nlm.nih.gov/nuccore/NC_013816.1?report=fasta) (We utilized different databases for designing the Axiom rice genotyping chip:1) Resequencing data of Korean World Rice Collection (KRICE) of 475 accessions composed of 417 cultivated and 58 wild accessions , 2) Riceiversity , 4) Affyiversity , 5) QTAR.go.jp/) , 6) Planoroplast  and mitooroplast  genomic iva/dna) , indica asta.gz) , and O. t=fasta) .® BioFx Services) service for initial probe screening. The priority was given as 0, 1, and 2, with 2 being the highest priority and 0 being the lowest. We assigned priority 2 to agronomically important genes, that have known pathways, and exist exclusively in either japonica or indica. After removing the tag-SNPs with a design score (pconvert)<0.6, a total of 3,204,347 SNPs met the Affymetrix probe designing criteria , and SNP/indel sites with MAF<0.05, and missing rate> 0.1 were removed. Obtained SNPs were further enriched with Affymetrix (Affy44K), High-Density Rice Array , and SNPs of selected genes from QTARO and Reactome databases, which resulted in a total of 7,682,442 markers. These markers were categorized into different classes viz., japonica, indica, and rufipogon specific, based on their sources Table S1Among technical suitable variants, we allocated SNPs based on genetic diversity and breeder’s interest, which were divided into five divisions , 220,135Furthermore, in the key research division, SNPs detected from 11 different genome-wide association studies (GWAS) for candidate genes were filtered with MAF< 0.01 Figure 2https://plantreactome.gramene.org) to retrieve SNPs involved in different pathways or processes in rice  71mer sequence (35 bp flanking sequence of target SNP) was to score the marker; (2) A marker was “not recommended” having one or more polymorphisms within 24 bases; (3) If a marker has the same recommendation for each strand, we recommended tilling the one with the highest pconvert value; (4) A marker/strand was recommended if: pconvert > 0.6, there are no wobbles, and poly count = 0; (5) A marker/strand was not_recommended if: duplicate count > 0, or poly count > 0, or pconvert< 0.4, or wobble distance< 21, or wobble count >= 3; (4) A marker was considered not_possible on a given strand if we cannot build a probe to interrogate the SNP in that direction.in silico validation with proprietary software. We retained one SNP marker every 200 bp to ensure a uniform distribution and high density of SNPs throughout the rice genome. The final 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip) contained a total of 581,006 SNP markers  for array design. The quality of each SNP was assessed again and designated as ‘recommended’, ‘neutral’, ‘not recommended’, and ‘not_possible’ using 1-F7 (209), breed , landraces (252), weedy (488), Rufipogon (96), wild type (245), and mutant lines (181) , backcross inbred lines (209), backcross lines , Fes (181) Table S3The traits clum or culm length (cm), heading date, and panicle length (cm) were evaluated for GWAS, and amylose content (%), panicle length (cm), number of grains/panicle, heading date, number of panicles/hill, and 100-grain weight were used in genomic selection analysis. Phenotyping was conducted at the Kongju National University, Yesan, South Korea, during the dry seasons of 2017 and 2018. Five plants were randomly selected from the middle row of the plot, and each parameter was recorded using the Standard Evaluation System (SES) . Days tohttp://www.affymetrix.com/). Briefly, genotype calling and QC metrics were performed using Affymetrix Genotyping Console™ (GTC) v.4.2. Samples with a development quality check (DQC) value<0.83 and call rate<0.97 were excluded from further analysis. GTC results were post-processed using the SNPolisher R package (v.3.0). The Ps_Metrics function was used to generate SNP metrics, and the Ps_Classification function with the default setting classified SNPs into six categories: PolyHighResolution , MonoHighResolution , Off-Target Variant, CallRateBelow-Threshold , NoMinorHom , and Other (more than one cluster property was below the threshold)  and quantified using a NanoDrop spectrophotometer . The DNA quality was checked using a 1% agarose gel. Genomic DNA (200 ng) from all lines was hybridized into arrays using the Affymetrix GeneTitan system, according to the manufacturer’s instructions. SNP genotyping, quality control (QC), and SNP filtering were performed according to the Axiom Genotyping Solution Data Analysis User Guide (reshold) . Furtherhttps://cran.r-project.org/web/packages/qqman/index.html) was used to draw Manhattan plots. A P value of 3.16 × 10−7 was used to consider marker-trait association (MTA) as significant.High-quality SNPs obtained from the Affymetrix chip were used in a genome-wide analysis of associations. A GWAS was performed on three phenotypic traits: clum length, heading date, and panicle length. Association analyses were conducted using the Genome Association and Prediction Integrated Tool (GAPIT)  and TASSThe ridge regression best linear unbiased prediction (rr-BLUP) model was implemented using the R package rrBLUP . The mody is an N × 1 vector of adjusted means for all genotypes, μ is the overall mean, Z is an N × M matrix of markers, u is a vector of marker effects as u ~ N, and e is the residual error with e ~ N.where GS was performed with fourfold cross-validation by including 80% of the samples in the training population and predicting the genomic estimated breeding values (GEBVs) of the remaining 20% of the samples. For the accuracy assessment, two 50 replication sets were performed, with each replicate consisting of five iterations.https://keras.io/callbacks/#earlystopping) was applied to control model overfitting  is a deep learning model that accommodates inputs distributed along with space patterns . In a CNrfitting . A poolirfitting .A detailed description of the detection, filtering, and final selection of SNPs included in the array is provided in the Methods section and Alignment of whole-genome resequencing of 1) 475 Korean World Rice Collection (KRICE) accessions and 2) 3,000 rice genome project (3 K-RGP) data accessions against both indica and japonica rice reference genomes were performed to identify sequence variations (SNPs and indels) . The aliRegarding the distribution of 500,725 japonica-specific SNPs in different parts of the genes, 82,666 (16.51%) SNPs were present in exons, 78,963 (15.77%) in introns, 27,139 (5.42%) in the UTR, and 26,3539 (52.63%) in the intergenic region Figure 4® platform. All samples passed the quality assessment with a high DQC value (>0.89) and call rate (>95%), and duplicate samples showed 99% SNP reproducibility. Thus, the genotyping results of 4,720 genotypes validated the chip performance with both a high sample success rate and genotyping call. The SNP genotyping results from the 4,720 genotypes were classified into six categories based on the Affymetrix quality control metrics  having the highest −log10p-value of 22 for a marker on chromosome 7 (chr07_ 29494004)  showed significant association with −log10p-value of 7<. In addition, significant SNPs mapped on the 2.4 Mbp region of chromosome 9 were belongs to 15 genes that include DENSE AND ERECT PANICLE1 , lecithine cholesterol acyltransferase (Os09g0444200), Serine carboxypeptidase 42 (Os09g0462875), gibberellin receptor (Os09g0455900), alpha-amylase isozyme 3A precursor (Os09g0457400), etc were significantly associated with panicle length , which was identified as being responsible for the early heading7-2 (EH7-2)/heading date2 (Hd2) QTL  was observed in the OsDEP1 gene (Os09g0441900) (Os09g0441900) was found to be the second most significant signal  for clum length  that encodes Remorin_C-containing proteins showed significant association with panicle length. Previously, two SNPs from the third and fifth exons of LP1 were reported to reduce panicle length  on chromosome 6 containing Os06g0285900 (embryogenesis transmembrane protein), Os06g0285100  Os06g0286310 (Oxidoreductase-like protein), Os06g0286400 (nitrate induced protein), Os06g0289200 (UDP-glucuronosyl/UDP-glucosyltransferase family protein), Os06g0286351 (Armadillo-type fold domain containing protein), Os06g0285400 (Serine/threonine-specific kinase-like protein), Os06g0296700 (C-type lectin conserved site domain-containing protein) which have not previously been shown to be associated with any trait in rice  QTL . In the 0441900) Table 2,0441900) . The same length . The clumosome 7 . TherefoGBLUP is the most robust method and the most commonly used tool in rice because it provides high predictability . In GS sThe current manuscript mainly focused on the development of an inclusive SNP array system that will help rice breeders for multiple breeding programs. Also, we have conducted validation analyses for the GWAS and genomic selection. In collaboration with DNA Link, Inc. (Korea), we provide microarray analysis services using our 580 K _KNU chip. Scientists and breeders in Korea are using it for a variety of applications, and we expect to see the results of studies on the use of our SNP array in the near future. As an accurate high-density genotyping tool, the 580 K _KNU chip is an excellent platform for GWAS, QTL mapping, evolutionary studies, genetic diversity, and genomic selection, especially genetic dissection of important traits of breeders’ interest that have not been fully identified. Hence, it will play a pivotal role in rice breeding applications.The original contributions presented in the study are included in the article/K-WK, BN and JH wrote the manuscript. K-WK, JN, BN and S-HC collected and analyzed the data. JH and Y-JP supervised and revised the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A4A1030348). This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Digital Breeding Transformation Technology Development Program funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322060031HD020). This work was carried out with the support of “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ015935), Rural Development Administration, Republic of Korea. This work was supported by a research grant from the Kongju National University in 2022.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.