Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies. Genome-wide association studies (GWAS) have identified hundreds of thousands of genetic loci associated with complex diseases. These associations have improved our understanding of the genetic architecture of human diseases . HowevermQTLs are characterized by polygenicity and pleiotropy ,7. PolygComprehensive annotations are necessary in order to gain functional insights into SNP–metabolite associations. Many resources are available to support SNP to gene annotation, such as VEP and SNiPA ,20. For Recently, cross-phenotype association analysis has gained increasing attention ,24,25,26There is a clear demand for dedicated bioinformatics resources to support mGWAS data analysis and interpretation. Our overall assumption is that by developing a centralized place for mGWAS datasets and performing deep annotation of the underlying SNPs and metabolites, users can gain valuable functional insights into the statistical associations identified from mGWAS results. A network is a valuable approach to depict mGWAS results and allows the dissection of polygenicity and pleiotropy. Heterogeneous networks comprising various types of nodes  and edges  have been remarkably useful in depicting the complex interplay across biological entities . These nhttps://www.mgwas.ca (accessed on 1 May 2022), a user-friendly web-based platform for network-based integrative analysis and visual exploration of SNPs, genes, metabolites, and diseases. Its key features include:Comprehensive collection and deep annotation of SNP–metabolite associations based on data from the 65 mGWAS to date.Support for SNP-based, gene-based, and metabolite-based network generation to facilitate interpreting results.Powerful network visual analytics system facilitating interactive exploration and built-in topological and functional enrichment analysis.Here, we introduce mGWAS-Explorer  and detailed tutorials. Together, these features comprise a powerful platform for functional interpretation and cross-phenotype association analysis of mGWAS datasets.p-value cutoffs are based on significance thresholds of the original studies, as the p-values and effect sizes of SNP-metabolite associations may differ across different studies due to the differences in sample sizes, population types, or the metabolomics platforms [Since the first study in 2008 , mGWAS wlatforms ,7. The main workflow of mGWAS-Explorer is summarized in There are five modules in mGWAS-Explorer corresponding to the five different types of input. Users can upload a list of SNPs, metabolites, or genes a–c; browp-value, and metabolite name  could be performed to maximize the results. Users also have the flexibility to include PPI networks, as well as to filter on biofluid or population types  in GWAS, where nodes are SNPs or phenotypes  and edges represent significant associations . The ‘SNon types a.For many GIMs, metabolites can be functionally connected to enzymes, or transporters . The ‘MeThe nearest gene mapping approach is suggested to be an effective indicator of true positive genes for mQTLs . In mGWAp-value threshold (1 × 10−5) for mGWAS-Explorer, resulting in 19 SNPs after LD clumping. mGWAS-Explorer revealed that the SNPs at the ABO  locus were in high LD (r2 > 0.8) with numerous other SNPs in this region associated with multiple metabolites and other human diseases, such as leucylalanine, citric acid [ABO has been linked to the risk of COVID-19 in several studies [ABO variants with levels and the ratios of fibrinogen A-α peptides  and venous thromboembolism  and FUT2 (fucosyltransferase 2) genes are associated with levels and/or ratios of fibrinogen A-α peptides. Additionally, FUT2 gene was also identified in the PPI network with the ABO gene  gene as reported by Lotta et al. [ABO locus. Indeed, previous epidemiological studies have reported that the associations of ABO group with coronary artery diseases are mediated by cholesterol [ABO blood group with type 2 diabetes were not consistent [ABO variants, T2D, and cholesterol levels. Furthermore, mGWAS-Explorer also revealed metabolites levels and their ratios identified in the previous COVID-19 case study shared associations with T2D loci. In fact, multiple studies have reported the comorbidity of T2D and COVID-19 [Around 250 genomic regions have been associated with type 2 diabetes (T2D) susceptibility in genome-wide association studies, some studies have highlighted the link to metabolomic profiles ,61,62. Wa et al. . Additiolesterol , althougnsistent ,66,67,68COVID-19 ,70,71. IURL links:http://metabolomics.helmholtz-muenchen.de/gwas/ (accessed on 1 May 2022).Metabolomics GWAS Server: https://github.com/statgen/pheweb (accessed on 1 May 2022)PheWeb: https://hdpm.biomedinfolab.com/netmage/ (accessed on 1 May 2022) (accept PheWAS summary statistics)NETMAGE: https://gephex.ega-archive.org/ (accessed on 1 May 2022).GePhEx: Establishing meaningful connections between diseases and deciphering molecular mechanisms that underpin shared genetic architectures are among the key objectives of GWAS. Our work shows that integrating mGWAS summary statistics, LD proxy search, and visual analytics can rapidly reveal multiple associations across metabolites and diseases, which can be utilized to better understand the ongoing global health crisis, such as the COVID-19 pandemic and type 2 diabetes.When looking at the cross-phenotype associations between metabolites and diseases, it is important to investigate the shared SNPs identified in the mGWAS-Explorer output to examine where the SNPs are located on the genome and the extent of overlapping of the SNPs. In fact, we consider mGWAS-Explorer as the initial stage in a pipeline for an in-depth mechanistic understanding of mGWAS before moving on to similarity analysis , colocalABO, much work needs to be done to fully understand the underlying mechanisms. Citric acid acts as a bridge between carbohydrate and fatty acid metabolism, promoting the growth and development of immune cells [ENPEP and FUT2 as potential candidate genes for COVID-19, although the association signal of these two genes were below the genome-wide significance threshold in the original study [ABO and ENPEP as COVID-19 risk genes; however, the evidence for the FUT2 gene is conflicting [FUT2 associated with critical COVID-19 diseases [FUT2 is responsible for the expression of histo-blood group antigens on the mucosal surface of gastrointestinal, genitourinary, and respiratory tracts. Inactivating FUT2 mutations lead to a non-secretor status, which confer resistance to norovirus and rotavirus gastrointestinal infections [FUT2 gene is drastically different between the European population (0.441) and the East Asian population (0.004) [FUT2 variants, fibrinogen A-α peptides, and COVID-19.While our first case study highlighted shared genetic variants regulating metabolite abundance  and COVID-19 at ne cells . Additione cells , which mal study . Many foflicting ,76,77. Idiseases . FUT2 isfections ,79. Furt (0.004) ,81, whicIncreases in throughput and decreases in cost will enable a growing number of mGWAS to be conducted in the near future. The web server will be regularly updated to incorporate the most up-to-date mGWAS datasets, disease associations, and additional SNP annotation data  to serve as a valuable bioinformatics platform for mGWAS researchers. With this context, we also intend to add support for peak annotations of untargeted metabolomics data obtained from high-resolution mass spectrometry.p-values and effect sizes of SNP–metabolite associations, we have included metadata from each publication, such as the type of biofluid, sample size, population type, genotyping platform, metabolomics platform, etc. (b) For SNP annotation, three options are provided, including HaploReg [2 values. With VEP, users can select either a specific distance or the nearest number of genes for SNP annotation. (c) SNP–disease and gene–disease associations were downloaded from the DisGeNET database [(a) mGWAS papers were searched from PubMed, Web of Science, bioRxiv, and medRxiv, resulting in 65 publications as of December 2021. The summary statistics were either downloaded from public databases or supplementary data of the original publications. Statistical associations between metabolites and SNPs were summarized and pre-filtered using study-specific significance thresholds. In addition to HaploReg , PhenoScHaploReg , and VEPdatabase . HMDB dadatabase . (d) KEGdatabase ,85,86. . Additional identifiers have also been included, such as genomic coordinates for SNPs , EnsembThere are two general types of relationships, including inter-omics and phenotype-specific links. Inter-omics connections are based on statistical associations (based on mGWAS), or knowledge-based associations . Phenotype-specific links allow users to identify variants that are associated with disease phenotypes. This information is obtained from DisGeNET  based onThe backend analysis was implemented using the R programming language (version 4.1.3). The whole framework was built based on the PrimeFaces component library (version 11.0.0). The integrated data are stored in a relational database using SQLite. The interactive visualization was developed based on the sigma.js and echarts.js JavaScript libraries for network view and 3D Manhattan plot, respectively.ieugwasr package with default parameters [p-value are retained, where SNPs in LD within a certain window are removed in LD clumping [2 = 0.8 were set as input parameters for LD proxy search.The datasets of COVID-19 and type 2 diabetes case studies were downloaded from their respective original publications ,63. LD crameters  prior toclumping . In bothThe default networks are built by querying for the direct mapping from the knowledgebase. Optionally, users can choose to expand the network by including PPIs in the SNP module. However, the result may suffer from the ‘hairball’ effect, which severely limits the usefulness and interpretability. Therefore, mGWAS-Explorer offers support to refine large networks based on node degree or betweenness values, batch filtering, or the shortest paths, as well as by computing minimum subnetworks based on the prize-collecting Steiner Forest (PCSF) algorithm . The detThe combination of network visualization and functional enrichment analysis is a valuable tool for gaining key biological insights. For SNP input, two types of enrichment approaches have been implemented—(1) directly testing in SNP-set library, or (2) testing on mapped genes for enrichment using hypergeometric tests. When the input is a gene or metabolite, the associated gene-set or metabolite-set enrichment analysis can be performed. The result tables will be displayed under the Function Explorer panel. Notably, clicking a row of the table will highlight the nodes contained in the corresponding function/pathway within the network. In addition, mGWAS-Explorer also permits enrichment analysis on the selected nodes of interests, for instance, from the batch selection panel.The Network Viewer page contains multiple advanced features for network visual exploration, including Network Layout, Global Node Styles, Module Explorer, Batch Selection, and Path Finder. Ten different network layout algorithms are available, including Force-Atlas, Fruchterman–Reingold, Circular, Graphopt, Large Graph, Random, Circular Bipartite/Tripartite, Linear Bipartite/Tripartite, Concentric, and Backbone layout. Three module detection algorithms are offered in the Module Explorer, including the WalkTrap, InfoMap, and the Label Propagation algorithms based on the igraph R package . These oWe have developed mGWAS-Explorer to allow users to easily explore the published mGWAS datasets, and to provide contextualized analysis for a given list of SNPs, genes, or metabolites. As demonstrated by our case studies of COVID-19 and type 2 diabetes, mGWAS-Explorer can facilitate hypothesis generation and reveal functional insights into the genetic basis of human metabolism to permit translational discoveries.


Characterizing the genetic diversity and population structure of breeding materials is essential for breeding to improve crop plants. The potato is an important non-cereal food crop worldwide, but breeding potatoes remains challenging owing to their auto-tetraploidy and highly heterozygous genome. We evaluated the genetic structure of a 110-line Korean potato germplasm using the SolCAP 8303 single nucleotide polymorphism (SNP) Infinium array and compared it with potato clones from other countries to understand the genetic landscape of cultivated potatoes. Following the tetraploid model, we conducted population structure analysis, revealing three subpopulations represented by two Korean potato groups and one separate foreign potato group within 110 lines. When analyzing 393 global potato clones, country/region-specific genetic patterns were revealed. The Korean potato clones exhibited higher heterozygosity than those from Japan, the United States, and other potato landraces. We also employed integrated extended haplotype homozygosity (iHS) and cross-population extended haplotype homozygosity (XP-EHH) to identify selection signatures spanning candidate genes associated with biotic and abiotic stress tolerance. Based on the informativeness of SNPs for dosage genotyping calls, 10 highly informative SNPs discriminating all 393 potatoes were identified. Our results could help understanding a potato breeding history that reflects regional adaptations and distinct market demands. It is consumed fresh or in various processed forms, as well as having some industrial applications. In Korea, potatoes are grown commercially all over the country. According to data collected by the Rural Development Administration (RDA) in 2021, Gangwon province, a cool and mountainous region, grows more potatoes than any other province, followed by Gyeongbuk, Chungnam, and Jeju Island. The potato breeding programs at the Highland Agriculture Research Institute (HARI), located at an elevation of 800 m above sea level in Gangwon province, were established in 1961, aiming to provide improved local potato cultivars with high yield and quality. In recent years, emphasis has been placed on developing early maturing varieties with a short dormancy period for double cropping and chip processing potatoes, which would benefit farmers and expand their market share. After the large-scale cultivation of Namjak (a.k.a. Irish Cobbler) potatoes from the 1960s to early 1980s, introductions like Sumi (Superior) (released in 1978) and Daeji (Dejima) (released in 1978) as fresh table potatoes, and Deaseo (Atlantic) (released in 1995) as a chip processing potato, have been widely cultivated in Korea2. The local potato varieties released over the past 20 years, such as Chubaek, Golden Ball, and Mangang, have been good alternatives to the previously cultivated foreign potatoes in terms of their early marketability and outstanding processing properties. Although the HARI Potato Breeding program has developed and released 28 cultivars, including 26 clonal selections, there is a need to develop new varieties which meet demands for high yields under low inputs,resistance against diseases and pests such as potato virus Y, late blight (Phytophthora infestans), and common scab (Streptomyces scabies),tolerance to abiotic stresses like high temperatures; favorable processing properties such as cold sweetening tolerance and long dormancy in storage; and improved health and nutritional properties.As the world’s third most important food crop in terms of human consumption after wheat and rice3.Breeding potatoes remains challenging because the complexity of their auto-tetraploidy and highly heterozygous genome, the complexity of their plant and crop physiology, the duration of their growth cycle, their low multiplication factor and the difficulties with the evaluation of their phenotype, have all resulted in slow progress, compared with diploid plants such as Arabidopsis, rice and tomato4, for example, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter simple sequence repeats (ISSR), inter-retrotransposon amplified polymorphism (IRAP) and simple sequence repeats (SSR). With the advantages of abundance, cost-efficiency, and high-throughput assays, single nucleotide polymorphism (SNP) markers have become increasingly important in crop genetic studies  enabled genotyping such as genotyping by sequencing (GBS) are the most popular high throughput genotyping platforms.Revealing potato genetic diversity and population structure is an essential step for breeding efforts, which involves identifying promising parental combinations from the germplasm collections, crossing the parents to generate genetic variation, and selecting clones with target traits. Although, in the genomics era, molecular breeding of polyploidy crops such as potato has lagged behind many diploid crop species, genetic evaluation of potato clones has been conducted using various molecular markers available  present in the potato germplasm have been developed and applied to investigate genetic diversity and population structure. Four open access software tools have been developed and used for genotype calling in polyploidy crop species based on array data, including Illumina GenomeStudio (https://www.illumine.com/techniques/microarrays/), ClusterCall R package7, a web-based software SuperMASSA12, and fitTetra R package6. The genetic diversity of 250 diverse North American potatoes was characterized using an 8 K SNP array10. They performed STRUCTURE analysis using the diploid genotype calls to demonstrate clear differences between cultivated potato clones and related wild species, and a minimal substructure within the cultivated potatoes. Also, clear separation between potato market classes was observed with pairwise kinship estimates. Igarashi et al. genotyped 164 Japanese potatoes based on a diploid model using a 12 K SNP array and compared them with North American and European potatoes13.In potato, high-throughput genotyping platforms.11 designed a 20 K SolSTW array and analyzed it using fitTetra software to genotype a total of 569 potato clones and identified introgression segments, selection, and founder signatures. Pandey et al.14 investigated the genetic diversity and population structure of 214 potato advanced clones selected and maintained in vitro over a 40-year period by the Texas A&M University Potato Breeding Program with the Illumina Infinium 22 K V3 Potato Array. They performed STRUCTURE and discriminant analysis of principal components (DAPC) using diploid genotypic calls and hierarchical clustering (HC) using tetraploid genotypic calls to divide the clones into three clusters.Vos et al15. Subsequent footprints of selection, known as selection signatures or selective sweeps, can be traced in the genomes of many crop plants that have been subjected to breeding programs to improve traits of interest, such as yield, pest resistance, and flesh color. Pandey et al.14 carried out a selection signature analysis using the PCAdapt, iHS, and XP-EHH approaches to identify candidate genes controlling potato flesh and skin color, length of plant cycle and tuberization, and carbohydrate metabolism.Plant domestication and artificial selection give rise to gradual changes in populations at the genomic level16. Recently, SNP quality assurance and control genotyping methods based on low-density SNPs have been investigated in maize16 and sweetpotato17. In sweetpotato, a 30 SNP-set with uniform distribution across chromosomes was selected to identify relatively similar mislabeling error rates as a high density SNP-set of 10,159 markers, while a minimum of 80 selected SNP markers was employed to distinguish each of the CIMMYT maize inbred lines (CMLs) entries from one another.Characterizing germplasm identity and purity is an essential component of breeding and germplasm managementThe objective of this study was to characterize the potato varieties and advanced breeding clones bred by Korean potato breeding programs at a genome-wide level in the context of global potato breeding history. To achieve this, a 110-line diversity panel that included the available local varieties and advanced clones, as well as foreign potatoes, was genotyped using 8 K SNP markers and dosage genotype calling methods to assess genetic diversity and population structure. Furthermore, we constructed a merged dataset using ClusterCall software to compare Korean potatoes with those bred by national potato breeding programs in Japan, the United States, Europe, and other countries, to illustrate how local/regional potatoes have evolved in relation to landrace potatoes (pre-1930). Based on the separation of the potatoes by country , we employed selection signature analysis to identify candidate loci that could be associated with local adaptations. The percent heterozygosity of the clones was calculated to reveal higher heterozygosity for Korean potatoes. Based on calculations of the informativeness for dosage genotype calls, 10 SNP markers were selected that could identify all 393 clones used in this study.find.clusters function, confirming structured population, except no admixture clones .STRUCTURE analysis provided an estimation of the number of populations in the Korean potato germplasm panel. The estimation of the delta K value, using Evanno's method, showed the highest peak at K = 3 , indicating that the 110 clones in the panel could be grouped into three clusters based on differences in their genetic makeup. For the DAPC analysis, the lowest Bayesian information criterion (BIC) value of K = 3 was obtained using the The Ward dendrogram generated using Nei’s genetic distance and hierarchical clustering also revealed the presence of three clusters in the population represented by the 110 potato clones , 26 (70.3%), and 28 (75.7%) of the 37 foreign clones, respectively. In the Cluster I (Namjak group), the average percentage of foreign clones that were common across all three population structure analyses was 87.9%. Six Korean potato varieties  were common in all three methods, and two more Korean potato clones (Hongjiseul and Jaekyo-P-15) were commonly present in the DAPC and HC results. Thus, the four colored potatoes , which are pigmented in both their skin and flesh, were grouped together in the Cluster I by DAPC and HC (Table S9). The remaining Korean potatoes (over 86.5%) were divided into two clusters, in which either cv. Daeseo or cv. Sumi were present. In the Cluster II (Daeseo group), the average percentage of Korean potato clones that were common across all three population structure analyses was 75.3%, while that of the Cluster III (Sumi group) was 73.3%. DAPC showed the highest average percentage of varieties common to more than two methods across all three clusters, suggesting that the DAPC results could be more reliable than the STRUCTURE or HC results as described inminorAllele in the adegent R package following the tetraploid model. We can easily calculate the number of transitions across the samples for the genotype calls of specific markers, so that the value indicates the informativeness of the SNP markers used in this study. Unlike the polymorphic information content (PIC), informativeness is calculated using dosage genotype calls. The PIC ranged from 0.08 to 0.38 with a mean of 0.30, whereas the informativeness ranged from 0.25 to 0.79 with a mean of 0.64. The average observed heterozygosity (0.51) calculated using diploid genotype calls was smaller than the average percent heterozygosity (0.63) calculated using dosage genotype calls. The average distance among the clones in the same cluster ranged from 0.32 to 0.37. The Cluster II showed the highest heterozygosity among the clones, indicating that it was highly diverse, whereas the other two clusters showed lower heterozygosity.We also calculated population genetics parameters, some of which came from diploid genotype calls, dosage calls, or both Table . The minst) measures the genetic distance between populations. The Cluster III had the highest Fst value (0.18), while the Cluster II had the lowest (0.07), indicating that the clones in the former group are not currently breeding with one another, whereas those in the latter group share their genetic material through high levels of breeding. Tajima’s D statistic was used to compare the observed nucleotide diversity against the expected diversity under the assumptions of selectively neutral polymorphisms and a constant population size20. The value (3.37) of Tajima’s D using diploid genotype calls was smaller than that (4.04) obtained after converting the dosage forms  into diploid forms  for use in analysis packages that do not support polyploid data. The value of 4.04 is close to that (4.29) obtained in a diploidized version described by Pandey et al.14.The fixation index . DAPC was performed and the lowest Bayesian information criterion value was found to be 6 . Similar to the DAPC analysis using the Korean potato germplasm panel, the DAPC analysis divided the Korean potatoes into two well-defined clusters according to their genetic structure, one group containing cv. Daeseo and the other cv. Sumi. Interestingly, when these clusters were compared with the 110-line panel DAPC clusters, almost all the admixed clones (18 of 20), determined by the 110-line panel STRUCTURE analysis, moved from the Cluster II  to the Cluster III . The four flesh-colored potatoes, Jayoung, Hongyoung, Hongjiseul, and Jaekyo-P-15, moved from the Cluster I to the colored group (Table S10).We further investigated the Korean potato clones in the Korean potato germplasm panel using an extended genetic diversity panel that included 94 Japanese potatoes21. The population genetic structure was projected by the first two reduced features of the KLFDAPC with σ = 2, for the Korean potato clones and the potato varieties released from Japan, the United States, and other countries  led to an easily recognizable visualization of the duplicates among the 393 clones, whose pairwise genetic distances were zero or almost zero. The identified duplicates were Namjak vs. Irish Cobbler, Sumi vs. Superior, Daeseo vs. Atlantic, Daeji vs. Dejima, CO97043_14 W vs. MSQ070-1, Rosa_hari vs. Rosa, Russet Norkotah_hari vs. Russet Norkotah-S8 vs. Russet Norkotah-S3, Norin1_hari vs. Norin1, and InkaRouge_2x vs. Inka-no-mezame_2x. The former potatoes were from the 110-line panel and the latter from the 393-line extended panel.In fact, the Korean varieties Namjak, Daeseo, Sumi, and Daeji, in the 110-line panel are introduced and renamed from abroad as the cultivars, Irish Cobbler (Unknown), Atlantic (USA), Superior (USA) and Dejima (Japan), respectively. In addition, the foreign potatoes  in the 110-line panel, were placed beside the original varieties from the extended panel with genetic distance = 0, indicating that the potato clones maintained in Korea have the same genetic identity as the original ones..13. The HC dendrogram showed the chip processing market potatoes grouped together, as were the pigmented potatoes and Russet varieties.InkaRouge_2x and Inka-no-mezame_2x were duplicated, as described by Igarashi et al.13.The percentage of heterozygous SNP loci (percent heterozygosity) for the 393 lines is shown in Fig. P < 0.001).The Korean potato clones exhibited a higher average percent heterozygosity (65.6%) than the clones from Japan, the United States, and other landraces potatoes  according to a non-parametric Wilcoxon test . The use of ten highly informative SNP markers could identify all 393 clones used in this study, including the duplicate clones , being a power of discrimination equal to a high density SNP-set of 3977 markers. The MAF values for the selected 10 SNP-set were ≥ 0.40 except two markers , an essential regulator of plant defense, and leucine-rich repeat (LRR) family proteins  were identified, whereas candidate genes such as nuclear factor Y (Soltu.DM.04G019240), the cystathionine beta-synthase family protein (Soltu.DM.04G020750), the zinc finger CCCH-type family protein (Soltu.DM.09G006620), and ascorbate peroxidase (Soltu.DM.09G006560) were identified for abiotic stress tolerance (Table S13).For example, candidate genes encoding the RPM1 interacting protein . For example, Korean potatoes suitable for chip processing were grouped together with foreign chip processing potatoes and the colored potatoes, cvs. Hongyoung, Jayoung, Hongjiseul, Jaekyo-P-15, and Daekwan2-60, were placed in the pigmented group that included Red Maria, Chieftain, All Blue, Shadow Queen, Purple Majesty, Winema, Aino-aka, Dragon Red, etc. We also looked at the groupings of landrace potatoes (pre-1930)  in the HC analysis  and the KLFDAPC. In the KLFDAPC, they were placed centrally, overlapping the more recently bred potatoes from Korea, Japan, and the United States in different directions, visually supporting the history of potato breeding and how potato varieties have diversified according to various breeding strategies . In the past, many foreign varieties were introduced and tested in local Korean environments. However, only a few varieties have been cultivated. For example, the potato varieties Atlantic, Superior, Irish Cobbler, and Dejima have been introduced and released under the registered names Daeseo, Sumi, Namjak, and Daeji, respectively22. Russet potatoes are unique to the United States, and are not selected by breeding programs in either Korea or Japan, taking consumers’ preferences into account. Approximately 35% of the potatoes in Japan are used for starch production13 and many modern Japanese varieties have T-type chloroplast DNA23, supporting the result that Japanese potatoes were not differentiated from European ones in our study. Unfortunately, most European varieties do not perform well in Korean environments. Korean potato programs have been pursuing the development of diverse market class potatoes, such as potatoes suitable for chip processing, French fries, and double cropping (spring/summer season products are used as seeds for winter season production in the south) under low input conditions. Accordingly, several promising varieties have been developed and released for agricultural deployment as alternatives to foreign varieties such as Daeseo (Atlantic) or Sumi (Superior). In terms of the high heterozygosity of Korean potatoes .Regarding the approaches employed in this study to reveal the genetic diversity and population structure of cultivated potatoes, dosage genotype calls could lead to more reasonable and accurate results than diploid genotype calls Table . If no pSoltu.DM.09G018840), LRR/NB-ARC domain-containing disease resistance proteins , nuclear factor Y (Soltu.DM.04G019240), the zinc finger CCCH-type family protein (Soltu.DM.09G006620), and ascorbate peroxidase (Soltu.DM.09G006560), were identified. RPM1-interacting protein 4 (RIN4) is a conserved plant immunity regulator that has been extensively studied and can be modified by pathogenic effector proteins25. RIN4 plays an important role in both pattern triggered immunity and effector-triggered immunity. Most disease resistance genes in plants encode nucleotide-binding site LRR proteins26. The nuclear factor Y complex plays multiple essential roles in plant growth, development, and stress responses27. CCCH genes are involved in plant developmental processes and biotic and abiotic stress responses29. The less-common CCCH type of zinc finger superfamily proteins are important in plant development and tolerance to abiotic stresses such as salt, drought, flooding, cold temperatures, and oxidative stress29. Ascorbate peroxidase catalyzes the conversion of H2O2 generated under environmental stress into H2O,therefore, it is of great importance as a key antioxidant enzyme in maintaining cellular homeostasis30. Although some important candidate genes were detected under selection, it is worth mentioning that the genome coverage of the current 8 K SNP array may be low, resulting in a lack of information on some important genomic regions harboring selection signatures. This issue may be addressed by using a greater density of SNPs.We identified several candidate genes, with 3977 SNP markers, related to biotic and abiotic stress tolerance that may be involved in adaptation to local environmental conditions. Candidate genes with putative functions, such as the RPM1 interacting protein  system and validated for routine use in breeding programs as well as germplasm conservation.In terms of methods to enable selection of a small number of SNP markers for the evaluation of germplasm identity and purity, we invented the number of transitions across the samples for the genotype calls of specific markers, rather than the use of the previously described selection criteria such as high minor allele frequency, sampling of clustered SNP in proportion to marker cluster distance and a uniform genomic distributionOverall, these results on the molecular characterization of cultivated potato clones could help understand how potato cultivars diversify for distinct market classes depending on each countries’ breeding strategies and could assist in genomics-facilitated breeding efforts to create new varieties that are better adapted to climate change and meet market demands.The germplasm used in this study comprised 110 diverse potato clones, including 73 Korean potato clones  selected over 40 years by a potato breeding program in Korea, and 37 potato collections from various countries  (Table S1). Although nine of the foreign clones had an unknown origin, they were selected for this study according to their agronomic performance. All potato clones are available as tissue culture plants or tubers for field evaluations at Highland Agriculture Research Institute, National Agrobiodiversity Center, Rural Development Administration in Korea. Plant materials has been obtained and all experimental protocols in the present study complies with international, national, and institutional guidelines.9, according to the manufacturer’s protocol . DNA was extracted from young leaf tissue from individual tissue culture plants, greenhouse-grown plants, or field plants using the QIAGEN DNeasy Plant Mini Kit , quantified using the DeNovix® DS-11 + Spectrophotometer (DeNovix Inc.), and adjusted to a concentration of 50 ng‧μL−1.The 110 lines in the Korean potato germplasm panel were genotyped using the Infinium 8303 Potato ArrayminorAllele in the R package adegenet31. After filtering, 6575 SNPs remained (Table S3) and were distributed across the 12 chromosomes . In addition, genotypes in nucleotide format were obtained in GenomeStudio, and a tetraploid format STRUCTURE input file (Table S4) was produced using a custom Python script. To determine the market class, phenotypic evaluations including tuber shape, tuber sucrose/glucose concentration, and chip color were carried out as described by Hirsch et al.10.The data were analyzed using Illumina GenomeStudio software according to the GenomeStudio® Polyploid Genotyping Module v2.0 Software Guide . The SNP genotype data were filtered to exclude SNPs that were monomorphic, had > 10% missing data, or mapped to duplicate places in the genome. In addition, the genotype data were filtered using < 0.05 MAF, calculated by the function 7, and polyBreedR  (https://polyploids.r-universe.dev/articles/polyBreedR/Vignette1.html), which have been developed to generate dosage genotype calls based on different models, were compared in terms of reproducibility for three independent replicates of the 16 Korean varieties (Table S5).The three software packages, GenomeStudio (Illumina software), ClusterCall (R package)The average number of loci with contradicting calls within these replicates after filtering  was only 0.2%, with a maximum of 0.3%, in GenomeStudio, whereas in ClusterCall, the number of markers with discordant calls between replicates was only 0.4%, with a maximum of 0.8%. There were no significant differences between the two software programs.geno_call of polyBreedR, which employed the normal mixture model implemented in the R package fitPoly, the average difference was 3.8%, with a maximum of 6.3%. Thus, ClusterCall was used to generate dosage genotype calls for the merged dataset from different sources of raw data, as described below .In contrast, for the function 13 were kindly provided by Prof. Kazuyoshi Hosaka of the Potato Germplasm Enhancement Laboratory, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido. The theta data for the United States and other countries  were from publicly available SNP data produced by Schmitz Carley et al.7. The XY data were converted to theta data, and ClusterCall software was used to obtain the dosage genotype calls. For the Korean dataset, in.idat format, dosage calls were also obtained using ClusterCall. The three dosage genotypes were merged into a single dataset based on the common markers across each dataset and were used for downstream analyses. The market class designations in the merged dataset were as described by Hirsch et al.10 and Igarashi et al.13.For the Japanese dataset, the XY data of 94 potatoes, including 88 Japanese varieties, four Japanese landraces, and two Japanese advanced breeding lines31 in R32 and the allele frequency divergence among the clusters, fixation index, and average distance among individuals in the same cluster were calculated using STRUCTURE v2.3.434. Tajima’s D was computed in TASSEL v5.035 using modified genotype calls, which were obtained by converting the dosage forms  into diploid forms . To compare the genetic diversity parameters of the dosage calls with those of the diploid calls, the allele frequency, PIC, heterozygosity, and inbreeding coefficient were calculated in the snpReady36 package in R, using the diploid genotype calls (Table S6).The dosage genotype call data were used to study the genetic diversity. The MAF was calculated in the adegenet package34 with an admixture model and five cluster-dosage genotype calls. Three replicates were performed for each value of K (number of populations) from 1–10 with a burn-in time and the number of Markov Chain Monte Carlo replicates after burn-in set to 10,000. The optimal number of subpopulations was obtained based on the delta K value calculated by the Evanno method using STRUCTURE HARVESTER38.Population structure was determined using STRUCTURE software31 in R was used to identify and describe clusters based on genetic relationships from tetraploid genotyping data. The find.clusters function was used to identify the number of clusters within the population.The adegenet package39 between clones was calculated with the StAMPP package40, using the dosage SNP genotype calls (Table S8). The resulting matrix was used to build a dendrogram using HC (method = “ward D2”) implemented in the dendextend41 and circlize packages42 in R.Pairwise Nei’s genetic distance21 in R was used to obtain the pre-processed data containing the computed 10 principal components, followed by computing the kernels of local genetic features. The σ values of the KLFDAPC were adjusted to change the shape of the first two reduced features.The dataset used for KLFDAPC analysis can be found in Table S7. The KLFDAPC package43 and the cross-population extended haplotype homozygosity (XP-EHH)44. iHS is known to be sensitive to ongoing or incomplete selection signatures, whereas XP-EHH is best at revealing the selection signatures close to fixation44. SHAPEIT245 set to the default options  was used to obtain phased haplotypes for iHS and XP-EHH analyses, implemented using the rehh package46 in R. Candidate selection sweep regions were defined as the SNP regions under selection by both the applied statistics. Genes spanning ~ 100 kb upstream and downstream of the candidate selection regions were retrieved from the genome browser window of the Spud DB database (http://spuddb.uga.edu/).Selection signature analyses were carried out using the filtered high quality 3977 SNPs for a total of 263 potato clones from Korea, Japan and the United States in the 393-line extended genetic diversity panel (Table S13) by applying two complementary statistical methods, the integrated haplotype homozygosity score (iHS)Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.Supplementary Information 14.


Haliotis discus hannai, performed genome‐wide association studies (GWAS) analysis for heat tolerance to detect single nucleotide polymorphisms (SNPs) and candidate genes, and assessed the potential of genomic selection (GS) in the breeding of abalone industry. A total of 1120 individuals were phenotyped for their heat tolerance and genotyped with 64,788 quality‐controlled SNPs. The heritability of heat tolerance was moderate (0.35–0.42) and the predictive accuracy estimated using BayesB (0.55 ± 0.05) was higher than that using GBLUP (0.40 ± 0.01). A total of 11 genome‐wide significant SNPs and 2 suggestive SNPs were associated with heat tolerance of abalone, and 13 candidate genes were identified, including got2,znfx1,l(2)efl, and lrp5. Based on GWAS results, the prediction accuracy using the top 5K SNPs was higher than that using randomly selected SNPs and higher than that using all SNPs. These results suggest that GS is an efficient approach for improving the heat tolerance of abalone and pave the way for abalone selecting breeding programs in rapidly changing oceans.Aquaculture is one of the world's fastest‐growing and most traded food industries, but it is under the threat of climate‐related risks represented by global warming, marine heatwave (MHW) events, ocean acidification, and deoxygenation. For the sustainable development of aquaculture, selective breeding may be a viable method to obtain aquatic economic species with greater tolerance to environmental stressors. In this study, we estimated the heritability of heat tolerance trait of Pacific abalone  AquaticHaliotis fulgens; Calderón‐Liévanos et al., Haliotis rufescens; Hart et al., Haliotis cracherodii; Ben‐Horin et al., Haliotis rubra), and green‐lip abalone  and genomic selection (GS). GWAS is a powerful tool for connecting phenotypes of organisms to genetics and for investigating the molecular mechanisms underlying specific phenotypes  and candidate genes, and evaluated the potential of GS in the breeding of abalone industry. Our results have guiding significance for the heat tolerance breeding of marine economic species to cope with the climate changes and food problems around world.In this study, a total of 1120 individuals of Pacific abalone 22.1The Pacific abalones used in this study were from a commercial breeding company . A total of 1121 individuals were randomly selected in the population constructed from 400 broodstocks with a random mating design.Seventeen‐month‐old abalone  were labeled and acclimated at 20°C for 2 months. A modified and more efficient method based on method published by Yu, Wu, et al.  was used2.2The foot muscle of 1121 tested individuals was collected and stored at −80°C for DNA extraction. Genomic DNA was extracted using the DNeasy 96 Blood & Tissue Kit (Qiagen) and quantified by Nanodrop2000 (Thermo Scientific). Sample genotyping was carried out by MOL BREEDING  using a 40K multiple single nucleotide polymorphism (mSNP) array and 87,959 SNPs were obtained. Quality control of sequencing data was performed using PLINK software  and a kinship matrix as fixed and random effects, allowing control of the effects caused by population stratification and relatedness  and the QQ plot of the original significance level  were produced by the qqman package  = 6.11) based on the Bonferroni test  = 5)  in PLINK before association analysis Figure . Then, e Turner,  in R. Th2.4ASReml‐R ; g is the random effects vector, which is assumed to follow ~N , where G is the genomic relationship matrix , where I is the identity matrix and σe2 is the residual variance. The phenotype variance (σP2) for HAD was calculated as: σP2=σg2+σe2, and the narrow‐sense heritability was computed as the ratio between additive genetic variance and phenotype variance: h2=σg2/σP2.The variance components for HAD were evaluated using π, and normally distributed with a mean equal to 0 and a locus‐specific variance with probability (1 − π). The Bayes B model is the following:y,Xb, and e are the same as terms described in Equation  are obtained from a Gibbs scheme‐based Monte Carlo Markov Chain (MCMC) iterations. A total of 100,000 iterations were used in the Gibbs sampling, and the first 3000 iterations were discarded as burn‐in cycles.BayesB is a mixed model, in which a priori SNP effect is assumed to be zero with probability bability  − π. The2.5The fivefold cross‐validation (CV) was used to compare the prediction accuracy of GBLUP and BayesB. The 1120 individuals were randomly separated into five groups of approximately equal size. Each group was used only once as a testing set in five analyses. For each analysis, the phenotypes of the testing set were set as “missing” and the remaining phenotypes were used as the training set. For the two methods, prediction accuracy was calculated as the Pearson correlation between phenotypes and GEBVs. The fivefold CV was repeated 10 times for each CV scenario, and the average values were calculated.The bias of the GEBV was calculated as the regression coefficient of observed phenotypes on GEBV. A regression coefficient of <1 or >1 indicates that GEBV is an overestimation or underestimation of the breeding value (BV). Therefore, a significant deviation from 1.0 is usually interpreted as prediction bias.2.6p values of GWAS in ascending order and randomly selected markers were developed. Datasets containing different numbers of SNPs were generated: 500, 1000, 5000, 10,000, 20,000, 40,000, and all SNPs. For each SNP subset, the GS model was built, and corresponding predictability was calculated through fivefold CV as described above.For investigating the appropriate numbers of SNP for the two GS models, two SNP panels based on the 33.1rd h and the cumulative drop rate reached 50% at the same time. The heat exposure lasted for 6 h and the final attachment rate, at the end of the 6‐h exposure, was 8.75%.The attachment curves of abalones under 32°C heat exposure are shown in Figure 3.2p value = 5.62e‐10; Table Eleven genome‐wide significant SNPs and two suggestive SNPs were found Figure . These Sl(2)efl), aspartate aminotransferase (got2), transmembrane domain protein (ptch1) and zinc finger protein family (znfx1). The candidate genes serve essential functions in multiple biological processes such as in transmembrane, metabolic pathways.After mapping onto the reference genome (unpublished), a total of 13 genes were identified across all 13 SNPs exceeding suggestive threshold Table , includi3.3The variance components and heritability for HAD estimated using GBLUP and BayesB are given in Table 3.4The predictive performance of GBLUP and BayesB methods with a fivefold cross‐validation was determined. The prediction accuracy of BayesB was 0.55 ± 0.05 which was higher than GBLUP (0.40 ± 0.01). The regression coefficients of GEBV on phenotypes were 0.98 ± 0.08 and 1.04 ± 0.02 for BayesB and GBLUP. The predictions obtained by both methods were similar.3.5Based on the GWAS results, the top 500, 1K, 5K, 10K, 20K, and 40K SNPs were selected as subset data for genomic prediction, and randomly selected SNPs were also performed for comparisons. The prediction accuracy using the top SNPs selected was higher than that of randomly selected SNPs and the accuracy predicted by the BayesB method was higher than the GBLUP method Figure . For the4Saccostrea glomerata had greater extracellular pH when exposed to elevated pCO2 compared to wild oysters, and were resistant to ocean acidification  highlighted that selective breeding programs and other more advanced biotechnological solutions  may play a key role to cope with a changing ocean , the heritability for heat tolerance‐related traits ranged from 0.41 to 0.51  was higher than that using GBLUP (0.40 ± 0.01). The difference between GBLUP and BayesB was largely dependent on the model of genetic variation used to simulate the underlying variation. Bayesian models often have an advantage over the GBLUP model when the architecture of the trait is (partly or entirely) controlled by a number of major quantitative trait loci (QTLs). However, Clark et al.  reportedOplegnathus fasciatus, the predictive abilities using informative SNPs sorted by GWAS showed a more stable upward trend compared with the predictability estimated by randomly sampled SNPs from the genome as the SNP numbers increased  and provided insights into the genetic basis of AHT in fish and will be helpful for heat‐tolerance improvement of large yellow croaker  belong to a group of human NFX1 transcription factors that may act as transcriptional repressors in regulating the duration of inflammatory responses efl) and the low‐density lipoprotein receptor (lrp5) also play important roles in the maintenance of protein homeostasis and transport of nutrients and vitamins (Jagla et al., A total of 13 candidate genes associated with the heat tolerance of abalone were identified. Among them, several related genes serve essential functions in multiple biological processes such as in transmembrane (May et al., In conclusion, ocean warming and marine heatwave events can be lethal to the survival of abalones in the aquaculture areas and cause ecological and economic losses to global aquaculture. However, it is a feasible way to explore the potential to augment the capacity of abalone to tolerate stress in an era of unprecedented climate change and food shortage problems around the world. The application of modern cutting‐edge molecular tools allows us to efficiently obtain abalone species/strains with heat tolerance. In this study, our results found that GS is an effective approach and can accelerate selective breeding process for heat tolerance of Pacific abalone. A total of 13 markers and 13 candidate genes were identified to be associated with heat tolerance of Pacific abalone. Overall, these molecular markers would further help to clarify the genetic architecture and molecular mechanisms of heat tolerance in abalone and support the development of marker‐assistant selection in abalone breeding programs.The authors declare that they have no conflict of interest.Fig S1Click here for additional data file.


During recent decades, the complexation of organic ligands toward several metal ions of s-p and d-block has been applied as a plan to enhance its antioxidant performance. Due to their wide range of beneficial impacts, coordination compounds are widely used in industries, specifically in the medicinal and pharmaceutical fields. The activity is generally improved by chelation consequently knowing that the characteristics of both ligands and metals can lead to the development of greatly active compounds. Chelation compounds are a substitute for using the traditional synthetic antioxidants, because metal chelates present benefits, including a variety in geometry, oxidation states, and coordination number, that assist and favor the redox methods associated with antioxidant action. As well as understanding the best studied anti-oxidative assets of these compounds, coordination compounds are involved in the free radical scavenging process and protecting human organisms from the opposing effects of these radicals. The antioxidant ability can be assessed by various interrelated systems. The methodological modification offers the most knowledge on the antioxidant property of metal chelates. Colorimetric techniques are the most used, though electron paramagnetic resonance (EPR) is an alternative for metallic compounds, since color does not affect the results. Information about systems, with their benefits, and restrictions, permits a dependable valuation of the antioxidant performance of coordination compounds, as well as assisting application in various states wherever antioxidant drugs are required, such as in food protection, appropriate good-packaged foods, dietary supplements, and others. Because of the new exhaustive analysis of organic ligands, it has become a separate field of research in chemistry. The present investigation will be respected for providing a foundation for the antioxidant properties of organic ligands, future tests on organic ligands, and building high-quality antioxidative compounds. The lipid radicals form stable spin adducts with 4-POBN, giving rise to the detected signal that corresponds to three doublets. The hyperfine coupling parameters of the formed adducts associated with the ROO− radical are aN = 15.77 G and aH = 2.86 G. Under severe conditions (presence of atmospheric O2 at 75 °C), the Mg-iso complex was able to scavenge the lipid radicals from both Longissimus lumborum and Psoas major muscles of fresh minced pork 3+ to [Fe(TPTZ)2]2+ is accompanied by a color change from light to dark blue. Formation of the reduced complex is detected by UV–vis spectroscopy by monitoring the absorption band at 593 nm 2+ to [Cu(dmphen)2]+ is accompanied by a change in color from blue to yellow for the reduced complex. The formation of the reduced complex is detected by UV–vis spectroscopy by monitoring the absorption band at a wavelength of 450 nm  7–10 (wherever M = Cd(II), Cu(II), Co(II), and Zn(II)) prepared as presented in  and wereectively .2]2 through bidentate 4-, 5-diazafluorene-9-one (dafo) and resulting organic ligands (L1–L3) (11–13) in the existence of (NH4)[PF6] yielded cations chelates having general formula [MLCl(arene)]PF6 {M = Ru, arene = benzene ; M = Ru, arene = p-cymene ; M = Rh, arene = Cp* ; M = Ir, arene = Cp* ;   studied the reaction of [(arene)MCl3) (13)] .To evalu2+ and Ni2+ and Cd2+ and Pb2+ (considered as chelates 26–29) were prepared through a new tetra-dentate organic ligand (L) (25), obtained through condensation of  developed a new organic ligand (25), in combination with the usage of less-expensive elements, through significant therapeutic potential and have prompted huge interest in the growth of organic ligands. Thus, four various metal chelates comprising Mnven here . Researc2, Co2+, Ni2+, and Cu2+ derived from the ligand 2-(thiophene-2- yl)-1-(thiophen-2-ylmethyl)-1H-benzo [d] imidazole. The antioxidant performance for tested ligand, as well the tested chelates estimated by DPPH process, were compared to the standard antioxidant. The data obtained exhibited that the antioxidant performance of the tested ligand, as well as their chelates, were moderate and that the Cu(II) chelate had a great performance, outdoing ascorbic acid. We could determine that the chelation of the tested ligand stimulated the antioxidant performance. The antioxidant performance considerably improved for the electron-withdrawing effect of the M2+ ion, which simplified the issue of H2 to decrease the DPPH radical NO3 chelate presented maximum free radical scavenging activity (IC50 ≥ 50 μg) between all of the studied chelates in association with BHA (IC50 ≤ 50 μg). An in silico test was completed through MOD with EGFR tyrosine kinase. The results displayed which tested materials had substantial anticancer and antioxidant performance ·2H2O (M:L ratio 1:3) (62), though the 1:1 forms were discovered in an aqueous medium at the optimal pH of 6 pH 10 ([Eu(CFA)] and [Eu(CFA)(OH)]) (50 for Eu(III)-caffeinate could not be determined using the DPPH approach because it precipitated at a higher concentration. In CUPRAC and FRAP tests, Eu(III)/CFA chelate showed decreasing assets, although lower than CFA -caffeinate and the CFA chelate and CFA, respectively ) . ElectroA)(OH)]) . The IC5ectively .2 or e− donation for EPM and Ascorbic acid, such as BHA, BHT, and α–tocopherol was projected through the DPPH. In this respect, the result exposed that the tested prepared EPM had not reported effective activity as a radical scavenger and did not have H2 donor performance. The EPM results demonstrated a definite Fe-binding potential, suggesting that their purpose as peroxidation protections might be linked to their iron-binding ability. The studied metal chelating performance of EPM and standards was studied toward decline into the demand of α-tocopherol < EPM < EDTA, which were 39.0%, 53.2%, and 85.7% at the final absorption, correspondingly [MedetalibeyogluH (2022) studied and synthesized the antioxidant activity of (EPM)2-ethoxy4-[-methyl]-phenyl-4-meth oxybenzoate . The tesondingly .2O)2] (65) and [V(L)(O) (H2O) (SO4)] (66) chelates prepared from an (E)- 2-(((2-((2-hydroxyethyl)amino)quinolin-3-yl) methylene) amino)ethan-1-ol ligand C14H17N3O2 (64), CoCl2·6H2O and VOSO4 in Me OH solutions (50 of 16.01 μg/ mL) than the free ligand, (VO) chelate. MOD study also recommended a lot of attention to the biological performance of the tested Co and VO chelates. Accordingly, molecules that have an antioxidant action could decrease the absorbance at 517 nm; this is related to the DPPH assay shifting hue during the recombination process pyridine-3,6(7H)-dicarboxylate) [TBHPC] (87) was generated by combining 6-tert-butyl 3-ethyl 2-amino-4,5-dihydrothieno pyridine-3,6(7H)-dicarboxylate with 2-hydroxy benzaldehyde. The metal chelates of the unusual organic ligands Fe(II), Co(II), and Ni(II) (88–90), as shown in , were pr2+, Pd2+, and Fe3+ ions interacting through CPTP (91) thiazole derivative ligand. In in vitro tests, the antioxidant performance of studied compounds was screened. All chelates revealed control than free ligand ineffective behavior, particularly the CPTPPd (92) compound  had a powerful antioxidant ability with only an IC50 value of 31 g/mL, which is not far from that of ascorbic acid -5,7thiazol ligand (MPTP) (95), as shown in 50 results to study their actual control of them. Thus, scavenging capabilities for the studied molecules were assessed for the color decay grade of DPPH -5,7-thiazol], as shown in 50 data. The chelates presented a vital antioxidant performance that was greater than the standard medicine. That might be associated with the coordination structure or redox environments. Moreover, the MOE docking approach clarifies all contact properties that occurred by 2 k4 l protein, which agrees through in vitro values  (103), [VO(Hdhbh)(Phen)]·1.5H2O (104), [Zn(Hdhbh)2] (105), [MO2(dhbh)H2O] (106), [MO2(dhbh)CH3OH] (107), [Ru(PPh3) (dhbh)Cl (H2O)] (108) and [Pd(Hdhbh)Cl] (109) complexes. The antioxidant performance of the tested complexes was assessed in relation to the DPPH radical, and it has been initiated that VO(IV) complex (104) demonstrated the maximum radical scavenging effectiveness equivalent to ascorbic acid as a reference antioxidant. The DNA binding assets of the tested compounds have been studied through electronic spectra with DNA cleavage through gel electrophoresis. The values presented in Abo afia et al. (2018) prepared and characterized new VO, Zn, Mo, Ru, and Pd chelates . AnalytiThe versatility of organic ligands and their derivatives’ prospect of modifying their possessions through varying the metallic chelate and the ligands open the door for the addition of other roles as well as antioxidant performance. While linking antioxidant active groups is promoting the antioxidant performance, it is still not as effective for discovering the antioxidant performance for a previously produced one, as well preparing novel antioxidant active group chelates with extra possessions. The molecular structure diversity of metal complexes has been recognized as promising for the discovery and application of new antioxidant compounds. This review covered the improvement achieved in their antioxidant activity with coordination to a wide range of metal centers and showed that various factors are involved in the antioxidant activity of a metal complex. In this context, coordination to a metal center can be seen as a useful tool to overcome the limitations of an organic antioxidant because it potentializes its activity by stabilizing the ligand structure in a more rigid geometry and because the metal complex is active by itself. For instance, the metal center acts as a modulating agent and in its high oxidation state the electron-withdrawing ability of the metal ion affects the antioxidant property of the resulting complex by transferring the electron density of the ligand to the metal center. A reverse behavior occurs when the metal is in its low oxidation state. These effects promote a considerable change in the electronic charge distribution of the ligand that facilitates the electron–proton loss and thus increases the radical scavenging ability of the resulting complex. Even while metal ions such as Mn, Fe, Cu, Co, and Zn are complex in many biological structures and play a crucial role in the functioning of organisms, it is important for reducing toxicity in the design of novel chelates antioxidants. The effective study of the sources of natural antioxidants and conniving new antioxidant molecules needs dependable systems of antioxidant performance estimation. Predictable techniques for the extent of antioxidant performance are desired and exact organizational procedures of chelates involve an extensive challenging stage. The working pH is a critical selection factor of the antioxidant assay. There seem to be examinations that are acidic (FRAP), neutral (CUPRAC), and alkaline . Furthermore, the antioxidant study’s sensitivity for both hydrophilic, as well lipophilic antioxidants, is critical. Although ABTS and CUPRAC analyses may detect hydrophilic and lipophilic antioxidants, certain systems  solely measure hydrophilic antioxidants, as others were exclusively applicable to hydrophobic systems (DPPH). Around the same period, the color combination in the feed solution can cause absorbance changes that have a greater negative effect on the event of discoloration effects (ABTS and DPPH) than on color-formation reactions (FRAP and CUPRAC). As a result, there is still considerable potential in this study area for generating new analytical techniques for evaluating the antioxidant potential of substances, particularly in food materials. The design of biomolecule senor and their usage in the antioxidant study, for example, could be of great interest and benefit in the analysis of the project planning phase. The large variety of biorecognition elements, including enzymes, aptamers, DNA/RNA, and whole cells, are critical in designing electrical biosensors for the study of antioxidants. The accessibility, efficient analysis, and use of a small sample amount are all benefits of biosensing in the research of antioxidants using complicated data.


Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57 K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2–10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50–100 kb which are usual distances between markers of the medium-density chip. Next-generation sequencing (NGS) has transformed the fields of quantitative, ecological and evolutionary genetics by enabling the discovery and cost-effective genotyping of thousands to millions of variants across the genome, allowing for genome-wide association studies (GWAS) of complex traits, genomic selection (GS) through accurate inference of relationships among individuals , inbreedTwo main methods are employed for large-scale and genome-wide SNP genotyping. Array-based methods use flanking probe sequences to interrogate pre-identified SNPs (often named “SNP chips”). The alternative genotyping-by-sequencing (GBS) methods call SNPs directly from the genome . In GBS Medium (∼thousands to tens of thousands of loci) and high (∼hundreds of thousands of loci) density SNP chips have been routinely developed for commercial species to perform genomic selection  and to iOncorhynchus mykiss) is a major academic model for a wide range of investigations in disciplines such as cancer research, toxicology, immunology, physiology, nutrition, developmental or evolutionary biology in addition to quantitative genetics and breeding  has then been developed  libraries , reducedeveloped  and prodeveloped , GWAS aneveloped . Howevereveloped . Of the eveloped , and in To overcome these limitations as well as to get access to a more powerful tool for GWAS and population genetics studies in rainbow trout, the aim of our study was to develop a high-density SNP array. To develop this resource for rainbow trout, we made use of a large set of resequencing data from 31 doubled haploid (DH) lines from Washington State University (WSU) and Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE). In the United States, 12 WSU DH lines have been created by androgenesis  while inIn this study, we describe how we overcame the limitations of duplications in the rainbow trout genome, in order to identify and locate polymorphisms. We describe the subset of detected SNPs that was selected for inclusion on a custom high-density SNP chip. It was used to genotype 463 samples from two different French commercial populations. We test the genotyping success rates, that is, the proportion of SNPs included on the array that are polymorphic and successfully genotyped. We demonstrate the utility of this SNP chip to infer linkage disequilibrium in the genome of this species.A second database (USDA2) with 17,889,078 SNPs coming from resequencing of 24 USDA samples was added to the initial USDA1 database. The samples were composed of 12 representatives from the USDA-NCCCWA odd-year class and 12 from the even-year class as previously described  coming from 11 isogenic lines. The second batch contained sequencing data from 17 samples  coming from 17 isogenic lines . Overall, 10 out of the 19 isogenic lines were sequenced twice. This resulted in a total of 8,911,630,867 paired reads with a median of 321,575,464 per sample.-stand_call_conf 30 -mbq 10), leading to 12 vcf files. A variant reference file containing 1,207,861 high quality SNPs was generated by keeping variants with QUAL ≥ 1,050 from the vcf files. This file was then used for the recalibration step, using GATK BaseRecalibrator and PrintReads. The recalibrated BAM files were then used as input for the variant calling step using GATK HaplotypeCaller in ERC GVCF mode. The resulting 12 GVCF files were then merged into a single vcf file containing 24,944,575 variants using GATK GenotypeGVCFs. The vcf file was then filtered as follows using GATK VariantFiltration: DP < 120; MQ < 30.0; QUAL < 600; AN<12. To filter out putative PSVs , we filtered out variants with heterozygous genotypes in at least two of the 12 doubled haploid samples. The filtered vcf file from the first batch contained 11,113,836 variants.Sequence reads from each of the 12 samples from the first batch were mapped to the Swanson rainbow trout reference genome . This constitutes the INRAE1 variants dataset which includes 14,439,713 SNPs.The final step for variant calling was to intersect the 3 calling datasets using VCFtools_0.1.12a , to keepUsing a vcf_filter.py homemade script to parse VCF file, INRAE1 dataset was filtered to keep only bi-allelic SNP localized on the 29 trout chromosomes or mitochondrial chromosome, fully genotyped for all 29 samples. As 10 isogenic lines were duplicated, we also checked genotype consistency and removed SNP with more than 1 isogenic line genotype discordance. Finally, we kept one sample per isogenic line (with the deepest sequencing) and filtered out SNP with more than 1 heterozygote genotype as they may represent duplicated genome regions. Among the 14,439,713 variants, we kept 10,286,009 SNPs (71.23%).We merged them using BCFtools with a second dataset INRAE2, containing 14,478,077 SNPs called from 60 samples of a commercial line from “Les Fils de Charles Murgat”  and whose resequencing was described in This merged dataset was filtered like the merged USDA dataset, to keep bi-allelic SNP localized on the 29 trout chromosomes of the Swanson reference genome or mitochondrial chromosome, with a MAF >1%. The final INRAE cleaned database contained 16,466,188 SNPs.https://doi.org/10.5281/zenodo.6657091).A total of 32,372,492 distinct SNPs were selected for consideration for the HD chip, from a combination (BCFtools merge) of the USDA and INRAE databases  may be reincluded. Thus, for a first SNP preselection, in addition to filtering SNPs with MAF ≥10% in both the USDA and INRAE databases, we applied filters on bi-allelism variant and on a maximum number of 4 heterozygote INRAE isogenic lines.1) Unicity of at least one side 35 bp-sequence for each SNP. This was done by blasting  Trimming of each side 50 bp sequence if it contained more than 3 successive N. Variants were kept if at least the shortest trimmed sequence contained 20 bp and the other 50 bp  according to their specifications.From this first submission to Affymetrix quality control, only 457,086 SNPs were qualified as recommended to be designable for the HD array and among them, only 351,755 were not ambiguous, meaning they were not of the type [G/C] or [T/A] that would require 4 probes instead of only two to distinguish the alleles.To get sufficient recommended variants and to avoid the selection of markers that will use twice the space used by the others on the HD array, we decided to resubmit a large second set of variants to Affymetrix quality check. The same procedure was applied to produce a second more relaxed preselected set of SNPs by keeping SNPs with a MAF ≥10% in the INRAE dataset only. This second preselection contained 533,637 additional SNPs. Among that additional set, 134,086 SNPs were specific to the INRAE dataset while the others were also present in the USDA dataset but with MAF below 10%.We merged the first recommended set of 457,086 SNPs with this additional set of 533,637 SNPs. Then we removed all ambiguous SNPs of type [G/C], [C/G], [T/A] or [A/T]. Finally, densities were adjusted such that in regions with more than 30 SNPs retained per 100 kb by the previous filters, we only kept SNPs with MAF ≥15% in at least one of the two INRAE or USDA databases.This procedure resulted in a selection of 815,525 SNPs for the final submission in October 2020 to Affymetrix for assessment of the suitability of the SNPs for inclusion on a custom AXIOM 96HT SNP chip. Of the submitted SNPs, a total of 623,544 SNPs were deemed to be “designable”  in either the forward or reverse flanking sequence based on the Affymetrix pconvert score.The INRAE and USDA research teams were willing to keep in the HD chip design the informative markers from the 57 K chip. Therefore 41,999 SNPs out of its 57,501 SNPs were designable in either forward and reverse directions and were kept for the HD chip design.At the only exception of 8 specific SNPs, all the markers had a unique position on the Swanson reference genome and MAF > 5% in at least one French or North American population. Among them, 38,826 SNPs were also put on a 200 K chip that was built on 120 resequenced mostly “wild” genomes from over 40 locations from Russia, Alaska Canada down through Washington, Oregon and California .In total 664,531 SNPs corresponding to 701,602 probesets  passed the Affymetrix final quality control to be designed on the custom HD Axiom array. Only 40,987 of the 41,999 SNPs from the 57 K chip remained on the HD final design.Among the selected SNPs, 664,503 were mapped on the 29 chromosomes of the Swanson reference genome , while 2Based on the Swanson reference genome mapping, the average (median) SNP density on the chromosomes was 293 (324) SNPs per Mb , with SNOncorhynchus mykiss as long reads-based de-novo assembly for a second WSU DH line, named Arlee line, had been performed. Because Arlee lineage was closer from the INRAE isogenic lines .Given that the objective of our work was to get a powerful genomic tool with a large number of SNPs useful in diverse rainbow trout populations, we included fish from two French commercial lines that were genetically distinct from the lines used from the SNP discovery step.We used fin samples collected from “Bretagne Truite”  and “Viviers de Sarrance”  commercial lines, hereafter named LB and LC lines respectively, that were sampled for the FEAMP project Hypotemp (n° P FEA470019FA1000016). Pieces of caudal fin sampled from 463 fish (288 from LB line and 175 from LC line) were sent to Gentyane genotyping platform  for DNA extraction using the DNAdvance kit from Beckman Coulter following manufacturer instructions and genotyping using the newly constructed HD SNP array.The first round of quality control was done by ThermoFisher software AxiomAnalysisSuite™ with threshold values of 97% for SNP call rate and 90% for sample call rate. All the 288 individuals of the LB line passed the preliminary control, while 174 out of the 175 individuals from LC line passed the control quality. Following array hybridization and imaging, genotypes were called using default settings in the Axiom Analysis Suite software and exported from the software in PLINK  format.In addition, we used the WGS information of 20 samples sequenced in www.cog-genomics.org/plink2) to calculate allele frequencies, filter SNPs at low MAF or individuals with high identity by descent (IBD) values and derive linkage disequilibrium (LD) measured as the correlation coefficient r2, using the mapping of the SNP probe flanking sequences to the Arlee genome.We then used PLINK v1.9  between all pairs of SNPs on the same chromosome and at physical distances up to 1 Mb was then calculated using the PLINK options “--r2 --ld-window 50000 –ld-window-kb 1001 –ld-window-r2 0.0”. The r2 values were binned into 2 kb units and per-bin averages calculated using R , the average SNP density on the chromosomes was one SNP per 3.8 kb, or 266 SNPs per Mb. The median inter-marker distance was 1.5 kb with only 7% of the distances between successive markers being above 10 kb. The largest gap was 4.16 Mb at the end of chromosome Omy6, the second largest gap was 2.94 Mb at the end of chromosome Omy10 and the third largest gap was 2.75 Mb at the end of chromosome Omy13 . Only fiwww.cog-genomics.org/plink2) was used for a final SNP filtering based on keeping for further analysis SNPs with call rate above 95% and a deviation test from Hardy-Weinberg equilibrium (HWE) with a p-value < 10e-7 within each population. For LB line, 571,319 markers  were kept after removing 1,136 miss genotyped SNPs and 3,663 ones with severe deviation from HWE. For LC line, 569,030 markers  remained after removing 2,574 miss genotyped SNPs and 4,592 ones with severe deviation from HWE.Finally, PLINK v1.9 software  SNP array that will be used for numerous applications, including genomic populations studies, GWAS or genomic selection. In fish, the first very high-density chip, named 930K XHD Ssal array, was developed for Atlantic Salmon using 29 fish from Aquagen lines and was a powerful tool to identify the key role of maturity  or the ematurity . A similmaturity . A similmaturity . Recentlmaturity .Based on the resequencing of 85 samples by USDA and 79 samples by INRAE, we identified 32,372,492 SNPs that were variants (MAF ≥ 1%) in either the USDA or the INRAE sets. More precisely, 29.0 and 16.4 million SNPs were identified in the USDA and INRAE datasets respectively for equivalent number of sequenced individuals. The higher number of SNPs detected in the USDA dataset probably resulted from the larger number of diverse populations included in the USDA dataset. The USDA database included 11 doubled haploid individuals and 50 individuals from 7 commercial, hatchery or wild populations, compared to the INRAE database that included 19 doubled haploid individuals derived from one experimental line and 60 individuals sampled from a single French commercial line. For comparison purposes, the influence of the numbers of sequenced individuals and populations or breeds on the number of identified SNPs can be exemplified in two large-scale projects, the 1000 human genomes project and the 1000 bull genomes project. In the human genome, a pilot phase identified ∼15 million SNP based on the WGS of 179 individuals from four populations ; increasIn this study, the average distance between two successive variants was 60 bp, indicating important polymorphism level in the rainbow trout genome. This is consistent with the average SNP rate over all chromosomes of one SNP every 64 bp previously reported by Across all chromosomes, the average SNP density per Mb was 16,483 SNPs, i.e., slightly higher than the ∼15,600 SNPs per Mb reported by There was also a heterogeneous distribution of SNPs along the chromosomes, with a minimum density per Mb as low as 451 SNPs, and a maximum of 31,819 SNPs. Areas with less SNP density generally located at the telomeric parts or the centromeric parts (for metacentric chromosomes) of some chromosomes  . Such heTaking advantage of the biological characteristics of fish , isogenic lines have been generated in some fish species  in the genome coverage. In particular, the extremely low coverage at the telomeric parts of most of the chromosomes or at the centromeric part of metacentric chromosomes have been drastically reduced and the 2 regions spanning over 10 Mb each without any markers on Omy13  spans over 100 kb  were moderate  compared to the ones observed at similar distances in cattle breeds  where r2We have demonstrated in this paper a substantial linkage disequilibrium between neighboring markers, suggesting the density of genotyped SNPs is well-designed to accurately tag most areas of the rainbow trout genome. We acknowledge that, by design, the minor allele frequency distribution of genotyped SNPs is skewed to common alleles, and variation has been predominantly sampled from common SNP shared by both French and North American farmed populations. While this may limit some analyses, we believe that the array will be an invaluable genomic resource for ongoing work investigating genetic diversity, genetic architecture of traits and adaptive potential in world-wide rainbow trout populations.


Rabbit breeding is an important branch of agricultural animal breeding; their fur color and weight are desirable traits for artificial breeding. Polymorphism can provide potential molecular markers for studying rabbit traits and improve rabbit breeds with such markers in the future. In this study, single-nucleotide polymorphism markers in genotyping-by-sequencing data were used to analyze rabbit traits. In total, three genes were identified to be associated with fur color and four with weight. The results of this study provide a data base for the research and improvement of rabbit breeding program.Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits.The domestic rabbit ( Oryctolagus cuniculus f. domesticus) are very important varieties for biomedical research and agricultural animal breeding because they are mild and easy to handle, restrict and reproduce [Domestic rabbits  markers. The great advantages of SNPs as genetic markers is that they have a relatively low mutation rate and are distributed on all chromosomes . CarneirGenotyping-by-sequencing (GBS) can acquire the whole genome variation information of a population composed of hundreds of individuals, aiming to shorten the complexity of the genome to be sequenced . The GBSIn this study, in order to explore the genetic diversity and population structure of indigenous breeds and other breeds, five breeds of rabbits were selected as experimental subjects to perform GBS, the California rabbit (CF), German Zika rabbit (ZK), Qixing rabbit (QX), SG and SW. We assessed the number, distribution and diversity of SNPs detected. Our aim was to identify SNP markers among the five breeds of rabbits to identify genetic changes in rabbit traits among different breeds.QX is a new breed of rabbit, which is of medium size, fast growth, high yield and strong disease resistance . ZK has The libraries were prepared by following the protocol developed by Qi et al. (2018). Genomic DNA was extracted from whole blood using GenElute blood genomic DNA kit . After having assessed DNA sample quantity and quality by NanoDrop 2000, the qualifying DNA samples were used for library construction using the SuperGBS technology . The resRaw reads were split from the down-machine data according to the barcode and restriction site information using the Stacks software  with optThe R package ggtree (v1.16.6) was used for visualizing the phylogenetic tree. PCA of the SNPs was performed using genome-wide complex trait analysis (GCTA) software (v1.26.0) .The diversity parameters such as expected heterozygosity (He), polymorphism information content (PIC), nucleotide diversity (Pi), observed heterozygosity (Ho), and coefficient of gene differentiation (Fst) were calculated for each SNP marker using VCFtools (v0.1.16) .The selective sweep analysis methods such as Fst, Pi, and joint analysis of Fst and Pi were calculated for each SNP marker. GO and KEGG enrichment were performed using the genes where the SNP sites of the selected regions were located and the sites not located in the gene body region  were removed.A total of 150 samples were collected in this project and divided into 5 groups . The number of clean bases obtained in each individual was between 1.26 gigabases (Gb)–2.06 Gb, and the ratio of clean base to raw base ranged from 93.88–97.25%; the number of clean reads was between 0.00863–0.0141 Gb, and the ratio of clean reads to raw reads was in the range of 94.07–97.58%; the range of Q30 was 89.44–91.34% . MoreoveBased on clean reads, a total of 831,035 SNPs were left after GATK identification and applying filtering control by VCFtools. These high-quality SNPs were used for further analysis. The number of SNPs on chromosome 6 and 21 was relatively small . The SNPIn order to further understand the genetic structure of the rabbit breeds, we used population genomics tools for analysis. The PCA, applied to study the genetic relationship among breeds, revealed a close relationship between CF and ZK and as expected also between SW and SG . SpecifiOryctolagus cuniculus) is the only recognized ancestor of the domestic rabbit. Therefore, we chose wild rabbits from France as a comparison to investigate to what degree the breeding process had reduced genetic variability. Regarding the value of He, French wild rabbits showed the highest levels of genetic diversity (He = 0.723) [The European rabbit (= 0.723) , followeWe further analyzed the selective sweep among the breeds and mined the selected genes in the different traits (fur color and weight) of the breeds. In view of this, we used SW as the control to screen the selected genes controlling fur color  and weight  compared with the other groups. First, according to the empirical distribution of Fst , candidaASIPs, MITFs and KITs) associated with fur color [ADCY3s, YAPs, FASs, and ACSL5s) related to weight in these pathways [Furthermore, genes with 32,144 SNP loci in the selective eliminated region were functionally enriched to screen out genes controlling specific functional traits. Most of these genes were involved in protein synthesis and cell proliferation in a variety of cellular and biochemical processes . By furtur color ,26, whicur color ,28, suchpathways ,30, and Rabbits can be divided into large, medium and small according to their body size. At present, the growth speed and body size of rabbits have an impact on meat quality . HoweverGBS is a genetic screening technology to find SNPs and genotype them. Sequence-based genotyping provides a low-cost alternative microarray for the study of genetic variation . PértillWe obtained a total of 831,035 SNPs in the markers panel through screening. Compared with other GBS studies, our analysis allowed the identification of relatively more SNPS, such as ducks  . In addiASIPs, MITFs and KITs. Mammalian coat color is mainly associated with the pigment distribution or proportion of eumelanin and brown melanin [MITF gene result in defective melanin transport, resulting in diluted coat color in mammals [MITF gene, which may be related to rabbit coat color.Advances in genomic selection technology and the shortening of the generation interval make it an effective method for animal breeding. Through selective scanning analysis, 39 common chromosomal regions and 102 protein-coding genes were found to be affected in broilers, and these genes were enriched in tight junction pathways and myosin complexes, which regulate meat production and quality . In our  melanin , in whic melanin . In addi melanin . However mammals . In our ADCY3s, YAPs, FASs, and ACSL5s. Grarup et al. [ADCY3s increases the risk of obesity and diabetes. SNP rs2419621 (C>T), which is common in the ACSL5s promoter, is associated with weight loss in obese white women [ACSL5s gene, which may influence weight changes. Furthermore, according to the functional annotation of candidate genes, several pathways are directly related to the growth and development of animals. For instance, the Hippo signaling pathway is a pathway that inhibits cell growth [In addition, we also identified genes related to weight , such as p et al.  reportedte women . In the l growth . The MTOl growth . Taken tASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s. Our results provide a theoretical reference for marker-assisted selection breeding for genetic improvement programs in rabbits.This study showed that GBS is an effective marker development technique for rabbit breed isolation and genetic studies. We found that PCA by SNP markers supported the segregation of different breeds. Similarly, the genetic differentiation among the five breeds was significant, and the genetic diversity was poor. Some genes with SNP loci may be related to the weight and color of rabbit breeds, such as